PROCEEDINGS

OFA

SYMPOSIUM

Ecological Land Classification:

Applications to Identify the Productive

Potential of Southern Forests

Editors

Dennis L. Mengel

D. Thompson Tew

Charlotte, North Carolina

January 7-9,199l

Sponsored by:

North Carolina State University

Southeastern Forest Experiment Station

Scott Paper Company

Union Camp Corporation

ROCKY MT. FOREST & RANGE

EXPERIMENT STATION

CONTENTS

REGIONAL OVERVIEWS Page Page

Forests as Landscape Ecosystems: Implications Forecasting Growth of Pine Hardwood Mixtures From

for Their Regionalization and Classification . . . . . . 3 vI Their Ecological Land Class . . . . . . . . . . . . 93

J. Stan Rowe F. Thomas Lloyd

Land Classification in the Western United States . . . . 9 1

Robert D. Pjister

Ecological Classification System for Classifying Land

Capability in Midwestern and Northeastern U.S.

National Forests . . . . . . . . . . . . . . . . . 18

Walter E. Russell and James K. Jordan

The Role of Ecological Land Classification Systems

in the Silvicultural Decision Process . . . . . . . . 96 L*/

Thomas R. Fox

Site Evaluation for Commercially Important Southern

Hardwoods: A Practical Field Method . . . . . . . 102

James B. Baker

History of Forest Site Classification in the South . . . 25 MAPPING/GIS APPLICATIONS IN

David H. Van Lear CLASSIFICATION SYSTEMS

CLASSIFICATION SYSTEM DEVELOPMENT

Land Classification in the Blue Ridge Province:

State-of-the-Science Report . . . . . . . . . . . . 37

W. Henry MeNab

No More Plots; Go With What You Know: Developing

a Forest Land Classification System for the Interior

Uplands . . . . . . . . . . . . . . . . . . . . . 48 tit’

Glendon W. Smalley

Landscape Ecosystem Classification for South

Carolina . . . . . . . . . . . . . . . . . . . . . 59

Steven M. Jones

Delineation and Classification of Wetlands in the

Southeast . . . . . . . . . . . . . . . . . . .

John M. Hefner and Charles G. Storm

Dynamic Digital Databases: The Key to Practical

Land Classification Systems . . . . . . . . . . . . 105

James L. Smith

Using a Geographic Information System to Classify

Forest Productivity in Northwestern California . . . . 110

John A. Brockhaus, Lawrence Fox,

and Nancy D. Tosta

Classifying Forest Productivity at Different Scales . . 115

Robin Lambert Graham

Landforms, Soils, and Forest Growth: Identification

and Integration With Geographic Information

Systems . . . . . . . . . . . . . . . . . . . . . 121

R. David Hammer

. 69 b/

POSTER PRESENTATIONS

Forest Habitat Regions: Integrating

Physiography and Remote Sensing for Forest Site

Classification . . . . . . . . . . . . . . . . . . . 73

Sampling Map Units to Characterize Forest Site

Quality . . . . . . . . . . . . . . . . . . . . . 133

Charles J. Everett and John H. Thorp

W. Frank Miller and Michael S. Golden

CLASSIFICATION SYSTEM

INTERPRETATION

Placing “Man” in Regional Landscape Classification:

Use of Forest Survey Data to Assess Human

Influences for Southern U.S. Forest Ecosystems . . . s 135

Victor A. Rudis and John B. Tansey

Utilizing a Site Suitability Guide for Site Potential

and Species Recommendations on Southern

Appalachian National Forests . . . . . . . . . . . 81

Richard D. Rightmyer and James E. Keys

Timberland Downtown?: Southern Forest Resources

Along the Urban-Rural Continuum . . . . . . . . . 137

Christopher E. DeForest, Thomas G. Harris, Jr.,

Frederick W. Cubbage, and Arthur C. Nelson

Use of Land Classification on the Cumberland

Plateau . . . , . . . . . . . . . . . . . . . . . 90

John C. Rennie

Spatial Characteristics of an Appalachian Forest Site . 139

S. J. Tajchman

iii
Classification and Evaluation of the Natchez Trace

State Forest, State Hesort Park, and Wildlife

Management Area for Timber and Wildlife Habitat .

 W. Smalley, Kenderick S. Arney,

Loorenda A. Shorber, and Hart W. Applegate

Soil/Site Influences on Forest Growth Within Three

Physiographic Regions of Tennessee . . . . . . . .

P. Alan Mays and Elizabeth R. Smith

. 141

SCS Forest/Soil Database of North Carolina . . . . . 144

Albert Cofley

Potential Productivity of Loblolly Pine Plantations

in the Southeastern United States . . . . . . . . . 146

S.R. CoJbert and H.L. Allen

. 142 GIS and Land Classification: Integrated Decision

Support for Industrial Forest Management . . . . . . 148

Brent J. Keefer

iV
PIREFACE

This volume contains 18 papers and 9 poster abstracts that were presented

at a symposium on Ecological Land Classification: Applications to Identify

the Productive Potential of Southern Forests in Charlotte, North Carolina,

on January 7-9, 1991. We appreciate the assistance of all those who

contributed to the success of the symposium. The papers are organized into

four general categories addressing broad areas related to the classification

process.

The symposium was held to assess the current status of site classification in

the Southeastern United States. Recurring themes running through all of

the papers would seem to indicate that we are speaking the same language

and looking at common issues, unlike a decade ago. Ongoing and completed

research reports in this volume makes us confident that we will soon have the

capability to classify many of the varied sites found in the Southeast.

Papers published in this proceedings were submitted by the authors in

camera-ready form, and authors are responsible for the content and

accuracy of their individual papers.
STEERING COMMITTEE

Dennis L. Mengel

formerly with N.C.

State University

CH2M Hill

F. Thomas Lloyd USDA Forest Service

Steven M. Jones Clemson University

Glendon W. Smalley

retired

USDA Forest Service

Laurie J. Shainsky USDA Forest Service

vi
REGIONAL OVERVIEWS

FORESTS AS LANDSCAPE ECOSYSTEMS

IMPLICATIONS FOR THEIR REGIONALIZATION AND CLASSIFICATIOI&'

J. Stan Row&'

Abstract.-- Because organisms cannot exist without environments, the supra-organism reality is the ecosystem that

includes both. To understand forests is therefore to understand them as landscape ecosystems whose soil, air and water

parts are equally as important as the biological community.

Landform, combining surficial materials and surface shape, is

an often overlooked key to differentiating dissimilar and

similar forest ecosystem sites.

Keywords: Site, multiresource forestry, new forestry

INTRODUCTION

Ideas of what forestry is all about are changing in tune with new understanding of the world

around us. The public is demanding not just forest management for sustained timber yield but forest ecosystem management "constrained by the intention of maintaining the forest system as a forest

system" (Behan 1990). A "New Forestry" has been

defined as taking an ecosystem approach to land

management with the aim of maintaining forests as

complex ecosystems rather than as tree factories

(Gillis 1990). Perhaps the most important task

for managers of land and water is figuring out the

meaning of "an‘ecosystem approach" where multiple

resources and values are concerned. Clarifying

the concept will also illuminate Ecological Land

Classification.

LANDSCAPES AS ECOSYSTEMS

Suppose that forested tracts of land are to be

managed as ecosystems. What meaning are we to

attach to that term? A consensus is lacking. The

commonest definition of ecosystem is the textbook

"community-plus-environment" that emphasizes the

biological component while consigning everything

else to avaguemilieu, usually represented as a

bundle of abstract factors: light, heat, moisture

and nutrients. The danger of this simple biological definition is the confusion introduced by the

I/Presented at the Symposium Ecological Land

Classification: Applications to Identify the Productive Potential of Southern Forests, Charlotte,

NC, January 7-9, 1991.

z'Professor emeritus, University of Saskatchewan, Saskatoon S7N OWO, Canada. Present address

1012 Josephine St. New Denver, B.C. VCG 1SO

inconstant nature of vegetation and by the wandering proclivities of animals. If an ecosystem is

no more than the extension of a community, then an

arctic ecosystem vanishes into thin air when its

dominant community members take wing and migrate

to the southern hemisphere. By the same logic,

ecologists have questioned the reality of the

visible ecosystem boundary where lake meets land,

because amphibians hop across it (McNaughton and

Wolf 1973).

A step above the biological definition -- offering deliverance from its dilemmas by tying it down

in space -- is the acceptance of soils as bona fide

ecosystem parts, granting them as much importance

as the biota. Years ago the California SoilVegetation Surveys attempted to bring these two

components together (Zinke 1960) as have many

others since. Berger and Pierpoint (1990) have

recently endorsed various forest ecosystem classifications because "they provide proper information

for both soil and vegetation components." Yet the

conception of forest ecosystems as primarily comprising vegetation and soils is also flawed (Rowe

1984).

The logic that accepts soil as integral to the

ecosystem, adding an earth-layer component to the

community of any geographic place, suggests the

consideration of still other volumetric parts: the

air, water, surficial materials and their topography that together make up the supportive matrix

of biota and soils. By this reasoning we come to

the definition of landscape ecosystems as substantial wholes, as fully functional entities, chunks

of the planet's living skin, as "units of nature

on the face of the earth," which is what Tansley

(1935) the originator of the term "ecosystem"

called them when he wrote:

Though the organisms may claim our primary

interest, when we are trying to think fundamentally we cannot separate them from their

3
spatial environment with which they form one

physical system. It is the systems so formed which, from the point of view of the

ecologist, are the basic units of nature on

the face of the earth.

Tansley's assertion that basic units of nature

exist on the face of the earth and that they are

the significant realities rather than organisms

per se, shifts attention to dimensional water- ---

scapes and landscapes as themselves more important than the organisms they encapsulate. A forest

we know is more than trees, undergrowth plants,

animals and micro-organisms in an abstract

factored environment. A forest is a landscape, a

spatial reality like a large terrarium, consisting

of an air stratum over a soil-sediment-water

stratum with organisms (including ourselves) the

bacon bits in the two-layered sandwich. Wherever

we go on the face of the earth -- out on the sea

or into a forest -- we immerse ourselves in ecosystems whose importance and reality exceeds our

own. The basis for this radical statement is the

fact that life is a function of ecosystems, not of

organisms. How long would any organisms, Homo

sapiens included, be "alive" without the vital

supportive matrix of air, water, sediments and

received sunshine?

The ecosystem approach, then, is geographically

comprehensive; it covers the entire system comprising land, air, water and wildlife..?/ Such a

holistic idea applied to forest landscapes at all

sizes, conceiving them as box-like terrariums,

implies that a wide range of land characteristics

can be drawn on to divide and classify in useful

ways. The idea has been expressed in a number of

US Forest Service papers such as "Ecoclass"

(Corliss and Pfister 1973) and "Land System

Inventory" (US Forest Service 1976). It is

relevant to this Symposium's purpose which, to

quote from the prospectus, "addresses the partitioning of landscapes into logical units based on

sound physiography or ecological principles."

REGIONALIZATION AN'D CLASSIFICATION

Two separate though related steps are involved

in the partitioning of landscapes into logical

units based on ecological principles. The first

is the partitioning and the second is the logical

grouping of the resulting units. We begin by

searching in the landscape at a chosen scale for

forest ecosystems, actual or potential, identifying them at their cores and differentiating them

from surrounding ecosystems at their boundaries

by criteria relevant to the purpose. Geographers

call this process of differentiating and mapping

units of the landscape "regionalization" (Bailey

1976). Soil surveys that produce maps are exercises in regionalization. In Canada the countrywide regionalization and evaluation of landscape

ecosystems began under the name "Bio-Physical Land

2'Ecosystems approach to water management.

United Nations, Economic Commission for Europe,

Economic 6 Social Council, ENVWA/WP.3/R.7/Rev.l

Classification," later changed to the more exact

"Ecological Land Survey" (Wiken 1980). The methodology has been used to survey the National Parks,

and its application in various land inventories of

the provinces provides for many of them the base

of a Geographic Information System (Holland and

Coen 1983, Rubec 1990).

Concurrently with regionalization, though a

step behind, a mental aggregating of the results

of landscape partitioning develops, evolving into

a map legend or a formal classification. Units of

the landscape with similar characteristics are

placed in the same class while others, judged to

be different, are assigned to different classes.

Both steps -- regionalization or survey (that

differentiates tracts of land) and classification

or typing (that differentiates kinds or classes of

land) -- interact and co-evolve, each influencing

the other. Regionalization is usually "from

above," proceeding analytically by division and

subdivision, with an eye on differences. Classification is usually "from below," proceeding synthetically by aggregation, with an eye on similarities (Rowe 1979).

The distinction between regionalization and

classification is important. The forestry literature frequently confuses the two by calling both

"site classification." Remember that classifications are exercises in logic; they can be developed from point or plot data, without dependence

on any prior survey-and-mapping exercises and

without any particular relevance to them. Many

forest site classifications, designed to slot

forest stands or deforested areas into appropriate

productivity or silvicultural treatment classes,

are developed without regionalization, without

maps. Some "Field Guides to Forest Ecosystems"

(Jones and others 1983, Corns and Annas 1986) are

of this type. They are useful for their specific

purposes -- which often are to add an ecological

dimension to forest cover-type maps -- but they

need not be derived from Ecological Land Regionalizations nor tied closely to them. Their

classes are more generalized, more abstract, than

those that an aggregation of mapped units and

complexes of units for a specific region or subregion yields.

MULTIDISCIPLINARY SURVEY

Visualize the continental parts of the world

as made up of a mosaic of spatial ecosystems,

three-dimensional, each consisting of an atmospheric layer overlying a soil or water layer with

organisms concentrated at the solar energized

interface. Our job is to sort out the mosaic,

differentiating meaningful forest ecosystems and

in disturbed places the sites of forest ecosystems.

Can it only be done in thematic ways, in single

disciplinary ways? We find that most land surveys

have been done in this fashion, by phytosociologists, geologists , geomorphologists, pedologists.

Each discipline defines its spatial units and

draws boundaries on maps according to its own

ideas of what is important -- which may not match

the disciplinary expectations of others. Years

ago Coile (1960) drew attention to the imperfec4
tions of agricultural soil surveys for forestry

purposes in the southeastern States, noting that

the soil auger in use at the time was too short

to sample deep strata important to tree growth.

If forest land is surveyed from an ecosystem

viewpoint rather than from the traditional thematic viewpoints, the result should be a more

generalized regionalization and a multi-purpose

classification -- which is the definition of a

"natural classification." It should help to

foster an integrated perception of the entire

spectrum of land uses, including with forestry

wildlife, agriculture, recreation. It should

tell something about sensitivity to acid rain

and to traffic, as well as about forest productivity and regeneration silviculture.

By consensus the most useful regionalization is

a multi-scale hierarchical system whose nested

units, from large to small, are delineated according to visible ecological relationships between

climate, landforms, soils and biota (especially

the vegetation). Here note.that no '!correct"

hierarchical scaling into domains is waiting to

be discovered "objectively" (Bailey 1984). We

divide the one big global ecosystem, the ecosphere, into units and at scales that best suit

our subjective purposes, which is not to say that

all conceptual approaches are equally valuable.

If the field is to be advanced different methods

should be compared by applying them on the same

terrain, as has at least been attempted in Europe

(Rowe 1984). Also each new exercise in regionalization and classification should be evaluated

as objectively as possible with respect to the

ends it purports to serve.

LANDFORM AND ITS GENETIC ROLE

Only one earth exists and there is no substitute for any patch of its surface. This relative

uniqueness does not mean that similar parts within

any extensive landscape tract cannot be picked out.

The question is whether there can be any logic to

the exercise.

Hans Jenny analyzed the problem from the viewpoint of a pedologist, identifying five relatively

independent "genetic" factors: climate, relieftopography, geological partent material, biota

and time. Hold any four steady and let the fifth

vary and the result is a sequence of soils: a

climosequence if climate is the variable, a chronosequence if time is the variable, and so on. The

same logic applies to the patterning of forest

ecosystems of which soils are the basal strata

(Jenny 1980). As with soils (or vegetation, or

local climate), the forms and functions of forest

ecosystems express the interactions over time of

climate, relief-topography, surficial geological

material and available biota.

Jenny's formulation can be simplified from five

factors to four by combining relief-topography

(surface shape) and geological parent material

(sub-surface composition and structure) into

landform, at all scales. Examples of local landforms are gravelly terraces, sandy loam levees,

sand dunes, lacustrine clay plains. When all the

components except landform are invariant -- that is

if the regional climate is relatively constant, if

the same biota is equally available in all parts

of the region and if the time for interaction of

ecosystem components within the region is everywhere the same -- then the chief source of withinregion variation or pattern in tundra, prairie,

savanna or forest landscapes is landform.

Landform -- the slowest changing and most con+

servative landscape element -- provides the best

taxonomic handle for terrestrial ecosystems. It

is the stage on which the more changeable players

- - surface climate, biotic community and soil --

wax and wane-as they act out their successional

roles. When they disappear temporarily for one

reason or another, the landform remains to provide

an identifiable place or "site" where a certain

kind of forest ecosystem or successional suite of

ecosystems will predictably develop. Landform is

the visible means -- clearly or dimly recognized --

by which the boundaries of soils and of potential

vegetation communities are extrapolated. If we

want to know where resources are and their spatial

distribution (Davis 1980), then landform is the

stable reference feature to which we must look.

Note that at smaller scales, attending to domains

and provinces, what we call physiographic divisions

are the homologues of landforms.

Another key role of landform, that has not been

explored in the ecological literature, is genetic

in the sense of exerting initial control over _

climate near the ground, over the constitution of

biotic communities and over the formation of soils.

The local fluxes of energy called "topoclimate" are

in fact the landform-mediated variations of the

overall regional climate. Phrased another way,

within large air-mass climatic regions, the suite

of landforms (the varying surficial materials and

their surface relief/topography expressions)

modify the intensity and quality of solar energy

input, as well as the reception and retention of

precipitation, dust, leaf litter and whatever else

is moved around on and in the soil, to form a

matching suite of sub-regional climates.

The parallel interacting controls of local

climate matched to local landform (for example,

the lower slopes of hills as compared to their

tops) constitute the mesh of the Darwinian sieve

that allows certain plant species and ecotypes to

colonize while excluding others. Particularly in

the early stages of succession the vegetation

communities are aggregated according to specific

landform-climate environments. Thus natural vegetation usually appears on aerial photos as repetitive catenary patterns of plant communities

matched to particular surficial materials and their

slope/exposure facets. Animals follow plants,

dependent on them for food and shelter, and the

patterns develop as gradients of biotic communities that, with landform-mediated drainage, give

rise also to distinctive catenas of soils.

5
In the eastern USA Nichols (1923) was one of

the first ecologists to note what stands out particularly in glaciated terrain: that within reasonably uniform climatic regions the differences

in vegetation and in successional patterns after

disturbance are primarily associated with differences in surficial materials; that is, with the

topography/soil complexes that surface landforms.

The same idea was discovered by Hills (1960) whose

influence imparted "a strong geomorphic bias" to

ecological land classification in Canada (Burger

and Pierpoint 1990). Barnes and others (1982)

drew attention to a parallel methodology developed

in Germany which he and his associates have

further illuminated (Simpson and others 1990,

Host and others 1987). And of course since 1972

in the National Forests of the Eastern Region the

US Forest Service has been using an ecological

land classification system that stresses landform

and geomorphology based on pioneer work by Dick

Alvis and by Wertz and Arnold (1972).

My emphasis on the primary role of landfonn in

shaping forest ecosystem development should not

be interpreted as a disparagement of the companion

roles of other components such as the soils and

the vegetation with its disturbance regimes and

successional processes. Superimposed on the

characteristics of natural forests entrained by

the controls of landform are all the effects of

secondary random influences: fire, flood, wind,

disease, animal depredation, chance migration,

competition that narrows habitat ranges, human

land-use practices and other mutualistic or antagonistic biotic interactions.

Nevertheless, the largest part of the repetitive

pattern in natural and semi-natural forests can

be traced to the repetitive patterns of landforms.

Without this consistent match-up the basis for

terrain analysis by remote sensing would largely

disappear. Here note that the question of scale

cannot be neglected because the relative size of

pattern-forming influences -- fires, insect infestations, wind storms, freezing belts, land uses --

either enhance or lessen the expected correspondences between landforms and their surface biological patterns.

Landform, then, is an essential component of

forest ecosystems at whatever scales they are

defined. It merits attention by forest ecologists

and forest managers equally with the trees, the

undergrowth, the soils, the climate. The classic

study "Geomorphology and Forest Ecology of a

Mountain Region in the Central Appalachians" by

Hack and Goodlett (1960) is worth study by everyone, as are recent contributions such as "Landform

effects on ecosystem patterns and processes"

Swanson and others 1988) and "Terrain shape index:

quantifying effects of minor landforms on tree

height" (McNab 1989).

Perhaps past indifference to landform as a key

to landscape understanding has been our fascination

with classification rather than with regionalization. One can classify forested land in various

ways using a range of criteria selected from

vegetation and/or soil withaut any necessary

reference to landform, but not so with survey and

mapping that force on the observer the importance

of surface shape and composition. The Australian

land surveys, conducted by interdisciplinary teams,

are literally grounded in geomorphology. Their

beautifully drawn block-diagrams of terrain,

matched with air photos and descriptions in table

form, impart ecological understanding with a high

degree of artistry (see Bellamy 1986, for example).

CRITERIA FOR ECOLOGICAL LAND CLASSIFICATION

"Ecological regionalizations use various criteria for bounding regions, with the assumption

that derived units reflect patterns of systems

that differ in response to management and resource

production capabilities" (Bailey and others 1985).

The adjective "ecological" placed before land

regionalization and land classification implies

that boundaries between forest ecosystem units

will have something more than narrow thematic

significance; they will have relational significance. Thus the ELC practitioner does not map soil

and vegetation separately, afterwards superimposing

one on the other in the vain hope that common

boundaries will emerge; s/he seeks rather to differentiate and map land as a unitary thing (Speight

1987), seeking forest ecosystem units suggested

by what can be seen and what is known of landformclimate-vegetation-soil parts in interaction,

aware of landscape processes, with understanding

continually sharpened by field experience.

Not all the important features are equally

accessible. The best way to regionalize, to map,

is to use visible surface features -- vegetation,

landform and its drainage patterns, land use.

Their ecological relationships provide the primary

stratification to which spot-sampled climate and

soils -- more difficult to observe but not less

important -- can be linked.

Various methods are used to arrive at ecologically significant boundaries. One method is

factorial, searching for clues in the terrain (or

on thematic maps of the same terrain) that show

changes in the intensity of key factors known to

be ecologically important to forests. Thus thermal sensing imagery or climatic maps at appropriate

scales may provide isolines of radiation, temperature, soil moisture, and so forth. These can

assist in bounding forest units provided that

they show some correlation with the natural landscape mosaic. A climatic map by itself is not an

ecological map.

A second method searches for terrain features

that control the intensity of key factors. Again

thematic maps may be useful. Landforms with their

control of radiation regime and retention of water

and other materials according to slope, aspect and

geological substance usually afford good clues to

boundary placement. The natural breaks in slope

angles, changes from convex-upward to concaveupward surfaces as well as the irregularities that

mark changes in surficial materials and soils

usually are paralleled by changes in forest vegetation. Nevertheless landform maps are not necessarily ecological maps. Geomorphologists sometimes
discount the importance of the soil skin on their

landforms, or map out different tracts according

to age rather than to ecological properties. Geomorphological units must be tested by their covariance with other landscape features.

A third method uses biological indicators:

vegetation, soils and the residual land-use marks

of disturbance and change made by animals and man.

But by themselves these too may not be trustworthy

for they may reflect unknown past events that have

little relevance to current conditions. Validation

as good ecological indicators requires that they

be checked against variations in physiography. A

vegetation pattern that does not make sense in

relation to the patterns of landforms, soils and

drainage patterns is suspect.

The success of these methods is predicated on

mapping areas of sufficient size to reveal repetitive patterns. Boundaries of ecological significance emerge from studies that reveal correlative

changes in vegetation, climate, soil, drainage and

landforms. Forest ecosystems are discriminated

by visible components that go together. This is

different from the attempt to synthesize ecosystems

by the addition of components initially defined

as thing-in-themselves, with no whole ecosystem

in mind.

Acknowledgement: The author thanks Ed Wiken

for generous assistance in literature survey and

for useful comments during the preparation of

this paper.

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Forest Service, Ogden, Utah 84401

Bailey, Robert G. 1984. Integrating ecosystem

components. In: Bockheim, James G., Proceedings

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20; Madison, WI. NCR-102 The north central

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Science, University of Wisconsin, WI 53706

pp. 181-188.

Bailey, Robert G.; Zoltai, Steven C.; Wiken, Ed B.

1985. Ecological regionalization in Canada

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Barnes, B.V.; Pregitzer, K.S.; Spies, T.A.;(and

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Behan, R.W. 1990. Multiresource forest management: a paradigmatic challenge to professional

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Corliss, John C.; Pfister, Robert D. 1973.

Ecoclass -- a method for classifying ecosystems.

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Corns, I. G. W.; Annas, R. M. 1986. Field guide

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Davis, L.S. 1980. Strategy for building a location-specific multipurpose information system

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Hack, John T.; Goodlett, John C. 1960. Geomorphology and forest ecology of a mountain region

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Jenny, H. 1980. The soil resource: origin and

behaviour. New York: Springer-Verlag.

Jones, R.K.; Pierpoint, G.; Wickware, G.M.;(and

others). 1983. Field guide to forest ecosystem

classification for the Clay Belt, site region

3E; Ontario Ministry of Natural Resources,

Downsview, Ontario, Canada.

McNab, W. Henry. 1989. Terrain shape index:

quantifying effects of minor landforms on tree

height. Forest Science 35(1):91-104.

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7
Nichols, G.E. 1923. A working basis for the ecological classification of plant communities.

Ecology 4:154-179.

Rowe, J.S. 1979. Revised working paper on methodology/philosophy of ecological land classification in Canada. In: Rubec, C.D.A., Applications of Ecological (biophysical) land classification in Canada; Ecological Land Classification Series No. 7, Lands Directorate,

Environment Canada, Ottawa KlA OH3 pp. 23-30.

Rowe, J.S. 1984. Forestland classification:

limitations of the use of vegetation. In:

Bockheim, James G., Proceedings symposium on

forest land classification: experiences, problems, perspectives; 1984 March 18-20; Madison

WI. NCR-102 The North Central Forest Soils

Committee, Department of Soil Science, University of Wisconsin, WI 53706 pp. 132-147.

Rubec, C.D.A. 1990. Thirty years of ecological

land surveys in Canada. Sustainable Development Branch, Environment Canada, Ottawa KlA OH3

29 PP.

Simpson, Thomas B.; Stuart, Philip E.; Barnes,

Burton. 1990. Landscape ecosystems and cover

types of the reserve area and adjacent lands of

the Huron Mountain Club. Occasional papers of

the Huron Mountain Wildlife Foundation, No.4.

Big Bay, MI 49808.

Speight, J.G. 1987. Land classification.

In: Gunn, R.H.; &nd others),Australian soil

and land survey methodology handbook. Inkata

Press, Melbourne.

Swanson, F.J.; Kratz, T.K.; Caine, N.;(and others).

1988. Landform effects on ecosystem patterns

and processes. Bioscience 38:92-98.

Tansley, A.G. 1935. The use and abuse of vegetational concepts and terms. Ecology 16:284-307.

Wertz, W.A.; Arnold, J.F. 1972. Land system

inventory. U.S. Department of Agriculture,

Forest Service, Intermountain Region, Ogden UT

Wicken, E. 1980. Rationale and methods of

ecological land surveys: an overview of Canadian approaches. In: Taylor, D.G., Land/

Wildlife integration; Environment Canada,

Ottawa KlA OH3. pp. 11-19.

Zinke, P.J. 1960. The soil-vegetation survey as

a means of classifying land for multiple-use

forestry. Proceedings Fifth World Forestry

Conference, Aug. 29 - Sept. 10, 1960, Seattle

WA.
LAND CLASSIFICATION IN THE WESTERN UNITED STATE&'

Robert D. Pfiste&'

Abstract.--Various approaches to classification and mapping

of forests, sites and landscapes have been developed during

the past few decades. These approaches vary in concepts,

objectives, methods, costs, accuracy, scale and application

to forest land management. Major systems are reviewed and

compared in terms of (1) basic classification concepts, (2)

ecological concepts, and (3) primary applications to

practical resource management.

Keywords: Site type, habitat type, land system, soils,

landform.

INTRODUCTION

My purpose is to provide an overview of land

classification activities in the western United

States. The period of time I will cover is the

last two decades. In reviewing these activities,

I hope to show the progress that has been made as

well as the problems that have been encountered.

Everyone has one perspective on land classification--their own! Rarely do two people share

the same perspective. Land classification sounds

very simple--but it is this apparent simplicity

that has led to major misunderstandings. YOU

would think that if we all communicate in English,

we wouldn't have that problem. However, in the

broad field of land classification, it often appears that people are not even using the same language. We are dealing with varied backgrounds in

terminology, concepts, experience, environments,

and a multitude of scales.

The fact that we hold these symposiums suggests that the subject is broader than one person

or discipline can encompass. I suggest that to

encompass everyone's interests and activities we

would have to define land classification as any

map that has been created to show polygons as individual entities within a larger whole. In other

words, land classification is clearly tied to the

process and products of mapping the landscape for

whatever purpose.

l/Presented at the Ecological Land

Classification: Applications to Identify the

Productive Potential of Southern Forests

Symposium. Charlotte, NC, January 7-9, 1331.

Research Professor, School of Forestry,

University of Montana, Missoula, MT 59812

With this broad viewpoint, at least three

disciplines need to look for common viewpoints.

These include: 1) mostly soil scientists, 2)

a few vegetation ecologists, and 3) a very few geographers. Each of these disciplines has made major contributions to the continually developing

field of land classification. However, I ah0

submit that the future challenges of land classification will require better communication and

cooperation among the different perspectives. I

hope this symposium helps meet that need.

Maybe I am a slow student, but as a student

of land classification for the past 20 years, I

would like to share what I have learned and the

basic concepts I use for teaching (so that my students do not have to take the same 20 years I took

to gain some understanding). If this approach

seems overly simple to some of you folks in the

audience, let me explain that we all are conditioned by past experience--what we hear is not necessarily what someone else hears after we each

pass it through our own unique filters. Practitioners in the field of land classification can

get into loud arguments over very small points because they do not have the same perceptions.

My perception of land classification began

with two different classification systems--soils

and vegetation. Before you say this is not land

classification, let me ask your indulgence. A map

of soil series is one kind of land classification.

A map of forest cover types is another kind of

land classification. A map of potential plant

community types is yet another kind of land classification. Some people may prefer to call these

nsitew classifications, or "vegetation" classifications, but once a map is produced--that map

identifies various pieces of land that are charac9
terixed by those specific criteria. The point

here is that--no one individual, agency, or discipline has a monopoly on the term "land classification . " The same thinking can be applied to the

subject of "ecosystem classification." With either land classification or ecosystem classification, no one understands what you are talking

about until you specify the criteria, the methods,

and the scales you are thinking about.

You may think this lack of common perception

and common objectives is a small matter. However,

it reaches far beyond one's wildest imagination in

the halls of academia, government agencies and

even within agencies. It has provided grist for

numerous articles, claims and counter claims.

SOIL TAXONOMY AND MAPPING

I was introduced to soil classification as

part of forestry training. Soil morphology, description, classification, and development were

part of my soils training along with soil physics,

fertility,etc. I was a student during the transition from the old 1938 classification, through the

‘7th Approximation," to the new "Soil Taxonomy."

We studied soil maps and the interpretive information that accompanied them.

This country has benefited from a national

standardized system. The foundation is in the

standard "Soil Taxonomy." Mapping is an attempt

to represent that taxonomy across the landscape,

although it rarely can be done as representation

of pure taxonomic units. With this foundation and

availability, soils have been a fundamental part

of many (but not all) land classification efforts.

HABITAT TYPE TAXONOMY AND MAPPING

In 1961 I was introduced to another method of

classification, the habitat type, that led me to

some confusion and curiosity. I was familiar with

vegetation classification, but this jump to habitat types as units of land capable of supporting a

specific climax plant association caused me some

problems. I was used to thinking of land in terms

of soils and obvious physical site factors that I

could measure easily, such as aspect, elevation,

slope and parent material. It was not until I

learned to recognize some of the plant species

other than the trees that I began to see that potential vegetation or habitat types provided another, at least equally valuable, perspective of

the landscape. I first learned to identify habitat types for individual plots, such as during an

inventory.

When I first mapped habitat types for a

3,600-acre experimental forest, I realized that

the vegetation was reflecting unique differences

among sites and landscape patterns. The perspective was different than two previous soil-mapping

projects had produced for the same area. When the

different maps were overlayed, you could see some

degree of correlation, as well as obvious lack of

correlation where the plant communities were reflecting site factors differently than previous

soil mapping had done.

For me, this was the beginning of an appreciation that more than one perspective can be valuable for understanding and managing forest ecosystems. This was fascinating as a researcher,

although managers usually wanted to invest in only

one all-purpose land classification and they usually hired soil scientists to provide this input.

LAND SYSTEMS INVENTORY

The U.S.D.A. Forest Service soil scientists

faced a difficult task in the early land classification days. Mapping of soil series was an impossibly expensive task considering the large acreages they were charged with. A classification

system was needed to get basic resource information efficiently for large areas of land.

The Land Systems Inventory approach was first

documented by Wertr and Arnold (1972) in the Intermountain Region and was developed concurrently

in the Northern Region. The principal criterion

for unit identification and mapping of Landtypes

or Landtype Associations was landform as identified on photographs of 1:60,000 to 1:40,000 scale.

Once the land units were mapped, a sample of them

was visited to identify and describe the soils,

vegetation, geology, and topography as a basis for

developing management interpretations for the

units (Nelson and Jordan 1987).

During the 1970's the Land Type or Land Type

Association mapping approach was proceeding in

most areas as an attempt to obtain a general land

inventory with sufficient characterization to use

for multiple-use planning at the resource allocation level. A similar approach, called Soil Resource Inventories, was used in the Rocky Mountain

Region and in the Pacific Northwest. In the Pacific Southwest, they called it a Terrestrial Ecosystem approach. In California, many different

approaches were being tried including detailed

soil-vegetation mapping. Improvements have been

made in the system by better integration of vegetation, landform and soils and finer resolution of

map units (Nelson and Jordan 1987). A 1984 figure

illustrates the different names of similar approaches in the different Forest Service Regions

(figure 1).

INTEGRATED CLASSIFICATION SYSTEMS

In 1971, the Washington office of the Forest

Service developed tentative guidelines for a national classification of ecosystems. The Chief

commissioned an interdisciplinary task force to

develop and document a standard ecosystem classification for the greater Pacific Northwest (Washington, Oregon, Idaho, Montana, Wyoming) based

upon existing knowledge. The task force produced

a document called ECOCLASS (Pfister and others

1973, Corliss 1974) that said a single classification was totally unrealistic, but that two terrestrial classification systems could be linked to

define ECOLOGICAL LAND UNITS. The double hierar10
LAND SYSTEMS ECOLOGICAL CLASSlFlCATlON

M0MflE0 ECOCLASS

ECOSYSTEM SURVEY SOIL RESOURCE INVENTORY

Figure l.--Forest Service ecological land

classification methods, March 1984 (from

Bailey i9ar).

thy of the ECOCLASS system was used in several areas in the 1970's to identify land units of similar topography, soils, and vegetation potential in

conjunction with Unit Planning.

VEGETATION SYSTEM LAND SYSTEM

FORMATION PROVINCE

SECTION

REGION

SUBSECTION

SERIES ASSOC

t- ECOLOGZCAL LAND UNITS t

Figure 2.-- Ecoclass vegetation system and

land system hierarchies (from Pfister and

others 1973).

ECOCLASS was not accepted outside of the regions for which it was developed. In addition,

the lack of agreement in basic inventory figures

of forest land among agencies at the national level provided impetus for another formal effort. A

Rocky Mountain Station research unit was given the

task of developing a standard national land classification for five agencies for the entire United

States (Driscoll and others 1984). (The Forest

Service put it in their manual in 1982, and the

Bureau of Land Management published it as a research note in 1983.)

POTENTIAL VEGETATION SOIL TAXONOMY

CLASS ORDER

SUBCLASS

FORMATION

SERIES

SUBORDER

GREAT GROUP

+L ECOLOGICAL RESPONSE UNITS f

Pigure 3.--An ecological land

classification system for the United

Statea (from Driscoll and others 1984).

Three independent hierarchies were provided:

1) Potential Vegetation, 2) Soil Taxonomy, and 3)

Landform. Taxonomies were available or underway

for much of the western United States for Potential Vegetation, and the national Soil Taxonomy

was readily available for the entire country.

However, a standard taxonomy for landform was not

available at that time. Therefore, the application of the system was limited to cross11
classification (identification) of inventory

points which then became Ecological Response Units

(ERU's), This document went through several reviews and rewrites in an attempt to reach consensus . It was finally pushed to completion, and

five agencies signed off on the basic concept.

However, there was still considerable resistance

by those whose needs were not addressed by the

document.

First of all, the system was primarily a taxonomic system and the main intended application

was to identify inventory points by their potential vegetation types and soil types (according to

standard taxonomies) so that inventory data could

be aggregated upward into higher levels of individual or linked hierarchies. Certainly, those

people who wanted this information at regional or

national levels could see the immediate value of

this application. (The Landform hierarchy was

also a desired component, but a standard landfonn

hierarchy was not yet available.)

Perhaps basic inventory and aggregation needs

were met by this system. However, any of the people who had been working on land classification

for planning purposes during the 1970's recognized

a basic problem. The National Land Classification

could not classify land by simply identifying

points in an inventory. It was not designed to

produce maps of land units. There was no clear

linkage with any of the land system mapping that

had been proceeding during the 1970's. What to

do?

Most regions simply proceeded with land mapping according to their regional procedures. If

the first level of mapping was at the Landtype Association, then the next task was to identify the

landtype level in the Land System hierarchy. This

was called level III soil mapping in some areas as

the Forest Service and the Soil Conservation Service began to rebuild working relationships toward

common terminology and increased cooperation.

Two symposiums were scheduled in the 1980's

to address the subject of land classification with

an emphasis on mapping. The first was in Albuquerque (Moir and Hendzel 1983) and the second was

in Madison (Bockheim 1984). This one appears to

be the third in the serie,s with a primary focus

on mapping approaches to land classification.

The proposed integrated methods have not been

widely accepted. Even today, individuals are interpreting the landscape independently by habitat

tw=s, soil types, range sites, landforms or remote sensing. Each have unique perspectives and

little hope of total coannunication unless they

have the opportunity to work as a team on the same

piece of ground.

why is this? Several reasons can be

suggested.

The first system was primarily aimed at mapping systems for application in land use planning.

The second system was aimed at identifying inventory points for aggregation of data. A second

major difference is the process of classification

and the distinction between taxonomy and mapping.

If you start with remote sensing, you are searching for ways to subdivide the world into map units

that appear to be relatively homogenous. If you

start on the ground, you are looking at grouping

similar stands or sites. Each group perceives a

complete hierarchy, but it is like trying to use a

20-foot extension ladder to paint a 15-foot flagpole when the two ethnic painters haven't figured

out how to put the extension ladder together yet!

GAINING BROADER PERSPECTIVES

In order to share my changes in thinking, I

need to go back to the late 1960's. My research

assignment had shifted from silviculture to the

development of forest habitat type classifications

(taxonomies) for the Northern Rocky Mountains. In

1970, I had the good fortune to work with the Intennountain Region Land Systems Inventory crew on

the Idaho Primitive Area. My role on the team was

to help train the soil scientists in habitat type

identification, which they were using to help

characterize their mapping units. In the process,

I gained an appreciation for the immensity of

their task--covering over a million acres in a

short summer.

In 1971, I had the opportunity to work on a

committee of the Northern Region in developing

methodology for an Ecological Approach to Unit

Planning. (This led naturally to involvement on

the ECOCLASS task force.) Habitat type mapping of

experimental areas and on entire National Forests

was progressing concurrently with independent mapping of landtypes. Obviously, this set the stage

for a lot of discussion, to say the least! Landtypes may have been adequate for certain planning

needs, but the resolution was unsatisfactory for

those managers who wanted to use a more direct

classification for interpretation of vegetation

potentials and related management information.

A major strength of the habitat type effort

was that the taxonomy was available to any field

person so they could identify types and make their

own maps. Furthermore, the management interpretations were tied to the classes of the taxonomy,

not to a specific map unit. Therefore, a foundation was available for building type-specific management information independent of mapping efforts. The habitat type mapping was also

relatively efficient.

Both habitat type maps and landtype maps were

useful input for planning. The landtypes provided

good characterization of landforms and general

soils (family level) characteristics important for

identifying constraints on management, but were

less useful for interpreting vegetation-related

potentials. The habitat types provided good information on potentials for multi-resource use related to vegetation potentials, but were much less

useful for interpreting constraints related to

physical site and landform variables. Planners

who had both sources of information at their disposal were more comfortable in their planning efforts.

12
I

TSUGA HETEROPHYLLA SERIES

iBlES GRANDIS SERIES

SEUDOTSUGA MENZIESII SERIES

Pigure 4. Illustration of the typical

distribution of series and species (from

Pfiater and othera 1977).

YIELD CAPABILITY CLASSES

I I I I 1

I

WYhi%)

unitlora)

V I IV I

III I I

(very low) ) WJW) I (moderate) ] (hi%, f

-.-A

I TSHE . CLUN 118-9

I%13

ABCR.LIB0

t PSME . “+,cA

kbreiiated under&

species name (Clintonia

I

I

I

1-

80 loo

Yield capability (ft’/ zacre/yr)

Figure 5. Illustration of the yield

capability information for habitat types

(from Pfister and other6 1977).

13
CONCEPTS, TERMINOLOGY AND METHODS

In 1977, the Society of American Foresters

was also interested in getting a better handle on

vegetation, site, and land classification so they

invited several authors to develop a special issue

for October 1978. Bob Bailey (geographer) and I

(plant ecologist) were invited to prepare a joint

paper on the basic concepts of land and resource

classification. We had little common ground, but

were determined to learn from each other and

broaden each other's perspectives. In the process, we looked at the basic science of classification to help provide perspective.

Classification embodies the separate approaches of taxonomy and mapping, and we encouraged the use of those terms. Soil taxonomy and

habitat type taxonomies are examples where a user

often does the identification of points or boundaries on the landscape. Users of taxonomy also

usually do the mapping rather than the developers

of the taxonomy. Mapping at scales of 15,840 or

larger can often be mapped as relatively pure (75

to 90 percent) taxonomic units, with a minimum of

complexes or associations. They are a classification system of choice for people working on the

ground.

Classification systems that start with identification of relatively homogenous units of landscape are usually done at scales smaller than

1:24,000 and are often termed regionalization. In

this process, mapping is usually the first step,

followed by sampling, description and interpretation.

The interaction with Bailey opened up a new

perspective for me, and helped me interpret some

of the integrated efforts. It helped me interpret

and characterize the National Classification effort (Driscoll and others 1984) as primarily a

"component site taxonomy ii--useful for identifying

points and providing a means for aggregating data

consistently. It bears little relationship to

land classification where mapping is the fundamental starting point, except for providing taxonomies that are useful for describing map units.

A. PROPOSED REVISION OF ECOCLASS

With much improved hindsight, I now see some

problems with the original ECOCLASS as a communication tool for mapping ecosystems. We can improve on the original concept by relating to current mapping efforts and identifying the scale at

which the mapping is usually done. We can also

make it specific to the Northern Rocky Mountains

to avoid regional terminology differences.

For the Potential Vegetation System, we first

can remove the term "formation“ because it is usually used in a taxonomic hierarchy rather than a

mapping hierarchy. At the upper level, Forest Regions have been mapped for Montana (Arno 1978)

where distributions of forest species and forest

habitat types are used to characterize different

effective climatic regions. (Or we can use Bailey's (1976) Ecoregions). Kuchler's (1964) Potential Natural Vegetation map is independent and one

level lower. Habitat Type or Habitat Type Phase

are the common operational mapping levels, usually

requiring a map scale of 1:15,840 or larger.

These can be aggregated upward to Groups or Series

as they are usually not mapped.

VEGETATION SYSTEM

~--.....- .,.,,..,,....,.....-. . . . . .

IREGION (ARNO) j

~KuCHLER-PPNVI

up--

SERIES

LAND SYSTEM ~~.-....-..---.....~.....~.....~

] PROVINCE

1 SECTION \

)

1 SUBSECTION i

t

L.... . ..-.............. ..,.- -.,. .-..".,,,.-"...d

$-"".".. ..--~...~~.~~...x~.~x.~x-._x~

SUB-SERIES /

1:60,()00 \LANDl-YPE ASSOC. 1

$

H.T. GROUP $

t

1:40,000 1L-JypE j

1 (Level III)

/HABITATTYPE 1

1:24.000 -

I~.~--~-~...~...~ . . . . . . . . . . . . . . . . . . . . . x . . . . . .

I

1

1:15,840 1 L. T. PHASE/

j (Level II)

1

1 H.T. PHASE 1

L. . . . . ..-........... . . . . x . . . .

[SOILS MAPS 1

.._x_____ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

L ECOLOGICAL LAND UNITS -4

(Combine Maps at Any Level With G.I.S.)

Pigure 6,--A proposed tevision of ECOCLASS

to reflect cutrent mapping efforts in the

Northern Rocky Uountsins.

Land System mapping is essentially completed

for the upper five levels. Landtype Associations

and Landtypes have been the focal point of Forest

Service mapping, usually at a scale of 1:40,000 to

1:60,000. Maps at scales of 1:15,840 have not

been prepared for many areas. However, several

private lands have been mapped by the Soil Conservation Service at Level' II, at scales ranging

from 1:12,000 to 1:24,000.

The lack of maps at the same scale for the

Potential Vegetation System and the Land System

has seriously hindered our ability to compare and

communicate. Defining Ecological Land Units is

not easy with maps of greatly different scales.

On Forest Service lands we rarely find a map at

the landtype Phase level to compare with a habitat

type map.

Soil scientists often state that the lowest

level of the land system is the same as ecological

land unit. Recent maps of habitat types and soil

types, both at 1:15,840 clearly illustrate that

the polygons have much less correlation than some

people care to admit. Both are valuable, but they

are different. Each provides a unique perspective

and unique interpretations. The principle of compensating factors is well established and cannot

be ignored. But look at the power the land manager has when the two are combined to develop ecological land units--areas of similar topography,

1 4
soils and potential vegetation. Neither the vegetation system alone, nor the land system alone

have yet provided this degree of resolution.

Some plant ecologists are attempting to develop "ecological type" classifications (Allen

1985), which are basically a subdivision of habitat types baaed on contrasting physical site features and/or productivity. The ecological type

appears almost synonymous with the lowest level of

the ecological response unit (Driscoll and others

1984) in a taxonomic sense, or with the lowest

level of the ecological land unit (Pfiater and

others 1973) in the mapping sense.

It is interesting to note that the scale of

1:24,000 is about where two major Forest Service

operational approaches meet. The foresters mapping habitat types are operating with patterns

they can see from the ground. The landtype specialists are mapping patterns that are not visible

from the ground. Until these people start operating on the same scale, or appreciate the unique

value of each map, or find a way to link mapping

efforts, there will be continuing communication

problems.

DISCUSSION

Can we avoid the term classification as much

as possible when referring to specific efforts?

Would it help our terminology, if we generally

talked about land classification as primarily a

mapping approach--or simply call it land mapping?

Can we use the word taxonomy for the classifications that are basically lists of types with keys

to help the uaer identify points or plots.

Could we generally reserve the term land mapping for scales smaller than 1:24,000 and use the

term site mapping for scales larger than 1:24,000?

We need pilot demonstrations of the different

techniques of land classification done on the same

pieces of ground. Experimental forests provide an

ideal laboratory for this kind of exercise. Until

you independently and objectively demonstrate different techniques, you have little basis for meaningful dialogue. This should include coat and

coat effectiveness comparisons for management applications.

The vegetation system will continue to be a

major component of any classification system in

the western United States because of the status of

developing taxonomies. Most of the forest lands

now have operational taxonomies that provide the

foundation for accumulating management information. However, formal mapping programs have been

limited to the Northern Rocky Mountains, perhaps

because available mapping dollars have been allocated to completing the landtype mapping. New

techniques are being developed to improve the preciseness and utility of the taxonomic systems, and

work is also progressing on developing auccesaional pathways relative to various management treatments.

Efficient techniques are also being developed

to extend the habitat type concept to areas where

few remnants of natural vegetation remain. For

example, we are currently completing a habitat

type classification of riparian and wetland areas.

We call this "habitat typing in the land of no

climax!" Existing knowledge of environments and

relative species amplitudes (plus minimum field

work) can be used to develop a first approximation

of the ‘Series" or "Sub-Series" level of habitat

type taxonomy on moat landscapes. It illustrates

the point that you can still use the concept of

developing a useful site classification based on

potential vegetation trends without old-growth

natural stands.

In evaluating use of different classification

systems, I have been impressed with the long term

value of taxonomic systems relative to mapping

systems. Look for opportunities to provide a taxonomy as a foundation for future work. Wlqen you

hand a forester a map, all they can do is use the

map for the intended purpose. When you provide a

taxonomy, other people can use it for identifying

points, as criteria for mapping, as description

for mapping units, and as a foundation for accumulating management knowledge. One kind of formal

taxonomy that would be very useful is a basic,

simple topographic or terrain classification. Another useful taxonomy is a local, simplified key

to soil types.

I wish you well in your symposium and in your

land classification efforts. A8 a person who believes that classification is a fundamental prerequisite of conceptual thought, I will be interested in your experiences, approaches, and

applications to land management

RECGMMFNDED READING

Several summaries of ecological land claaaification have been written in an attempt to provide an overview and an extensive list of references . The following are recommended for their

breadth and references:

0vervJ

Description, Classification, and Mapping of Forest

Ecosystems, Chapter 16 in the book, Forest

Ecology (Kimmins 1987)

Nature of land and resource classification--a re

view (Bailey, Pfister and Henderson 1978)

Ecogeographic Analysis: A guide to the Ecological

Division of Land for Resource Management

(Bailey 1988).

An ecological land classification framework for

the United States (Driscoll and others 1984)

' . westezznlaited StaU Classlficat=

ECOCLASS--a Method for classifying ecosystems.

(Pfister and others 1983; Driacoll 1984)

1 5
Ecological Land Classification in Montana and

Idaho (Pfister 1977)

Land classification based on vegetation- applications for resource management symposium (Ferguson, Morgan and Johnson 1989)

Forest habitat type classification in western

United States (Pfister 1984)

Workshop on southwestern habitat types --

Albuquerque, NM (Moir and Hendzel 1983)

(Copies of my symposium papers are available by

writing to me at the School of Forestry, University of Montana, Missoula, MT 59812)

LITERATURE CITED

Allen, B. H. 1985. Ecological type classification

for California: the Forest Service approach.

Gen. Tech. Rep. PSW-98. Berkeley, CA: U.S.

Department of Agriculture, Forest Service,

Pacific Southwest Forest and Range Experiment

Station. 8 p.

Arno, S. F. 1980. Forest regions of Montana. U.S.

Department of Agriculture. Forest Service Research Paper INT-218. Intermountain Forest

and Range Experiment Station, Ogden, UT 39 p.

Bailey, R. G. 1976. Ecoregions of the United

States. U.S. Department of Agriculture, Intermountain Region, Ogden, UT. Map. Scale

1:7,500.

Bailey, R.G. 1984. Integrating Ecosystem Components (p. 181,188). ;En: Forest Land classification: Expriences, problems, perspectives.

Proceedings of a symposium: March 18-10,

1984. University of Wisconsin, Madison

Bailey, R. G. 1988. Ecogeographic analysis; a

guide to the ecological division of land for

resource management. Washington, DC: U.S.

Department of Agriculture, Forest Service,

Miscellaneous Publication 1465. 16 p.

Bailey, R. F.; Pfister, R. D.; and Henderson, J.

A. 1978. Nature of land and resource classi-,

fication -- a review. Journal of Forestry

76: 650-655.

Bockheim, J. G. (ea.1 1984. Forest land classification: Experiences, problems, perspectives.

Proceedings of a symposium; 1984 March 18-20;

Madison, WI: University of Wisconsin. 276 p.

Corlis, J.C. 1974. EOCCLASS--A method for claS

sifying ecosystems. IQ: Foresters in landuse planning. 1973 National Convention, Society of American Foresters, p. 264-271

Driscoll, R. S.; Minkel, D. L.; Radloff, D. L.;

Snyder, D. E.; Hagihara, J. S. 1984. An ecological land classification framework for

the United States. Misc. Pub. 1439. Washington, DC: U.S. Department of Agriculture,

Forest Service. 56 p.

Ferguson, D. E.; Morgan, P. J.; Johnson, F. D.

(compilers). 1989. Proceedings--land classifications based on vegetation: applications

for resource management; 1987 November 17-19;

Moscow, ID. Gen. Tech. Rep. INT-257. Ogden,

UT: U.S.Department of Agriculture, Forest

Service, Inter-mountain Research Station. 515

P.

Kimmins, J. P. 1987. Forest ecology. New York:

MacMillan Publ. Co. 531 p.

Kuchler, A.W. 1964. Manual to accompany the mappotential vegetation of the conterminous

United States. Am. Geo. Sot. Spec. Pub1 36.

116 p. t map (scale 1:1,000,000)

Moir, W.H.; Hendzel,L. 1983. Proceedings of the

workshop on southwestern habitat types, April

6-8, 1983, Albuquerque, NM. U.S. Department

of Agriculture, Forest Service, Southwest Region. 110 p.

Nelson, D.; Jordan, J.K. 1987. Application of

multi-factor land classification on national

forests. Economic and Social Development--A

role for forests and forestry professionals.

Proc. 1987. Sot. Am. For. Nat. Conv. p. 162-

165

Pfister, R. D. 1977. Ecological classification

of forest land in Idaho and Montana. (p. 329-

326) In: 1977 Proc., Ecological Classification of Forest Land in Canada and Northwestern USA, Univ. of British Columbia, Vancouver, CAN.

Pfister, R. D. 1982. Designing successional

models to meet management needs. In: Forest

Succession and Stand Development Research in

the Northwest. Proceedings of the Symposium

held 26 March 1981 at Corvallis, OR. Forest

Research Laboratory, Oregon State University,

p. 44-53. (Joseph E. Means, editor).

Pfister, R. D. 1984. Forest habitat type classification in the western United States. In:

Symposium on Site and Productivity of Fast

Growing Plantations, Proceedings of a Symposium held in Pretoria and Pietermaritzburg,

South Africa, 30 April-11 May 1984. International Union of Forestry Research Organizations. (Editorial Committee: D. C. Grey,

A. P. G. Schonau and C. J. Schutx) volume lp.149-162.

Pfister, R. D. 1988. Site classification and

productivity in the western United States.

(p. 81-91) In: Williams, T. M. and C. A.

Gresham, Ed. 1988. Predicting Consequences

of Intensive Forest Harvesting on Long-term

Productivity by Site Classification. IEA/BE

Project A3, Report No. 6. Baruch Science Institute of Clemson University, Georgetown, S.

C. USA

1 6
Pfister, R. D. 1989. Basic concepts of using

vegetation to build a site classification

system. In: Symposium, Land Classifications

Based on Vegetation: Applications for Resource Management, MOSCOW, ID, November 17-

19, 1987.

Pfister, R. D. and S. F. Arno. 1980. Classifying

forest habitat types based on potential climax vegetation. Forest Science 26:52-70.

Pfister, R. D.; Kovalchik, B. L.; Arno, S. F.;

Presby, R. C. 197-l. Forest habitat types of

Montana. USDA For. Serv. Gen. Tech. Rep.

INT-34, 114 p. Intermountain Forest and Range

Exp. Sta., Ogden, UT.

Pfister, R. D.; Corliss, J. C.: [ and others 1.

1973. Ecoclass--a method for classifying ecosystems --a task force report to the Chief of

the Forest Service. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.

52 p.

Wertz, W. A.: Arnold, J. F. 1972. Land system

inventory. Ogden, UT: U.S. Department of Agriculture, Forest Service, Inter-mountain Region. 12 p.

17
ECOLOGICAL CLASSIFICATION SYSTEM

FOR CLASSIFYING LAND CAPABILITY

I N

MIDWESTERN AND NORTHEASTERN U.S. NATIONAL FORESTS L'

Walter E. Russell and James K. Jordan -2r

Abstract.--The Ecological Classification System used by the

National Forests in the U.S. Eastern Region is a framework to

facilitate integrating appropriate physical and biological

factors to stratify forest landscapes into homogeneous resource

capability units. The framework provides seven hierarchical

levels to accommodate land capability-suitability analyses

at different levels of resolution, depending on management

information needs.

Keywords: Ecological Type, Ecological Unit, Ecological Land

Type, Land Type Association, Ecological Land Type Phase,

Opportunity Area.

INTRODUCTION

The National Forest System (NFS), USDA, .Forest

Service, is responsible for administering and

managing the 156 National Forests and 17 National

Grasslands in the United States. The Eastern

Region (R-9) of the NFS includes nearly 12 million

acres of public land in 17 National Forests distributed across 20 midwestern and northeastern

states. R-9 contains roughly 6 percent of the

total National Forest System land, and approximately half the Nation's human population.

BACKGROUND AND NEED

National Forest management is guided by a number

of federal public laws and regulations, beginning

with the Organic Act of 1897. Besides the Organic

Act, some of the more notable laws include the

Multiple Use-Sustained Yield Act (MUSY) of 1960,

National Environmental Policy Act (NEPA) of 1969,

Resources Planning Act (RPA) of 1974, and the

National Forest Management Act (NFMA) of 1976,

which amended the RPA. Some of the significant

i/Presented at the Symposium on Ecological

Land Classification: Applications to Identify the

Productive Potential of Southern Forests,

Charlotte, NC, January 7-9, 1991.

21 - Regional Soil Scientist, Eastern Region,

Milwaukee, WI; and Forest Soil Scientist, Ottawa

National Forest, Ironwood, MI, respectively,

Forest Service, U.S. Department of Agriculture.

requirements woven through these laws and their

implementing regulations are: (1) The National

Forests will be managed as ecosystems, (2)

productivity of the land will be sustained, and

(3) environmental effects will be evaluated.

The Forest Planning process developed pursuant

to the National Forest Management Act began to

bring to a head the need for more and better

spatial information about forest ecosystems,

including land capability. The completion and

subsequent implementation of Forest Plans brought

more focus to this need. The focus is being

further sharpened through the evolution of resource management issues, as the public further

defines the values and uses for which they want

their National Forests maaaged.

Agency Policy h Guidance

Overall National Forest policy on ecosystem

classification and inventory is outlined in

Forest Service Manual (FSM) 2060. This policy

states in part:

"1. The Forest Service shall use ecological

type classification to coordinate and integrate

resource inventories to stratify land and

resource production capability and responses

to management.

"2.. Ecological Units shall be identified in

inventory, evaluation, planning, and resource

management on National Forest System (NFS)

lands."

18
Forest Service Handbook (FSH) 2090.11 provides

overall procedural guidance for ecological classification and inventory, and lists the following

components, as appropriate for classification and

characterization of ecological types:

-Vegetation (Potential Natural Community)

-Soils

-Water (Hydrology)

-Geology (stratigraphy, lithology, landform)

-Climate

-Topography (elevation, slope gradient, aspect)

Definitions (FSM 2060)

Ecological Type is defined as "a category of

land having a unique combination of potential

natural community, soil, landscape features,

climate, and differing from other ecological

types in its ability to produce vegetation

and respond to management."

Ecological Unit is defined as "a mapped landscape unit designed to meet management objectives, comprised of one or more ecological

types."

Thus, Ecological Type is a classification unit,

while Ecological Unit is a mapping or inventory

unit.

EASTERN REGION (R-9) ECS

Structure

The R-9 Ecological Classification System (ECS)

is structured as a hierarchical framework, similar

to that of the Land Systems Inventory concept that

was developed earlier in the Western United States

(Wertz & Arnold, 1972). This nested hierarchy

facilitates development of Ecological Units at

different levels of resolution, based on management needs (Nelson, Russell, and Stuart, 1984).

The hierarchical levels are illustrated in Table 1 .

Application of the hierarchical levels

Provinces and Sections are derived from those

in Fenneman's Physiography of the Eastern United

States (Fennemar, N.M., 1938). The Eastern

Region contains all or parts of eight provinces

and sixteen sections. These broad, natural physiographic divisions help explain and organize

information about natural environmental differences and similarities among the National Forests

in the Region. The Provinces and Sections are

illustrated in Figure 1.

Subsections have been used on some R-9

National Forests, such as the Green Mountain,

White Mountain, and Mark Twain. The Forest Service is involved in a cooperative project with

the Upper Great Lakes Biodiversity Committee;

The Nature Conservancy; Michigan, Minnesota, and

Wisconsin State Heritage Programs; and others,

developing subsections in the Upper Great Lakes

area, which includes eight National Forests.

They are focusing on macroclimatic zones and

major glacial physiographic landforms. Present

and future applications are expected to include:

Regional biodiversity;, Landscape Ecology, correlation of Ecological Units among National Forests,

beginning of ecological classification of State,

County, and privately-owned lands, Forest Plan

revisions, etc.

The Land Type Association (LTA), Ecological

Land Type (ELT), and Ecological Land Type Phase

(ELTP) levels are sometimes referred to as the

"working levels" of the ECS, because they have to

date received by far the most use. These are the

levels that are most useful at the Forest level,

for land and resource management planning, and in

the implementation of Forest Plans.

The LTA level: Land Type Associations generally number between about 10 and 20 per National

Forest. This level was used extensively in the

Forest Planning process in the Eastern Region.

The primary use was to aid in allocating land to

Management Areas, and developing desired future

conditions.

The ELT level: A few National Forests used

Ecological Land Types for Forest level planning.

More commonly, however, the ELT level is used for

more detailed planning on subdivisions of National

Forests -- Management Areas, or Opportunity Areas.

ELTs commonly repeat across the landscape in a

predictable pattern within an LTA.

The ELTP level is the most detailed, sitespecific level that is normally mapped on an

operational basis. It usually provides the level

of detail of land capability-suitability information that is needed for project level applications. ELTPs commonly repeat across the landscape

in a predictable pattern, within a given ELT.

ELTPs can be thought of as mapped representations

of sites.

The Site level:. The site is the classification

unit - the ecological type, as defined earlier;,

the primary data collection unit. Sites by themselves are not usually delineated on maps, except

for special purposes where there is need for

extremely site-specific information.

Background

Classification and inventory of ecological

units began in R-9 on a limited basis during the

early 1970'9, The R-9 Ecological Classification

System (ECS) was institutionalized in 1979 when

the ECS Handbook Chapter was issued. (FS H

1909,21, chapter 30). Subchapter 32, Riparian

Area Subsystem, was added to the Regional ECS

Handbook in 1981.

The early emphasis of the ECS was to obtain

relatively rapid, inexpensive inventories for

broad level planning purposes. The broad level

inventories , primarily at the LTA level, provided

most of the land capability information that was

thought to be needed for the first round of

Forest Planning under the National Forest Management Act. Forest Plan implementation, however,

requires a more detailed level of land and ecb19
Table 1 .--Hierarchical levels of R-9 Ecological Classification System (ECS) (Forest Service

Handbook 1909.21 - Eastern Region Land and Resource Management Planning Handbook, Chapter 30,

1979, working draft revision.)

LEVEL

Province

Section

Subsection

PRIMARY

DIFFERENTIATING CRITERIA

Geomorphology, Climate

Geomorphology, Climate,

Vegetation

Climate, Geomorphology,

Vegetation

TYPICAL

SIZR

Multi-State

Thousands of

square miles

Tens to

Hundreds of

square miles

APPLICABLE

PLANNING LEVEL

National-Regional

Regional-Subregion

Multi-Forest

State

Landtype Landforms, Natural Tens to Forest

Association Overstory Communities, Thousands

(LTA) Soil Associations of acres

Ecological Landform, Natural Tens to Ranger District,

Landtype Vegetative Communities, Hundreds Management Area,

(ELT) Soils of Acres Opportunity Area al

Ecological Soils, Landscape position, Ones to Tens Project

Landtype Natural Vegetative of Acres

Phase (ELTP) Communities

Site Soils, Landscape Position, Less than one Individual Site

Natural Veg. Community acre

fi'opportunity Area: A land area identified as providing best opportunities to work toward

Forest Plan goals and objectives in an integrated manner. Each National Forest is divided

into a number of Opportunity Areas for Forest Plan Implementation (USDA, Forest Service,

Eastern Region, 1985, pp. 12-15).

system stratification. Resolution of some of today's public issues call for still more detailed

and data-intensive applications of ecological

classification. Today's publics are calling for

more emphasis on a wider array of values and uses

that National Forests can provide; for example,

biodiversity, Old Growth forests, threatened and

endangered species habitat, etc. These demands

call for more detailed, data-intensive applications of the ECS, so that it can provide better

information on ecosystem dynamics, as well as

land capability to sustain uses and provide goods

and services.

Most ECS emphasis to date has been on terrestrial applications. There is growing awareness,

however, of the need to devote more attention to

developing ecological classification of aquatic

ecosystems.

Operational application

The ECS has been applied in R-9 through the

Soil Resource Inventory (SRI) activity. Ecological

unit inventory operations are coordinated with the

National Cooperative Soil Survey (NCSS). The

integration of landform, vegetative, and climatic

parameters enhance the soil mapping units,

particularly from descriptive and interpretive

perspectives.

Ecological Units are used to determine land

capability for a wide range of resource management

prescriptions, evaluate costs and benefits, and

predict effects of actions or non-action applied

to any given piece of land..

The ECS does not replace timber or other vegetation inventory systems. The ECS identifies the

potential natural vegetation community, but not

the current vegetative cover. Other inventory

systems (Vegetative Management Information System

[VMIS] in R-9, for example) identify current

vegetative conditions and temporal changes in

vegetative cover, but not potential natural

vegetation. So the ECS and other established

vegetative information systems compliment each

other very well.

Examples of Forest Applications

Recognizing that Ecological Type classification and Ecological Unit mapping are evolving

sciences and arts, both the National and Regional

direction allow for some latitude in approaches

on individual National Forests. Following are

some examples of ECS development and application

on some of the National Forests in the Eastern

Region.

2 0
I +-3\ r-??-b-&PERlOk UPLANDS

OlSSECTED TILL PLAINS -.

-cd.

CENTRAL LOWLANDS

OZARK

“lLLS PLik””

PI ATCA ‘” COASTAL D-LnlLn PLAIN

0 ..lDy-

- PROVINCE8

- - SECTIONS

Figure l.--Provinces and Sections of the Eastern Region

The White Mountain National Forest is in the

States of New Hampshire and Maine, in the New

England Province, and the White Mountain Section.

Richard Alvis began mapping Ecological Land Types

(ELTs) on the White Mountain National. Forest about

1973. Alvis had had previous experience with Land

Systems Inventory on National Forests in the

western United States. On the White Mountain,

Alvis based the RLT delineations on core differentiating characteristics of geomorphic

process, soil substrata, and vegetative potential

(climax community). In addition to the core,

differentiating characteristics, several associated

characteristics were identified, which helped to

characterize and describe each ELT. Management

interpretations were developed, which indicated

suitabilities and predicted responses of each ELT

to various management practices and uses.

Ecological Land Type mapping was completed on the

White Mountain National Forest about 1983, and was

heavily used in developing the Forest Plan.

The Land Type Association (LTA) level was not

applied on the White Mountain. Four subsections

were defined and delineated, to help organize and

understand similarities and dissimilarities among

certain ELTs.

The Ecological Land Type Phase (ELTP) level is

being applied selectively on the White Mountain.

Ecological Land Type Phases follow the "Habitats"

developed by William Leak, of the Northeast

Forest Experiment Station at Durham, NH (Leak,

1982).

The approach on the Green Mountain and Finger

Lakes National Forests in the States of Vermont

and New York is similar to that on the White

Mountain.

The Mark Twain National Forest in southern

Missouri, is nearly all in the Ozark Plateau

Province, and mostly in the Salem Plateau Section

(Miller, 1981). Ecological classification on the

Mark Twain began during the mid 70's. The Mark

Twain utilized a team approach to develop

ecological classification largely from a synthesis

of existing data and information on soils and

vegetation.

The Forest was initially divided into 5 Subsections, within the 2 Provinces and 3 Sections

that the Forest is part of. The 5 subsections

were based on differences in land surface form,

geology, soils, and natural vegetation. The subsections were subdivided into 18 Land Type

Associations (LTAs), based on a finer break-down

of land surface form, surface geology, and potential vegetative community. The soils information

was from the cooperative soil survey which was ongoing at the time. The vegetative information was

from the Natural Communities classification,

developed by the Missouri Natural Areas Committee

of the MO Dept. of Natural Resources.

The Land Type Associations and Ecological Land

Types were used heavily to determine land capability and suitability for the Forest Plan, The

2 1
Ecological Land Type information is,heavily used

in implementing the Forest Plan.

The Mark Twain National Forest ECS is currently

being updated, based on re-evaluation of infonnation needs, and "new" data and information that

was not available during the first go-round. The

"new" data and information includes a completed

soil survey, and increased knowledge of the

Missouri Natural Plant Communities. The revision

will include additional ELTs with more detailed

and accurate definitions and descriptions, and

may include some application of the Ecological

Land Type Phase level.

The Mark Twain and White Mountain are two of

the few National Forests in the Eastern Region

that have implemented Aquatic Ecological Classification of their surface waters.

The Ottawa National Forest is located in the

western part of the Upper Peninsula of Michigan,

in the Southern Superior Section of the Superior

Uplands Province. Ecological classification

began on the Ottawa during the early 7Os, in

response to a need for land capability information

for Forest Planning (USDA, Forest Service, Eastern

Region, 1987, pp. 51-67, 131-158). Very little

information about basic resources (soils, natural

vegetation, glacial geology) was available at

that time.

The Forest was first divided into 21 Land Type

Associations based on major glacial landforms,

areas of bedrock control and outcrop, and major

post-glacial erosional landforms. The Forest was

also divided into three distinct macroclimatic

zones, based on climatic differences caused by

proximity to Lake Superior. The Land Type

Associations and climatic zones provided the basic

land capability-suitability information used in

the Forest Planning process.

The Ottawa National Forest Leadership Team

recognized the need for more detailed levels of

Ecological Classification in 1977. Subsequently,

the Forest entered into a cooperative agreement

with Michigan Technological University to fully

characterize and analyze, stratified, randomly

selected sample areas representative of all Land

Type Associations, a two percent sample of the

Forest. Within each sample area, systematic sampling was completed for soils, landforms, and

total vegetation. Through the use of computer

ordination models, vegetation relationships were

established. Site concepts (Ecological Types)

were developed and mapping unit (Ecological Units)

concepts were developed based on the observed recorded, and analyzed soil,Iandform, and vegetation

relationships.

From the detailed analysis of soils, vegetation, and landforms, the Ottawa develops Ecological Types, and from the Ecological Types, they

build Ecological Land Type Phases (ELTPs) for

mapping. Each ELTP is composed of a major site

unit (Ecological Type), and usually one or more

minor site units (mapping inclusions). A few

ELTPs have 2 or even 3 major site units; these are

mapping complexes. ELTPs are used to evaluate

site potential, predict ecological processes such

as successional pathways, and predict environmental effects at the individual project level of

detail. ELTPs are aggregated upward into ELTs

for more generalized Opportunity Area Analyses

and Planning. The process of developing, classifying, and mapping ELTPs continues today, and

presently covers approximately 75 percent of the

U.S. owned land in the Ottawa National Forest.

However, not all of the Forest will be mapped to

the ELTP level; areas where management information

needs do not require that level of detail have

been identified, and are or will be mapped to the

ELT level. Concepts, mapping, verification, and

development of interpretations continues with

involvement of scientists from the North Central

Forest Experiment Station, and Universities in

Michigan, Minnesota, and Wisconsin. Correlation

with adjoining National Forests has begun and

will strengthen the use of the Ecological Classification System.

The Huron and Manistee National Forests are

located in the northern part of the Lower

Peninsula of Michigan, in the Great Lakes Section

of the Central Lowlands Province. These 2 National

Forests are managed under 1 Forest Supervisor,

and are generally referred to as the Huron-Manistee.

Similarly to other National Forests in the

Great Lakes States, Land Type Associations on the

Huron-Manistee are keyed to glacial landforms.

Initiation of a more detailed level of ecological

classification was precipitated in the early '80s

by a recognition that the "conventional" soil

survey was not effectively differentiating some

units of landscape that were ecologically significant (USDA, Forest Service, 1987, pp. 15-17,

30-41, 68-97, 186-240).

Since 1984, the Huron-Manistee has been working

with Michigan State University and the North

Central Forest Experiment Station, developing an

Ecological Type classification for the Forests.

The Forest Service and University collaborators

have taken an analytical approach, emphasizing

multiple factors and functional relationships

between ecological variables (Cleland, 1982).

Plots for sampling multiple factors were located,

using a landform-based stratified random sampling

design. Detailed observations in each plot were

recorded for all vegetation layers, soils, and

local landform characteristics. Compositional

patterns detected in multivariate analysis of

floristic data were used to form ecological

species groups, and relate vegetation patterns to

environmental factors (Host, 1987). Ecological

Types are based on a combination of Ecological

Species groups, soils, and landforms, and form

the basis for the Ecological Unit inventory,

primarily at the Ecological Land Type Phase level.

The ELTP inventory on the Huron-Manistee is

being used to implementthe Forest Plan, and provide a sound ecological basis for responding to

current public issues.

22
The Allegheny National Forest is located in

northwestern Pennsylvania, in the Unglaciated

Plateau Section of the Appalachian Plateau

Province. The Allegheny is involved in a unique

approach to Ecological Classification, in collaboration with Dr. Lew Auchmoody of the Northeastern

Forest Experiment Station. The collaborators

there are attempting to construct Ecological Units

with a Geographic Information System, using plot

data to supplement existing data from such sources

as soil surveys, contour maps, digital elevation

models, geologic mapping, and existing vegetation

inventories.

Trends Across the Region

Activities in ecological classification are on

the increase on National Forests all across the

Eastern Region. On the Hoosier National Forest

in Indiana, Ecological Types are being developed

in a partnership among Forest resource managers

and scientists from Purdue and Indiana Universities, the Indiana Natural Heritage program, and

the North Central Forest Experiment Station. A

similar cooperative program is about to get underway on the Wayne National Forest in Ohio. The

Chippewa National Forest in Minnesota is developing Ecological Land Type Phases in cooperation

with the Minnesota Natural Heritage program. The

Chippewa has also done some pioneering work on

aquatic ecological classification of lakes (USDA,

Forest Service, 1987, pp. 159-181).

On the Superior National Forest in northeastern

Minnesota, LTAs, ELTs, and ELTPs were developed in

the mid 1970's and played a prominent role in

developing and implementing the Forest Plan

(Prettyman, 1982). The Superior's interdisciplinary (ID) monitoring team made Forest-wide field

visits in 1990 to monitor implementation and

effectiveness of the Plan; standards and guidelines are ELT dependent and are used in project

activity planning (Siderits, 1981). The Ecological Classification System on the Nicolet National

Forest in northern Wisconsin is currently being

updated, using recent technology developed on the

Ottawa and Huron-Manistee National Forests. The

Hiawatha.National Forest in Michigan's Upper

Peninsula recently completed a cooperative project with Michigan State University, developing

upland ELTPs, using technology developed earlier

on the Huron-Manistee National Forests. The

Hiawatha is presently cooperating with Michigan

Technological University, to develop wetland

Ecological Types. The ELT inventory is about to

be completed on the Chequamegon National Forest

in Wisconsin (USDA, Forest Service, 1987, pp.

182-184). On the Monongahela National Forest in

West Virginia, an effort to develop "provisional"

Ecological Land Types, based on existing soils,

geology and landform information, is just

beginning.

Ecological Classification is both researchable

and operational., The Ecological Classification

project on the Huron-Manistee National Forests

has provided research for 6 Doctoral and 3 Master

of Science candidates (Cleland, D.T., Personal

communication). However, Ecological Classification is at least as much a research tool as a

research topic. Ecological Classification defines

differences and similarities between geographic

areas, thereby greatly enhancing extrapobility of

research results. The researchers at the North

Central Forest Experiment Station's Landscape

Ecology unit at Rhinelander, Wisconsin, are finding the ECS to be an indispensable tool in defining landscape heterogenity (Crow, T.R., talk at

Madison, WI, Nov. 28, 1990).

SUMMARY

The American people, through their elected

representatives, have directed that National

Forests are to be managed as ecosystems, to provide

a sustained yield of a wide array of values, uses,

goods, and services. National Forest policy

directs that ecological classification and inventory shall be used to help accomplish this.

Ecological Types are classified based on a combination of multiple factors of soils, potential

vegetation community, geology, topography, hydrology, and climate. Ecological Units are mapped

representations of Ecological Types.

The Eastern Region (R-9) of the National Forest

System uses a multi-level, hierarchical Ecological

Classification System (ECS). The hierarchical

framework facilitates mapping of ecological units

at different levels of site specificity in order

to satisfy different management needs.

The ECS in R-9 has been evolving since the

early to mid 70's, in response to changing management needs. The original intent was to provide a

relatively rapid method to inventory natural

resources and estimate land capability for Forest

Planning. Today, the trend is toward more detailed, data-intensive applications, in order to gain

better understanding of ecosystems, to respond to

today's resource management issues.

ACKNOWLEDGEMENTS

The authors thank their co-workers who have

worked diligently over the years to develop,

apply, and constantly improve ecological classification and inventory of the National Forests

in Region 9, and who have offered suggestions to

help develop and improve this paper. Recognizing

that when names are named where so many have contributed, some will be omitted, we especially

thank: Dave Cleland, Huron-Manistee National

Forests; Eunice Padley, Hiawatha National Forest;

Barbara Leuelling, Superior National Forest;

David Shadis, Chippewa National Forest; Patrick

Merchant and Frank Voptas, Wayne-Hoosier National

Forests. We also thank our able typist, Eleanor

Sczesny, who cheerfully, competently, and professionally, gave extra effort to make this

publishable.

23
LITERATURE CITED

Cleland, David T.; Hart, James B.; Pregitzer,

Kurt S.; [and others]. 1985. Classifying Oak

Ecosystems for Management. In: Proceedings,

Challenges in Oak Management and Utilization;

1985 March; Madison, WI: pp. 120-131. [Available from Cooperative Extension Service,

University of Wisconsin, Madison, WI.]

Fenneman, N.M. 1938. Physiography of the Eastern

United States, McGraw-Hill Book Co., 714 pp.

Host, George E. 1987. Spatial Patterns of Forest

Composition, Successional Pathways, and Biomass

Production Among Landscape Ecosystems of

Northwestern Lower Michigan. East Lansing:

Michigan State University. 180 pp. Dissertation.

Jordan, J.K.; Sommerville, J.L.; Meunier, J.L.;

[and others]. 1984. Ecological Classification

System, Its Application in Development and

Implementation of Forest Planning on the

Ottawa National Forest (poster display). In:

Proceedings, Forest Land Classification:

Experiences, Problems, Perspectives; 1984

March; Madison, WI: p. 251. [Available from

University of Wisconsin, Madison, WI.]

Jordan, J.K. 1982. Application of an Integrated

Land Classification. In: Proceedings,

Artificial Regeneration of Conifers in the

Upper Great Lakes Region; 1982 October;

Green Bay, WI; pp. 65-82. [Available from

Michigan Technological University, Houghton,

MI.]

Leak, W.B. 1982. Habitat Mapping and Interpretation in New England. USDA, Forest Service,

Northeastern Forest Experiment Station,

Research Paper NE-496, 22pp.

Miller, M.F. 1981. Ecological Land Classification,

Terrestrial Subsystem, Mark Twain National

Forest Missouri. USDA, Forest Service, U.S.

Government Printing Office. 56 pp.

Nel son, D.O.; Russell, W.E.; and Stuart, G.W.

1984. The Ecological Classification System

in the National Forest System, Eastern Region.

In: Proceedings, Forest Land Classification:

Experiences, Problems, Perspectives; 1984

March; Madison, WI: pp. 270-276. [Available

from University of Wisconsin, Madison, WI.]

Prettyman, Donald H. 1982. Integrated Land Inventory and Conifer Regeneration. In: Proceedings,

Artificial Regeneration of Conifers in the

Upper Great Lakes Region; 1982 October;

Green Bay, WI; pp. 83-llO.-[Available from

Michigan Technological University, Houghton, MI]

Siderits, Karl. 1981. Mammals of the Superior

National Forest. Duluth, MN. U.S. Department

of Agriculture, Forest Service, Superior

National Forest. pp. 44-52,

USDA, Forest Service, Eastern Region. 1985.

Working Together for Multiple Use, Integrated

Resource Management. U.S. Government Printing

Office, 27 pp.

USDA, Forest Service, Eastern Region, 1987.. Proceedings, Ecological Classification System

Workshop; 1987 May; Traverse City, MI:

[Available from USDA, Forest Service, 310 W.

Wisconsin Ave., Milwaukee, WI 53203.1

Wertz, W.A.; and Arnold, J.F. 1972. Land Systems

Inventory. U.S. Department of Agriculture,

Forest Service, Intermountain Region, Ogden,

UT. 12 pp.

2 4
HISTORY OF FOREST SITE CLASSIFICATION IN THE SOUTH?'

David H. Van Lear?'

Abstract.--Potential productivity has been a cornerstone of

most land characterization systems used in the South.

Soil-site studies identified many variables related to site

quality in the exploited southern landscape. Early

attempts to use county soil isurvey maps to delineate site

quality were generally unsuccessful, although maps refined

to reflect soil and site fea,tures important to tree growth

have been successfully used :for the past 20 years. More

recently, landform has been recognized as the primary

factor controlling repetitivle patterns on the landscape, a

realization which has markedly increased understanding of

forest-site relationships. (Current research focuses on

development of landscape eco,system classification systems

which integrate relationship,s among landform, soil, and

vegetation, and which can be linked with modern remote

sensing capabilities.

Keywords: landform, landscape ecosystem, GIS, physiography

INTRODUCTION

The evaluation of site quality and development

of site classification systems have been among the

dominant themes of American forestry for nearly

all of the 20th century. Because land is the

foundation on which the art and science of forest.

management must be built, foresters have long

appreciated the need to understand the relationships between forests and their physiographic

environment. "Students of forestry in the United

States are constantly demanding a guide to the

topography, drainage, soils, and climatic features

of the country." So stated Isaiah Bowman of Yale

University in the preface of his book Forest

Physiography, published in 1914. We now have many

guides, or land classification systems, that

enhance our understanding of forest-site

relationships.

Most site classification systems used in

Southern forestry have been primarily concerned

with grouping land units on the basis of productivity. Foresters needed to know what the land

could produce. This issue, or subtle variations

of it, has been the stimulus of much of the site

classification research over the past seven

'Presented at the Ecological Site

Classification symposium, Charlotte, NC,

January l-9, 1990.

'/Professor, Department of Forest Resources,

Clemson University, Clemson, SC.

decades. Although certainly an important issue,

one can see that there is some historical

justification to the criticism that foresters are

often too production oriented. We would have

saved ourselves much grief if we had been more

concerned in the beginning about ecological, as

well as economic, values of sites. Unfortunately,

the discipline of ecology was still in its

formative stages when foresters were first

becoming concerned about site classification.

The South is a region of extremely variable

physiography (Hunt 1974). Soil, topography,

climate, and biotic factors interact to produce a

multitude of forest sites in the different

physiographic provinces (Ralston 1978; Zahner

1984; Smalley 1986; McNab 1987). In addition, the

land has a history of human occupation for

thousands of years, during which time it has been

exploited to various degrees by all manner of

people from the original Indians (Hudson 1976) to

the timber barons of the late 19th century (Healy

1985). This long and complex land-use history

superimposed on a complex physiography presented a

great challenge to foresters seeking to

characterize the land.

FOREST SITE CLASSIFICATION IN TIiE SOU!l'Ii

An Appropriate Expression of Site Quality

Early in this century foresters were primarily

concerned with forest protection and the productive potential of the land. Productivity is

25
a complex concept and difficult to define. It

basically deals with the ability of land to

produce biomass, not just at the present time but

in the future as well. Productivity is a function

of both biotic factors and abiotic factors and

their interaction (Switzer 1978). Biotic factors

which influence productivity include species,

stocking, competition, the incidence of disease

and insects, and the past history of the stand.

Abiotic, or non-living, factors of the environment

include soil and site features which control the

availability and supply of soil moisture and

nutrients for plant use. Site quality was

considered -table by early foresters, but it is

now recognized that land has an inherent

productivity which can be increased or decreased

by management activities.

Productivity was a logical basis for site

classification. Early foresters recognized the

need for, as well as the difficulty of developing,

a standard method of expressing site productivity.

Some argued that site quality should be expressed

in terms of volume growth (Bates 1918}. However,

problems in using volume at this time were abundantly apparent (lack of yield tables, difficulty

of measurement, differences in units and merchantability limits, periodic vs. final yield, evenage

vs. uneven age stands, etc.). Consequently, this

concept did not receive wide endorsement.

Another school favored the use of forest sitetypes, using plant indicators (Zon 1913). This

method failed to gain favor in the eastern United

States because it was thought that too many

factors other than site influence composition and

development of understory plant species,

especially in the highly disturbed forests of the

South.

A third group, which prevailed, favored the use

of height at a given age as an index of site

quality (Frothingham 1921). Site index was

favored because it was directly related to volume

growth in normally stocked stands, easily

measured, and considered free of the effects of

stand density over a rather wide range of stand

density. In 1923, the Society of American

Foresters recommended that site index, in

conjunction with soon to be developed yield

tables, be used throughout the country to express

the productive potential of forest land.

The theory of using tree height as an index of

site quality has been widely criticized (Mader

1963; Grigal 1984; Monserud 1984). Space limitations prevent discussion of these criticisms, many

of which are valid. Alternatives have been

proposed, but none have gained the wide acceptance

of site index. Gale and Grigal (1987) and

Henderson et al. (1990) recently proposed a

productivity index in which the sufficiency of

soils to support root growth is related to forest

growth and yield. While this concept is

attractive potential, it tacitly assumes that the

relationship between above-ground biomass and root

biomass of forest trees is the same for all sites.

This assumption may or may not be true, but in any

event requires further research. Despite the fact

that site index has many problems associated with

its use, its simplicity, general applica-bility,

and long tradition insures that it will continue

to be a commmon method of expressing site quality.

Soil-Site Studies

The Society of American Forester's decision to

recommend the use of site index as the appropriate

expression of site quality initiated a half

century of soil-site research. In the South,

foresters pondered the question of how could sites

be classified into productivity classes in the

absence of suitable forest cover? Much of the

South's forest had been heavily cut over and

cleared for crop production by 1935. Crop land

acreage in the South (excluding Texas and

Oklahoma) peaked in the mid 1930s at about 65

million acres and between 1900 and 1925 the South

led the nation in timber volume cut (Healey 1985).

It was against this background of worn-out

farmland and cutover forests that the first soilsite studies were conducted in the South.

In 1935, T. S. Coile of Duke University

published a paper which concluded that the site

index of Piedmont land in North Carolina for

shortleaf pine was related to the nature of the

subsoil and the thickness of the A horizon.

Similar relationships were later found for

loblolly pine (Coile 1948). At about the same

time, Turner (1938) found that site index of

loblolly and shortleaf pines in the Coastal Plain

of Arkansas was related to surface soil depth,

subsoil texture, and internal drainage. Turner

observed that topographic features such as slope

position and steepness correlated with site index.

Coile also recognized the relationship between

topographic position and site index, but did not

include it in his regression equations because

slope position was correlated to soil depth and

did not improve the precision of his equations.

The regression approach used in these studies

was apparently addictive, because the pioneering

efforts of Coile and Turner were followed by

decades of similar studies throughout the southeastern United States and the nation. Numerous

investigators (see Carmean 1975) identified many

soil and site variables related to site index of

different species in various locations and

physiographic regions. Variables associated with

site quality were those that directly or

indirectly influenced availability and supply of

water and nutrients for tree growth.

The same soil factors that affect the availability and supply of water and nutrients may also

correlate with the quality of the growing space

for tree roots. However, topographic features of

the landscape may insure a supply of water and

nutrients via internal drainage which could

compensate for a lack of quality growing space for

roots on some sites. Topographic features such as

aspect, slope position, and slope shape

consistently relate to site index (Car-mean 1975).

26
In mountainous or hilly terrain, topographic

features are often equally or more important to

site quality than soil factors (Doolittle 1957;

Ike and Huppuch 1968; McNab 1987; and Rightmyer

1988). Aspect in steep terrain influences

evapotranspiration, which affects development of

water stress and subsequent tree growth (Lee and

sypo1t 1974). Subsurface flow of water (Hewlett

1982) benefits trees growing on sites located on

lower slope positions.

It has been difficult to determine relationships between measured soil/site variables and

site quality for hardwoods in the Coastal Plain

(Broadfoot 1969), probably because of the

complexity of drainage patterns in bottoms.

However, Baker and Broadfoot (1977) devised a

technique of site evaluation for eight southern

hardwoods which combined objective and subjective

evaluations of the relative importance of soil

physical condition, moisture availability during

the growing season, nutrient availability, and

aeration. This method is somewhat similar to the

concept of a productivity index mentioned earlier

(Gayle and Grigal 1987, Henderson et al. 1990),

except here the sufficiency of each soil and site

factor was related to site index rather than to a

soil rooting potential. Baker-and Broadfoot

developed their method from long years of field

experience and found that, when tested, the

technique accurately predicted site index of eight

southern hardwoods.

Soil-site studies identified many features of

the environment that related to tree growth and

site quality. However, the relationships are not

general for all sites. The essence that one can

derive from all these studies is that various

combinations of site factors influence the

magnitude and timing of supplies of soil moisture

and nutrients, the effective properties that

control site quality (Stone 1984). Within the

confines of necessary assumptions, stone's

conception allows one to make some order out of

the myriad of physical and chemical site factors

shown to be related to site quality, i.e., they

all affect the fundamental availability and supply

of water or nutrients.

In retrospect, soil-site studies essentially

provided a compilation of factors related to site

quality, and were not readily applicable to the

real management need for spatial delineation of

repetitive units of the landscape, i.e., maps.

The regression equations produced were often

cumbersome and difficult for managers to use.

The heterogeneity of forest sites made sampling

difficult for practitioners. Researchers themselves ignored the variablility of soil properties

within the experimental plots used to develop the

equations (Powers 1987). Even if properly

sampled, relationships between site quality and

any given soil/site parameter were seldom linear

(Fisher 1984). For all these reasons, experienced

field foresters often had a better intuitive feel

for site quality than could be obtained from

rather complicated eguations.

Use of Soil Surveys

The National Cooperative Soil Survey of the

Soil Conservation Service (SCS) had been mapping

soils in the South since the 1940s. With so many

soil-site studies indicating relationships between

soil properties and site quality, foresters

naturally tried to utilize soil survey maps when

available. However, they were generally

disappointed. They found that SCS soil surveys

too often pleased soil taxonomists but lacked the

user orientation needed by forest managers (Grigal

1984, Smalley 1986).

Most studies found that site quality varied too

widely within SCS soil series and mapping units to

be of practical use to foresters (Carmean 1965;

Van Lear and Hosner 1967; Broerman 1977). Not

only was site quality poorly related to soil

taxonomic units, response of the land to management was also unrelated. For example, Ku&la and

Fisher (1980) found that the response of slash

pine to fertilization was related to soil drainage

class and depth to and nature of the B horizon,

but was not related to soil series. The reason

for the lack of correlation between soil mapping

units and site quality or response to management

is obvious--those soil and site factors important

to tree growth are often not the same ones

considered in soil taxonomy and mapping.

Among the shortcomings of SCS soil surveys was

the fact that they failed to incorporate knowledge

that productivity is related to land-use history,

landform, and climatic conditions, as well as to

soil properties. Rowe (1984) suggested that the

problem originated when pedologists began

perceiving soils as natural bodies and things-inthemselves, rather than associating soils with

their ecological significance.

SCS soil surveys are general purpose surveys.

Soil surveys for forest management purposes should

consider relationships between productivity and

landform or moisture gradients (drainage) and

soils should be mapped on the basis of properties

known to be related to site quality and response

to management. There is hope. Soil taxonomists

increasingly recognize that the genesis and

distribution of soils are best understood when

studied in a landscape context, rather than at the

level of individual pedons or classification units

(Graham and Buol 1990). Arnold (1984) noted that

while the recognition of soil individuals is the

basis of the soil taxonomy used by SCS, there is a

need for a similar type of definition of

individual land areas that can be recognized and

delineated as ecological response units.

For about the last 28 years, forest industries

in the southeastern United States have been

mapping their own forest lands. These companies

chose either not to use published SCS county soil

surveys or to refine them to better meet their

special needs. Forest industries need an

inventory of their soil resources and a site

classification on the basis of productivity and

27
need for silvicultural treatment (Haines and

Haines 1981). Their mapping emphasizes landscape

features, such as shape of the landform, its

geologic origin, and the position on the

landscape. Special attention is given to the

relationship between surface and subsurface soil

properties and site quality (Everett and Thor-p

1990). Industrial experience in the South

indicates that foresters and soil mappers can work

together to produce maps that better characterize

forest land, not only for productivity but also to

provide information on potential erosion hazard,

regeneration potential, trafficability, and other

management considerations for different soils.

Physiographic Classification

Physiographic classification represents an

attempt to use physiography, i.e., the physical

expression of geologic history, topography, soils,

and climate of an area, to define broad land areas

within which the local landscape can be subdivided

into visually discrete landforms. The concept of

physiographic land classification was first

developed in Canada (Hills 1961). In 1975, Wertz

and Arnold proposed a similar system for the

United States to help standardize land classification and facilitate land-use planning, a topic

of great public interest in the early 1970s.

In the South, the physiographic land classification concept was first used by Hodgkins et al.

(1979) to map (scale of l:l,UOO,OOO) and describe

forest habitat regions and subregions of Alabama

and Mississippi from landsat imagery. Upper level

land classification units of province, region, and

subregion were broadly defined by geology,

topography, soil, and climate. Habitat regions

are primarily useful for national and regional

evaluations of forest resource conditions, and are

not intended to be the basic units of operational

land management where site specific decisions must

be made. However, habitat regions do provide the

foundation upon which the landscape can be further

subdivided. Other southern states with completed

habitat maps are Louisiana (Evans et al. 1983),

Georgia (Pehl and Brim 1985), and South Carolina

(Meyers et al. 1986).

Forest soil scientists often incorporated

topographic features into their regression models

during the heyday of soil-site studies. However,

the practicality of using easily recognizable

landforms in site classification systems in the

South was slow in coming. Coile (1952), in his

review of the relationship of soil and forests,

briefly discussed the importance of topographic

features to tree growth, but the concept of using

landforms as the integrator of the landscape

ecosystem (Rowe 1984) was not mentioned.

The term landform did not appear in early soilsite studies in the South, although its surrogate,

drainage class, did. Turner (1938) first

documented the relation betweeen drainage class

and site quality of southern pines in Arkansas

where excessively drained upland sands were poor

sites and loamy soils in floodplains with good

28

internal drainage were superior sites. In 1956,

Beaufait related site quality for willow oak to

topographic features such as ridges and flats (he

aid not call them landforms) and certain soil

properties. In the mid 197Os, Weyerhaeuser

Corporation developed a site classification system

for the Coastal Plain of North Carolina in which

soil features were stratified by landform

(pocosins, flats, clay uplands) to yield

regression equations which adequately predicted

site index of loblolly pine (Campbell 1978).

Until the data were stratified by landform,

accuracy of equations for estimating site index

was unacceptable.

The trend toward using landform (or drainage

class) as the basic component of site classification systems gained momentum in the 1980s.

Fisher (1981) described a site classification

scheme for the Coastal Plain based on productivity

differences which were related to drainage class,

depth to and nature of the B horizon, and

character of the A horizon. Drainage classes

reflect, in part, subtle differences in elevation

between lowlands and the Sandhills of the Coastal

Plain. Responses to management activities such as

site preparation and fertilization were related to

site classes, an important feature in the

increasingly domesticated forests (Stone 1975) of

the Coastal Plain.

Switzer and Shelton (1984) divided the upland

landscape of the Gulf Coastal Plain into five

landform components: crest, shoulder, backslope,

footslope and toeslope. Productivity was

subjectively related to landform with the poorest

sites on crests and shoulders and the best sites

on the toeslopes. Differences in productivity

among landforms were attributed to corresponding

differences in nutrient and moisture regimes.

Between 1979-1986, Smalley (1986) developed a

comprehensive and practical physiographic site

classification system for the Cumberland Plateau

and Highlands Rim provinces of the Interior

Uplands of the Southeast. His hierarchal system

progressively reduces complex landscapes to easily

identifiable landforms called landtypes.

Landtypes have resulted from similar climatic,

geologic, and pedologic processes and are

repetitive units of land with distinct potential

for growing trees and/or similar management

limitations and hazards. For each landtype, the

geographic setting, dominant soils, depth to

bedrock, soil texture, drainage, relative water

supply and fertility, and general vegetation is

described, as well as management interpretations.

Smalley's system is practical because it

identifies discrete units (landtypes) of the

landscape easily visualized by the forest manager

and because it has mapping capability at a scale

of delineation to meet most management objectives.

It provides information concerning species

suitability, competition, equipment limitations,

erosion hazard, and other factors, as well as

productivity. Vegetation is relegated to a

position of minor importance because current

vegetation was not considered to reflect site
potential and often did not coincide with site

boundaries. Sites are further subdivisions of

landtypes, but usually not mapped. In mountainous

or steep terrain, site conditions often vary

dramatically over short distances due to

interactions between parent material, depth to bed

rock, slope steepness and shape, aspect, terrain

stability, vegetation, climate, and drainage and

water supply from adjacent sites (Zahner 1984).

McNab (1987) published a first approximation of

a site classification system for the Southern

Appalachians similar to that of Smalley's for the

Interior Uplands. Slope features, such as slope

type, slope aspect slope position, slope shape,

and gradient are incorporated into the system at

different levels to divide the mountainous

landscape into increasingly smaller units until

landtype phases can be displayed on maps with a

scale of l:ZO,OOO or larger. These landtype

phases are the units appropriate for normal forest

planning and management. As with Smalley's

system, landtype phases are equivalent to an

ecological site type if vegetation information is

included in the description.

The transition from regression-based soil-site

studies to multifactor physiographic site

classification greatly increased understanding of

forest-site relations and the feasibility of

putting this knowledge to work in forest

management. As Rowe (1984) pointed out, landform

represents the most stable surface component of

landscape ecosystems and, over long periods of

time, becomes the primary correlate of soils and

vegetation in areas of similar regional climate.

Landforms are the prime cause of the repetitive

patterns of soil and vegetation seen on the

landscape. Thus, landforms with their associated

soils and biotic communites are the logical basis

on which site classification systems should be

developed. If site quality varies too widely

within landforms, than soil and vegetative

features can be used to stratify the landform into

units of more narrowly defined site quality.

Ecological Site Classification

Productivity concerns will always be an

important part of site classification systems.

However, because the forester is a steward of the

land and all its resources, the public is

increasingly demanding that equal consideration be

given to other values of the forest, e.g., wildlife habitat, watershed protection, endangered

plants and plant communities, etc. National

forests are especially vulnerable to public

pressures, but private forests will also come

under closer public scrutiny in the future. There

is no single site classification system currently

in use by the U. S. Forest Service. However, the

numerous systems in use by the agency all attempt

to delineate and describe units of land that are

fairly homogenous with respect to the relationships among vegetation, soil, and landform. The

lack of a uniform system of classifying potential

natural vegetation has hindered the full

incorporation of vegetative components into a

nationally standardized system (Larson and

Schlatterer 1984).

The concept of ecological site classification

was developed in Germany after World War II and

has been the basis for their multiple-use management for decades (Barnes 1984). Ecological site

classifications are similar to the multifactor

physiographic site classification systems just

described. However, in an ecological site

classification system, the three components of the

landscape ecosystem, i.e., landform, soil, and

vegetation, are integrated simultaneously in the

field. The local climax vegetation is identified

and groups of species with narrow ecological

amplitude, i.e., site specific, are determined and

used to delineate site unit boundaries.

Vegetation is given more consideration in

delineating site classes than in the physiographic

systems described previously.

In the South, ecosystem classification has been

applied to a portion of the Coastal Plain in South

Carolina (Jones, et al. 1984; Van Lear and Jones

1987). Late successional, near-climax hardwood

communities were identified along a landform

moisture gradient in the Hilly Coastal Plain

province of South Carolina. Site types were

identified by these late successional hardwood

communities, including both overstory and

understory species, that occupied specific

landforms. Community identification was based on

a relatively small number of character, or

diagnostic, species which tend to occur on certain

sites in conjunction with common species which

have a wider ecological amplitude. In addition to

the late successional hardwood communities

associated with different landforms, earlier

successional communities that precede them were

also identified. In this regard, this system is

similar to the habitat type approach developed by

Daubenmire (1952) for the northern Rockies and now

used extensively on national forests in the West

(Pfister 1989). Jones (1989) has recently

expanded this ecosystem classification system to

the Piedmont of South Carolina.

There are numerous reasons why information

about plant communities and successional trends

should be included along with landform and soil

components in land classification systems. By

including vegetation, landscape ecosystem classification provides more complete information about

ecosystem diversity and functioning. This information is essential if planners and managers are

to stabilize and reverse the disturbing trend of

landscape fragmentation of the landscape now so

common throughout the South. It will be necessary

to incorporate the best elements of land use

planning and landscape ecology to preserve the

landscape mosaic of wildlands (Brown 1989).

Inclusion of information about potential climax

or late-successional vegetation and seral

communities in a classification system gives

insight about the composition and structure of

old-growth communities that would prevail in areas

protected from timber harvesting. Such areas often

exceed 30 percent of the land base in Southern

29
national forests. In addition, if the seral

communities preceeding late-successional

communities are known, managers can decide which

vegetative stage is desirable on various sites in

the landscape. For example, the longleaf pinewiregrass ecosystem of the Coastal Plain once

covered up to 86 million acres, but now occupies

no more than 5 million acres (Noss 1989). Accounts

of early explorers suggest that much of the

PreColumbian landscape of the Coastal Plain was in

wet prairies and open savannahs, now rare communities that can only be created and maintained

by frequent burning. It is important for ecoloqical reasons to restore a portion of that original

ecosystem. A landscape ecosystem Classification

describing successional sequences of vegetative

communities on various site types with and without

prescribed fire would aid in delineating those

areas where restoration of this endangered

ecosystem is best suited.

Forested wetlands commonly found along many

coastal streams, rivers, lakes, and bays are among

the most extensive types of forest sites in the

southern United States. Although development of

wetlands has slowed in the South in recent

decades, the quality of wetlands continues to

decline. To reverse this disturbing trend,

wetlands must be delineated and their relationships to surrounding systems identified. Bxown

(1989) has proposed an landscape ecosystem

classification system for wetlands which

incorporates landscape position, nutrient

availability, and hydrologic regime, in addition

to successional trends, as a first step in

protecting wetlands.

Riparian zones and streamside management zones

throughout the South require similar

consideration. Land-use pLanners and managers

must give greater attention to the protection and

functions of these sensitive and ecologically

important ecosystems and how their management

affects associated aquatic ecosystems.

No site or land classification system will

satisfy all management needs or resolve all

conflicts arising from opposing views on use of

specific areas of land. As long as people have

opinions and wants, there will be conflicts over

land uses. All classification systems are

contrivances of man to organize ideas in useful

ways (Cline 1963). As such, they will never be

perfect. However, classification systems that

integrate the major ecosystem components of

Landforms, soils, and vegetation provide a

relatively sound basis upon which individuals,

companies, public agencies, and society can make

Long-term decisions about the land that make sense

both ecologically and economically.

New Technoloqies

Maps have historically depicted the spatial

relationships of land, including its

characteristics and boundaries, and will continue

to be important tools for storing and conveying

spatial information. Unfortunately, as forestry

became more sophisticated, the rate at which these

maps became outmoded also increased. This problem

can now be addressed through the use of digital

computers and software for handling geographic

data. Geographic Information Systems (GIS) and

Digital Elevation Models (DEM) are increasingly

used to input, store, manipulate, and display

geographically referenced data to provide the

current information needed by forest managers.

GIS and DEM are commonly used for land-use

planning and resource inventory, but researchers

are just recently discovering their potential for

site classification. Several papers in this

proceedings address this new technology, which has

exciting possibilities for increasing our ability

to map site and landscape features. Hammer (this

proceedings) suggests that these new technologies

can be used as research tools to generate new

data, rather than as just hi-tech ways of

producing maps and managing data. Certainly the

future is bright for this new technology. Its

potential for illustrating relationships between

landforms, soils, and vegetation are almost

unlimited, as is its potential for expanding our

understanding of the values and functioning of

these ecosystem components.

Forest site classification had its origins near

the turn of the century shortly after forestry

began in this country. The early history of site

classification dealt with finding an appropriate

expression of site quality, since it was generally

recognized that productivity was a basic criterion

for delineating sites. Site index, despite its

problems, was selected and remains the most

commonly used measure of site quality even today.

In the South, decades of soil-site studies

established the rather obvious fact that site

quality was related to those soil and site

features that affect the availability and supply

of water and nutrients to forest trees. The

compilation of factors related to site quality,

while a necessary first step in the exploited

forests of the South, did little to solve the

problem of how to spatially delineate units of

land with differing growth potential.

Foresters attempted to use general purpose SCS

soil survey maps to delineate sites of different

quality. However, these maps were generally not

suitable for intensive forest management purposes.

Site index varied widely within mapping units and

responses to management activities often did not

coincide with soil series. Soil and site features

important to tree growth were obviously not the

properties important to soil taxonomy. However,

industrial forestry experience has shown over the

past 20 years that foresters and soil mappers can

work together to develop soils maps suitable for

forest management purposes.

The importance of landform in classifying

forest sites was not widely recognized in the

South until the late 1970s. Landform naturally

integrates climatic, hydrologic, soil, and

vegetative variables, and forms the stable

3 0
repetitive feature of the landscape. Most

importantly, landforms are readily mapped.

Multifactor physiographic classification systems

which separate landscape components on the basis

of geology, topography, and soils into visually

identifiable landtypes have greatly increased

understanding of forest site relationships.

Ecosystem classification is similar to multifactor physiographic classification except it

places greater emphasis on vegetation. Identification of late successional or near climax plant

communities that occupy these repetitive landforms

and the seral conununities that preceed them is an

integral part of the system. Such a system

provides a broader ecological base upon which the

patterns and processes of landscape ecosystems can

be interpreted. However, it must be recognized

that landscape ecosystem classification, nor any

other system, is a panacea that will solve all

land classification problems.

In the last decade, great progress has been

made in the carteographic expression of geographic

data using GIS and similar systems. Integrating

site classification systems into these new technologies will improve our ability to manage forest

land for maintenance and enhancement of productivity, and at the same time enable us to give due

consideration to other values of the forest.

LITERATURE CITED

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Madison, WI. University of Wisconsin. 148-166.

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American Foresters. 175-178.

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forest growth: identification and with

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Applications to identify the productive

potential of southern forests. 1991

January 7-9; Charlotte, NC. (in press)

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American south. The Conservation Foundation,

Washington, D.C. 333 pp.

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David F. 1990. Can measurable soil properties

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s. P.; Lacate, D. S.; Weetman, G. F.; Powers,

R. F., eds. Sustained productivity of forest

soils. Proceedings of the 7th North American

Forest Soils Conference. 1988 July; Vancouver,

BC. University of British Columbia. 137-154.

Hewlett, John D. 1982. Principles of Forest

Hydrology. The University of Georgia Press,

Athens, GA. 183 pp.

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Forest Resources Rep. no. 46. 204 pp.

Hodgkins, E. J.; Golden, M. S.; Miller, W. F.

1979. Forest habitat regions and types on a

photomorphic-physiographic basis: A guide to

forest site classification in AlabamaMississippi. Alabama Agricultural Experiment

Station and Mississippi Agricultural Forestry

Experiment Station Southern Coop. Bull. 210.

64 PP.

Hudson, Charles. 1976. The Southeastern Indians.

The University of Tennessee Press. Knoxville,

TN. 573 pp.

Hunt, Charles B. 1974. Natural regions of the

United States and Canada. W. H. Freeman and

co., San Francisco. 725 pp.

Jones, S. M.; Van Lear, D. H.; S. K. Cox. 1984. A

vegetation landform classification of forest

sites within the upper Coastal Plain of South

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111:349-360.

Jones, Steven M. 1989. Application of landscape

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Proceedings of the 1989 National Convention,

Society of American Foresters; 1989

Sept. 24-27; Spokane, WA. 79-83.

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1303-1306.

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analysis of function and characteristics in

site evaluation. Journal of Forestry 61:193-

198.

32
McNab, W. Henry. 1987. Rationale for a multifactor

forest site classification system for the

Southern Appalachians. In: Hay, Ronald L.;

Woods, Frank W.; DeSelm, Hal, eds. Proceedings

of the Central Hardwood Forest Conference VI;

1987 February 24-26; Knoxville, TN. Asheville,

NC, U. S. Department of Agriculture, Forest

Service, Southeastern Forest Experiment

Station. 283-294.

Meyers, Richard; Zahner, Robert; Jones, Steven M.

1986. Forest habitat regions of South Carolina

from Landsat imagery. Clemson University

Department of Forestry, Forest Research Series

no. 42. 31 pp., map supplement.

Monserud, Robert A. 1984. Problems with site

index: an 21 opinionated review. In: Bockheim,

James G., ed. Forest land classification:

experiences, problems, perspectives. 1984 March

18-20; Madison, WI. University of Wisconsin.

167-180.

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regions of Georgia, Landsat 4 imagery. Georgia

Agricultural Experiment Station Special

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27, 1989; Spokane, WA. 74-78.

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soil management practices: Avoiding "future

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of America, Madison, WI. 391-403.

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physiography, and soils. In: Tippin, Tom, ed.

Proceedings of a symposium on principles of

maintaining productivity on prepared sites;

1978 March 21-22; Mississippi State University.

6-13.

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Limitations of the use of vegetation. In:

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University. l-10.

1s

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Wildlife and Fisheries, The University of

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March 21-22; Mississippi State University.

14-27.

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of productivity: Some observations. In: Karr,

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of site index and soil mapping units--poor for

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loblolly pine ecosystem, west region. In: Karr,

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20-22; Jackson, MS: Mississippi State University Cooperative Extension Service: 17-32.

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Forest Experiment Station.

33

CLASSIFICAsTION SYSTEM

DEVELOPMENT

LAND CLASSIFICATION IN THE BLUE RIDGE PROVINCE:

STATE-OF-THE-SCIENCE REPORT'

W. Henry McNab2

ABSTRACT.--An ecological land classification system based on

vegetation, landform, and soil relationships is being

developed for the Blue Ridge Province. The classification

overcomes many problems associated with site index, has the

potential for a high level of accuracy, and can be used for

forest resources other than timber. It can be applied using

a range of methods including field mapping, taxonomic

classification, and geographic information systems. Results

of a test in the Bent Creek Experimental Forest suggest that

five major landscape units, consisting of relatively uniform

overstory species composition occurring below 3500 feet

elevation, can be mapped from recognizable landforms and

information on soil maps, and predicted using topographic

factors measured on-site.

Keywords: Landscape units, geographic information systems,

landforms, soil, site quality, site classification.

INTRODUCTION

The Blue Ridge Physiographic Province extends

almost 600 miles from the Susquehanna River in

southern Pennsylvania to the vicinity of Mount

Oglethorp in northern Georgia (Figure 1). Fenneman

(1938) describes the province, including its

geomorphic history, and subdivides it into two

sections at the Roanoke River Gap in central

Virginia. The northern section is a single

mountain range averaging about 3000 feet elevation

and less than 15 miles wide. The other section,

commonly known as the southern Appalachians (Braun

1950) broadens from the Roanoke River into a hilly

plateau that gradually changes to rugged mountain

ranges and cross ranges, with many summits

exceeding 6,000 feet elevation. With an area of

over 12 million acres, this province is

characterized by igneous and metamorphic rock

formations that were intensely folded and faulted

during several erogenic periods, then eroded over

the past 600 million years into a strongly

dissected landscape of mountain ranges with

broadly rounded summits.. Climate varies with

latitude and elevation, which ranges from about

'Paper presented at the Symposium on Ecological Land

Classification: Application to Identify the Productive

Potential of Southern Forests, Charlotte, NC, January 7-9.

1991.

?t esearch Forester. USDA Forest Service. Southeastern

Forest Experiment Station. Bent Creek Experimental Forest.

Rt. 3. Box 1248, Asheville, NC 28806.

1000 to 6684 feet. Precipitation, which ranges

from about 40 to over 80 inches annually, is

strongly affected by elevation, local topography,

and proximity to the Blue Ridge escarpment. Rock

formations are typically acidic in reaction, and

can vary in mineral content and resistance to

erosion over rela

complex landforms 5

ively short distances to form

consisting of ridges, coves,

and connecting sideslopes. Soils formed from

these formations are generally low in fertility

and have moisture regimes strongly influenced by

local topography. Evapotranspiration can exceed

precipitation during the late growing season,

causing soil moisture deficits (McNab 1991),

especially on sites with southerly aspects. This

variation in climate, geology, topography, and

soils produces a wide range of sites suitable for

over 30 commercially valuable timber species, most

of which are deciduous. Braun (1950) placed this

area in her Oak-Chestnut Forest Region and

attributed the great variety of vegetation and

large number of species mainly to "the diversity

of topography and range of altitude" and partly to

variation in soils that formed from a range of

geologic formations. Catlin (1984) presents a more

detailed description of the rocks, fauna, flora,

and past land use along the Blue Ridge Parkway,

which extends through much of the province.

3 Defined as: "Any physical, recognizable form or feature

of the earth's surface. having a characteristic shape, and

produced by natural causes; it includes major forms such as

a plain, plateau, on mountain, and minor forms such as a

hill, valley,' slope, esker, or dune." (Driscoll and others,

1984 )

37
Figure l.--Location of Blue Ridge Physiographic

Province within the southeastern United States

(after Braun 1950). Arrow indicates location of

Roanoke River, which subdivides the province into

northern and southern sections.

Silvicultural prescription and stand management

in the Blue Ridge Province require a system for

delineating areas with homogeneous species

distribution and site productivity. At present,

such a system is lacking. Site index is widely

used to estimate stand productivity, but it has

many limitations (Beck and Trousdell 1973). The

most serious is the frequent lack of suitable

sample trees. Considerable work has been done to

determine soil-site relationships for the

commercially important tree species in the Blue

Ridge and adjoining provinces (McNab 1988a).

Often, however, application of these relationships

is risky without a means of classifying sites

first for species suitability. Soil surveys often

are of doubtful value for forest land

classification in mountainous areas. Few counties

have been surveyed since 1970, mapping is often

inadequate for stand-size areas in complex

topography, soil series-site quality data are too

generalized, and species recommendations often

stress conifers and are misleading for hardwoods.

The USDA Forest Service initiated a research

program at the Southeastern Forest Experiment

Station in 1981 to develop a classification system

with the following attributes:

Ecological basis for integrating vegetation

with climatic, geologic, topographic, and soil

environmental factors.

Hierarchical structure to allow application at

appropriate scales.

Quantitative in the lower levels to allow

objective application, predictive modeling, and

computer-based application.

Classification based on recognizable features

to facilitate mapping.

The system follows USDA Forest Service guidelines

for classifying terrestrial ecological types (U.S.

Department of Agriculture, Forest Service 1986)

and will be applicable to over 12 million acres of

land, 65 percent of which is classed as commercial

forest.

Classification of forested landscapes is a

two-phase task: (1) establishing classification

units based on similarity of environmental

attributes, and (2) developing relationships

between the classification units and the resources

of interest. Briefly summarized in this report are

the classification units, which are presented in

greater detail elsewhere (McNab 1987a). Also

presented are published and preliminary results

that illustrate relationships between the

classification units and distribution of the

vegetation resource in the Bent Creek Experimental

Forest. Summarized elsewhere are results of

soil-site studies that correlate tree growth with

the classification units in and adjacent to the

province (McNab 1988a).

METHODS

Land classification terminology and landform

definition follow a glossary presented by Driscoll

and others (1984). A landform omitted from this

glossary but commonly used in the southern

Appalachians is "cove": a small, straight valley

extending into a mountain or down a mountainside

(Bates and Jackson 1980). Hack and Goodlett (1960)

use the term "hollow" to describe this landform.

Ecological Basis

Results of numerous studies in the southern

Appalachians have indicated that vegetative

composition and productivity are highly correlated

with a range of broad-scale environmental factors,

including climatic zones and geologic formations.

For example, Whittaker (1956) and Golden (1974)

found that the species composition of tree and

shrub vegetation was correlated with environmental

gradients associated with elevation, which affects

growing-season length and precipitation patterns.

Geologic formations influence relief (Fenneman

1938), soil properties (Hack and Goodlett 1960).

vegetation distribution (Rohrer 1983), and

productivity (McNab 1986, McNab and Merschat

1990). Rowe (1984) suggests that climate, geology,

and landform are the principal factors that affect

soil formation and development of biotic

communities. When evaluated simultaneously,

climatic and geologic zones delineate relatively

homogeneous areas for application of land

classifications.

In mountainous areas of uniform climate and

geology, landform strongly influences ecological

relationships because of its effects on local

climate (Bailey 1988), water relations (Helvey and

others 1972) and tree growth (McNab 1984). As

early as 1921, broad classes of landform were used

to rate productive potential of forested areas

(Frothingham 1921). Whittaker (1956) and McLeod

(1988) reported that species composition of

vegetation was correlated best with topographic

variables and next best with soil variables.

3 8
Landform is a significant factor affecting

ecological relationships (Rowe 1984) and

successional pathways of forest ecosystems (Host

and others 1987). However, there is no generally

accepted method to classify landforms, as there is

for soils (Bailey 1981, Driscoll and others 1984).

Soil characteristics generally have a lesser

influence on moisture regimes than landform in

mountainous areas. Within a landform, soil

influences species composition mainly through

water storage capacity as expressed by solum depth

and texture (Carmean 1975). Mowbray and Oosting

(1968) reported strong correlations of vegetative

associations with several soil physical

characteristics. Braun (1950), and Hack and

Goodlett (1960) reported that soil characteristics

often account for local variation in species

composition and productivity. In areas of reduced

relief, such as the Asheville Basin where

conditions are similar to the Piedmont Province,

soil characteristics likely have a more important

effect on vegetation than does landform (Jones

1989).

Ecological forest site classification requires

an understanding of the interrelationships among

vegetation, landform, and soil. As Barnes and

others (1982) explain in greater detail,

ecologically similar units of land usually support

similar associations of overstory, understory, and

ground cover species. Growth and reproduction of

these species are responses to specific climate,

soil moisture, and soil fertility. A landscape

unit is defined as a group of sites with similar

species, hazards, and productivity (Barnes and

others 1982). Equivalent landscape units can have

,dissimilar landforms and soils that interact in

ways which provide similar site conditions. This

concept of equivalent site conditions is essential

to the development of a workable and accurate land

classification system. A classification system is

necessary to reduce the complex array of

topographic and soil variables into a few

homogeneous landscape units for practical field

application.

There.are two general approaches to developing

a classification: regionalization and aggregation.

Regionalization is done by successively dividing

large land areas into smaller units of more

uniform characteristics. It often is applied to

landscapes consisting of perceived units that are

difficult to quantify. Aggregation (or taxonomy)

combines similar small units into successively

larger groups with similar properties. Maps of

physiographic provinces are developed by

regionalization while soil maps are developed by

aggregation. Bailey and others (1978) provide a

more complete description and application of each

classification method.

Classification Framework

The classification system is a modification of

a seven-level hierarchy developed by Wertz and

Arnold (1972) for stratifying land units of

various scale into homogeneous areas. This general

hierarchy has also been used in modified'form for

ecological classification in Region 9 of the USDA

Forest Service (Nelson and others 1984) and in the

Interior Uplands (Smalley 1984). The hierarchical

framework I am using is:

VII

VI

V

IV

III

I I

I

PHVSIOGRAPHIC PROVINCE--A large area of

similar geologic structure, geomorphic

history, and climate. (Example: Blue Ridge

Province).

SECTION--Stratifications of provinces that

account for variation in climate often

associated with elevation and local relief.

(Example: zones of precipitation >60 inches).

SUBSECTION--Subdivisions of sections to

account for variation in geologic formations

that affect general soil properties. (Example:

Anakeesta formation).

LANDTYPE ASSOCIATION--Aggregations of

landtypes with similar mesoscale landforms,

soil properties, and local climate. (Example:

mountain upland).

LANDTYPE--Groupings of landtype phases with

similar solar radiation and soil moisture

which lead to similar species composition.

(Example: steep sideslope with north aspect).

LANDTYPE PHASE--Clusters of sites with similar

microscale landforms which lead to similar

vegetative growth. (Example: concave land

surface).

SITE--Smallest, most uniform components of

landscapes that Wertz and Arnold (1972) define

as IIa final integration of all environmental

elements that occur together at a specific

location" and that affect growth and

reproduction of individual trees and shrubs.

Sites are classified and clustered to form

landtype phases, and are the observations for

simulation models.

Che upper three levels of the hierarchy

represent environmental landscape components that

act largely independently and are generally

unapparent to the observer. The next three levels

represent interrelated landscape components based

mostly on recurring topographic features, which

can be observed and quantified. Sites vary in

size, depending on objectives of the classification, and for this application are about l-acre

in area. The basis of this hierarchy is presented

in greater detail elsewhere (McNab 1987a).

This classification scheme has a high degree of

compatibility with a system of land classification

developed for the Interior Uplands (Smalley 1984).

The frameworks have similar overall structure and

bases for stratification in the upper three

levels. In the next three levels of the hierarchy,

however, I use taxonomic methods. Sites of similar

species and productivity are grouped into landtype

phases, which are then combined into landtypes and

landtype associations. Landtypes are equivalent to

landscape units and provide ecologically similar

conditions that affect species composition of

vegetation. Even though methodology differs

somewhat between the two classifications, both are

based on about the same criteria at the landtype

level: landform, aspect, slope position and soil

.properties. The main difference is that forest

communities were associated with landtypes after

the classification was developed for the Interior

Uplands (Wheat and Dimmick 1987), but I use

vegetation to initially identify ecologically‘

similar landscape units.

39
Landform Quantification

Unlike the topographic variables of aspect and

gradient, landform could not be easily defined and

measured. Nevertheless, it was an essential

variable in prediction models. It therefore was

necessary to develop methods for measuring

landforms to ensure accurate application in the

field, in modeling, and in GIS. Currently, I use

two scales to quantify landform: (1) the

microscale surface shape of the site, as measured

by the terrain shape index (McNab 1989); and (2)

the mesoscale shape of the land surface

surrounding the4site, which is measured by the

landform index. These scales of landform are

compatible with concepts presented by Bailey

(1988) and extend his continental-scale hierarchy

down to a localized level. Techniques for

quantifying landform ware evaluated throughout the

Blue Ridge Province using permanent plots

established to study growth of yellow-poplar

(Liriodendron tulipifera L.) (McNab 198713).

Bent Creek Landscape Model

The classification framework and landform

quantification techniques were tested at Bent

Creek Experimental Forest, a 6000-acre watershed

located in western North Carolina, about 10 miles

southwest of Asheville. The watershed, which forms

a U-shaped, northeast-facing valley averaging

about 5 miles long by 2 miles wide, has a

dendritic drainage pattern (Figure 2) and

landforms ranging from ravines and coves to ridges

and knobs. Summers in the area are long and warm,

and winters are short and mild. Annual

precipitation averages 45 inches at the

headquarters site (elevation 2100 feet) and is

evenly distributed, with little occurring as snow.

Elevation ranges from 2102 to over 4000 feet. Mean

mor$hly temperature is 36 F for January and

740F for August, with an annual average of

55 F. The predominant rock formation consists of

muscovite-biotite gneiss. Soils are generally deep

(>40 inches) and typically consist of Hapludults

and Dystrochrepts. Slopes range from almost level

to very steep.

Vegetation consists of late successional

species in associations that have developed during

the past 100 years on sites that once were

cultivated, pastured, or selectively logged. The

only recent major large-scale disturbance has been

the loss of American chestnut (Castanea dentata

(Marsh.) Borkh.) during the 1930's. The

distribution of overstory species is closely

related to the soil moisture regime and perhaps

ambient temperature. Dry slopes and ridges are

dominated by xerophytic species, most of which are

drought tolerant oaks: chestnut (Quercus prinus

L.), scarlet (Q. coccinea Muenchh.), black (9.

velutina Lam.), and sometimes post oak (Q.

stellata Wangenh.). Sourwood (Oxydendrum arboreum

(L.) DC.) is a common associate, along with

mockernut hickory (Carya tomentosa (Poir.) Nutt.)

and pitch pine (Pinus rigida Mill.) on some dry

sites. Mesophytic species such as yellow-poplar,

northern red oak (Q. rubra L.) and black locust

(Robinia pseudoacacia L.) are found on moist

slopes and coves. White oak (p. alba L.), red

maple (Acer rubrum L.), and flowering dogwood - -

(Cornus florida L.) are found across a range of

siteizonditions. Alluvial floodplains

provide conditions suitable for sycamore (Platanus

occidentalis L.), river birch (Betula nigra L.),

boxelder (Acer negundo L.) and other species. A

host of understory and ground cover species also

occur in the Bent Creek drainage. A preliminary

inventory of vegetation in a portion of the Bent

Creek Valley occupied by The North Carolina

Arboretum (Figure 2) revealed 474 vascular plant

taxa (Pittillo 1989), including 76 tree and 66

shrub species.

I PW.E

Figure 2.--The drainage pattern of Bent Creek

Experimental Forest with locations of The North

Carolina Arboretum and section lines (A-A', B-B',

C-C').

4

McNab, W.H. 1991. A field index to quantify the effect

of landform on forest site quality. Unpublished draft on

file: Asheville, NC: Southeastern Forest Experiment Station.

4 0
Mapping Units

Application of the land classification requires

definition of mapping units at the landtype

association and landtype levels of the hierarchy.

In general, physical form of the lower end of the

Bent Creek Valley is nearly identical to the

intermontane basin model proposed by Gile and

others (1981), as presented in Driscoll and others

(1984). Based on their findings, and my

observations in the field, the following three

landtype associations are proposed:

1. MOUNTAIN UPLAND--An elevated land surface more

than 1000 feet above surrounding lowlands and

generally having steep sides (>25 percent

slopes). Soils typically consist of fine-loamy,

typic Hapludults and fine-loamy typic

Dystrochrepts.

2. PIEDMONT SLOPE--The dominant gentle slope at

the foot of a mountain that grades into an

alluvial flat. Soils typically consist of

clayey, typic Hapludults on slopes that range

in gradient from 2 to 25 percent.

3. ALLUVIAL FLAT--A nearly level surface

consisting of unconsolidated elastic material

deposited by running water including gravel,

sand, silt, clay and various mixtures of these.

Typically, soils consist of fine-loamy,

fluvaquentic Dystrochrepts.

A typical arrangement of these landtype

associations in the Bent Creek valley is presented

in Figure 3, which illustrates a cross-sectional

profile along section line A-A' of Figure 2. The

sequence and presence of landtype associations

varies with location in the valley. For example,

mountain uplands are adjacent to alluvial flats at

section line B-B', and only mountain uplands are

present at C-C'. These and other arrangements of

landtype associations are probably typical of many

large valleys in the Blue Ridge Province.

BENT CQEEK

.-

. ..*-.*. I

:

: IDEALIZED

I..

-... / \ :

a... I

I

----../....

/

\ ’

--....... \...’.-

.--

I

f-*-- . . . . . . . . . --

I I

1

I I

I

~OVNTAIN I P I E DMONT

I

I A LLUVIAL I

UPLAND I SLOPE ) FL-Al- 1

Figure 3.--Profile section A-A' across Bent Creek

Valley in comparison to landforms of an intermontane basin described by Gile and others (1981).

Except for alluvial flats, each landtype

association consists of three component landforms:

ridges, sideslopes, or coves. When combined with

aspect, slope position, and soil properties, each

component landform becomes a potential mapping

unit. Ecologically equivalent mapping units are

dominated by vegetation of similar species

composition. Alluvial flats have a single

landform--sideslopes--which can vary widely in

soil properties, depending on drainage.

Field sampling

A tentative landscape classification model

based on overstory and understory vegetation was

developed in a 3000-acre portion of the

Experimental Forest, located mainly on the

northern side of Bent Creek and below 3500 feet

elevation. Overstory vegetation, landform, and

soil were evaluated on 135 permanent, 0.20-acre

plots previously randomly established in upland

hardwood stands. Basal area was determined by

species for trees greater than 4.5 inches d.b.h.

To evaluate application of the classification by

field mapping, landtype association, landtype, and

slope position were determined subjectively, and

aspect and gradient were measured on each plot.

For application by prediction, the following site

variables were measured: elevation, aspect,

gradient, landform index, and terrain shape index.

Soil characteristics were determined by reference

to an unpublished 1:24,000 scale map pryduced by

USDA Soil Conservation Service in 1977. Soils

data gathered on sites were ignored because I

wanted to classify only from mapped data.

Overstory landscape units were determined from

field observations and cluster analysis. Field

observations and summary tables were used to

determine relationships between landscape units,

landform, and soil for use in mapping. Canonical

and multivariate discriminant analyses were used

to develop a mathematical model for predicting

cover types based on topographic variables. The

mapping units and the prediction model were tested

with data from an independent set of 69 validati8n

plots. Field procedures and analytic methods for

the prediction mop1 are explained in greater

detail elsewhere.

A preliminary study was made of understory

vegetation to determine if its distribution was

associated with landform and soils. Field methods

were identical to those for the overstory.

Landform and soil variables were measured on 81

temporary plots where the understory vegetation

was of similar species composition and occupied a

5 Interim soil survey report for Buncombe County, North

Carolina. July 1977. 241 PP.

6 McNab, W.H. [In press]. Prediction of forest type Using

topographic variables in Bent Creek Experimental Forest.

Proceedings of the sixth biennial southern silvicultural

research conference; 1990 October SO-November 3. Memphis.

TN. Asheville, NC: U.S. Department of Agriculture. Forest

Service, Southeastern Forest Experiment Station4 1
site of at least 1 acre. Classification of

understory landscape units was based primarily on

the presence of one or two dominant species. Six

prominent landscape units were identified and

sampled: (1) a combination of two low-growing (<36

inches) ericaceous species (low ericads), dryland

blueberry (Vaccinium vacillans Torrey) and black

huckleberry (Gaylussacia baccata (Wang.)K.Koch);

(2) mountain laurel (Kalmia latifolia L.); (3)

rosebay rhododendron (Rhododendron maximum L.);

(4) common alder (Alnus serrulata (Aiton) Willd.);

(5) tree seedlings (dbh cl.0 inch) of all species;

and (6) miscellaneous fern and herbaceous species,

which included species such as New York fern

(Thelypteris noveboracensis (L.) Nieuwland) and

indian turnip (Arisaema triphyllum (L.) Schott).

Deerberry (V. stamineum L.) was often associated

with mountain laurel on Piedmont slopes, but was

not sampled as a landscape unit.

As with the overstory, field reconnaissance was

used to associate understory vegetation with

landform and soil for use in mapping. Multivariate

discriminant analysis was used to develop a

prediction model of understory landscale units,

but the model was not validated. This preliminary

test provides no basis for combining overstory and

,understory landscape units because field sampling

for each was conducted separately. Although ground

cover vegetation can be useful in land

classification (McNab 1988b), it was omitted from

this preliminary evaluation, except for the

collective grouping of miscellaneous ferns and

herbaceous species.

RESULTS AND DISCUSSION

Landscape Units

Five overstory landscape units of species

composition were identified that likely represent

a moisture gradient ranging from xeric to

subhydric:

Moisture gradient Landscape unit

Xeric Scarlet oak (SO)

Subxeric Chestnut oak (CO)

Intermediate Mixed oaks (MO)

Mesic Yellow-poplar (YP)

Subhydric Sycamore (S)

Landform characteristics account for much of the

distribution of landscape units. Mountain uplands

provided a range of soil moisture regimes that

were correlated with land surface shape, slope

position, gradient, and aspect. Chestnut oak,

often in association with scarlet and black oaks,

occurred mainly on dry sites along ridges and man,y

south-facing slopes. Yellow-poplar occupied coves

and lower north facing slopes. Mixed oaks occupied

a range of sites considered to be intermediate in

soil moisture. Piedmont slopes were dominated by

scarlet oak in association with black and white

oaks, and sometimes post oak on the most xeric

sites. Yellow-poplar, red maple, and white oak

often formed stands on mesic lower slope positions

of Piedmont slopes adjacent to alluvial flats.

Alluvial flats were classified as subhydric, but

sometimes ranged to mesic depending on soil parent

material and properties, gradient, and past land

use (such as logging). Crayfish (Cambarus -)

often inhabit the wettest alluvial sites. Location

of these over- story landscape units in relation

to physiography is shown in Figure 4 and a

tentative, generalized classification is presented

in Table 1.

so

n C O

SLw-&S Auuv IAL

FLA-TS

tiOR7-H SOUTH

Figure 4.--Idealized overstory landscape units in

relation to landtype associations and landtypes in

Bent Creek Experimental Forest. (CO-Chestnut oak,

MO-Mixed oaks,

S-Sycamore.)

SO-Scarlet oak, YP-Yellow-poplar,

Table 1 .--Tentative classification of selected

overstory landscape units.

I. Low elevation landscape units (<3500 feet).

A. Alluvial flats --Loam to sandy loam soil,

nearly level gradients.

SYCAMORE-RIVER BIRCH

B . Piedmont slopes--Gentle to steep gradients,

clay loam to sandy clay loam soils.

1 . Ridges and upper slopes.

SCARLET-WHITE-BLACK-POST OAKS

2 . Sideslopes.

SCARLET-WHITE-BLACK OAKS

3 . Coves and lower slopes.

SCARLET-WHITE OAKS, YELLOW-POPLAR

C. Mountain uplands--steep to very steep

gradients, loam to stony loam soils.

1 . Ridges and upper slopes.

CHESTNUT-SCARLET-BLACK OAKS

2 . Mid slopes with southerly aspects.

CHESTNUT-BLACK OAKS

Mid slopes with northerly aspects.

MIXED OAKS

3 . Lower slopes with southerly aspects.

MIXED OAKS

Lower slopes with northerly aspects.

YELLOW-POPLAR, N. RED OAK

4. Coves. YELLOW-POPLAR, N. RED OAK

II. Middle elevation landscape units (>3500 feet).

Not sampled
Understory vegetation was also associated with

specific landscape positions considered to

represent a moisture gradient:

Moisture gradient Understory vegetation

Ultraxeric Low ericads (LE)

Xeric Mountain laurel (ML)

Intermediate Tree seedlings (TS)

Submesic Rosebay rhododendron (RR)

Mesic Fern-herbaceous (FH)

Subhydric Common alder (CA)

Species considered as xerophytic were associated

with ridges and slopes with southerly aspects.

Coves and slopes with steep north aspects provided

conditions suitable for species considered to be

mesophytic. Tree seedlings were present on almost

all sites, but were most apparent on areas not

dominated by one of the other understory units.

Rosebay rhododendron dominated some moist sites,

especially steep, northerly slopes, and along some

streams, but was absent from other similar

situations. Typical positions on the landscape

occupied by these units are shown in Figure 5, and

a tentative classification of understory landscape

units was developed (Table 2).

FLATS

tJ0R-W SOIITW

Figure 5. --Idealized understory landscape units in

relation to landtype associations and landtypes in

Bent Creek Experimental Forest. (TS-Tree

seedlings, DE-Low ericads, FH-Fern-herbaceous,

ML-Mountain laurel, RR-Rosebay rhododendron,

CA-Common alder.)

In general, understory vegetation appears to

occupy a narrower range of landscape positions

than overstory species, perhaps as a result of

shallow rooting depths and greater sensitivity to

soil moisture regimes and other conditions

including fertility and past land use. Because

overstory and understory were sampled separately,

a classification including both landscape units

could not be developed. However, I observed that

certain overstory units tended to be associated

with specific understory units. For example, four

understory landscape units were found beneath

chestnut oak overstories: low ericads, mountain

laurel, tree seedlings, and rosebay rhododendron

(Table 3).

Groups of species must be defined to apply this

classification system. But grouping oversimplifies

the distribution of species in relation to

environmental conditions. While my overstory

landscape units are similar in composition to

natural communities of the North Carolina Natural

Heritage Program (Schafale and Weakley 1990) and

are almost identical to vegetation types described

by McLeod (1988), they still differ in relative

abundance of each species present. This is because

abundance of a species varies continuously from

near absence to near complete dominance of stands

in response to site conditions, which also vary

continuously. For example, yellow-poplar dominated

coves but was also present on ridges. Likewise,

mountain laurel dominated dry slopes but was also

present beside streams. Boundaries drawn between

landscape units are therefore likely to be

arbitrary and artificial. As Schafale and Weakley

(1990) stated "Occurrences near a boundary of a

category may be more similar to some occurrences

in the adjacent category than they are to members

at the opposite end of their own category."

Table 2.--Tentative classification of selected

understory landscape units.

I . Low elevation landscape units (<3500 feet).

A. Alluvial flats. COMMON ALDER

B. Piedmont slopes.

1 . Ridges, upper slopes with southerly

aspects. LOW ERICADS

Upper slopes with northerly aspects.

MOUNTAIN LAUREL

2 . Middle slopes with southerly aspects.

LOW ERICADS-MT. LAUREL

Middle slopes with northerly aspects.

MOUNTAIN LAUREL

3 . Coves. TREE SEEDLINGS

C. Mountain uplands.

1 . Ridges, upper slopes with southerly

aspects. LOW ERICADS, MT. LAUREL

Upper slopes with northerly aspects.

MT. LAUREL, TREE SEEDLINGS

2 . Middle slopes with southerly aspects.

LOW ERICADS, MOUNTAIN LAUREL

Middle slopes with northerly aspects.

TREE SEEDLINGS, RHODODENDRON

3 . Coves. FERN-HERBACEOUS

II. Middle elevation landscape units (>3500 feet).

Not sampled.

Table 3.-- Tentative associations of overstory and

understory vegetation landscape units.

Understory landscape units

Overstory landscape units

SO CO MO YP S

Low ericads X X

Mountain laurel X X X

Tree seedlings X X X X X

Rosebay rhododendron X X X

Fern/herb X

Common alder X

(Key to overstory landscape units: SO=Scarlet oak,

CO-chestnut oak, MO-mixed oaks, YP-yellow-poplar,

S-sycamore.)

4 3
Test and Evaluation

The discriminant model correctly predicted

about 75 percent of overstory landscape units

using topographic and soil factors (Table 4). The

most accurate classifications were for scarlet oak

and yellow-poplar, which are at opposite ends of

the assumed moisture gradient. Accuracy was

somewhat better for mapping than for prediction

methods.

Results of this preliminary test suggest that

understory landscape units also occupy specific

sites and can be accurately predicted (Table 5).

Overall, prediction accuracy using the

discriminant function was 77 percent, about

comparable to accuracy obtained for the overstory

landscape units. Much of the error resulted from

two landscape units. Tree seedlings were found

across a range of sites and are likely better

associated with disturbance of the overstory than

with the soil moisture regime. About a third of

the misclassified landscape units were occupied by

mountain laurel but were classified as low-ericad

sites. Because site conditions for these two

landscape units overlapped in the field, the

discriminant function was also somewhat

inaccurate. If the low ericads and mountain laurel

landscape units were combined into a single xeric

group, classification success would increase to

about 83 percent. It is likely that soils differeNd

beneath these two landscape units, but soils were

not mapped at a scale sufficient to indicate

different series.

These results suggest that landscape units

consisting of overstory and understory species can

be identified and mapped using landforms and

soils, and that the approach is feasible for land

classification in the Blue Ridge Province.

Inclusion of vegetation in the mapping process

will increase accuracy. However, even if

vegetation is not present and the sites have been

recently disturbed, use of landform and soils

alone should allow reasonably accurate land

classification. A reasonable level of accuracy to

achieve might be to produce a map that is 80

percent accurate at the 95 percent confidence

level.

Geographic Information Systems

Computer application of the classification

system was impossible because algorithms for

calculation of the landform and terrain shape

indexes were not available. When the algorithms

are developed, the Bent Creek model may be readily

applied by computer methods because all required

site variables can be calculated from a single

digital elevation data base. Using a raster-type

geographic information system, the discriminant

model can predict the probable landscape unit for

each l-acre site using stored physiographic and

edaphic attributes. Other models also can be

applied to determine productivity and hazards

associated with each site. The land manager can

then delineate stands based on manageable-sized

areas and other criteria (Figure 6).

Table 4.--Results of mapping and predicting

overstory landscape units on 69 plots.

Actual Classified landscape unit

landscape Scarlet Chest- Mixed Yellow- Sycaunit oak nut oak oaks poplar more

- - - - _ -percent- _ _ _ _ _ _ _ _ _

MAPPING METHOD

Scarlet oak 7 9 16 5 0 0

Chestnut oak 0 89 11 0 0

Mixed oaks a 8 a4 0 0

Yellow-poplar 0 6 0 9 4 0

Sycamore 0 0 0 0 100

PREDICTION METHOD

Scarlet oak 8 1 19 0 0

Chestnut oak 6 72 16 6

Mixed oaks 1 0 10 70 1 0

Yellow-poplar 0 25 0 7 5

Sycamore

Sycamore landscape unit was not tested by

prediction.

Table 5.--Results of predicting understory

landscape units on 81 plots.

Actual understory Predicted understory unit

unit LE ML TS RR FH CA

_ - - - Percent - - - - -

Low ericads 9 4 6 0 0 0 0

Mountain laurel 2 9 5 3 12 0 0 6

Tree seedlings 0 2 0 53 20 0 7

Rosebay rhododendron 0 0 0 92 8 0

Fern-herbaceous 0 0 8 0 8 5 8

Common alder 0 0 0 0 0 100

LE-Low ericads; MGMountain laurel:

TS-Tree seedlings; RR-Rosebay rhododendron;

FH-Fern and herbaceous; CA-Common alder.

MO

YP w

+MO YP

no YP I

Figure 6.--Example of application of the

classification on l-acre sites using a geographic

information system to predict occurrence of five

overstory landscape units. Thick line indicates

probable stand boundaries. (CO=Chestnut oak,

MO-Mixed oaks, S-Sycamore, SO-Scarlet oak,

YP-Yellow-poplar.)
Future Work

LITERATURE CITED

Although considerable progress has been

achieved, several major phases of the

classification work remain. Productivity and

hazards must be described and evaluated for each

landscape unit. Because vegetative composition is

influenced by climatic and geologic variables,

additional stratifications will be needed to

extend the Bent Creek model to other parts of the

Blue Ridge Province. Initial success in

quantification of micro- and meso-scale landforms

suggests that macro-scale relief surrounding the

site can also be quantified and could be useful in

predicting effects of changing climate on

distribution and growth of species. For example,

radial growth of yellow-poplar is closely

correlated with patterns of growing-season

precipitation (Beck 1984), which is likely I

associated with local relief. Development of a

model to predict variation in local rainfall will

require a means of quantifying relief. Fractal

geometry offers a promising method of doing this

(Barenblatt and others 1984). The apparent

advantage is that the fractal dimension of a large

landscape can be calculated with the same digital

data set used by the geographic information system

for land classification.

In summary, results of this preliminary

evaluation suggest that the integrated, ecological

approach is well suited for classifying forest

land in the Blue Ridge Province. The proposed

system has potential for purposes other than

,timber management, including classifying habitat

suitability for wildlife, estimating water yield

from watersheds, and controlling competing

vegetation. The classification can be applied by

three methods: (1) field mapping landscape units

that consist of vegetation, landform, and soil;

(2) on-site classification; and (3) computer

prediction using digital data bases. Availability

of more detailed maps will allow soil information

to play a greater role in the classification.

Development, evaluation, and modification of this

system is a continuous process. Because of the

broad range in climate, geology, landform, and

soil of the Blue Ridge Province, a final

approximation of the classification will likely

never be achieved.

ACKNOWLEDGMENTS

The author thanks Director George Briggs and

Mr. Ronald W. Lance, The North Carolina Arboretum,

for providing information on taxa of the Arboretum

and on distribution of understory vegetation in

relation to environmental factors.

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NE-148. Radnor, PA: U.S. Department of

Agriculture, Forest Service, Northeastern

Forest Experiment Station. pp. 475-488.

McNab, W.H.; Merschat, C. E. 1990. Geologic

variables associated with height of

yellow-poplar stands in the Bald Mountains of

North Carolina. Journal of the Elisha Mitchell

Scientific Society. 106(2):25-31.

4 6
Mowbray, T.B.; Oosting, H.J. 1968. Vegetation

gradients in relation to environment and

phenology in a southern Blue Ridge gorge.

Ecological Monographs. 38(4):309-344.

Nelson, Devon 0.; Russell, Walter E.; Stuart,

Gordon W. 1984. The ecological classification

system in the national forest system, Eastern

Region. In: Bockheim, J.G., ed. Proceedings of

the symposium on forest land classification:

experiences, problems, perspectives; 1984 March

18-20; Madison, WI. Publ. NCR-102. Madison, WI:

University of Wisconsin, Department of Soil

Science. pp. 270-276.

Pittillo, J. Dan. 1989. Botanical resources

inventory for the Western North Carolina

Arboretum. Asheville, NC: The North Carolina

Arboretum. 89 pp.

Rohrer, J.R. 1983. Vegetation pattern and rock

type in the flora of the Hanging Rock Area,

North Carolina. Castanea. 48:189-205.

Rowe, J.S. 1984. Forest land classification:

limitations of the use of vegetation. In:

Bockheim, J.G., ed. Proceedings of the

symposium on forest land classification:

experiences, problems, perspectives; 1984 March

18-20; Madison, WI. Publ. NCR-102. Madison, WI:

University of Wisconsin, Department of Soil

Science. pp. 132-147.

Schafale, M.P.; Weakley, A.S. 1990. Classification

of the natural communities of North Carolina,

third approximation. Raleigh, NC: North

Carolina Natural Heritage Program. 325 pp.

Smalley, Glendon W. 1984. Landforms: a practical

basis for classifying forest sites in the

Interior Uplands. Proceedings, 12th annual

hardwood symposium of the Hardwood Research

Council; 1984 May 8-11; Cashiers, NC.

Unnumbered publication. Memphis, TN: Hardwood

Research Council. pp. 92-112.

U.S. Department of Agriculture, Forest Service.

1986. Ecosystem classification handbook FSH

2090.11. Washington, DC: U.S. Department of

Agriculture, Forest Service. 13 pp.

Wertz, W.A.; Arnold, J.F. 1972. Land systems

inventory. Ogden, UT: U.S. Department of

Agriculture, Forest Service, Intermountain

Region. 12 pp.

Wheat, R.M., Jr.; Dimmick, R.W. 1987. Forest

communities and their relationships with

landtypes on the western Highland Rim of

Tennessee. In: Hay, R.L.; Woods, F.W.; DeSelm,

H ., eds. Proceedings of the 6th central

hardwood forest conference; 1987 February

24-26; Knoxville, TN. Knoxville, TN: University

of Tennessee, Department of Forestry, Wildlife,

and Fisheries. pp. 377-383.

Whittaker, R.H. 1956. Vegetation of the Great

Smoky Mountains. Ecological Monographs.

:l-80.
NO MORE PLOTS; GO WITH WHAT YOU KNOW:

DEVELOPING A FOREST LAND CLASSIFICATION SYSTEM

FOR THE INTERIOR UPLANDSU

Glendon W. Smalley2/

Abstract.--A five-level forest site classification system

was developed for the 29 million acres of the Cumberland

Plateau and Highland Rim physiographic provinces. Six

published regional guides that describe the system and how

it is used to classify and evaluate forest sites are

available. Landtypes, the most detailed level, are

described in terms of nine elements, are evaluated in terms

of productivity and desirability of selected hardwoods and

conifers for timber production, and are rated for five siterelated problems. The system permits on-site determinations

of site productivity and provides a framework for forest

management planning, operations, and research.

Keywords: Cumberland Plateau, Highland Rim/Pennyroyal,

landforms, land stratification, site productivity, soils,

timber management.

INTRODUCTION

The Keynote Session for this Symposium provided

an excellent historical perspective of ecological

land classification in Canada and the United

States. Suffice it to say that many methods have

been employed, but no one system applicable to all

the diverse forest sites, forest types, and forest

conditions has been developed. In fact, Pierpoint

(1984) suggested that "...it is unrealistic idealism to expect to develop a comprehensive so-called

universal classification hierarchy that will serve

all users, beyond providing a broad regional

framework for broad land-use planning."

Professor Rowe (1984) has eloquently submitted

that landform is a synthesizing supplement to vegetation and soils and argues that vegetation not

be used as the basis for classifying forest land.

In his words, "a universal system of forestland

classification... will only develop if agreement

is reached on concepts as to the nature of forestland, and on purposes to be served in dividing it

I/Presented at the Symposium--Ecological Land

Classification: Applications to Identify the

Productive Potential of Southern Forests,

Charlotte, NC, January 7-9, 1991.

L/Principal Soil Scientist (retired),Southern

Forest Experiment Station, Forest Service, U.S.

Department of Agriculture, Sewanee, TN, and

Consultant--Forest Land Classification and

Evaluation, Route 1, Box 541, Sewanee, TN 37375.

4 8

and classifying it.... The managers of forestland

need area1 units defined according to criteria

that make them relevant to such multi-use aspects

of management as silviculture, tree harvesting,

wildlife renewal, and watershed protection.

Such needs subtly encourage classifiers to take

account of climate, soil, and landform in addition

to vegetation when devising their typologies.

Even more important is the related conceptualizing

of spatial ecosystems..., as landscape segments

that are not shadowy extensions of vegetation

and/or soil but real structure objects-of-interest

based on landform. By its modification of the

fluxes of solar energy and precipitation, the

shape and substance of the land (landform) control

the expression of local climate, biota, and soil

in site-specific ways. Thus landform provides the

integrating framework of other landscape

components. Perceived covariances of vegetation

and landform in the landscape patterns provide the

means for mapping."

Today forest managers are faced with the challenge of producing more wood on diminishing

acreage of commercial forest land, and the need

for site classification and productivity information continues to be of "high priority"; witness

this symposium.

Before the system developed for the Interior

Uplands is described here, the title needs to be

explained. The "no more plots; go with what you

know" was a restriction placed on this author by

the then Assistant Director of the USDA Forest

Service's Southern Station, James L. Stewart,
after it had been determined that development of a

forest land classification system was feasible and

of high priority, i.e., this author was not to

initiate any new plot research, but rather was to

base all efforts on personal experience,

familiarity with the region, and existing

information.

CONDITIONS AND RATIONALE

The Cumberland Plateau and Highland Rim physiographic provinces (fig 1) extend from central and

eastern Kentucky and southwest Virginia through

middle Tennessee and northwest Georgia

into northern and central Alabama (Fenneman _

The Plateau is bordered on the east by the Ridyc.

and Valley province. The Highland Rim, called the

Pennyroyal in Kentucky, lies west and north of the

Cumberland Plateau, surrounds the Nashville Basin

in Tennessee, and borders on the Bluegrass and

Western Coalfields in Kentucky. On the west, both

the Plateau and Rim border on the Upper Coastal

Plain.

On the Plateau, soils are derived from thick,

mostly horizontal strata of sandstones, siltstones, and shales. Topography, ranging from gentle to rugged and complex, is characterized by

Figure 1. Physiographic provinces and Iregions of the Interior Uplands.

49
Udendritic drainage patterns with winding, narrow

ridges and deep, narrow valleys. In places, the

degree of dissection is weaker, and broader interfluves are common. The southern end of the

Plateau in Alabama is also well dissected but is

less rugged. The Plateau is bounded on the east

and west by prominent sandstone escarpments.

Elevation ranges from about 1,000 feet in the

south to 3,000 feet in the Cumberland Mountains; a

few peaks exceed 4,000 feet. Local relief ranges

from 100 to 150 feet in the smoother places to

1,000 feet at the Plateau margins and to nearly

2,000 feet in the vicinity of the tallest mountains.

On the Rim, topography ranges from gentle to

rugged and complex, and slope gradient ranges from

nearly level to very steep. Degree of dissection

ranges from young to mature; sinkholes are common

in some areas. Elevation ranges from 800 to 1,100

feet over most of the Rim but diminishes to about

500 feet south of the Tennessee River in northern

Alabama. Local relief ranges from 200 to 400 feet

but is 50 feet or less in the smoothest parts.

Much of the Rim is covered with 2 to 4 feet of

loess. Soils are derived from this loess and several strata of limestone of varying purity. Soils

with fragipans are common on parts of the Rim.

Along the boundary with the Upper Coastal Plain,

soils are topographically stratified on the basis

of parent material--1oess; unconsolidated sands,

gravels, and clays: and cherty limestone.

The Interior Uplands have a temperate climate

characterized by long, moderately hot summers and

short, mild to moderately cold winters. According

to Thornthwaite's (1948) classification of climate, it is humid mesothermal. Daily and seasonal

weather is controlled largely by alternating cold,

dry continental air masses from Canada and warm,

moist air from the Gulf of Mexico. During the

summer, complete exchanges of air masses are few,

and tropical maritime air masses persist for extended periods.

Mean temperature ranges from 55 to 61 OF. The

frost-free period is 200 or more days in northcentral Alabama and 160 to 180,days in the

Cumberland Mountains and on the Northern

Cumberland Plateau. Annual precipitation, ranging

from 46 to 61 inches with a decreasing trend south

to north, is ordinarily well distributed all year,

but short periods of very wet or very dry weather

are common. Precipitation is highest from December

through March and lowest from August through

October. Thunderstorms with high-intensity rainfall and occasional hail occur on more than 50

days each year, mostly in late spring and summer.

Snowfall seldom exceeds 6 inches and melts in a

few days; it is greater and persists for longer

periods at the highest elevations.

The Interior Uplands encompass about 29 million

acres, and over half is forest land. This area,

like much of the eastern hardwood forest region,

has a long history of indiscriminate cutting,

burning, grazing, and clearing for agriculture.

Parts of the Plateau and Rim have been cleared,

farmed, and abandoned several times. Some abandoned land now supports forests over 100 years

old. Much of the steeper land was never cleared,

but it was logged, burned, and grazed. Also, the

demise of American Chesnut has drastically altered

forest composition and structure. Consequently,

the existing forests are a mosaic of stand

conditions, with seemingly fortuitous species

5 0

composition. Productivity is far below potential

because of poor stocking, an undesirable mix of

species, and the presence of defective and lowvigor trees. Too few suitable stands exist to

obtain a direct measure of site potential.

At the onset of this project, very little treesoil-site information was available, and practically none of that was applicable to the Plateau

and Rim. Available information was developed

mostly by the factorial approach, which was not

always successful. Often, sample selection and

statistical manipulation were much less sound than

they appeared to be. Also, products of these

studies were graphs and equations, but the tools

necessary for forest planning and management are

maps and inventories.

Less than a quarter of the Plateau and Rim counties had had soil surveys published since 1967,

and less than half of these surveys contained

"Woodland Suitability" sections. Lack of

communication between foresters and soil

scientists has resulted in surveys that appeal to

soil taxonomists but disappoint forest managers,

who find little information or guidance on how to

apply soil surveys to their specific and pragmatic

goals for managing forest resources.

From a practical standpoint, there is little

justification for making the usual medium-intensity soil survey (typical county survey) for most

forest management activities associated with

"regulated forests" (Stone 1975). Bartelli and

DeMent (1970) concluded that low-intensity surveys

would provide a reasonable balance between cost

and value of the survey for forest management purposes. Boundaries of soil mapping units in lowintensity surveys more often coincide with natural

features of the landscape.

Because soils are closely related to landforms

and topography, a strong argument can be made for

subdividing landscapes instead of mapping soils.

Even where soils are to be identified and mapped,

the mapping is more meaningful when done by landform. In rugged terrain, landforms may have as

many, or even more, recognizable relationships

with tree growth than do soil series. Landforms

can be easily recognized by foresters and other

potential users of land classification systems

without formal training in soil science. Rowe

(1984) has suggested 'I . ..that forest land managers

need a scheme that sorts out the patterns of

landscapes with which they deal. Climate, soil,

vegetation, and landform are all important, but in

themselves they are not enough. Forestland

managers need defined terrain units or elements of

land that comprise all four components in interaction. [Furthermore].. .landfonns are the spatial

synthesizers of site components, and only in the

context of landforms can forestland patterns make

sense. '1

Commercial forest land in the five-state area is

owned primarily by private individuals. Tract

size varies as much as the occupations and/or interests of the owners. Land owned by forest

industries and various Federal, State, and local

government agencies represents only a small

percentage of the total. Considerable acreage of

the privately owned land receives no professional

forestry input and qualifies as "unmanaged or exploited forests," but some can be classed as

"regulated." Most of the forest industry acreage

bean be classed as "intensively managed or

domesticated" (Stone 1975).
Thus, this author endeavored to devise a forest

site classification system that was practical,

relatively easy to use, flexible in application,

and integrated--not a system consisting of a

compilation of site components but rather one

composed of discrete units of the landscape with

reasonably homogeneous potential for growing trees

and/or for management limitations and hazards.

The system should be applicable to all sizes and

classes of ownership. It should have a mapping

capability, and the scale and detail of

delineations should be appropriate to meet the

management objectives of both "regulated" and

"domesticated" forests. Lastly, the system should

be hierarchical so the units can be aggregated or

disaggregated to meet the needs of land managers

as well as regional, State, national, or corporate

planners and executives.

DESCRIPTION OF THE SYSTEM

This site classification system was adapted from

Wertz and Arnold's (1975) Land System Inventory.

The system can best be described as a process of

successive stratifications of the landscape.

Stratifications were based on the author's

knowledge of the interactions and controlling

influences of ecosystem components--physiography,

climate, geology, soils, topography, and

vegetation. Macroclimate does not vary much

across both physiographic provinces, but

microclimate does vary because of local relief.

Vegetation was relegated to a position of minor

importance because, generally, existing forests do

not indicate site potential, and present stand

boundaries may or may not coincide with site

boundaries.

The five levels of this system (proceeding from

the least detailed to the most detailed) are:

physiographic province, region, subregion, landtype association, and landtype. Landtypes are visually identifiable areas that have resulted from

similar climatic, geologic, and pedologic processes.

The Cumberland Plateau was divided into four regions--Cumberland Mountains and Northern, Mid- and

Southern Plateau (fig.2 and table 1). The Highland Rim was divided into two regions--Eastern and

Western. A guide for each of these six regions

(Smalley 1979a; 1980, 1982, 1983, 1984b, 1986a)

plus a combined edition (Smalley 1986b) have been

I vy

i

‘2

i

“7.

../ (

/ ./ k4

,. STRIKE RIDGES

C. CUYILILAWO-STONE YOUNTAIW

0. STONE -POWELL MOUNTAIN

2. THRIJGT BLOCK INTERIOR, WARTBURG BASIN, 8 JELLICO MOUNTAINS

•j E. Y,OOLESGORO SYNCLINE

F. PlNE YOVNTAIN T”N”ST PLATE SYNCLINE

0. WAN,G”RO GASIN -JCLLICO MOUNTAINS

3. MAJOR RIVER GOTTOYS

“. CUYSERLANO RIVER

Figure 2.--Subregions and landtype associations of the Cumberland Mountains region

5 1
Table I .--Regions, subregions, and landtype associations of the Cumberland Plateau and Highland Rim/Pennyroyal

tumherland Plateau Hinhlw-xi Rim/ppnnvrnval

Region Subregion Landtype association Region Subregion

Cl umberland Strlke rldges A. Walden Ridge, Fork Mountaln, & Eastern Hlghlana I. Hlghland Rim Plateau

M ountalns Rim-Pennyroyal

. Landtype association

Cnestnut Ridge

B. Pine Mountain

C. Cumberland-Stone Mountaln

D. Stone-Powell Mountain

A. Strongly dlssectea plateau

8. Moderate alssected plateau

C. Weakly alssected plateaugray soils (Barrens)

D. Weakly alssected plateaured solls

2. Hlghland Rim-Nashville E. Tennessee Knobs

Basin transltlon F . Outer Basin

3. Pennyroyal-Bluegrass

transltlon

G. Western KentUCky Knobs

H. Eastern Kentucky Knobs

4. Moulton Valley 1. Strongly dissected valley

J. Weakly dissected valley-rea

SOlIS

K. Weakly dIsseCted valleybrown soils

5. Ma]or river bottom5 L. Tennessee Rwer

M. Elk, Duck, & Cumberland

Rivers

N. Barren, Green, Rolling

Fork, Salt, Kentucky, &

Red Rivers

I. Hlghland Rim plateau A. Strongly dIsSeCted plateau

B. Moderately dlssected plateal

C. Weakly alssected plateaugray solls

0. Weakly dissected plateaured solls

2. Hlghlnd Rim-Nashville

Basin transltlon

E. TennesseeKnobs

F. Outer Basin

3. Karst plain G. Weakly dissected Dlaln

H. Moderately dlssected elaln

I. Slum0 area

4. Major river bottoms J. Tennessee River

K. Buffalo River

L. Cumberlana. Harpeth, Duck,

&Elk Rivers

M. Green & Barren Rivers

2. Thrust block Interior. E. Mlddlesboro syncllne

Wartburg Basin, & F . Pine Mountain thrust plate syncllne

Jelllco Mountains G . Wartburg Basin & Jelllco Mountains

3. Major river bOttOmS 1 H. Cumberland River & major trlbutarles

N orthern

C umberland

P lateau

I. Mountains & coalfields A. Rugged eastern area

B. Low hills belt

C. Western escarpment facing the

Eastern Highland RlmlPennyroyal

D. western escarpment facing the

Bluegrass

E . Cumberland & Kentucky RlverS R

major trlbutarles

A . Weakly dissected plateau surfzxe

B. Moderately dlssected plateau surface

C . Strongly dlssected plateau surface

D. Crab Orchard Mountains

A . Weakly dlssected plateau surface

B . Moderately dlssected Dlateau surface

C . Strongly dlssected plateau surface

A . Weakly dlssected plateau surface

C. Strongly alssectea plateau surface

A . Weakly dlssected plateau surfacesandstone

8. Moderately dissected plateau surfacesandstone

C. Strongly dlssected plateau surfacesandstone

A . Weakly dIssected plateau surface8. sandstone

Moderately dissected plateau surfacesandstone

C . Strongly dlssectea plateau surfaceI

sandstone

A Weakly dlssected plateau surfacesandstone

B . Moderately dIsSeCted plateau surface2. MaJOr river b0ttOmS

M ia- I True plateau

C umberland

Plateau

Western Hlghland

2. Walden Ridge Rlm-Pennyroyal

strongly alssected

southern Dortlon

C outhern

C umberland

P lateau

I, Table plateaus--Sand

& LOOkOUt MOUntalnS

2. Moderately alssected

plateau-Brlndley &

Llttle Mountains

3. strongly dissected

western Dortlon

sandstone

4. shale nllls C. Strongly alssected plateau surfacesandstone

0. moderately alssected plateau surfaceshale

E. Strongly dlssected plaeau surfaceshale

published. Several published papers that describe

the overall system and its use (Sims 1987; Smalley

1979b, 1984a, 1985, 1989) are available.

The division of the Cumberland Plateau into regions was mostly arbitrary except for the

Cumberland Mountains, which are higher than the

adjacent Cumberland Plateau and the Ridge and

Valley physiographic provinces. The division of

the Highland Rim follows the traditional partition

made in Tennessee where the Nashville Basin nearly

separates the Rim east and west at the Kentucky

and Alabama boundaries. The east-west division

extends into Kentucky on the basis of soil parent

material--relatively high-grade limestone versus

cherty and shaly limestones. In Alabama the division is arbitrary.

Subregions were defined mostly on the basis of

well-recognized geographic, physiographic, or geologic areas (table 1). Upland landtype associations were defined mostly on the "degree of dissection" of the landscape and occasionally on

broad soil groups. Landtype associations of major

river bottoms reflect differences in mineralogy of

the sediments. Landtype associations are equivalent to soil associations delineated at the state

level by the Soil Conservation Service (SCS)

52
(Hajek, Gilbert, and Steers 1975; Perkins and

Shaffer 1976; Soil Conservation Service 1975,

1979; Springer and Elder 1980.)

Landtype associations were divided into 193

landtypes-- on the Plateau and 98 on the Rim.

Some landtypes are common to more than one region,

so the total number of distinct landtypes is probably about 150.

LANDTYPE DESCRIPTIONS

Each landtype is described in terms of nine elements. A sample description (Landtype 24 in the

Cumberland Mountains) is shown in table 2. The

GEOGRAPHIC SETTING provides an overall description

of the landtype, specifying both where it occurs

on the landscape and its relation to other landtypes.

The most prevalent soil series are listed under

DOMINANT SOILS. These series reflect the most recent designations in soil classification and link

this site classification system to county soil

surveys published by SCS.

The kind of BEDROCK or PARENT MATERIAL of the

soil and DEPTH TO BEDROCK are given next. TEXTURE

of the surface soil is described in terms of the

12 conventional classes (Soil Survey Staff 1951).

Internal SOIL DRAINAGE is described in terms Of

the seven conventional classes (Soil Survey Staff

1951). RELATIVE SOIL WATER SUPPLY of each landtype is rated in five classes: very low, low,

medium, high, and very high. This qualitative

rating is based on the available water-holding capacity of the dominant soils, but allowances are

made for the influence of soil drainage, topographic position, and aspect.

SOIL FERTILITY is described on the basis of

seven classes: very low, low, moderately low,

moderate, moderately high, high, and very high.

Because most soils of the Plateau and Rim are

fairly acid and derived from rocks having limited

weatherable minerals, soils with high or very high

fertility are not conunon.

The most common woody species in approximate order of abundance are listed under VEGETATION; some

distinctive shrub and herbaceous species are included.

FOREST MANAGEMENT INTERPRETATIONS

In the regional guides opposite each landtype

description is a table summarizing information on

the PRODUCTIVITY of selected tree species, severity ratings for five MANAGEMENT PROBLEMS that can

affect forest operations, and SPECIES DESIRABILITY

ratings for timber production (e.g., table 3).

Productivity of commercially valuable species is

expressed as site index and as average annual cubic growth. With few exceptions, site indices for

naturally occurring species are the means of values from soil survey interpretations issued by SCS

for the dominant soils in each landtype. Curves

for most species are based on 50 years, although

I a”,- L.--n Dell I ,p,c. ,511 ,u,,p “_a”, ‘p’“’ I. I ‘““‘““‘cw.3, I.,“-.”

Description of Landtype 24: Colluvial mountain slopes, benches, and coves--north aspect

Deep, loamy, often gravelly, cobbly, or stony soils on sloping to steep lower two-thirds to three-fourths of north slopes in

Subregions 1 and 2. The linear to concave slopes rangIs from 15 to 60 percent. Soils formed in loamy colluvium from acid

siltstone, shale, and sandstone. Landtype 24 is more common in LTA’s E and G than in LTA’s D and F, and it occurs below

Landtype 22 and above Landtypes 19 and 20. Surface mnes (Landtype 26) occur extensively in association with Landtype

24. In places, this landtype merges with major river bottoms (Landtypes 29 and 30).

p

Jefferson, Grimsley, Shelocta, and Rigley. Zenith and Cutshin occur at higher elevations (above 2,500 ft) on slopes and in

coves, particularly in LTA’s E and G. Often mapped as soil complexes.

Bedrock Siltstone, shale, coal, and clay; possibly sandstone and conglomerate.

!i%z

Forty to 120 inches or more.

Jexturct Loam, fine sandy loan, and silt loam; occasionally sandy clay loam and clay loam. Rock fragment content varies considerably

over short distances. Boulders and cobbles are common on the surface, particularly in coves.

Well-drained.

Medium to high.

water sw2k

Soil Moderate to moderately high.

Veaetation Yellow-poplar, northern red oak, white oak, hickories, black oak, red maple, and American beech; occasional sugar maple,

cucumbertree. yellow buckeye, eastern hemlock, eastern white pine, white ash, blackgum, white basswood, and black birch.

Flowering dogwood, mountain-laurel, American hornbeam, vacciniums, grape, viburnums, hydrangea, alder, and smilax are

common understory species.

5 3

--.- - -
Table 3.--A sample table of forest management interpretations. From

Smalley 1964b

Forest management interpretations for Landtype 24:

Colluvial mountain slopes, benches, and coves-north

aspect

PRODUCTIVITY

Species Site index Average annual growth

(feet) (cubic feet per acre)

E. White pine 6 5 145

Shortleaf pine 7 5 136

Virginia pine 7 5 120

White oak 7 5 5 7

N. red oak 6 0 6 2

Yellow-poplar 100 107

MANAGEMENT PROBLEMS

Plant Seedling Equipment Erosion Windthrow

competition mortality limitations hazard hazard

doderate to Slight to Moderate to Moderate to Slight

revere moderate severe severe

SPECIES DESIRABILITY

Most

desirable

E. white pine

White oak

N. red oak

Black oak

Yellow-poplar

White ash

Acceptable

Hickories

American beech

Cucumbertree

Sugar maple

Yellow buckeye

White basswood

Least

desirable

E. hemlock

Black birch

American hornbeam

Serviceberry

Sumac

Red maple

Blackgum

Flowering dogwood

Sourwood

younger base ages are common for fast-growing

species grown in short rotations. When site

indices were available for one species, estimates

for other species were sometimes made using site

index comparisons. When necessary, SCS site index

values were adjusted for aspect and slope position

based on the author's experience and soil-site research. When available, site indices for a

specific region or subregion were used instead of

averages for the soil series. In a few cases,

when no values were available, site indices of

important species were estimated. In the tables,

these estimated values are enclosed in

parentheses.

Average annual cubic growth was calculated from

available yield tables. Yield tables represent

either normal or fully stocked conditions. Annual

growth rates for all naturally occurring species

or forest types were averaged over 50 years.

Average annual growth for loblolly and shortleaf

pine plantations was based on 40 years and for

eastern white pine on 35 years, the oldest ages

reported in the respective yield tables. Because

yields are not expressed in a common merchantability standard, care must be exercised in

comparing average annual yields of species, both

within and between landtypes.

t Prom

For the most part, the five management problems

--PLANT COMPETITION, SEEDLING MORTALITY, EQUIPMENT

LIMITATIONS, EROSION HAZARD, and WINDTHROW HAZARD-

-and ratings of slight, moderate, and severe

follow SCS definitions (Soil Conservation SerViCe

1987).

Three categories are used to rate species desirability of tree species that commonly occur On

each landtype. MOST DESIRABLE species are those

that have the potential for fast growth or high

value, or both. ACCEPTABLE species are those

having moderate growth rate or value. LEAST

DESIRABLE species are those having slow growth or

low value, or both. These ratings represent the

average situation of a region. The presence of

local markets could result in a species being

assigned to another category.

USING THE SYSTEM

The system is designed to allow resource professionals to make on-site determinations of site

productivity and provides a site-dependent framework for forest management planning and forest research.

To make on-site determinations on a specific

tract of land, users should trace the location of

the tract through the classification hierarchy to

the level of landtype association, verify the

location on the reference map (fig. 2 in each

regional guide), (e.g., fig. 2) and ascertain all

possible landtypes by referring to the proper

table (table 4 in each regional guide, except

table 3 in the guide for the Southern Cumberland

Plateau). Landscape descriptions (e.g., table 2)

and landform drawings (e.g., fig. 3) enable users

to identify specific landtypes. Most tracts

smaller than 500 acres seldom contain as many as

12 landtypes. Once a landtype has been

identified, users should refer to the accompanying

table (e.g., table 3) to obtain information about

productivity, severity of management problems, and

species desirability.

A logical vehicle for converting this site

classification system into a valuable forest management tool is a landtype map (fig. 4), which can

be used in all phases of management, from day-today activities to long-range planning. The number

and scale of maps will depend on size of ownership

and how intensively one wishes to manage.

Landtypes can be mapped at scales of l:lO,OOO to

1:60,000, and at these scales, areas as small as

2.5 acres can be recognized on the larger scale

maps. Smoothness of the terrain will determine

maximum size. In the Interior Uplands, maximum

size of landtypes probably will not exceed 50

acres. The U.S. Geological Survey 7.5-minute

quadrangle sheets (1:24,000) make excellent base

maps on which to delineate landtypes for

"regulated" forests. Topographic base maps at a

scale of 1:12,000 are more appropriate for

"domesticated" forests. Black and white or color

aerial photos, particularly stereopairs, can also

serve as base maps. A reasonable amount of ground

checking should be part of the mapping process.

Owners of large tracts should explore the capabilities of mapping, storage, retrieval, and manipulation of this spatial information with a geographic information system.

This site classification system provides a sound

ecological basis for forest management planning

because it recognizes inherent site differences

and soil-related hazards. When the system is

5 4
LEGEND

2.Shallow soils and sandstone outcrops

I9.Hountain footslopes, fans, terraces, and

streambottoms with good drainage.

22,Upper mountain slopes--north aspect,

23,Upper mountain Slopes--South aspect.

24. Colluvial mountain slopes, benches, and covesnorth aspect.

25,Colluvial mountain slopes,benches,and covessouth aspect.

26. Surface mines.

27.Narrow shale ridges, pointsand convex upper

slopes.

28.Broad shale ridges and convex upper slopes.

Figure 3.--Landtypes characteristic of Landtype Associations E (Middlesboro syncline)andG (Wartburg

Basin-JellicoMountains) in Subregion 2 of the Cumberlandf-lountains. From Smalley 1984b

-igure 4.--A samplelandtype map showing Landtype AssociationG(Wartburg Basin andJellicoMountains)

in Subregion 2 of the CumberlandNountains. Map covers atractof about700 acres in the northeast corner a

the northwest quarter of the WindrockQuadrangle, Anderson County, TN.The contour interval is 20 feet.

Numbers refer to specific landtypes described in Smalley 1984b.

5 5

~_~~_~-~-~~~_-~~---_~.~ ---- _._-..~-,~- ----il--_____l___
adopted, landtypes become the basic unit of land

management rather than existing forest stands

whose boundaries are, most likely, artifacts of

past land use. Landtypes are landscape units

having reasonable homogeneous site potential and al

particular set of management constraints and problems. Once landtypes are defined and mapped, existing forest type and inventory information can

be merged with the landtypes and forest planning

begun. The actual conversion to a landtype system

of management can be made gradual as each management unit (e.g., compartment) is entered in the

normal sequence of forest operations.

Detailed descriptions of the physical character--

istics of each unit of land aid land managers in

divising management scenarios that will protect

the soil and water, form the basis of silvicultural practices, and promote the maintenance of

site productivity.

For forest researchers, this site classification

system provides a basis for stratifying study areas (e.g., Cremeans and Kalisz 1988). The system

also aids in identifying and isolating problems

that need to be researched. Finally, the system

provides researchers with a vehicle for quick

transfer of research results to the practitioner.

Results can be reported on the basis of their applicability to specific landtypes.

Development of the system is a continuing

process. Additional research, experience in application, and feedback from users will result in

revision of productivity data, refined landtype

descriptions, improved interpretations for timber

management, and extension of interpretations to

other forest resources.

The next step is to study the relationships between plant communities and the landscape units on

selected, minimally disturbed areas. The goal of

such research is the capability of predicting

which community(s) grow on each unit and to ascertain the successional pathways resulting from various disturbances.

APPLICATIONS AND PERIPHERAL STUDIES

This land classification system is gradually being accepted and applied as a basis for the management of timber and wildlife. Several studies

have been completed that confirm the efficacy of

the system as a sound ecological basis for forest

management. Extension of the system to other

physiographic provinces is contemplated.

The Tennessee Division of Forestry (TDF) uses

the regional guides as a comprehensive training

manual to orient new employees and is gradually

adopting the system as a basis for the management

of the State forests on the Plateau and Rim. The

regional guides also provide important baseline

information for use in advising and preparing

management plans for nonindustrial private

landowners. Over 150 public and industrial

foresters have had on-the-ground training in the

system.

For over a decade, the Tennessee Wildlife

Resources Agency (TWRA), in cooperation with

Tennessee Valley Authority and Forest Service, has

been developing a land classification system for

150,000 acres of wildlife management areas (WMA)

as part of a long-term wildlife-forest management

program (Hughes 1987). If valid relationships

between plant communities and landscape units can

be found, TWRA can use the units as a faster,

cheaper method to define and map habitat for many

wildlife species. The alternative is costly

inventories of vegetation on every acre.

Several studies have been conducted to determine

these relationships. On the 20,000-acre Cheatham

WMA on the Western Highland Rim west of Nashville,

vegetation on each of six landtypes was significantly different from that on all other

landtypes.2' The forest communities (landtypes)

can be used in a geographic information system to

model wildlife habitat, assess site productivity,

and extrapolate from one location to another with

similar landtypes and history.

Wheat and Dimmick (1987) studied plant coaununity-landform relationships on two other Western

Highland Rim tracts. Three ridge landtypes supported similar communities, and distinctive communities existed on cherty north slopes, cherty

south slopes, and in streambottoms having good

drainage.

Plant community-landform relationships have also

been studied on the 26,000-acre Prentis Cooper

State Forest and Wildlife Management Area on the

south end of Walden Ridge (Mid-Cumberland Plateau)

west of Chattanooga (Arnold 1990). Discriminant

analysis of 138 plots located on four extensive

landtypes revealed that landtypes had relatively

distinct forest cover. Contrariwise, cluster

analysis revealed common communities on most

landtypes.

During the last periodic survey of forest land

in Tennessee by Forest Service's Southern Forest

Experiment Station, each survey point on the MidCumberland Plateau was classified as to landtype.

Forest type, stand volume, site index, and other

stand data were analyzed by landtype in an effort

to substantiate the productivity values and

description of overstory vegetation on Mid-Plateau

landtypes (Rennie 1991).

An intensive study of the soils and vegetation

on three major landtypes on the Mid-Cumberland

Plateau near Crossville, TN, has been completed

(Hammer 1986). Landtypes significantly affected

magnitudes of temporal and spatial soil variability (Hammer, O'Brien, and Lewis 1987). The morphological features of soils, when precisely described and interpreted with respect to landtypes,

are indicators of patterns of movement and

relative amounts of available soil moisture and

can be a valuable aid in predicting potential site

productivity for Mid-Plateau forest soils. The

forest land classification system is apparently a

valuable method of grouping Mid-Plateau forest

soils into landform units having relatively

homogeneous chemical and physical properties.

In Kentucky and Tennessee, WESTVACO is in the

process of adapting the land classification system

to their Highland Rim lands and expanding the concept to lands in the Upper Coastal Plain. In West

Virginia, WESTVACO researchers are developing a

similar land classification system for their

J'Clatterbuck, W.K. 1988. Classification and

analysis of forest plant communities on Cheatham

Wildlife Management Area in north-central

Tennessee. Report submitted to Tennessee Wildlife

Resources Agency. Nashville, TN: 110 pp.

56
Allegheny Plateau and Ridge and Valley woodlands.

Evaluations will incorporate existing forest inventory data plus ratings for logging and other

intensive forest management activities.

Smalley 41 has recently completed a land

classification of the 45,000-acre Natchez Trace

State Forest, State Resort Park, and Wildlife

Management Area (NTSF) on the Upper Coastal Plain

in west Tennessee for TDF and TWPA. The forest

plan for NTSF was roundly criticized by a wide

variety of publics. The land classification was

developed to form a sound ecological basis on

which to revise the plan and eventually to manage

the resources of NTSF. Landtype mapping was

completed in 1990. The Laurel Hill WMA on the

Western Highland Rim in south-central Tennessee is

currently being mapped.

For too many years, forest land management decisions have been made without knowledge of the productive capacity and management restrictions of

the land. One does not have to look far to see

mistakes resulting from this lack of information.

With the application of this system, sounder management decisions can be made for forests of the

Cumberland Plateau and Highland Rim/Pennyroyal.

LITERATURE CITED

Arnold, David H. 1990. Distinctness of forest

communities within landtypes on the Prentice

Cooper State Forest, Marion County, TN. 130 pp.

Thesis

Bartelli, L.J.; DeMent, J.A. 1970. Soil survey--a

guide for forest management decisions in the

Southern Appalachians. In: Youngberg, C.T.;

Davey, C.B., eds. Tree growth and forest soils.

Proceedings, third North American forest soils

conference; 1968 August; Raleigh, NC.

Corvallis, OR: Oregon State University Press.

PP. 427-434.

Cremeans, D.W.; Kalisz, P.J. 1988. Distribution

and characteristics of windthrow

microtopography on the Cumberland Plateau of

Kentucky. Soil Science Society of America

Journal 52(3): 816-821.

Fenneman, Nevin M. 1938. Physiography of eastern

United States. New York: McGraw-Hill Book Co.,

Inc. 714 pp.

Hajek, B.F.; Gilbert, F.L.; Steers, C.A. 1975.

Soil associations of Alabama. Agronomy and

Soils Departmental Series 24. Auburn, AL:

Auburn University, Alabama Agricultural

Experiment Station and U.S. Department of

Agriculture, Soil Conservation Service. 30 pp +

map.

a/Smalley, Glendon W. Classification and

evaluation of forest sites on the Natchez Trace

State Forest, State Resort Park, and Wildlife

Management Area in West Tennessee.

Hammer, Richard D. 1986. Soil morphology, soil water, and forest tree growth on three Cumberland

Plateau landtypes. Knoxville, TN: University of

Tennessee. 2 vol. 319 pp. Dissertation.

Hammer, R. David; O'Brien, Ralph G.; Lewis,

Russell J. 1987. Temporal and spatial soil

variability on three forested landtypes on the

Mid-Cumberland Plateau. Soil Science Society of

America Journal 51(S): 1320-1326.

Hughes, Thomas, W. 1987. The Tennessee Wildlife

Resources Agency wildlife-forest management

program. In: Hay, Ronald L.; Woods, Frank W.;

DeSelm, H., eds. Proceedings, sixth central

hardwood forest conference; 1987 February 24-

26; Knoxville, TN. Knoxville, TN: University of

Tennessee. pp. 517-518.

Perkins, H.F.; Shaffer, M.E. 1976. Soil associations and land use potential of Georgia soils

(map 1:750,000). Athens, GA: Georgia

Agricultural Experiment Station and U.S.

Department of Agriculture, Soil Conservation

Service.

Pierpoint, Geoffrey, 1984. Forest land classification - rationale and overview. In: Bockheim,

J.G., ed. Forest land classification: experience, problems, perspectives. Proceedings of a

symposium; 1984 March 18-20; Madison, WI.

Madison, WI: University of Wisconsin. pp. 6-10.

Rennie, John C. 1991. Use of land classification

of the Cumberland Plateau. In this proceedings.

Rowe, J. Stan. 1984. Forestland classification:

limitations of the use of vegetation. In:

Bockheim, J.G. ed. Forest land classification:

experience, problems, perspectives. Proceedings

of a symposium; 1984 March 18-20; Madison, WI;

Madison, WI. University of Wisconsin. pp. 132-

147.

Sims, Daniel H. 1987. How to classify upland forest sites. Forest Farmer 46(3): 8-9.

Smalley, Glendon W. 1979a. Classification and

evaluation of forest sites for timber

production: Introduction of the new system for

classifying forest sites based on the physical

features of the landscape. In: Forest Soils and

Site Quality Workshop; 1979 May 8-9; Auburn,

AL. Auburn, AL: Auburn University. pp 28-47.

Smalley, Glendon W. 1979b. Classification and

evaluation of forest sites on the southern

Cumberland Plateau. Gen. Tech. Rep. SO-23. New

Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment

Station. 59 pp.

Smalley, Glendon W. 1980. Classification and evaluation of forest sites on the western Highland

Rim and Pennyroyal. Gen. Tech. Rep. SO-30. New

Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment

Station. 120 pp.

Smalley, Glendon W. 1982. Classification and evaluation of forest sites on the Mid-Cumberland

Plateau. Gen. Tech. Rep. SO-38. New Orleans,

LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station. 58

PP.

57
Smalley, Glendon W. 1983. Classification and eval,-

uation of forest sites on the eastern Highland

Rim and Pennyroyal. Gen. Tech. Rep. SO-43. New

Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment

Station. 123 pp.

Smalley, Glendon W. 1984a. Landforms: a practical

basis for classifying forest sites in the

Interior Uplands. In: Proceedings, 12th annual

hardwood symposium; 1984 May 8-11; Cashiers,

NC. Asheville, NC: Hardwood Research Council,

pp. 92-112.

Smalley, Glendon W. 1984b. Classification and

evaluation of forest sites in the Cumberland

Mountains. Gen. Tech. Rep. SO-SO. New Orleans,

LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station. 84

PP.

Smalley, Glendon W. 1985. Landforms: the basis for

classifying and evaluating forest sites in the

Interior Uplands of the eastern United States.

In: Proceedings, IUFRO workshop working party

S1.02.06: Forest site classification methods:

1985 October 7-10; Fredericton, New Brunswick,

Canada: IUFRO. pp. 114-142.

Smalley, Glendon W. 1986a. Classification and

evaluation of forest sites on the northern

Cumberland Plateau. Gen. Tech. Rep. SO-60. New

Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment

Station. 74 pp.

Smalley, Glendon W. 198613. Site classification and

evaluation for the Interior Uplands: forest

sites of the Cumberland Plateau and Highland

Rim/Pennyroyal. Tech, Publ. R8-TP9. Atlanta,

GA: U.S. Department of Agriculture, Forest

Service, Southern Region. 518 pp.

Smalley, Glendon W. 1989. Site classification and

evaluation for the Interior Uplands. In: Clark,

F. Bryan, tech. ed.; Hutchinson, Jay G., ed.

Central Hardwood Notes, Chapter 4.03. St. Paul,

MN: U.S. Department of Agriculture, Forest

Service, North Central Forest Experiment

Station. 5 pp.

Soil Conservation Service. 1975. General soil map,

Kentucky (1:750,000). Washington, DC: U.S.

Department of Agriculture. In cooperation with:

Kentucky Agricultural Experiment Station and

Division of Conservation, Department of Natural

Resources and Environmental Protection.

Soil Conservation Service. 1979. General soil map,

Virginia (1:750,000). Washington, DC: U.S.

Department of Agriculture. In cooperation with:

Virginia Polytechnic Institute and State

University.

Soil Conservation Service. 1987. National forestry

manual, subsection 537.11, amendment 4.

Washington, DC: U.S. Department of Agriculture.

Soil Survey Staff. 1951. Soil survey manual.

Agric. Handb. 188. Washington, DC: U.S.

Department of Agriculture. 503 pp.

Springer, M.E.; Elder, J.A. 1980. Soils of

Tennessee. Stn. Bull. 596. Knoxville, TN:

University of Tennessee Agricultural Experiment

Station. 66 pp. + map.

Stone, E.L. 1975. Soil and man's use of forest

land. In: Bernier, G.: Winget, C.H., eds.

Forest soils and forest land management.

Proceedings, fourth North American forest soils

conference; 1973 August; Quebec,Canada. Quebec,

Canada: les Presses de 1'Universite Laval. pp.

l-9.

Thornthwaite, C.W. 1948. An approach toward a rational classification of climate. Geographical

Review 38(1):55-94.

Wertz, W.A.: Arnold, J.F. 1975. Land stratification for land-use planning. In: Bernier, G.;

Winget, C.H., eds. Forest soils and forest land

management. Proceedings, fourth North American

forest soils conference; 1973 August; Quebec,

Canada. Quebec, Canada: les Presses de

1'Universite Laval. pp. 617-629.

Wheat, Robert M., Jr.; Dimmick, Ralph W. 1987.

Forest communities and their relationships with

landtypes on the western Highland Rim of

Tennessee. In: Hay, R.L.; Woods, F.W.; DeSelm,

H ., eds. Proceedings, sixth central hardwood

forest conference; 1987 February 24-26;

Knoxville, TN. Knoxville, TN: University of

Tennessee. pp. 377-383.
LANDSCAPE ECOSYSTEM CLASSIFICATION FOR SOUTH CAROLINA&'

Steven M. ;Jonesz'

Abstract.--Effective and efficient land resource management

is dependent upon accurate estimation of site productivity

and identification of sites thtit respond similarly to

management practices. The landscape ecosystem

classification approach relies <on relatively undisturbed

vegetation in place of traditional site indices to classify

lands into units that are productive equivalents.

Variation in landform and soils are then related to the

classification units identified by the vegetation. Once

the land classification model is developed, classification

units can be identified and interpretations made based on

landform and soil information. In South Carolina,

landscape ecosystem classification models have been

developed for lands in the Piedmont and upper coastal

plain.

Keywords: landform, soils, vegetation, productive

potential, GIS.

INTRODUCTION

The purpose of ecological land classification

is to identify units of land distributed across

the landscape that are similar relative to type,

structure, and productivity of vegetation. Within

a classification unit, the similar parcels of land

are assumed to be ecological equivalents and have

been traditionally referred to as "site types."

Historically, productivity of forest site types

has been defined in terms of site quality as

indicated by the maximum timber crop the land can

produce in a given time (Daniel and others 1979).

Site index estimates represent an attempt to

quantify forest land productivity. It has become

so established in our routine for so long that we

tend to forget that there is a probability of

misclassification associated with the estimate.

The shortcomings of using site index as an

estimator of site quality are presented at length

by Van Lear (1991) and Lloyd (1991) in this

proceedings and by Monserud (1984).

The esoteric nature of a classification based

on the single land value of timber production has

not been of particular concern to forest resource

L/Presented at the Ecological Land

Classification Symposium, Charlotte, NC,

January 7-9, 1991.

E/Assistant Professor, Department of Forest

Resources, Clemson University, Clemson, SC

29634-1003.

managers of past decades. Obviously, in most

situations the value of greatest interest

continues to be timber; however, with greater

frequency across the south, forest land managers

are finding themselves in situations where they

must make value judgements for natural resources

other than timber. Today's forester is managing

lands that society views as a source of quality

water, wetland habitats, endangered species

habitat, endangered plant communities, wildlife

game species, and diversity of landscapes and

species.

No single classification can be all things to

all people; however, an ecologically based land

classification not driven by a single value can be

interpreted for many different values.

CLASSIFICATION CONCEPTS

Plants, through their failure or success of

establishing viable populations, can be considered

as integraters of all possible combinations of

environmental factors.

In the absence of disturbance, the distribution

of individual species in competition with their

associates is a function of environmental conditions. Those species which have a narrow

ecological amplitude are considered "diagnostic"

and are indicative of their associated environmental conditions. Species with a broad

ecological amplitude are considered as "constant"

species and are not indicative of a certain set of

59
environmental conditions (Mueller-Dombois and

Ellenberg 1974). Species with similar environmental requirements have overlapping distributions

and form associations (Figure 1). It is these

associations of diagnostic species under undisturbed conditions that are used in the "site

indicator" sense. The presence and absence of

diagnostic species are used in place of timber

productivity (site index) as a means of determining which land units are equivalent in terms of

potential biological productivity.

Under relatively undisturbed, near steady-state

or steady-state conditions, the associations of

diagnostic species (vegetation types) are related

to landfonn and soils. Landform factors may

include slope gradient, slope position, aspect,

and slope shape, while the soil component may

include drainage, chemistry, and physical

properties, such as depth of clay, amount of clay,

or thickness of sandy epipedon. Because the

interrelationships of vegetation, landform, and

soil are known, the resulting land classification

is ecologically based. Approaches which overlay

single factor classifications to produce a

component classifica-tion of climate, soil,

landform, and vegetation have been developed and

are in use but are not necessarily ecological

(Rowe 1978).

This approach for South Carolina parallels the

work of Barnes in Michigan (Barnes and others

1982)., The Michigan approach developed the terminology of "landscape ecosystem classification"

(which has been adopted for South Carolina. Landscape ecosystem classification (LEC) expresses the

interrelationships (1) between vegetation and

landform, (2) between vegetation and soils, and

(3) between landform and soils. The term

"landscape" is used as a modifier to emphasize

that ecosystems are geographic units extending

horizontally over the land (Barnes 1989).

Landform is the key component because it is

permanent and relatively easy to recognize. Soil

information is used to refine the classification,

while vegetation is used as a check-and-balance.

The approach is hierarchical and adopts the

regional classification of South Carolina by Myers

and others (1986) for the upper levels of the

hierarchy. Broad units were defined from differences in geologic material, topography, soils, and

climate which results in variations in species

distributions. Within this regional classification, South Carolina is delineated into seven

major provinces (Figure 2), 14 regions, and 15

subregions; a total of 23 map units:

Figure l.--Gaussian species distributions along an environmental gradient interpreted from the first axis

of a detrended correspondence analysis. The data are taken from relatively undisturbed, late successional

upland, blackwater river and redwater river sites on the Savannah River Site, South Carolina. Species are,

by number, (1) bald cypress, (2) water tupelo, (3) lizard's tail, (4) laurel oak, (5) swamp gum, (6)

yellow-poplar, (7) dog-hobble, (8) climbing hydrangea, (9) chain fern, (10) red bay, (11) water/laurel oak,

(12) dogwood, (13) white oak, (14) black oak, (15) pipsissewa, (16) sand hickory, (17) post oak, (18)

deerberry, (19) blackjack oak, (20) broomsedge, (211) dwarf huckleberry, (22) turkey oak, (23) goat's rue.

swamps wet

flats

floodplains

terraces upland slopes upland

flats

thick

sands
Figure 2.--Physiographic provinces of South Carolina (Myers and others 1986).

#

Ei

NUII

q

II

Blue Ridge Yountain

Hilly Coastal Plain

Middle Coastal Plain

Flatlands Coastal Plain

Alluvial Bhdplahs L

River Terraces

Coastal Hxr& d r Islands

Blue Ridge Mountain Province

Blue Ridge Mountain Region

Chauga Ridges Region

Piedmont Province

Piedmont Foothills Region

Subregion: Upper Foothills

Lower Foothills

Midlands Plateau Region

Subregion: Interior Plateau

Charlotte Belt

Carolina Slate Belt

Southern Piedmont Hills

Kings Mountain Region

Hilly Coastal Plain Province

Sandhills Region

Upper Loam Hills Region

Subregion: Upper Loam Hills-gentle relief

Upper Loam Hills-moderate

relief

Redhills Region

Middle Coastal Plain Province

Southwestern Loam Hills Region

Subregion: Southwestern Loam Hills

Clay Hills

Northeastern Loam Plains Region

61
Flatlands Coastal Plain Province

Coastal Flatwoods Region

Subregion: Upper Terraces

Lower Terraces

Alluvial Floodplains and River Terraces

Province

Redwater River Floodplains and Terraces

Region

Subregion: Alluvial Floodplains

River Terraces

Blackwater River Floodplains Region

Coastal Marsh and Islands Province

Coastal Marsh and Islands Region

Since the vegetation, soil, and landform

relationships vary by region, it is necessary to

develop a separate landscape ecosystem classification for each region. For some situations, it may

be necessary to modify the regional classification

to accommodate differences at the subregional

level. Each landscape ecosystem model further

classifies lands into landform associations and

site units. The landform association expresses

differences in parent material, topography, and

relief. Within each landform association, the

site units are identified on the basis of soil

physical properties or micro-relief, such as

aspect, slope position, slope gradient, or slope

shape. The site unit is the level where

individual stand management considerations are

made.

METHODS

The landscape ecosystem classification approach

has been applied within two physiographic

provinces of South Carolina. These are the Upper

Loam Hills Region and Sandhills Region of the

Hilly Coastal Plain Province and the Midlands

Plateau Region of the Piedmont Province (Myers and

others 1986). Forest stands across the range of

upland and bottomland site conditions were sampled

within the Hilly Coastal Plain Province. Within

the Piedmont Province only the range of upland

conditions were sampled. Within the Midlands

Plateau Region of the Piedmont Province, the

landscape ecosystem modelling was restricted to

lands on gneiss-schist derived parent materials.

These lands occur primarily within the Interior

Plateau and Charlotte Belt subregions. Landscapes

associated with Carolina slate are currently under

study, and plans are underway to initiate a study

on gabbro-diabase derived soils in the near

future.

Within both study areas, relatively

undisturbed, steady state or near steady state

stands were sampled to identify the

interrelationships of vegetation with soil and

landform variables. Forest stands representing

major successional. and disturbance conditions,

including plantations, were sampled across the

range of site conditions. Approximately 350

stands were sampled in developing and verifying

the models.

Sampling on 0.1 acre plots included quantitative vegetation measurements of all strata,

correlation of soils, description of soil

morphology, particle size distribution (in the

Piedmont), slope position, aspect, and landform

type. Data were analyzed and vegetative classifications developed through multivariate analysis

techniques (ordination and cluster analysis).

Soil and landform data were related to the vegetative classifications through informal, visual or

empirical recognition of pattern in variables and

through discriminant analysis procedures. Species

associations that are characteristic of a certain

set of environmental. conditions (diagnostic

species) were identified through synthesis table

construction. Plot design, measurements, and

analytic procedures have been described in detail

elsewhere (Jones and others 1984; Jones 1988a and

1988b).

RESULTS

Hilly Coastal Plain

Within the Hilly Coastal Plain Province, seven

late successional, hardwood forest types were

identified. An association of diagnostic species

was described for each hardwood forest type. The

distribution of selected diagnostic species for

the well drained uplands is given in Table 1.

With respect to the four vegetation associations

within the uplands landform association, thickness

of the sandy epipedon was the major discriminating

soil variable. Internal drainage was the major

discriminating variable in relation to the three

vegetation associations within the alluvial

floodplains. Each of the seven site units

corresponded with a unique combination of major

landform type and soil characteristics which

determine the nature of the late successional,

hardwood vegetation (Figure 3).

In the absence of vegetation or under disturbed

conditions, the site unit can be determined in the

uplands by the thickness of the sandy epipedon and

in the blackwater alluvial floodplains by the

degree of gleying:

I. Upland landform association.

A. Sandy epipedon >80 inches thick.

Xeric Site Unit

B. Sandy epipedon 40-80 inches thick.

Subxeric Site Unit

C. Sandy epipedon 20-40 inches thick.

Submesic Site Unit

D. Sandy epipedon <20 inches thick.

Mesic Site Unit

II. Blazater alluvial floodplain landform

association.

A. Thin, black surfaces.

1. Gray subsoils at a depth greater than

40 inches or gray mottles at a depth

greater than 20 inches.

Well Drained Site Unit

2. Underlain with subsoils that are gray

throughout.

Poorly Drained Site Unit

6 2
Table l.--Distribution of selected diagnostic Site Unit Management/Successional Type

species by site unit for well drained uplands B. Thick, black surfaces; underlain with

within the Upper Loam Hills, Moderate Relief subsoils that are gray throughout.

Subregion of the HiILly Coastal Plain Province 3. Very Poorly Drained Site Unit

Xeric

Species Site Unit

Xeric Subxeric Submesic Mesic

Longleaf Pine-Turkey Oak-Wiregrasss

Longleaf Pine-Turkey Oak-Bracken

Fern

Aristida

stricta

Gaylussacia

dumosa

Quercus

laevis

Tephrosia

Virginia

Quercus

margaretta

Quercus

incana

Quercus

marilandica

Quercus

stellata

Quercus

alba

Cornus

florida

Chimaphila

maculata

XxXx-XxXx

MXXXXXXXXXXXXXX

XXXXXS

xxxxxmGmaxxxxXxxxxxxx

xxxxxxXXXXXXXXXXX

XXXX

xxxxxxxx

XXXXXXXX

xxxxxxxx

xxxxxxxx

Detailed descriptions of the vegetation for each

site unit are published elsewhere (Jones and

others 1981; Jones and others 1984; and Van Lear

and Jones 1987).

For each site unit, the vegetative associations

of successional or management forests were also

described. These are forests with artificially or

naturally established overstories. A particular

site unit may have more than one management/

successional vegetation type associated with it:

Subxeric

Submesic

(tentative)

Mesic

Well Drained

Poorly

Drained

Very Poorly

Drained

Longleaf Pine-Moneywort

Longleaf Pine-Sassafras

Longleaf Pine-Blackgum-Sand Hickory

Loblolly Pine-Black CherryHoneysuckle

Slash Pine-Black Cherry-Water Oak

Slash Pine-Sassafras-Dollarleaf

Slash Pine-Blackgum

Southern Red Oak-Hickory

Piedmont Province

Loblolly Pine-Sweetgum-Broomsedge

Sweetgum-Water Oak

Southern Red Oak-Hickory

Loblolly Pine-Sweetgum-Redbay

Loblolly Pine-Redbay-Cane

(tentative)

Loblolly Pine-Swamp Gum-Naked

Witherod

The upland, hardwood forest stands representing

steady state, undisturbed conditions were classified into five forest types, and the associated

diagnostic species were identified (Table 2). The

five vegetative associations occurred across a

range of site conditions extending from xeric

upland flats and upper slopes to mesic lower

slopes. Thus, the endpoints of an environmental

gradient were defined by extremes in landscape

position (Figure 4). Detailed descriptions are

published elsewhere (Jones 1988a; Jones 1988b).

To date, work in the Piedmont bottomlands has not

been initiated.

Figure 3. --Landscape ecosystem classification model for the Hilly Coastal Plain Province, South Carolina.

VERY POORLY

DRAINED

v*wpoplarswamp gum

lbly Sl-90+

POORLY WELL YESlC SIJBMESK: SUBXERlC XEFllC

DRAINED DRAINED high bottoms rbpes uplandtlats

t4sckjack osk- bl~J)ack ask- twkq oakswestgum- swestgum- WhltS oak-po# oak d-&Y dwarf potsI o5k dw5rt huckbbcwfy

IUdbSy- mdbay lbly 51-85-90 lbly Sl-50-85 Iglf 51-60-75 lglf Sl<60

bly 51-90-105

i

loOIt

80 In

lhbrk

Rlleaquuns

owltic

numaqueptr

some Hlstorols

T~plc Arsnlc

ochraquults ochraqums

Typic h AwkTYPIC

Paleaqul#ts udmlNsnt8

Tvplc

TYplo

PSlSUdinS

ssml~20ln

Amnk

Palsuduna

~nd20-4Oln

(;rOSSStiC Quwtzlpsamments

RlMlAtS

SSlld>Whl

ssnd40-80ln

63
Table 2.--Distribution of selected character species by site unit for the Midlands

Plateau Region of the Piedmont Province. Numbers 1, 2, and 3 following the names

of woody species represent tree, sapling, and seedling size classes, respectively

Species Site Unit

Mesic Submesic Inter. Subxeric Xeric

Tiarella cordifolia

Anemonella thalictroides

Fagus grandifolia 1

Asimina triloba 2

Thelypteris hexagonoptera

Hepatica acutiloba

Polystichum acrostichoides

Cercis canadensis 2 & 3

Sanguinaria canadensis

Cimicifuga/Ligusticum

Geranium maculatum

Rhus radicans

Desmodium nudiflorum

Quercus rubra 1

Calycanthus floridus 2 & 3

Smilacina racemosa

Fraxinus americana 2

Aristolochia serpentaria

Polygonatum biflorum

Viola hastata

Quercus velutina 1

Quercus coccinea 1

Vaccinium stamineum

Quercus stellata 1

Vaccinium vacillans

xxxxxxxx

XXXXXXXX

XXXXXXXX

XXXXXXXXXXX

xxxXXXXXXXXXXXXXXXXxxxxx

xxxxxxxxXXXXXXXX

xxxxxxxx

xxxxxxxx

Figure 4.--Landscape ecosystem

classification

model for the

Interior Plateau

Subregion of the

Midlands Plateau

Region of the

Piedmont Province,

South Carolina.

MESIC- AMERICAN SEEC” - NORTHERN RED OAK - CHRISTMAS FERN

SUBMESIC- NORTHERN RED OAK - W”,TE OAK - WLO GERANIUM

INTERMEDIATE-r WHITE OAK - NORTHERN RED OAK - FALSE SOLOMON!3 SEAL

SUSXERIC+ WHITE OAK - SCARLET OAK .- DEERBERRY

XERIC- POST OAK - BUCK OAK - LOWSUShI BLUEBERRY

-.---------_~-

6 4
By combining values of slope position and

aspect, a single value rating for landform was

developed. The landform rating expresses the

degree of exposure of a given site.

The two soil factors found to be related to the

distribution of vegetation associations were depth

of the horizon where clay percentage was a maximum

and the maximum percentage clay. Quantification

of these two soil factors were combined into a

single value soil rating expressing soil aeration

and available water holding capacity within the

upper 24 inches of soil.

2. Soils clay loam to sandy clay loam

(27% to 40% clay).

a. Maximum clay horizon within

24 inches of surface.

Intermediate Site Unit

III. Mid-lower (20-40%) slope positions.

A. Southerly to westerly aspects (135O to

315O).

1. Soils clay to sandy clay (>40% clay).

a. Maximum clay horizon within

24 inches of surface.

Subxeric Site Unit

2. Soils clay loam to sandy clay loam

In relation to the five vegetative associations, the landform and soil indices were nearly

equal in their discriminating power with landform

being slightly more important in terms of explaining the variation in vegetation. Each of the five

site units corresponded with unique combinations

of slope position and aspect which interact with

soil characteristics (amount and depth of clay) to

produce a unique association of plant species.

When the vegetation is disturbed or absent, the

site unit can be determined quantitatively by

calculating a total score from the landform and

soil ratings (Jones 1988a) or qualitatively and

more generalized through a descriptive key:

I. Ridge flats to slight slopes; or upper

(O-20%) slope positions.

A. Any aspect.

1. Soils clay to sandy clay (>40% clay);

a. Maximum clay horizon within

12 inches of surface.

Xeric Site Unit

b. Maximum clay horizon within 12 to

24 inches of surface.

Subxeric Site Unit

(<27% clay). Maximum clay horizon at

any depth.

Submesic Site Unit

II. Mid-upper (60-80%) to mid (40-60%) slope IV. Lower (<20%) slope positions.

positions. A. Any aspect.

A. Southerly to westerly aspects (135O to

315O).

1. Soils clay to sandy clay (>40% clay);

a. Maximum clay horizon within

12 inches of surface.

Xeric Site Unit

1. Soils clay loam to sandy clay loam

(27% to 40% clay).

a. Maximum clay horizon within

24 inches of surface.

Submesic Site Unit

b. Maximum clay horizon within 12 to

24 inches of surface.

Subxeric Site Unit

2. Soils clay loam to sandy clay loam

(27%-40% clay).

a. Maximum clay horizon within

12 inches of surface.

Subxeric Site Unit

b. Maximum clay horizon within 12 to

24 inches of surface.

Intermediate Site Unit

B. Northerly to easterly aspects (316" to -

2. Soils sandy clay loam to sandy loam

(<27% clay). Maximum clay horizon at

any depth.

Mesic Site Unit

Preliminary results of current research indicate that vegetation patterns within the pine

management/successional types were not a function

of environmental conditions; rather, variation in

vegetation was due to conditions of stand establishment or subsequent anthropogenic influences.

The pine management/successional types were

separated into Virginia (Pinus virqiniana),

134O).

1. Soils clay to sandy clay (>40% clay).

a. Maximum clay horizon within

12 inches of surface.

Subxeric Site Unit

b. Maximum clay horizon within 12 to

24 inches of surface.

Intermediate Site Unit

(27% to 40% clay).

a. Maximum clay horizon within

12 inches of surface.

Subxeric Site Unit

b. Maximum clay horizon within 12 to

24 inches of surface.

Intermediate Site Unit

B. Northerly to easterly aspects (316O to

134O). -

1. Soils clay to sandy clay (>40% clay).

a. Maximum clay horizon within

24 inches of surface.

Intermediate Site Unit

2. Soils clay loam to sandy clay loam

(27% to 40% clay).

a. Maximum clay horizon within

12 inches of surface.

Intermediate Site Unit

b. Maximum clay horizon within 12 to

24 inches of surface.

Submesic Site Unit

3. Soils sandy clay loam to sandy loam

), and loblolly (P. taeda)

pine types. The Virginia pine type was subdivided

into a Virginia pine-hardwood type and a Virginia

pine-grass type, while the loblolly pine type was

separated into three types: loblolly-sweetgum

(Liquidambar styraciflua), loblolly-water oak

(Quercus niqra), and loblolly-partridge pea

(Cassia fasciculata). In addition, a pine65
hardwood (oak and hickory) mixed type was

identified as a later successional stage.

The hardwood .Eorest immediately succeeding the

pine management/successional and pine-hardwood

types was identified as a successional red oak

type. The predominant oaks were southern red

(Quercus falcata) and scarlet oak (Q. coccinea),

although white oak (Q. alba) and hickory (Carya)

are commonly present. The late successional oaks,

such as post oak (Q. stellata), white oak (&

alba) and northern red oak (Q. rubra) succeed the

red oak forest depending on site conditions and

was described in general as the disturbed white

oak forest. As a result of disturbed conditions,

yellow-poplar (Liriodendron tulipifera) and white

ash (Fraxinus americana) are common associates on

moist sites; scarlet oak under drier conditions;

and hickories and black gum (Nyssa sylvatica)

under all site conditions. Species composition

was found to vary across the five site units for

both the successional red oak stage and the

disturbed white oak stage.

IMPLICATIONS

This approach to land classification on an

ecological basis attempts to take into account

variation due to major environmental variables by

recognizing regions and subregions. For instance,

within a given physiographic region or subregion

major climatic patterns would not significantly

vary. Likewise, when parent material differences

are known to affect major soil properties and

alter plant species composition and productivity,

lands are subdivided into physiographic regions or

subregions. As a result, the physiographic

classification approach of Myers and others (1986)

is implemented in a hierarchical sense, with

landscape ecosystem classification modelling the

micro-climatic and micro-site variability within a

region or subregion. This corresponds to the

microscale of Bailey (1988).

Although requiring a greater investment in time

and financial resources, the advantage of developing landscape ecosystem models for each region is

an increase in accuracy. The models at this scale

can be refined to account for minor variations in

soil and landform that result in fluctuations in

vegetation. These differences can be appreciated

at the individual site level.

The landscape ecosystem models are flexible;

that is, that can be refined to take into account

new information. For a given landscape ecosystem

model, this is accomplished by merely subdividing

existing site units into one or more new site

units. In addition, there is flexibility in the

modelling approach to take into account the shift

in relative importance of landform to soils across

regions. Within the Hilly Coastal Plain, soils

are the driving variable in the model. Landform

influences are apparent only at a broad level.

Landform associations are subdivided into site

units based on differences in soil characteristics. In contrast, within the southern

Appalachians, landform is the major discriminator

of site differences. Landform associations may be

subdivided into site units based on landform

variables or a combination of soil and landfonn

variables or perhaps soils alone.

The major criterion in measuring the usefulness of a classification approach is its

adaptability to mapping procedures and the

production of accurate, useful maps from which

interpretations of land productivity and other

resource values can be made. Obviously, mapping

site types based on existing or potential climax

vegetation would have limited use in the identification of land productivity in the southern United

States. As a result of intensive forestry,

including widespread conversion to pine forests,

and other widespread anthropogenic impacts, the

south's forests are predominately composed of

successional species whose presence are a reflection of disturbance conditions rather than

environmental conditions.

The use of soil surveys is also perceived as

having limited application in delineating site

units with similar productive potential. Soil

taxonomy is often criticized because soil series

are classified based on morphological features

often unrelated to site productivity. This

problem is overcome by combining soils at the

series level into groups that represent ecological

equivalents, that is, those soils that produce the

same type of late successional vegetation on a

given landform.

Early results of efforts to integrate landscape

ecosystem classification models into geographic

information system data layers are promising

(Lloyd and others 1990). Landform is expressed as

digital elevation data and soils expressed through

digitized soil survey which are grouped and

remapped according to their ecological equivalent

groups. Predicted site unit boundaries and potential vegetation are mapped through modelling the

interaction of landform and soils (Figure 5).

Mapped site unit boundaries can be refined in the

field by observing the distribution of diagnostic

species when they have not been eliminated through

land use practices. Since landscape ecosystem

classification simplifies all the various combinations of soil variables and landform variables

into relatively few site units for a given region,

mapping the landscape becomes simplified.

A map of site units derived from landscape

ecosystem classification through application of

GIS can be used in making multi-value planning

decisions. For example, predicting potential

habitat for endangered species, wetlands delineation, and ecological restoration projects. In

the management of diversity, predictions can be

made relative to species diversity at a site

level. Perhaps more importantly in terms of

managing spatial variability, it is possible to

quantify the potential for landscape level

diversity. That is, for a given area we can

address the number (richness) and relative amounts

(evenness) of potential vegetation types directly

from maps of site units. Of course, traditional
Figure 5.--Predicted site units map of the Mill Creek Area, Savannah River Site, South Carolina.

Uplands

# Xeric

# Subxeric

q Submesic

q Mesic

Bottomlands

f# Ifell Drained

q Poorly Drained

Very Poorly Drained

Other

JJ Altered Soil or Water

i&

e l/z 1IllI

67 I
forestry interpretations (productivity,

trafficability, etc.) are possible as well.

LITERATURE CITED

Bailey, R. G. 1988. Ecogeographic Analysis: A guide

to the ecological division of land for resource

management. USDA Forest Service Misc.

Publication 1465, Washington, DC. 18 pp.

Barnes, B. V.; K. S. Pregitzer; T. A. Spies;

V. H. Spooner. 1982. Ecological forest site

classification. Journal of Forestry

80(8):493-498.

Barnes, B. V. 1989. Old-growth forests of the

northern lake states: A landscape ecosystem

perspective. Natural Areas Journal 9(1):45-57.

Daniel, T. W.; J. A. Helms; F. S. Baker. 1979.

Principles of Silviculture. 2nd ed., McGrawHill, New York. 500 pp.

Jones, S. M. 1988a. Old-growth, steady state

forests within the Piedmont of South Carolina.

Ph.D. Dissertation. Department of Forestry,

Clemson University, Clemson, SC. 83 pp.

Jones, S. M. 1988b. Old-growth forests within the

Piedmont of South Carolina. Natural Areas

Journal 8(1):31-37.

Jones, S. M.; D. H. Van Lear; S. K. Cox. 1981.

Major forest community types of the Savannah

River Plant: a field guide. Savannah River

Plant, National Environmental Research Park

Program, U.S. Department of Energy SRO-NERP-9.

79 PP.

Jones, S. M.; D. H. Van Lear; S. K. Cox. 1984. A

vegetation-landform classification of forest

sites within the upper Coastal Plain of South

Carolina. Bulletin of the Torrey Botanical Club

111(3):349-360.

Lloyd, F. T. 1991. Linking a growth and yield model

with landscape ecosystem classification for

natural pine-hardwood mixtures. [These

Proceedings].

Lloyd, F. T.; R. M. Chubb; S. M. Jones. 1990.

Using a GIS-implemented ecological land

classification model to determine where pinehardwood regeneration practices work on the

Savannah River Site (Abstract). P. 28. In:

E. R. Smith (ea.), Proc., First Annual Southern

Appalachian Man And The Biosphere Conference.

Tennessee Valley Authority. Gatlinburg, TN.

Monserud, R. A. 1984. Problems with site index:

An opinionated review. P-167-180. In: J. G.

Bockheim (ea.), Proc. Forest Land zassification: Experiences, Problems, Perspectives. NCR102 North Central Forest Soils Committee,

Society of American Foresters, USDA-Forest

Service, and USDA Soil Conservation Service.

Madison, WI.

Mueller-Dombois, D.; H. Ellenberg. 1974. Aims and

Methods of Vegetation Ecology. John Wiley and

Sons, New York. 547 pp.

Myers, R. K.; R. Zahner; S. M. Jones. 1986. Forest

habitat regions of South Carolina. Clemson

University, Department of Forestry Research

Series No. 42. 31 p.+map.

Rowe, J. S. 1978. The common denominator of land

classification in Canada: an ecological

approach to mapping. P.195-198 In: Proc.

Ecological Classification of Fo&t Land in

Canada and Northwestern U.S.A. For. Ecol.

Working Group, Can. Inst. Forestry and Centre

for Continuing Education. Univ. British

Columbia, Vancouver.

Van Lear. 1991. History of forest site classification in the south. [These Proceedings].

Van Lear, D. H.; S. M. Jones. 1987. An example of

site classification in the southeastern coastal

plain based on vegetation and land type.

Southern Journal of Applied Forestry

11(1):23-28.
DELINEATION AND CLASSIFICATION OF WETLANDS IN THE SOUTHEAST;

John M. Hefner and Charles G. Storrsi'

Abstract--The National Wetlands Inventory of the U.S. Fish

and Wildlife Service has prepared large scale wetland maps

for over 70 percent of the Southeast. Maps are produced

through interpretation of high altitude aerial photographs.

Wetlands are identified based on hydrology, soils, and

plant species. Classification is in accordance with

Cowardin et al. 1979.

Keywords: Classification, inventory, maps, photo

interpretation, trends, wetlands.

INTRODUCTION

The National Wetlands Inventory (NWI) of the

U.S. Fish and Wildlife Service (Service) has been

mapping and classifying wetlands, and analyzing

wetland trends since the late 1970's. The data

collected and disseminated by the NW1 is intended

as a tool to foster wise management of this

important resource.

The NW1 is the fourth wetland inventory

carried out by the Federal Government. The

first two inventories, conducted in 1906 and

1922 by the Department of Agriculture, were

intended to identify lands that could be improved

by drainage and converted to productive croplands.

The Service's previous wetland inventory was

conducted in 1954 to identify important wetland

habitat for wildlife, especially waterfowl. The

release of the findings in Wetlands of the United

States, usually referred to as Circular 39 (Shaw

and Fredine 1956). marked a major turning point

in wetland conservation.

Since that survey, wetlands have undergone many

changes, both natural and man-induced. These

changes, coupled with our increased understanding

u Presented at the Symposium on Ecological Land

Classification: Applications to Identify the

Productive Potential of Southern Forests,

Charlotte, NC, January 7-9, 1991.

ii Regional National Wetlands Inventory

Coordinator and Assistant Coordinator.

respectively. Fish and Wildlife Service,

U.S. Department of Interior, Atlanta, Georgia

of wetland values, led the Service to establish

the NWI. During its 15 year history. the NWI

has developed a variety of cartographic and

narrative products. However, the project's

principal products are detailed large scale

wetland maps and periodic reports of the status

and trends of the nation's wetlands. Wetland maps

are in wide use for impact assessment of sitespecific projects including facility and corridor

siting, oil spill contingency plans, natural

resource inventories, habitat surveys and other

studies. National estimates of the current status

and trends (i.e., losses and gains) of wetlands

have been used to evaluate the effectiveness of

existing Federal programs and policies, and to

identify national or regional problems. The

initial trend study by the NWI increased general

public awareness of wetlands and was instrumental

in stimulating several pieces of important wetland

legislation.

WETLAND CLASSIFICATION

At the inception of the NWI. a variety of

regional wetlands classification schemes were in

use. However, no single classification fully met

the needs of a nationwide project. Therefore, a

new classification system (Cowardin et al. 1979)

was developed by a team of wetland ecologists,

with the assistance of local, State. and Federal

agencies, as well as many private groups and

individuals. After extensive field testing and

four major revisions, the classification was

officially adopted by the Service in 1988.

Familiarity with the classification is essential

to making maximum utility of NWI products.

The Service's wetland classification defines

wetlands in the following manner: "Wetlands are

6 9
lands transitional between terrestrial and aquatic

systems where the water table is usually at or

near the surface or the land is covered by shallow

water. For purposes of this classification

wetlands must have one or more of the following

three attributes: (1) at least periodically, the

land supports predominantly hydrophytes. (2) the

substrate is predominantly undrained hydric soil,

and (3) the substrate is nonsoil and is saturated

with water or covered by shallow water at some

time during the growing season of each year"

(Cowardin et al. 1979). Lists of wetland

plants (Reed 1988). and hydric soils (U.S. Soil

Conservation Service 1987). have been developed

in support of this definition and are now also

an integral part of the Federal methodology for

identifying jurisdictional wetlands (Federal

Interagency Committee for Wetland Delineation

1989).

The classification is hierarchical. At

the most general level, wetlands and deepwater

habitats are separated into five systems - Marine,

Estuarine, Riverine. Lacustrine. and Palustrine.

Each system groups wetlands and deepwater habitats

according to hydrologic, geomorphologic. chemical

and biological similarities. Estuarine and

Palustrine are primarily wetland systems, while

Marine, Riverine, and Lacustrine are predominantly

deepwater systems. At the next level of the

hierarchy, subsystems subdivide the systems on

the basis of general hydrologic characteristics.

At The taxonomic level below the subsystems

are the classes, followed by subclasses. The

11 classes are based on either vegetative life

form or substrate and flooding regime. Classes

describing vegetated wetlands include Aquatic

Bed, Moss-Lichen, Emergent, Scrub-Shrub, and

Forested. Classes describing nonvegetated

wetlands include Rock Bottom, Unconsolidated

Bottom, Unconsolidated Shore, Rocky Shore.

Streambed and Reef. Subclasses provide

additional life form detail (e.g. needleleaved evergreen), or substrate information

(e.g. sand). The classes and subclasses

are easily recognized and can normally be

identified by using remote sensing technologies.

At the most precise and detailed level of the

classification are dominance types. These are

named for the dominant plant species in vegetated

wetlands or the predominant sedentary or sessile

macroinvertebrate species in nonvegetated

wetlands. At this point, the classification

is open-ended and dominance types can be

identified and named as required. In general.

dominance types for forested wetlands can be

related to Forest Cover Types as described by

the Society of American Foresters (Eyre 1980).

For example, Forest Cover Type 98. Pond Pine

would be classified: SYSTEM: Palustrine;

SUBSYSTEM: none; CLASS: Forested; SUBCLASS:

Needle-leaved Evergreen; DOMINANCE Type:

Pinus serotina.

To more fully describe wetlands. a series of

modifiers have been included in the classification

that document hydrology. water chemistry, soil

tw , and the impact of beavers or man. Modifiers

can be applied at the class, subclass, and

dominance type levels.

WETLAND MAPPING

Due to the magnitude of this national effort,

wetland mapping by the NW1 is primarily a remote

sensing project. High altitude photography is the

basic data source. Since 1980. the NW1 has

utilized 1:58,000 scale color infrared photography

acquired for the U.S. Geological Survey's National

High-Altitude Photography Program. Satellite

capabilities are regularly investigated and may

eventually prove useful for monitoring wetland

changes, updating NWI maps. and for producing maps

in unmapped areas.

The preparation of NWI maps is a highly

structured 11 step process combining photo

interpretation. field work, interagency review of

draft maps, along with numerous quality control

checks (Tiner 1990). Photo interpretation is a

manual process in which wetland boundaries and

classifications are penned on a clear overlay

affixed directly to the photographs. During the

interpretation process, careful attention is paid

to collateral information, especially county soil

surveys and topographic maps. When delineations

are complete and have received a satisfactory

review by NWI project personnel, the linework and

classifications are transferred from 1:58.000 to

1:24.000 scale. Linework is then superimposed

onto the corresponding topographic quadrangle

to form the wetland map. The maps are then

distributed to a variety of Federal and State

agencies for review and field checking. Editorial

comments are compiled, maps are corrected, and

final maps are prepared. This entire process

takes from 2 to 3 years from photo acquisition to

final map production.

NWI maps can be quite detailed and

cartographically complex. The minimum size

wetland unit displayed on the maps in Southeastern

U.S. is between one and three acres. The level of

classification detail is also high. Wetlands are

described to the subclass level with modifiers in

accordance with Cowardin et al. (1979). Literally

hundreds of categories of wetlands are described.

some of which infer dominance types. For example,

the classification "Estuarine, Intertidal,

Forested Wetland, Broad-leaved Evergreen,

Regularly Flooded" along the Florida coast,

usually is equivalent to the red mangrove

(Rhizophora mantle) Similarly, dominance type.

the classification "Palustrine, Forested Wetland,

Needle-leaved Deciduous, Semipermanently Flooded"

in the Southeast describes cypress (Taxodium

distichum or L ascendens) dominated wetlands.

The strict adherence to proven mapping

procedures has enabled the NW1 to achieve a high

level of accuracy. Although only one formal study

of NWI map accuracy has been conducted (Swartwout

et al. 1981). the maps have successfully passed

intense scrutinity during usage. Perhaps the best

gauge of overall quality is the willingness of

outside agencies to share the cost of map

70
production and provide field review of the maps.

In 1990. over $1.5 million were provided from

outside sources.

To date, over 30,000 maps covering over

65 percent of the contenninous United States

and 20 percent of Alaska, as well as, Hawaii,

Guam, Puerto Rico, and the U.S. Virgin Islands,

have been prepared. Over one million map copies

have been distributed. Mapping is 70 percent

complete for the 10 States comprising the

Southeast Region of the Service. Maps are

available for all of Kentucky, Tennessee, and

Florida.

An important strength of the NWI is the

accessibility of its products. Maps are

routinely distributed to the U.S. Army of Corps

of Engineers, the U.S. Environmental Protection

Agency. and the U.S. Soil Conservation Service,

as well as, State agencies which have expressed

interest in receiving then. Maps are also

available for purchase to anyone for a $1.75 each

by calling toll free l-800-USA-MAPS. In addition,

they can be obtained from 27 State distribution

centers, which in the Southeast are located in

South Carolina. North Carolina, Georgia, Florida,

Alabama, and Kentucky.

In addition to preparing hard-copy maps. the

NW1 is constructing a georeferenced database for

users of automated geographic information system

(GIS) technologies. Copies of the database files,

in a variety of formats, can be purchased from

the NW1 Offices in St. Petersburg, Florida. at

(813) 893-3873. The database currently includes

digital data for almost 6,000 maps covering

11 percent of the continental United States. The

database is complete for Washington, Illinois,

Indiana, Maryland, and New Jersey, and is nearing

completion for Virginia.

WETLANDS STATUS AND TRENDS REPORTS

Recognizing that maps are a static representation of wetland conditions, the Service conducts

periodic studies to determine wetland gains and

losses nationwide. The first wetland trend study

was completed in the early 1980's and evaluated

wetland changes from the mid-1950's to the mid1970's (Frayer et al. 1983; Tiner 1984). A

second study which will be released in early 1991.

developed trend information for the mid-1970's

to mid-1980's (Frayer In press.).

A stratified random sampling design was used.

Aerial photographs taken at the start and the end

of each study period were interpreted and wetland

acreages measured for 3,629 sample plots, each

four-square miles in size. Estimates of wetland

acreages were then generated through statistical

analysis of the data obtained from the sample

plots.

The initial study revealed that over 11 million

acres of wetlands were lost from the 1950's to the

1970's, with an average annual net loss rate of

458,000 acres. Agricultural development was

responsible for 87 percent of the losses.

These and other findings were instrumental in

the passage of important wetland legislation,

such as the Emergency Wetlands Resources Act

of 1986 (P.L. 99-645) and the Swampbuster

Provisions of the Food Security Act of 1985

(16 U.S.C. 3801-3845).

Preliminary results from the recently completed

trend study indicate that the average annual net

loss of wetlands from the 1970's to 1980's has

declined by a third, to 290.200 acres. It also

appears that the impact of agricultural development had lessened. Estuarine wetlands experienced a relatively small decline, concentrated

primarily along the coasts of Texas and Louisiana.

Palustrine forested wetlands exhibited greater

losses than any other type, a net loss 3.4 million

acres. Most of the forested wetland losses were

identified in the Lower Mississippi Valley and

South Atlantic States.

The design for the national status and trends

studies has been utilized for analyzing wetland

changes in smaller geographical areas by intensifying the sampling effort. In this manner

trend studies have been completed for the

Southeastern States (Hefner and Brown 1984).

the Mid-Atlantic States (Tiner and Finn 1986;

Tiner 1987). Florida (Hefner 1986). and the

Central Valley of California (Frayer et al. 1989).

Additional localized studies are planned for 1991.

DISCUSSION

Since the early 1980's. the NWI has been single

most accurate, accessible and extensive source of

cartographic information related exclusively to

wetlands. Admittedly. no land cover mapping

project based primarily on remote sensing can be

without limitations. Aerial photography which

is processed poorly or taken under adverse

conditions such as periods of severe drought or

flooding can affect interpretation accuracy. In

addition, some wetland habitats, particularly

pine dominated wetlands in the Southeast. are

inherently difficult to interpret from aerial

photographs. Tiner (1990) describes the special

problems related to inventorying a variety of

forested wetland types throughout the U.S. Map

users need to be aware the delineations of some

wetland types are necessarily approximate and

detailed on-site study is necessary for accurate

boundary determinations.

A deliberate effort has been made by the NWI to

make its products available to the greatest number

of people possible. However, new NWI map users

are sometimes deterred by the seemingly complex

classification displayed on the maps. To overcome

this, NWI personnel located in each Regional

Office of the Service are available to provide

assistance in understanding the maps. In

addition, formal training sessions in wetland

classification and mapping procedures are

regularly scheduled.

71
Maps are now available for over 65 percent of

the contenninous U.S. and completion is scheduled

for 1998. However, many areas for which maps were

produced have changed dramatically. For a few

locations, like coastal Georgia. South Carolina,

and Louisiana, as well as. southern Florida,

updated maps have been prepared with funding from

outside sources. Many other areas, like the Lower

Mississippi Valley, are in serious need of

remapping.

As mapping nears completion, the future scope

of NWI is under consideration. The NWI will

continue to conduct periodic wetland trend studies

as mandated by the Emergency Wetlands Resources

Act. yet many other tasks need doing. A series

of reports describing the findings of the NWI in

each State needs to be prepared. All NWI maps

need to be digitized to NWI standards so that

automated analyses can be conducted. Wetland

acreages for every State and county need to be

determined. In addition, it may be time to

expand NW1 efforts to evaluate the functional

health and value of this important resource.

LITERATURE CITED

Cowardin, L.M.; Carter V.; Golet, F.C.; LaRoe,

E.T. 1979. Classification of wetlands and

deepwater habitats of the United States.

Washington. D.C.: U.S. Fish and Wildlife

Service. 103 pp.

Eyre, F.H., ed. 1980. Forest cover types of

the United States and Canada. Washington,

D.C.: Society of American Foresters. 148 pp.

Federal Interagency Committee for Wetland

Delineation. 1989. Federal manual for

identifying and delineating jurisdictional

wetlands. Washington. D.C.: U.S. Army Corps

of Engineers, U.S. Environmental Protection

Agency, U.S. Fish and Wildlife Service,

and U.S. Department of Agriculture, Soil

Conservation Service. 76 pp.

Frayer. W.E. In press. Status and trends of

wetlands and deepwater habitats in the

conterminous United States, 1970's to 1980's.

Houghton: Michigan Technological University.

32 PP.

Frayer, W.E.; Monahan, T.J; Bowden, D.C.;

Graybill. F.A. 1983. Status and trends of

wetlands and deepwater habitats in the

conterminous United States, 1950's to 1970's.

Fort Collins: Colorado State University.

32 PP.

Frayer. W.E.: Peters, Dennis D.: Pywell. H. Ross.

1989. Wetlands of the California Central

Valley: status and trends, 1939 to mid-1980's.

Portland, OR: U.S. Fish and Wildlife Service.

28 PP.

Hefner. J.M. 1986. Wetlands of Florida. 1950's to

1970's. In: Estevez, E.D., Miller, J.; Morris.

J.; Hamman. R. eds.. Proceedings, conference

on managing cumulative effects in Florida

wetlands; 1985 October 17-19; Sarasota, FL.

New College Environmental Studies Prog. Publ.

No. 37. Madison, WI: Omnipress. pp 23-31.

Hefner. J-M.; Brown, J.D. 1984. Wetland trends

in the Southeastern United States. Wetlands

4:1-11.

Reed, P.B.. Jr. 1988. National list of plant

species that occur in wetlands: national

summary. Serv. Biol. Rpt. 88(24). Washington,

D.C.: U.S. Fish and Wildlife Service. 244 pp.

Shaw. S.P.; Fredine, C.G. 1956. Wetlands of the

United States. Circular 39. Washington, D.C.:

U.S. Fish and Wildlife Service. 67 pp.

Swartwout. D.J.; MacConnell, W.P; Finn, J.T.

1981. An evaluation of the National Wetlands

Inventory in Massachusetts. In: Proceedings.

workshop on in-place resource inventories; 1981

August 9-14; Orono, ME: University of Maine.

pp. 685-691.

Tiner, R.W.. Jr. 1984. Wetlands of the United

States: current status and recent trends.

Washington, D.C.: U.S. Fish and Wildlife

Service. 59 pp.

Tiner, R.W., Jr. 1987. Mid-Atlantic wetlands:

a disappearing natural treasure. Newton

Corner, MA: U.S. Fish and Wildlife Service and

U.S. Environmental Protection Agency. 28 pp.

Tiner. R.W., Jr. 1990. Use of high-altitude aerial

photography for inventorying forested wetlands

in the United States. Forest Ecology and

Management 33/34:593-604.

Tiner. R.W., Jr.; Finn, J.T. 1986. Status and

recent trends of wetlands in five Mid-Atlantic

States: Delaware, Maryland. Pennsylvania.

Virginia, and West Virginia. Newton Corner,

MA: U.S. Fish and Wildlife Service and

U.S. Environmental Protection Agency. 40 pp.

U.S. Soil Conservation Service. 1987. Hydric Soils

of the United States. Washington, D.C.: U.S.

Department of Agriculture. 200 pp.

72
INTRODUCTION

FOREST HABITAT REGIONS: INTEGRATING PHYSIOGRAPHY

AND REMOTE SENSING FOR FOREST SITE

CLASSIFICATION'

W. Frank Miller and Michael S. Golden2

Abstract.--This is a hierarchical system originally

developed with the aim of providing a physiographicallybased classification with regional consistency throughout

the southern U.S. The approach applies remote sensing for

delineation of landscape units which are based primarily on

physiographic characteristics and relate well to

topographic, vegetation, and land-use patterns. The focus

is primarily on use of satellite imagery for delineation of

"provinces," "forest habitat regions" and "subregions", but

information from a variety of sources, including geologic

maps, soils maps, and field checking, is applied to the

description and refinement of classification units.

Keywords: Site suitability, satellite imagery, southern

U.S.

The goal of any forest land classification

scheme ultimately should be to provide the

information base required for assessment of site

suitability. As we are using the term "site

suitability" in this paper, we refer to the

concept of "attractiveness" and "vulnerability."

Attractiveness can be expressed in terms of

economics (growth and yield of fiber), aesthetics,

or environmental attributes. Vulnerability is

used to evaluate the impact of the proposed land

use or activity upon the ecosystem; e.g., do we

have a fragile soil and an inelastic ecosystem, or

do we have an ecosystem with a high degree of

elasticity? These concepts were first formulated

by McHarg (1969) and developed as a design tool

'Presented at the symposium, Ecological Land

Classification: Applications To Identify The

Productive Potential Of Southern Forests.

Charlotte, NC, January 7-9, 1991.

2Professor of Forestry, Mississippi State

University, Mississippi State, MS; Associate

Professor of Forestry, Auburn University, Auburn,

AL.

for landscape architects. With slight

modification these ideas fit very nicely into the

concepts of forest site classification and

ecosystem elasticity.

BACKGROUND

The hierarchical classification system

presented in this paper provides the information

required to evaluate site suitability for a wide

range of uses. It was originally developed with

the aim of providing a physiographically based

system with regional consistency for the 13

southern States. This system has its roots in the

early work of the Society of American Foresters

Forest Site Classification Committee of the

Southeastern Region (Hodgkins, 1965). Soon after

the launch of the U.S. s first earth-observing

satellite system, Landsat 1, Miller (1973a)

utilized the concept of physiographic

classification based on satellite imagery for a

large area in northern Alabama. The results were

presented to the Southern Forest Environmental

Research Council and a regional project to develop

a unified South-wide land classification

methodology was proposed. The project was

approved, and researchers in Alabama and

Mississippi worked jointly to develop the

classification scheme and map these two states

(Hodgkins et.al., 1976; 1979).

73
The utilization of remotely sensed data for

analysis of landforms and landscape units is by no

means a new or even a recent concept; geologists,

civil engineers and foresters have been

interpreting conventional panchromatic photography

since it became available in the late 1930 s.

However, with the launch of the Landsat series of

Earth Observing Satellites (originally Earth

Resources Technology Satellites -- ERTS), we

gained an entirely new perspective of the surface

of the Earth. The repetitive and synoptic

characteristics of the satellite data have

provided us with a unique tool for interpreting

spatial relationships of surface features.

DEVELOPMENT OF THE CLASSIFICATION SYSTEM

Classification levels

I. Physiographic Province

These are major systems of landform-geologic

materials. A Province is determined much as Hills

(1961) described in his approach--the superimposition of a regional climatic pattern on a given

geologic substrate.

II. Habitat Region

These are broad areas identifiable by some

uniformity of landform pattern at the 1:500,000

scale of the landsat imagery.

III. Habitat Subregion

Subregions are delineated when a further

subdivision of the broad pattern of

landform/geology is discernible on the 1:500,000

scale imagery. Within both this and the previous

class there is considerable diversity in

topography and soils, but the region or subregion

does define narrow the range of local habitat

conditions. These units (region or subregion) are

the lowest divisions separable on the Landsat

imagery.

IV. Land Type

A land type is defined as an area marked by a

uniformity in geomorphology; general topography,

dominant geologic substrate, and general soil

associations.

V. Land Subtype

This classification unit corresponds to Hills

(1961) Physiographic Site Type--a more closely

defined range of terrain features, dissection,

geologic substrates, and soil series.

VI. Habitat Type

Where an observable variability in habitats

still exists within a Land Subtype, habitat types

can be mapped to further refine productivity and

use suitability. For example, within a given

Subtype, both Alfisols and Ultisols may occur in

mixture. Since there will probably be significant

differences in management strategy between the

habitats occupying each soil order, a split must

be made at the Habitat Type level.

Procedure

A number of different Lendsat data products

were evaluated for their capability to provide

separability of the complex landforms of northern

Alabama. Hardcopy products from the various

wavelength bands were tested as well as color

composites of three channels of data at scales of

1:500,000 and 1:1,000,000. The "best" combination

of data products was the Multispectral Scanner

(MSS) bands 5 and 7, the red and near infrared

bands, respectively. Band 5 gave good

differentiation of cultural features versus

forested lands; band 7 gave excellent land/water

contact, wet soils, and subdued vegetation

signatures while enhancing terrain features.

All levels in the hierarchy cannot be

interpreted from the same source. The classification unit, the suggested mapping and publication

scales, and the data sources are indicated in

Table 1. A thorough review of existing geologic

maps, soil association maps, and Soil Conservation

Service county soil surveys are used extensively

in coordination with interpretation of the

appropriate imagery.

For all scales of imagery,, the key to analysis

of landforms and geologic substrates is the

identification of surface drainage patterns.

Landform is defined as the type and degree of

dissection and the continuity of form. Both local

and regional drainage patterns reflect the

landforms. The types of drainage patterns have

been illustrated by various authors and the

nomenclature is standard (Miller and Miller, 1961;

Way, 1973). Various formal definitions of

drainage pattern have been given, but a common

working definition is the spatial arrangement of

rivers and streams. A surface drainage pattern

consists of two elements, the type of pattern and

the density of the pattern. In areas underlain by

bedrock these patterns are somewhat related to the

depth of developed soil materials, but the major

control of pattern is by rock type and geologic

structures. In areas of unconsolidated materials,

such as the coastal plain provinces, the drainage

pattern types more clearly reflect the thickness

and nature of the soils and substrates.

The degree of dissection is evaluated by the

density or texture of the individual pattern

types. The following texture classes were

developed by selecting a number of classes in each

type and measuring the distances on 1:500,000

scale Lendsat Multispectral Scanner (MSS)

hardcopy. A coarse-textured pattern is defined as

one with approximately 12 mm or more between major

tributaries within a river basin, and 3mm between

first order streams emptying into the major

tributaries. For medium texture, the distances

are 8 mm and 1 mm, respectively, for tributaries

and first order streams. Both regional and local

patterns were studied with particular emphasis on

degree of dissection (density) and spatial

7 4
arrangement (type), vegetation, and land use.

Geologic maps and soil surveys were examined in

order to refine boundary delineations at all

levels of classification. In a number of cases,

traversing suspected boundaries by vehicle was

necessary.

Table I.--A hierarchical land classification

system

Scales

Division Mapping Publication Sourcesa

Province

Region

Subregion

Land Type

1:500,000 1:1,000,000

1:500,000 1:1,000,000

1:500,000 1:1,000,000

1:120,000 1:250,000

Land Subtype 1:58,000 1:80,000

Habitat Type 1:12,000 1:24,000

Landsat MSS

bands 5 & 7;

geology maps

Landsat MSS,

bands 5 & 7;

geology &

soil assoc.

maps

(Same as

region)

NAP or TM

imagery

NAP color IR

& black and

white IR

Low altitude

CIR imagery

aAbbreviations: MSS = Multispectral scanner;

NAP = National Aircraft Program; TM = Thematic

Mapper; IR = infrared.

Example

The habitat region map as developed for Alabama

and Mississippi is shown in Figure 1, with a small

area in east central Mississippi circled for an

illustration of potential interpretation. In a

closeup view of this area of the map (Figure 2),

the area of interest includes Regions 20, 29, and

"a", and Subregion 18A. A high altitude aircraft

infrared image of the area (Figure 3--the original

is in color) allows interpretation of terrain

detail to the level of Habitat Type. The

description here will proceed from the west to the

east side of the area. In the western portion

(left) of the area, the open farmland with a

medium-textured parallel and subparallel drainage

pattern (Region 20, the "Blackbelt Region")

indicates a homogeneous, fine-textured substrate,

with flat to gently rolling terrain. Moving

eastward, there is a distinct ecological break.

Although the drainage pattern remains the same,

the land cover changes from open farmland to oldfield pine and pine/hardwood in the area just west

of the highly dissected hills in the center. A

field trip and reference to a county soil survey

readily established the cause--alkaline clays on

the west and acid clays on the east. The highly

dissected fine dendritic pattern in the center

(Subregion 18A, the "Upper Loam Hills") is a

result of gully erosion of a coarse loamy

substrate, the Tombigbee Sand Member of the Eutaw

Formation. Further east is the alluvial plain of

the Tombigbee River and the adjacent low (recent)

terraces (Region "a", "Miscellaneous Alluvial

Floodplains").

Figure 1 .--The physiographic habitat region map of

Mississippi and Alabama. The example area

described in the text is circled.

Figure 2. --Closeup of the example area, which

involves Region 20, Subregion 18A, and Region 29.

The area is bisected by the Tombigbee River (in

Region "a").

7 5
Figure 3.--High altitude aircraft imagery of the

example area (original is 1:58,000 scale color

IR). The top is north.

The highly reflective area on the eastern

(right) side of the frame (Figure 3) represents

the Old Terrace Region (Region 29), a series of

two distinct terrace levels caused by downcutting

of the river and movement to the west. The nature

of the drainage pattern on old terrace is unique--

a combination of coarse parallel and dendritic,

with the drains themselves not deeply entrenched.

This indicates flat terrain with a high percentage

of soils in the Fragic Great Group.

At this point, we should have amassed

sufficient information on soils and terrain to

begin our evaluation of site suitability and the

resource management decision-making subsequent to

data collection.

In addition to the original one for Alabama

and Mississippi (Hodgkins et al., 1976; 1979),

forest habitat region maps have been published for

the States of Louisiana (Evans et al., 1983),

Georgia (Pehl and Brim, 1985), and South Carolina

(Meyers et al., 1986). One for Tennessee is in

preparation.

Refinements

The Subregion level is the point in the

classification where the 80 meters resolution of

the Landsat MSS data generally ceases to yield

additional site-related information. Currently,

mapping at the Subregion and Land Type levels may

be refined by using Landsat Thematic Mapper (TM)

data, which has a resolution of 30 meters, or by

using the panchromatic band of the French Spot

satellite, which provides a resolution of 10

meters. Also, with the advent of the National

Aircraft Program (NAP, formerly NHAP), every state

has been covered at least once during leaf-off

period with 1:80,000 black and white infrared and

1:58,000 color infrared imagery.

As indicated in Table 1, the Habitat Type is

the lowest level in the physiographic delineation

of land units of similar properties and use

suitability. However, in order to properly

evaluate use suitability, an ecological component

must be added. The classification has been

developed one additional step to include an

ecological component comparable to Hills (1961)

"Ecological Site Type." A "Forest Habitat Mapping

Unit" (Level VII) is defined as a recurring forest

community with distinct physiognomic characteristics recognizable on color infrared aerial imagery

of scales from 1:6,oo0 to 1~15,840.

As an example of the differentiation of forest

habitat mapping units, a stand of bottomland

hardwoods composed of cherrybark and water oak

(Quercus pagoda Raf. and Q nigra L.), hickory

(Carya spp. Nutt.),

- -

and sweetgum (Liquidambar

styraciflua L.) of sawtimber size and normal

density would be mapped as a separate unit from a

stand of sugarberry (Celtis laevigata Willd.),

green ash (Fraxinus pennsylvanica Marsh), and

boxelder (Acer negundo L.) of pole size and normal

stocking. This type of mapping has proven to be

of value in estimating not only timber volumes,

but also upland game (Table 2) and waterfowl

habitat (Miller, 1973b).

Table 2 .--Habitat quality ratings for four game

species as related to selected forest habitat

management units--Tombigbee Sand Hills Ecosystem.

"Best"= 1 to "worst"= 5.

Forest Habitat Quality

Typea Density Deer Squirrel Turkey Rabbit

MOH NORMAL 3.0 2.1 3.0

MOH SPARSE 2.6 3.6 3.2

I S NORMAL 2.6 5.0 3.5

P NORMAL 3.5 ::: 4.5 3.5

aMOH = mixed bottomland red oaks, hickories,

and sweetgum; 12 inches dbh. IS = invader

species: sugarberry, green ash, and boxelder; 6-12

inches dbh. P = natural pine (generally loblolly

(Pinus taeda L.)); 6-10 inches dbh. - -

CONCLUSION

The development of a uniform hierarchical

mapping system that is closely related to

interpretation of satellite and aircraft imagery

has produced a classification that is constant

across the Southern Region. This provides a

standard mapping protocol and nomenclature for the

divisions of a hierarchical classification scheme.

With this technique, it is possible to arrive at a

level of classification refinement that permits

7 6
the evaluation of use suitability as well as

potential productivity estimates.

LITERATURE CITED

Evans, D.L.; Burns, P.Y.; Linnartz, N.E.;

Robinson, C.J. 1983. Forest habitat regions of

Louisiana. Baton Rouge, LA: Louisiana

Agricultural Experiment Station, School of

Forestry and Wildlife Management, Research

Report no. 1. 23 pp.

Hills, G. Angus. 1961. The ecological basis for

land use planning. Ontario Department of Lands

and Forests, Research Branch Report no. 46. 204

PP.

Hodgkins, Earl J. 1965. Southeastern forest

habitat regions based on physiography. Auburn,

AL: Alabama Agricultural Experiment Station,

Forestry Department Series no. 2.

Hodgkins, E.J.; Cannon, T.K.; Miller, W. Frank.

1976. Forest habitat regions from satellite

imagery: states of Alabama and Mississippi.

Map supplement to Southern Cooperative Series

Bulletin 210, "Forest habitat regions and types

on a photomorphic--physiographic basis."

Departments of Forestry, Alabama and Mississippi; Auburn, AL: Alabama Agricultural Experiment

Station.

Hodgkins, Earl J.; Golden, Michael S.; Miller, W.

Frank. 1979. Forest habitat regions and types

on a photomorphic--physiographic basis: a guide

to forest site classification in Alabama--

Mississippi. Southern Cooperative Series

Bulletin no. 210. Auburn, AL: Alabama Agricultural Experiment Station. 64 pp.

McHarg, Ian. 1969. Design with nature. Garden

City, N.J.: Natural History Press. 197 pp.

Miller, J.C.; Miller, C.F. 1961. Photogeology. New

York: McGraw Hill Book Co., Inc.

Miller, W. Frank. 1973a. The use of ERTS imagery

for forestry and agriculture. Final Report,

Contract #GSA-73-20. Tuscaloosa, AL: Geological

Survey of Alabama, Drawer 0.

Miller, W. Frank. 1973b. Interpretation of aerial

data for ecological evaluations: the TennesseeTombigbee Waterway. In: Soilers, Scott, ed.

Remote sensing for environmental analysis.

Washington, D.C.: Office of the Chief, U.S.

Army Engineers.

Myers, R.K.; Zahner, Robert; Jones, S.M. 1986.

Forest habitat regions of South Carolina from

Landsat imagery. Clemson, SC: Clemson University Department of Forestry, Forestry Research

Series no. 42. 31 pp.

Pehl, C.E.; Brim, Roy L. 1985. Forest habitat

regions of Georgia, Landsat 4 imagery. Athens,

GA: Georgia Agricultural Experiment Station,

Special Publication 33. 12 pp.

Way, D.S. 1973. Terrain analysis: a guide to site

selection using aerial photographic interpretation. Stroudsburg, PA: Dowden, Hutchinson and

Ross, Inc. 392 pp.

7 7

CLASSIFICATION SYSTEM

INTERPRETATION

UTILIZING A SITE SUITABILITY GUIDE FOR SITE POTENTIAL AND SPECIES

RECOMMENDATIONS ON SOUTHERN APPALACHIAN NATIONAL FORESTS L/

Y

Richard D. Rightmyer and James E. Keys

ABSTRACT. --Direct estimates of site productivity

are often difficult in stands common to Appalachian

forests. A guide has been developed for indirect

estimates of productivity using topographic and

soil factors allowing recommendations of suitable

tree species best suited to the site. A field

study of the guide conducted in 1988 indicated site

potential is often misclassified without the aid of

soil and site data.

KEYWORDS: Land classification, site productivity.

INTRODUCTION

A continuous problem facing forest land

managers of the Appalachian forests is the

classification of forest lands based on their

site potential and making selection of suitable

species for management. Direct estimates of

productivity is not always practical. High

grade harvesting in the early 1900s with little

stand management often created stands where

accurate prediction of site potential is

difficult from the existing stems. A method has

been sought to evaluate site potential of such

stands on the Chattahoochee National Forest and

aid in recommending suitable commercial species

for these sites.

Public concerns over conversion of hardwood

stands to pine and increasing utilization of

natural regeneration methods were identified

during the preparation of the Forest Plan (USDA

Forest Service 1985). These two issues along

with increasing emphasis on natural ecosystem

management support the need for accurate site

classification. To meet these challenges a

guide has been developed on the Chattahoochee

National Forest which provides an indirect

measure of site productivity using field evaluation of soil and topographic variables providing a recommendation for management types.

A/ Paper presented at the Ecological

Land Classification Symposium, Charlotte, NC,

January 7-9, 1991.

2/ Forest Soil Scientist, ChattahoocheeOconee National Forests, USDA-Forest Service,

Gainesville, GA 30501. and Regional Soil

Specialist, Southern Region, USDA Forest

Service, Atlanta, GA 30367

SITE QUALITY STUDIES

In developing the Chattahoochee Guide a

review of current literature was done to help

decide landform soil and site factors most

critical to growth and perhaps those most easy

to observe and measure.

Measurement of site quality is an age-old

problem of forest management with numerous

efforts completed in ways to measure different

sites and work with different species. Heiberg

and White (1956) defined site as a complex of

many factors influending development of a forest

and that a forester must be aware of all the

effective factors contributing to this development.

Carmean (1975) produced an exhaustive report

on forest site quality evaluation. He states

that the first step to intensive forest land

management is to determine productive capacity

and site quality of the land for alternative

tree species. With this knowledge one can

compare potential yields to identify the most

productive and valued species for the site. The

problem arises when deciding to use direct or

indirect measures of the site potential.

Direct estimation can be used where age and

height can be measured from free to grow,

dominant or codominant trees on the site. Where

trees are not present or unsuitable due to past

management one must use indirect estimates. In

stands of this description the relationship of

soil and site factors to growth of commercial

timber species can be measured to predict growth

of a desired species.

8 1
Carmean (1970) indicates soil depth, soil

texture, soil porosity and parent material are

always critical. These characteristics

influence the quality and quantity of growing

space for roots. In particular his work has

shown that changes in the depth of the surface

layer; the zone where nutrients and available

moisture are most abundant, are the most

significant to tree growth.

Soil texture influences the content and

movement of moisture; levels of organic matter

and the cycling or availability of nutrients

(Eigel et al. 1982). Medium textured soils have

higher productivity than coarser textured due to

higher levels of organic matter, less nutrient

leaching and better moisture holding conditions.

Studies by Doolittle (1957). Trimble and

Yawney (1968). Trimble and Weitzman (1956), and

McNab (1985, 1986, 1988) demonstrate the strong

influence of topographic variables on site

productivity. Higher productivity for hardwoods

is generally found on north facing slopes, lower

on south facing slopes. This is generally

attributed to increased available soil moisture

and deeper rooting depths on the north aspects.

Landform position and landform shape has

also been strongly correlated with productivity

(McNab 1984). Aspect and landform position

affect solar radiation reception which

indirectly affects soil moisture losses

resulting from evaporation and transpiration.

Position on the landform affects soil moisture

movement and drainage. For example ridges and

upper sideslope positions lose moisture while in

contrast lower slope positions are in moisture

gaining situations.

DEVELOPMENT OF CHA'ITAHOOCHEE GUIDE

Guide development focused on three

objectives:

-- Define soil and topographic variables

influencing site productivity which could be

classified in the field setting;

-- Design a format for measuring these

variables during standard field examination:

-- Correlate guide recommendations with stand

classification data.

During literature review a previous study

was found which identified soil and site

relationships pertinent to the objectives. Ike

and Huppuch (1968) conducted an extensive study

on the Chattahoochee National Forest which

focused on growth response of seven hardwoods

8 2

and three pines on sites. They found very

strong correlations with several topographic

variables and weaker associations with soil

variables. The findings of this and other

studies in the South along with personal

experiences of foresters and soil scientists

involved in land classification guided the

selection of variables used in the field guide.

Field use of the guide requires measurement

of topsoil depth; identification of landform

position from a topographic map or on the site;

identification of soil texture in the upper 18

inches of soil material and measure of aspect.

Solum depth is estimated from field observations

or soil survey reports. The guide can be

utilized on a stand basis but it is generally

desirable to traverse the entire stand: evaluate

average site and landform conditions and develop

recommendations for the species best suited to

the stand. The Chattahoochee National Forest

Site Suitability Guide (Rightmyer 1988) is found

in Appendix A.

The following definitions describe the

variables used in the site classification and

species recommendations:

-- Landform Position: position on the

landscape or landform

categories evaluated: ridges (ridgetop and

upper sideslope). upper sideslope, middle

sideslope. lower sideslope

-- Aspect: measure of azimuth from north, - -

exposure, may be described as general direction,

i.e. northerly, northeast, southerly

categories evaluated: north to east, south

to west

-- Solum Depth: the depth of soil material

available for effective root growth, free of

restrictive layers and materials, generally the

depth of soil material overlying bedrock or

parent material

categories evaluated: shallow: 0 to 20

inches, moderately deep: 20 to 40 inches, deep:

40 inches plus

-- Topsoil Horizon Depth: the depth of the

surface layer of soil material, area of

concentrated soil moisture and available

nutrients, zone of feeder (fine) root growth,

generally refered to as topsoil

categories evaluated: shallow: less than 2

inches (<2"), moderately deep: 2 to 6 inches,

deep: greater than 6 inches (>6")
-- Soil Texture: composition of soil in terms

of relative proportions of sand, silt and clay

particles; dominant texture in the A and B

horizons

categories evaluated: fine: (clays, silty

clays, sandy clays), medium: (loams, silt loams,

sandy loams), coarse (sands, skeletal-rocky)

Effects of Selected Variables on Site Quality

To completely utilize the Guide one must

have a basic understanding of the effects of the

soil and topographic variables on site quality

and growth potential. The interrelationships

between each variable is complex and often

difficult to measure, however a basic estimation

can be achieved.

The location of a site on a landscape

influences growth in terms of the gain or loss

of soil moisture due to the effects of gravity

on flow and protection from climatic

influences. Moisture amounts generally increase

as distance from the ridgetop increases due to

gravitational flow down the slope. Lower slope

positions tend to be gaining, upper slopes are

in losing positions.

The amount of solar radiation, sunlight, a

given site receives affects growth in terms of

evaporation and transpiration controlling the

level of available soil moisture. The exposure

of a slope controls the temperature of a site

which influences the rate of moisture loss, the

rate of chemical reaction to breakdown nutrients

and protection of the site from adjoining

landforms. For example a south facing exposure

(south aspect) will receive longer periods of

solar radiation resulting in increases in soil

temperatures, evaporation and transpiration

rates and reduced availability of soil moisture

to trees for growth.

Soil depth typically increases in a similar

manner, increasing from the ridgetop to the

lower slope. The interaction of these two

situations is the increased soil depth and

moisture available for growth on lower slopes.

Additionally lower slope positions may be

protected from winds and long periods of solar

radiation which allows soil moisture to be

retained for longer periods.

The depth of soil material is basic to tree

growth, controlling the volume of space

available for storage and delivery of soil

moisture and nutrients: and for root growth,

critical to the amount of intake capacity to

acquire these items for growth. The volume of

soil material that is available for effective

root growth, that allowing unrestricted root

movement, will determine the potential of the

site to produce vegetation. In general the

greater the total depth of soil material the

higher the site potential, other factors

considered (i,e. stone content, compacted

layers, etc.).

Topsoil depth (surface or Al horizon) also

influences the availability of soil nutrients

and moisture. Typically this portion of the

soil material contains the inflow of nutrients

from decomposing organic matter and has a

texture allowing soil moisture to be more

available. Growth of small feeder roots is

generally concentrated in this area. Loss or

absence of the surface horizon removes this

critical area of nutrient and moisture uptake.

Sites with a deeper Al horizon are more

conducive to development of larger and more

effective root systems which translates to

increased above ground biomass production.

Soil texture describes the proportional

makeup of soil particle sizes which directly

controls the movement of moisture in the soil

material. Soils having a high percentage of

clay particles (fine-sized) tend to hold water

tightly, therefore unavailable to plant uptake.

These soils can also have poor aeration

conditions and restricted root growth.

Generally soils with medium textures (loamy)

have an increased potential for plant growth due

to the increased movement and availability of

moisture and nutrients.

Coarse textured (sandy or stony) soils tend

to allow rapid movement of moisture ouof the

soil material therefore losing essential

moisture too quickly.

Each tree species found in the southern

Appalachians differs in growth requirements:

general evaluations cannot be made for broad

areas but instead one must analyze specific

sites and species.

FIELD STUDY

A field study was undertaken in 1988 and

1989 to validate the Guide and test it's application to site productivity recommendations.

Plot data was collected to compare the Guide's

recommendations with those of foresters using

normal site classification procedures.

Secondary objectives included acquainting the

foresters with use of a soil/site guide and

gaining increased use of soils information.
Study Area Features

The portion of the Chattahoochee National

Forest assigned to the Blue Ridge Province of

the Appalachian Mountains made up the study

area. Rugged mountains and ridges ranging from

1000 to 3400 feet with peaks up to 4000 feet are

characteristic of the area. Geology consists of

rocks such as granite, schists, quartzite.

gneiss, mica-schists and metasedimentary rocks

such as metagraywacke and phyllite.

Figure l.--Study area, Chattahoochee National

Forest, northeast Georgia.

Soils range from loamy to clayey in texture

and shallow to deep depending on slope, landform

position and parent material. Soils are all

mesic, classified in the Typic Hapludults and

Typic Dystrochrepts families on sideslope and

ridgetop positions; in Umbric Dystrochrepts on

colluvial positions and Fluventic Dystrochrepts

on alluvial positions. Annual precipitation

ranges from 52 inches on the west side of the

Forest to a high of 80 inches at the northeast

corner near Clayton, GA.

Study Procedure

Study data was collected at the sample plot

locations selected by the forester during stand

examination. A variable plot, defined by a BAF

10 prism, determined the plot size used for the

classification of forest and management type.

The Region 8 Silvicultural Examination and

Prescription Handbook (USDA-Forest Service 1988)

defines forest type as "species of trees that

comprise the main crown canopy, i.e. dominants

and co-dominants". Management type as defined

is what should be produced on the site to best

the goals and objectives of the Forest Plan.

This differs from a strictly timber oriented

management type which is defined as "the forest

type that optimizes the productive capability of

the site to produce quality material.

The seven foresters participating in the study

were directed to use their standard procedures

and experience for site classification and in

recommending a management type. Because of

differences in definition one-to-one correlation

was not expected between the Guide and the

foresters. The Guide is based on the factors

controlling the productive capacity of a site

while the forester may be constrained by management direction or bias of direct estimates.

Data was collected at the plots on aspect,

landform position, topsoil depth, solum depth,

elevation and the forest type. The forester

entered their management type recommendation and

the guide's recommendation based on the soil and

topographic factors in the matrix.

A total of 241 plots were examined arrayed

across the entire Forest. Sample plots were

taken in the stands being examined as part of

the normal entry into areas being considered for

treatment.

Data was summarized for each of the guide

factors using a SAS program (Statistical

Analysis System 1985). Summaries were made for

each of the soil/site factors in the guide with

comparisons then made between the recommendations of the guide and the foresters.

RESULTS AND DISCUSSION

Landform Position

Landform position was the easiest factor to

measure however agreement was variable. Agreement was highest on the ridgetops, both guide

and forester recommending pine.

Landform Agree Differ

(percent)

Ridgetops

Sideslopes i; $7

Colluvial 48

Alluvial

All Plots :z

i;

Moving downslope the level of agreement

declines, particularly on the lower sideslopes.

On a significant number of plots the guide

recommends hardwood due to deeper, loamy and

clayey soils and favorable moisture conditions.

In contrast the foresters primarily recommended

pine; probably influenced by the current stand

condition. Although only 7 plots were sampled

in alluvial positions the level of agreement was

very low; the guide recommending hardwood, the

foresters pine.
Aspects

The amount of agreement for aspect did not

readily explain differences between the guide

and the foresters. On sideslopes the percentage

was about the same regardless of aspect.

Aspect Agree Differ

(percent)

North-East 51 49

South-West 48 5 2

Again the Guide's consideration of soil and

topographic factors favors hardwoods on most

north facing positions and lower slopes of south

facing aspects. The foresters recommended pine

over hardwood or mixed types.

Topsoil Thickness

Data comparison based on surface horizon is

inconclusive when comparing recommendations.

This was expected due to the minimal experience

of the foresters in sampling soil conditions for

either soil depth or texture. A 12 inch auger

was used for this procedure.

The data collected showed higher agreement

for the O-2 inch and 6 inch+ classes; lower for

the 2-6 inch class. This category of data does

not provide significant information to support a

change in the guide.

Surface Agree Differ

(inches) (percent)

0 to 2 5 9 4 1

2 to 6 3 9 6 1

Management Type

The level of agreement on hardwood and pines

was not significantly different; however for

mixed typesthe disagreement level is high. This

may be due in part to limited experience in

classifying the mixed types and past policy

limiting use of such types. This points out an

excellent future use of the site suitability

guide; identifying soil and site conditions

favoring mixed management types.

Managment Type Plots Percent

Differ Agree Differ Agree

Hardwood

Mixed

Pine

(number)

3 3 4 4

29

4 2

4 3

a5

3 4

Of the 241 plots examined 124 plots or 52

percent were classified as pine mangement

types. Hardwood management types were assigned

to 80 plots or 33 percent and mixed types to

only 37 plots or 15 percent.

SUMMARY AND CONCLUSIONS

Estimating site potential by use of soil

and topographic factors was found to be

practical in recommending suitable management

types on the Chattahoochee National Forest. In

comparing the recommendations of the site

suitability guide and foresters levels of

agreement were about equal when considering

landform position and topsoil depth. Agreement

on aspects was not conclusive.

Considering the assignment of management

types for the entire sample indicates an

emphasis of pine management over hardwood or

mixed types. The suspected inaccuracy in using

direct estimates of site potential is also

evident; a large number of the stands are made

up of low quality hardwood stems at present not

displaying true site potential.

The evaluation process of the site suitability guide has extended application to a

Geographic Information System. The integrated

format of the soil and topographic factors

should be compatible with the layers typically

input in a GIS format.

ACKNOWLEDGEMENTS

The authors thank W. Henry McNab, Research

Forester, USDA-Forest Service, Southeastern

Forest Experiment Station, for his support to

this project. Henry provided analysis of the

plot data, advice on the design of the Guide and

the field study and a morale boost when the

going got tough. A special thanks to Mr. Pat

Thomas, Forest Supervisor (now deceased) on the

Chattahoochee National Forest who set the

process in motion to develop the Guide and

emphasized it's use in the management of the

multiple resources of the National Forest.

LITERATURE CITED

Carmean, W. H. 1970. Site quality for eastern

hardwoods. In: The silviculture of oaks and

associated species. Upper Darby. PA: USDAForest Service, Northeastern Forest Experiment

Station Res. Pap. NE-144. pp. 36-56.

85
Carmean, W. H. 1975. Forest site quality

evaluation in the United States. Advances in

Agronomy 27:209-269.

Doolittle, Warren T. 1957. Site index of scarlet

and black oak in relation to southern

Appalachian soil and topography. Forest Science.

3(2):114-124.

Eigel, R. A., R.L. Blevins, R.F. Wittwer and

C.J. Lieu. 1982. Site index of yellow-poplar in

relation to soil and topography in eastern

Kentucky. In: R.N. Muller, ed. Proceedings of

the Fourth central hardwood forest conference;

Lexington, KY. pp. 207-219.

Ike, Albert F. and C.D. Huppuch. 1968.

Predicting tree height growth from soil and

topographic site factors in the Georgia Blue

Ridge mountains. Georgia Forest Research

Council, Macon, GA.: Georgia For. Res. Pap.54.

llp.

Heiberg. Svend 0. and Donald P. White. 1956. A

site evaluation concept. Journal of Forestry.

54:7-10.

McNab, W.H. 1984. Geomorphic factors associated

with quality of yellow-poplar sites in the

southern Appalachians. In: Shoulders, Eugene,

ed. Proceedings, 3d biennial southern

silvicultural conference, 1984, November 7-8:

Atlanta, GA. Gen. Tech. Rep. SO-54. New

Orleans, LA: USDA Forest Service, Southern

Forest Experiment Station. pp. 331-336.

McNab, W.H. 1986. Integrated site classification

in the southern Appalachians: geologic variables

related to yellow-poplar productivity. In:

Phillips, Douglas, ed. Proceedings 4th biennial

southern silvicultural conference, 1986 November

4-6, Atlanta, GA. Gen Tech. Rep. ~-42.

Asheville, NC: USDA Forest Service, Southeastern

Forest Experiment Station. pp.: 253-258.

McNab, W.H. 1987. Yellow-poplar site quality

related to slope type in mountainous terrain.

Northern Journal of Applied Forestry.

4(4):18g-192.

American Foresters, Washington, D.C. pp.

226-240.

Rightmyer, Richard D. 1988. Site Suitability

Guide for Management Type Recommendations on

the Chattahoochee National Forest. USDA Forest

Service, Gainesville, GA. 10 pp.

SAS Institute Inc. 1985. Statistical Analysis

System. Version AOSVS SAS 5.04. SAS Institute,

Inc., Cary, N.C.

Trimble, George R. and Sidney Weitzman. 1956.

Site index studies of upland oaks in the

northern Appalachians. Forest Science. 2(3):

162-173.

Trimble, George R. and H. W. Yawney. 1968. Oak

soil-site relationships in the Ridge and Valley

region of West Virginia and Maryland. USDAForest Service, Northeastern Forest Experiment

Station Res. Pap. NE-96.

U.S. Department of Agriculture, Forest Service.

1985. Chattahoochee-Oconee National Forests Land

and Resource Management Plan. Gainesville, GA.

320 PP.

U.S. Department of Agriculture, Forest Service.

1988. Region 8 Silvicultural Examination and

Prescription Handbook. USDA Forest Service,

Southern Region. Atlanta, GA. Forest Service

Handbook FSH 2409.268. 250 pp.

McNab. W.H. 1988. Hardwoods and Site'Quality.

In: Smith, Clay, ed. Proceedings-Guidelines for

regenerating Appalachian hardwood stands, 1988,

May 24-26. SAF Publication 88-03. Society of

8 6
Appendix A. --Site Suitability Guide, Chattahoochee NF, Georgia.

CHATTAHOOCHEE NF SITE SUITARILITY GUIDE (l/88) L/

LANDFORM SOIL SURFACE SOIL RECOMMRNDED HARDWOOD

POSITION DEPTH DRPTH TRXTURE MGMT.TYPE* POTENTIAL

RIDGETOPS (Aspect not applicable)

<20" >6" CLAYEY HARDWOOD MRDIUM

LOAMY MJXFD MEDIUM

COARSE PINE LOW

2-6" CLAYEY MIXED MEDIUM

LOAMY PINE LOW

COARSE PINE LOW

<2" ALL PINE LOW

----------------------"------------------------------

>20" >6" CLAYEY HARDWOOD MEDIUM

LOAMY HARDWOOD MEDIUM

COARSE MIXED MRDIUM

2-6" CLAYEY HARDROOD MRDIIJM

LOAMY MIXED MRDIUM

COARSE PINE Low

<2" ALL PINE LOW

-------------------_-----------------------------------------------------------

UPPER SIDESLOPES

ASPECT: N-E

O-20" >6" CLAYEY HARDWOOD MEDIUM

LOAMY HARDWOOD MEDIUM

COARSE MIXED MEDIUM

2-6" CLAYEY HARDWOOD MEDIUM

LOAMY MIXED MRDIUM

COARSE PINE LOW

<2" ALL PINE LOW

----------------------------------------------------

20-40" >6" ALL HARDWOOD MEDIUM

2-6" CLAYEY HARDWOOD MEDIUM

LOAMY HARDWOOD MEDIUM

COARSE MIXED MEDIUM

<2" CLAYEY MI2U%D MEDIUM

LOAMY MIXED MHDIUM

COARSE PINE LOW

----------------------------------------------------

40"+ ALL ALL HARDWOOD HIGH

------------------------------------------------------------------

ASPECT: S-W

O-20" ALL ALL PINE LOW

_________-------------------------------------------

20-40" ALL ALL PINE LOW

40"+ ALL CLAYEY MIXED MRDIUM

LOAMY PINE MRDIUM

COARSE PINE LOW
LANDFORM SOIL SURFACE SOIL RECOMMENDED HARDWOOD

POSITION DEPTH DEPTH TEXTURE MGMT.TYPE* POTENTIAL

MIDDLE SIDESLOPES

ASPECT: N-E

O-20" <2" CLAYEY HARDWOOD MEDIUM

LOAMY NIXED MJZDIUM

COARSE PINE LOW

2-6" CLAYEY HARDWOOD HIGH

LOAMY HARDWOOD HIGH

COARSE MIXED MEDIUM

>6” ALL HARDWOOD HIGH

______------------__-------------------------------

20-40" <2" CLAYEY HARDWOOD HIGH

LOAMY HARDWOOD HIGH

COARSE MIXED MEDIUM

2-6" ALL HARDWOOD HIGH

>6” ALL HARDWOOD HIGH

-___-----___---____-____________________---------------------------------------

ASPECT: S-W

O-20" <2" ALL PINE LOW

2-6" CLAYEY MIXED MEDIUM

LOAMY PINE LOW

COARSE PINE LOW

>6” CLAYEY MIXED MEDIUM

LOAMY MIXED MEDIUM

COARSE PINE LOW

-----_------------__--------------------------------

20-40" (2" CLAYEY MIXED MEDIUM

LOAMY PINE LOW

COARSE PINE LOW

2-6" CLAYEY MIXED MEDIUM

LOAMY MIXED MEDIDM

COARSE PINE LOW

>6” ALL MIXED MEDIUM

-----__------_----__--------------------------------

401’+ ALL CLAYEY HARDWOOD MEDIUM

LOAMY MIXED MEDIUM

COARSE PINE MEDIUM

LOWER SIDESLOPES

ASPECT: N-E

O-20" <2" CLAYEY HARDWOOD HIGH

LOAMY HARDWOOD HIGH

COARSE MIXED MEDIUM

2-6" ALL HARDWOOD V.HIGH

>6” ALL HARDWOOD V.HIGH

-------------------_--------------------------------

20-40" ALL CLAYEY HARDWOOD V.HIGH

LOAMY HARDWOOD V.HIGH

COARSE MIXED HIGH

40”+ ALL ALL HARDWOOD V.HIGH
LANDFORM SOIL SURFACE SOIL RECOMMENDED HARDWOOD

POSITION DEPTH DEPTH TEXTURE MGMT. TYPE" POTENTIAL

U)WER SIDESLOPES

ASPECT: S-W

O-20" <2" CLAYEY MIXED MEDIUM

LOAMY MIXED MEDIUM

COARSE PINE LOW

2-6" CLAYEY HARDWOOD MEDIUM

LOAMY MIXED MEDIUM

COARSE MIXED MEDIUM

>6" CLAYEY HARDWOOD MEDIUM

LOAMY HARDWOOD MBDIUM

COARSE MIXED MEDIUM

----------------------------------------------------

20-40" <2" ALL MIXED MBDIUM

2-6" CLAYEY HARDWOOD MEDIUM

LOAMY MIXED MEDIUM

COARSE MIXED MEDIUM

>6" ALL HARDWOOD HIGH

-----------_----------------------------------------

40"+ ALL CLAYEY HARDWOOD HIGH

LOAMY HARDWOOD HIGH

COARSE MIXED MEDIUM

DEFINITIONS OF GUIDE FACTORS

*RECOMMENDED MANAGEMENT TYPE- general category recommendation; specific

management types should be based on the forest type in place. site potential

and management direction.

SOLUM DEPTH TOPSOIL DBPTH

O-20" SHALLOW <2" SHALLOW

20-40" MODERATELY DEEP 2-6" MODERATELY DEEP

40"+ DEEP >6" DEEP

SOIL TEXTURE

CLAYEY: CLAY, SILTYCLAY

LOAMY: LOAM, SILT LOAM

COARSE: SANDY. STONY

ASPECT: N-E: AZIMUTH 330 TO AZIMUTH 120; S-W: AZIMUTH 121 TO AZIMUTH 329

LANDFORM POSITION: FROM ON-SITE OR TOPOGRAPHIC MAP INTREPRETATION

--Toeslopes typically included in lower slopes, coves may be included in both

lower and middle slope positions, ridgetops typically include portions of upper

sideslopes.

SOURCES OF SOIL INFORMATION: ON-SITE EVALUATION OR SOIL SURVEY REPORTS

HARDWOOD SITE POTENTIAL 2/ RECOMMENDED MANAGEMENT TYPE GROUP

VERY HIGH: SITE INDEX 95+ FOR YELLOW-POPLAR HARDWOOD

HIGH: SITE INDEX 75-95 FOR OAKS HARDWOOD

MEDIUM: SITE INDEX 65-75 FOR OAKS MIXED OR PINE, CK ASPECT

LOW: SITE INDEX <65 FOR OAKS PINE

L/ Rightmyer. Richard D. 1988. Site Suitability Guide for Management Type

Recommendations on the Chattahoochee National Forest, Georgia. USDA Forest

Service, Gainesville. GA. 30501

g/BASED ON 50 YEAR CURVES.
USE OF LAND CLASSIFICATION ON THE CUMBERLAND PLATEA&/

John C . RenniZ'

ABSTRACT. Smalley (1980, 1982, 1983 & 1984) developed a comprehensive forest site classification system for the Cumberland

Plateau, Highland Rim and Pennyroyal regions of Tennessee and

adjoining states. Past abuses of forests make the trees in

many stands poor indicators of site quality and potential

productivity. This paper describes the use of landtype as a

descriptor of site quality in a growth and ylfield predictor on

the Cumberland Plateau.

INTRODUCTION

Tennessee has extensive forest resources,

with about 50 percent of its land in forests.

Three quarters of the forests are hardwoods.

Ninety percent of forested lands is in private

ownership (May in press) with much of the hardwood

forests in small tracts in nonindustrial private

ownership. Sound forestry practices are applied

to only a small portion of these lands (Birdsey

1983). Increasing the level of forest management

on nonindustrial private ownerships is a goal of

forestry extension programs, the Tennessee Division

of Forestry, consulting foresters, and industrial

landowner assistance programs.

Growth and yield predictors are basic tools

used in timber management, allowing foresters to

predict stand volume and stems per acre, usually

by diameter class, at various ages using initial

or intermediate stocking, and, site quality.

Mortality and volume growth for five or ten year

periods can also be predicted from the same stand

variables.

Currently available growth and yield predictors

for hardwood types are not appropriate for

Tennessee. They were developed using data from

other regions, or from data obtained so long

ago so as to be of questionable value. New

predictors that are based on current Tennessee

data need to be developed.

11 Invited paper presented at "Ecological Land

Classification: applications to Identify the

Productive Potential of Southern Forests", 7-9

January 1991, Charlotte, North Carolina.

2.1 Associate Professor, Department of Forestry,

Wildlife and Fisheries, The University of Tennessee, Knoxville, 37901-1071.

The work described here uses the Smalley site

classification system based upon landtype to

quantify site quality in a growth and yield

.predictor for the Cumberland Plateau of Tennessee.

Although a few intensive studies have been conducted using this site classification system,

the one reported here i s the first with data from

a large area.

PAST WORE

Smalley (1980, 1982

;

1983 & 1984) presented a

,system of land classif cation for the Cumberland

Plateau and Highland Rim of Tennessee. Landtypes

are defined to include land of about equal

productivity. Smalley described the geology,

soils, vegetation, productivity and management

problems expected on sites within each landtype.

This information was drawn from a variety of

published material and, in some cases, is based

upon extrapolation.

Three studies have examined soil or vegetation

within Smalley's landtypes. Hammer (1986) studied

relationships among soil morphology, soil water,

and forest tree growth on three landtypes at two

locations - Catoosa Wildlife Management Area and

Fall Creek Falls State Park - both located on the

Cumberland Plateau. He concluded that the forest

land classification system appeared to be a

viable method of grouping soils into units suitable for forest management.

Clatterbuck (in press) used Smalley's site

classification to initially stratify vegetation

in the six landtypes on the Cheatham Wildlife

Management Area in the Western Highland Rim.

From this he developed a community classification

system that integrated vegetation and landform to

serve a basis for multiresource land management

decisions.
Arnold (1990) studied vegetation communities in

four major landtypes that occur in Prentice Cooper

State Forest on the Cumberland Plateau. He found

that the landtypes examined had relatively

distinct forest cover, although some forest types

occurred in more than one landtype. He indicated

the need for additional variables to distinguish

between similar communities that occur on differert

landtypes.

PRESENT WORK

Much of the Cumberland Plateau and Highland Rim

of Tennessee is in mixed hardwoods for which there

are no applicable growth and yield predictors.

Use of site index as a variable is limited in most

of these stands since their history is unknown

and many are not even-aged. Landtypes offer an

alternative to site index for these mixed stands

since they are designed to include areas of

similar communities that occur on different landtypes.

This study uses data collected by U.S.D.A.

Forest Service, Forest Inventory and Analysis

(Forest Survey) field crews. Crews were trained

in using Smalley's landtypes on the Cumberland

Plateau. At each cluster (sample plot) consisting

of ten sample points, landtype (or landtypes) and

boundaries were recorded on a special data sheet;

disturbance(s) were also recorded where they

occurred. Landtype data were keypunched by

county, sample plot and sample point.

Standard plot and tree data for the previous

and current measurements were provided by the

USDA Forest Service. Data were reviewed on a

plot by plot basis; plots with significant cutting

or other disturbances were removed from the data

set.

There were 319 sample plots with useable landtype data, representing 16 counties covering the

Cumberland Plateau in Tennessee. Plots occurred

in all 20 landtypes described by Smalley (1982).

The number of plots ranged from 2 in each of 3

landtypes (upper shale slopes, north; lower shale

slopes, south; terraces, streambottoms and

depressions with poor drainage)to 54 in the broad

undulating sandstone upland landtype.

Nineteen USFS forest types were identified on

the sample plots. Eleven forest types were

represented by fewer than six plots each. Seven

forest types are represented by 12 to 24 plots

each. One forest type (white oak, red oak,

hickory) was found on 148 plots.

Two hundred and fifty plots had no evidence of

harvest activity since the last measurement while

59 showed some signs of partial cut . Nine plots

had been clearcut and one plot had a salvage cut.

Two hundred and ninety nine plots had no evidence

of management since the last measurement, while

the remainder had indications of management

activities including thinning (1 plot), stand

improvement (5), stand conversion (3), site

preparation (5) and natural disturbance (6).

Preliminary analyses consisted of contingency

tables examining USFS site class by aspect and by

parent material. Site classes 1 and 2 were pooled

because only one plot was in class 1. Landtypes

were pooled into north, south, ridges and drains.

A significant difference was found in the distribution of plots by site class over these four

categories of aspect. When a fifth categoryoutcrops - was added, the distribution continued

to be significant. The distribution of plots by

site class was not significant over parent

material.

FUTURE WORK

Development of a growth and yield predictor

will be attempted using plots on which there was

little or no disturbance. Because most forest

types were represented by only a small number of

plots, it may be necessary to use only the white

oak-red oak-hickory forest type (n=148). However,

within this forest type, one landtype is not

represented and eleven landtypes are represented

by fewer than ten plots. Combining landtypes may

be necessary. Possible combination are upper and

lower slope or north and south aspect.

Results reported above are for sample plots on

which there was a single or dominant landtype.

Data were collected on an additional 75 sample

plots that did not have a dominant landtype.

Within each plot there are ten sample points each

with a landtype or disturbance code. If sample

points within a sample plot are grouped by landtype, the number of forest types and landtypes in

the data set will increase. This may increase the

number of landtypes included in the growth and

yield predictor and decrease the amount of

combining needed to achieve adequate representation in combinations of landtype and forest type.

LITERATURE CITED

Arnold, D.H. 1990. Distinctness of forest communities within landtypes in the Prentice Cooper

State Forest, Marion County, Tennessee. Knoxville: University of Tennessee. 130 pp. Thesis.

Birdsey, R.A. 1983. Tennessee Forest Resources.

Resource Bulletin SO-90. New Orleans, LA: U.S.

Department of Agriculture. Forest Service,

Southern Forest Experiment Station. 35 pp.

Clatterbuck, W.K. A kommunity classification

system for forest evaluation: development,

validation, and extrapolation. Forest Science

(in press).

Hammer, R.D. 1986. Soil variability, soil water,

and forest tree growth of three forested

Cumberland Plateau landtypes. Knoxville:

University of Tennessee. 319 pp. Dissertation.

May, D.M. Timber Resources of Tennessee. Resource

Bulletin. New Orleans> LA: U.S. Department of

Agriculture. Forest Service, Southern Forest

Experiment Station (in press).

9 1
Smalley, G.W. 1980. Classffication and evaluation

of forest sites on the Western Highland Rim and

Pennyroyal. Gen. Tech. Rep. SO-30. New Orleans,

LA: U.S. Department of Agriculture. Forest

Service, Southern Forest Experiment Station.

120 pp.

Smalley, G.W. 1982. Classification and evaluation

of forest sites on the mid-Cumberland Plateau.

Gen. Tech. Rep. SO-38. New Orleans, LA: U.S.

Department of Agriculture. Forest Service,

Southern Forest Experiment Station. 58 pp.

Smalley, G.W. 1983. Classification and evaluation

of forest sites on the Eastern Highland Rim

and Pennyroyal. Gen. Tech. Rep, SO-43.

New Orleans, LA: U.S. Department of Agriculture

Forest Service, Southern Forest Experiment

Station. 123 pp.

Smalley, G.W. 1984. Classification and evaluation

of forest sites on the Cumberland Mountains.

Gen. Tech. Rep. SO-50. New Orleans, LA: U.S.

Department of Agriculture. Forest Service,

Southern Forest Experiment Station. 84 pp.
FORECASTING GROWTH OF PINE-HARDWOOD MIXTURES

FROM THEIR ECOLOGICAL LAND CLASS

F. Thomas Lloyd’

Abstract-A conceptual framework is outlined for an alternative to

using site index as a piedictor variable in growth and yield models.

Site quality differences are captured in ecological land classification

units which are predicted from topographic and edaphic data. These

units represent a moisture gradient that translates into site quality

differences. Separate growth curves are developed for each ecological

unit. The resulting growth predictions can be applied directly to

particular land areas when the topographic and edaphic factors are

registered in a computer-based spatial data system.

Keywords: Stand-level models, site index, growth and yield.

INTRODUCTION

The purpose of this symposium is to share research

results and study updates on assessment of the productive

potential of southern forests through ecological land

classification. Wood volume is a major economic

component of productive potential, so linking growth and

yield prediction systems to ecological land classification

models is of interest. Finding a strong link is the objective

of the study described here. More specifxally, I hope to

use ecological land classification to incorporate site quality

into a growth forecasting system for natural pine-hardwood

stands in the Piedmont physiographic region. This

research is not complete (permanent plots are still being

installed), so this progress report describes: 1) the rationale

behind using land classification in the place of site index,

2) the model building strategy to be employed, and 3) the

permanent plot design.

WHY NOT SITE INDEX?

Model developers and model users have often clashed

over the use of site index as a measure of land productivity

‘Presented at the Symposium on Ecological Land

Classification: Applications to Identify the Productive

Potential of Southern Forests, Charlotte, NC, January 7-9,

1991.

‘Project Leader, Southeastern Forest Experiment

Station, USDA Forest Service, Clemson, SC.

in growth forecasting systems. The user appreciates the

practical difficulties in measuring site index and relating it

to a particular landscape. Their dissatisfaction is supported

by a large body of literature describing disappointing

attempts to relate an assortment of physical site attributes

to site index. Model builders are not blind to the problems

of users, but they tenaciously cling to the strong

relationship between the site index and the stem volume.

It is true that site index describes a major dimension of

cumulative stand volume and makes statistically significant

contributions to the empirical fit of stand volume

expressions to data. In a field where improvements in tits

are hard to find, model builders are very reluctant to give

up site index as a primary expression of site quality.

However, the justification that “it works” is becoming less

convincing as evidence of flaws in the site index concept

accumulates.

A most disabling flaw is the failure of the foundational

assumption that density does not affect height growth

across the range of densities encountered in “normal” stand

management. Jones (1977) and Lloyd and Jones (1983)

showed for loblolly and slash pines (P. taeda L. and P.

elliottii Engelm.) that height growth is meaningfully

reduced by increasing densities encountered in normal

management practice and that the trend becomes

increasingly pronounced with increasing stand age. Lloyd

and Jones go on to show how volume projections after 20

years are over-estimated by 46 percent in the densest

stands (6-feet by 6-feet spacing) when a common site index
value is used. The site index curves we used accurately

described height growth for the widest spacing (U-feet by

15feet), but increasingly over-estimated height as planting

density increased. The conclusion, at least for southern

yellow pine, is that density affects height growth and

height growth (that is, site index) affects density growth.

This feedback mechanism must be addressed in the growth

modeling process in a way that site index does not allow.

Research on the precision of the site index estimator

(Lloyd and Hafley 1977, Lloyd 1980, and Lloyd et al.

1982) verifies another problem that has been intuitively

clear to site index users for a long time. Variance of the

site index estimator can be quite large, depending upon

sample size and stand age. An imprecise estimate of site

index produces estimation bias when it is used as an

independent variable in a least-squares model fitting

process. Furthermore, this bias is not uniform across the

prediction space because of the way variance increases

with decreasing stand age. This measurement error can be

large, and the resulting bias may help to explain our

inability to accurately predict site index from site

attributes.

USING LAND CLASSIFICATION FOR SITE

QUALITY

Ecological land classification offers an alternative for

incorporating site quality into growth and yield prediction.

Success will depend on the extent to which the land units

defined by the classification model represent site quality

and the extent they can be predicted from physical

attributes of the land.

The land classification system used in this investigation

(Jones 1989) is built on landform, soil, and late

successional vegetation interrelationships on Piedmont

sites. Its land units represent a moisture gradient that

relates well to site quality. Expressions of landform,

which describes the attributes of the landscape, include

aspect and slope positions. Key soil properties include

depth to and percentage of tine textured material in the

maximum clay horizons. The modeling approach will use

the vegetation-defined ecological land classification units to

predict height and density growth.

MODELING APPROACH

Modeling will start with the relatively easy task of

projecting changes in closed-canopy stands over relatively

short periods (no more than 10 years). The projection

interval is restricted because we plan to speed the

model-building process by looking backward in time. That

is, we will use stem analysis and increment cores from

today’s trees to reconstruct past conditions on permanent

plots. Past mortality can only be inferred for relatively

brief periods. Closed-canopy stands will be used in this

initial model so as to avoid prediction problems due to

rapid composition changes in young stands resulting from

death of shade intolerant species that failed to reach an

overstory position during early stand development.

The stand-level modeling approach will build on the

widely recognized expression:

V=kBH (1)

where:

k = a constant to be estimated,

B = present stand basal area, and

H = present height of the dominant stand.

Variations of this fundamental, geometrically-based volume

construct serves as the underpinning for most stand-level

growth and yield models.

This particular application in mixed pine-hardwood

stands will partition volume between the pines and the

hardwoods, so total stand volume is expressed as:

v = v* + v,. (2)

Equation (l), therefore expands to:

V =kr,BpHr, + k&,Hh. (3)

The subscripted terms are the same as k, B, and H defined

above for the respective pine and hardwood stand

components, and total stand basal area equals the sum of

BP and B,. Equation (3) simply says that total stand

volume is the sum of the component volumes.

The final step in the modeling approach consists of

incorporating a growth element. It is easier to envision

this process by returning to Equation (1) without the

component subscripts. It is assumed for some projection

interval that the stand grows dB square feet in basal area

and dH feet in height, so future density and dominant

height are (B + dB) and (H + dH). The projected

volume becomes:

V = k(B +dB)(H + dH). (4)

It is a straight-forward process to obtain a model for

the partitioned stand volume by adding subscripts like

those in Equation (3), yielding the model:

V = 1cp(Bp + dB,)oI, + d%)

+ kh(Bb + WJO% + d&J (5)

where the basal areas and heights BP, B,,, HP, and Hh are

given conditions in the present stand and the growth

increments dB,, d&, dHr, and dH,, must be predicted. The

modeling task of this research will be to develop predictors

for these dB and dH increments.

9 4

__- . ~ _ ~~- w---m
There is much literature on the structure for these

growth expressions, but the final forms will be determined

from a combination of geometrical, biological, and

empirical arguments. Basal area growth (dB) will be

related to present basal area (B), stand age, and site

quality. The site quality effect will be incorporated by

developing separate growth functions for each of the

ecological land groups, thus producing a family of growth

curves. A similar approach will be used for height growth

(dH), which will likely be some expression of present

height (H), density (B), and stand age. The growth and

yield model is thus linked to the landscape though the

parameters associated with ecological units, which are

defined by combinations of aspect, slope position, and

depth of rooting zone.

PERMANENT PLOT DESIGN

Approximately 125 permanent plots will be distributed

uniformly across stands from 15 to 85 years old. They

will be installed in natural pine-hardwood stands on

National Forests and on the Clemson University

Experimental Forest in the Piedmont physiographic region.

Presently, 43 plots have been installed.

It was already pointed out that growth will be obtained

from increment cores taken at breast height on all

merchantable trees (4.6 + inch diameter at breast height)

and stem analyses from two trees (one pine and one oak)

near each plot, but no closer than 1 chain. The plots are

circular in shape and l/5-acre in size. Diameter at breast

height is measured on all tree species (including

nonmerchantable trees). All merchantable-sized trees are

numbered clockwise on azimuths starting at North and the

distance of each from the plot center is recorded. The

crown position of each tree is classed as dominant,

codominant, intermediate, or suppressed, and total height

of every fifth tree in each l-inch diameter class is

measured in both the pine and hardwood components. For

diameter classes with less than five trees, height is

measured on one randomly selected tree.

The plot center is identified with a metal post. Four

large nails are driven into the soil beneath the litter layer

to help relocate plot centers if center posts are removed

before the next remeasurement. Plots are located on the

ground by coordinate readings from global positioning

equipment.

We hope to use mortality data from the Forest

Inventory and Analysis Unit to adjust initial stocking

obtained from the increment cores. Stand density and

volume of survivor trees will be adjusted to include basal

area and volume estimates of trees that died during the 10

and 20 years periods prior to plot installation. In this

manner, net growth (as opposed to survivor growth) will

be approximated and the projection interval will be

lengthened.

CONCLUSION

The justification for trying something new is strong,

and interest among those who use growth and yield models

is high. It is no doubt true that the precision of growth

predictions will be reduced by the presence of some

variation of site quality within an ecological classification

unit. However, the traditional site index approach

produces an over-confident sense of precision from the fact

that families of curves can be constructed arbitrarily close.

The closeness of the site index curves essentially

defines the width of the site index class, and Lloyd and

Hafley (1977) showed how the probability of

misclassification increased with deceasing class size. My

assessment is that 3 to 5 productivity classes are probably

the best we should expect form either of the above

approaches. What is gained by using ecological

classification units is the ability to link growth models to

the landscape and the ability to more appropriately

describe density and height growth as interrelated functions

of site quality.

LITERATURE CITED

Jones, Steven M. 1989. Application of landscape

ecosystem classification in identifying productive

potential of pine-hardwood stands. In: Waldrop,

Thomas A., ed. Proceedings, pine-hardwood mixtures:

a symposium on management and ecology of the type;

1989 April 18-19; Atlanta, GA. Gen. Tech. Rep.

SE-58. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southeastern Forest

Experiment Station. pp. 64-69.

Jones, Earle P., Jr. 1977. Precommercial thinning

of naturally seeded slash pine increases volume and

monetary returns. Res. Paper SE-164. Asheville, NC:

U.S. Department of Agriculture, Forest Service,

Southeastern Forest Experiment Station, 12 p.

Lloyd, F. Thomas. 1981. How many tree heights

should you measure for natural, Atlantic Coastal Plain

loblolly site index? Southern Journal of Applied

Forestry 5(4):180-183.

Lloyd, F. Thomas; Jones, Earle P., Jr. 1983.

Density effects on height growth and its implications

for site index prediction and growth projection. In:

Proceedings, Second biennial southern silvicultural

research conference; 1982 November 4-5; Atlanta, GA.

Gen. Tech. Rep. SE-24. Asheville, NC: U.S.

Department of Agriculture, Forest Service,

Southeastern Forest Experiment Station. pp. 329-333.

Lloyd, F. Thomas; Hafley, William L. 1977.

Precision and the probability of misclassification in site

index estimation. Forest Science 23(4):493-499.

Lloyd, F. Thomas; Muse, H. David; Hafley, William

L. 1982. A regression application for comparing

growth potential of environments at different points in

the growth cycle. Biometrics 38:479-484.

95
THE ROLE OF ECOLOGICAL LAND CLASSIFICATION

SYSTEMS IN THE SILVICULTURAL DECISION PROCESS"

Thomas R. Foxe'

Abstract.-- An applicable land classification system

can provide the type of information required to make

site specific silvicultural prescriptions. Since the

stand is the basic unit of silviculture, the spatial

scale of the land classification system must

coincide with that of the stand. An appropriate land

classification system must also emphasize the

features of a region that most strongly affect

productivity and management. Integration of a land

classification system with a geographic information

system increases the flexibility and accessibility

of the information. The development of a

silvicultural decision support system ("expert"

system) is a potential outgrowth of this

integration.

Keywords: Soil Mapping, Geographic Information

Systems, Decision Support

INTRODUCTION

Biologically sound silvicultural

decisions must be made on a species and

site specific basis. Perhaps the two most

important factors influencing the

silvicultural decision process are the

inherent productivity of each site and the

potential to affect site productivity

through silvicultural manipulations. In

todays complex environment, informed

silvicultural decisions also require

forest managers to synthesize data on a

large number of additional factors ranging

from the management objectives of the

landowner, to the economic climate of the

region, to the social and political

consequences of a particular decision. It

is my contention that some type of land

1/ Presented at Symposium on Ecological

Land Classification: Applications to

Identify the Productive Potential of

Southern Forests, Charlotte, NC, January

7-9, 1991.

2/ Silvicultural Research Coordinator,

ITT Rayonier, Inc., Southeast Forest

Resources, Forest Research Center, P.O.

Box 819, Yulee, FL 32097.

classification system, either implicit or

explicit, that takes these factors into

consideration is a part of all sound

silvicultural decisions.

There are two objectives of this paper.

The first is to describe, in a general

manner, the attributes I believe are

necessary to successfully integrate a land

classification system into the

silvicultural decision process. Second,

by example, I hope to illustrate how

silvicultural decisions can be simplified

with information derived from an

ecological land classification system.

TYPES OF LAND CLASSIFICATION SYSTEMS

An applicable land classification system

can provide the type of the detailed

information upon which informed

silvicultural decisions can be based. It

is not my intention to review in detail

the numerous land classifications systems

that have been developed. However, some

brief comments on the process of land

classification and the general types of

classification systems that exist would be

valuable and will help to provide a

framework for discussion. Those
interested in more specific information

should refer to the other papers in this

volume and several recent reviews and

symposia on the sub.ject (Carmean, 1975;

Bockheim, 1984; Wickware and Stevens,

1986; Williams and Gresham, 1988).

. .

The Process of Isfication

Land classification can be a descriptive

or a predictive process. Descriptive land

classification systems attempt to

characterize the biotic and/or abiotic

feature of an area and establish unique

groups with similar features. This type of

classification system often includes a

mapping component. Examples of this type

of land classification includes the soil

mapping done by the USDA Soil Conservation

Service and various industrial forest

products companies, and the vegetation

mapping currently being done by the US

Fish and Wildlife Service as part of the

National Wetlands Inventory. Predictive

land classification systems attempt to

relate a property of interest to some

measurable feature of the site. The

property of interest is usually site index

or site quality. These systems may be

quantitative and utilize a regression

approach, or semi-quantitative and award

points for various features. The advantage

of these systems is that once the

relationship is established, the site

quality of any land can be evaluated

rapidly with a minimum of effort. Detailed

mapping of the land base is usually not

done. An example of the regression type of

site classification system is the soilsite work of Coile and Schumacher (1964).

Examples of the semi-quantitative approach

include some of the soil-site work of

Zahner (1957) and the site quality rating

system developed by International Paper in

New England (Saviello, 1979).

In practice, many land classification

systems combine predictive features into a

descriptive framework. This is usually a

normal progression following the

implementation of a descriptive system.

Correlating properties of the various

classification units with forest

productivity can lead to an improved

understanding of the functional

relationships involved. This may in turn

lead to an improved classification system.

. . . . .

Tvves of Descrivtive Lias$ Classification

Svstema

The land classification systems that

employ the descriptive approach can be

divided into four broad groups: 1) those

that utilize general landscape and/or

vegetation relationships on a more-or-less

regional scale; 2) those that utilize

soil-site relationships as the basis for

mapping; 3) those that utilize some type

of soil grouping procedure; and 4) those

that utilize the individual soil series

approach.

Landscape Relationships

In the South, the best example of a land

classification system that utilizes broad

scale landform relationships is the system

developed for the Interior Uplands of

Kentucky, Tennessee, and Alabama

(Smalley, 1980; 1982). This system

integrates physiography, geology, soils,

topography and vegetation to develop

landtypes for the region. These landtypes

are evaluated from the standpoint of

forest productivity and related management

limitations.

Soil-Site Relationships

Soil-site relationships such as those

derived by Coile and Schumacher (1964)

have been incorporated into a soil mapping

system. In the Coile mapping system, soils

are grouped into map units based on site

index determined from features such as

drainage class, texture, and depth of

surface horizons. A large amount of forest

land in the South has been mapped

utilizing this system.

Soil Groups

There have been several attempts to

group soils on the basis of similarities

in physical properties into relatively

narrow classes that respond to management

in a like manner. The woodland suitability

groups developed by the SCS is one example

of this type of classification system

(Lemmon, 1970). The CRIFF soil groups

(Fisher and Garbett, 1980) are probably

the most widely used land classification

system in the South based on soil groups.

In this system, the forest soils of the

southern Coastal Plain are divided into

eight groups based on drainage class, the

presence of spodic and argillic horizons,

and the depth to these subsurface

horizons. This system was developed to

group soils based on the likelihood of a

growth response following forest

fertilization. It has proven useful in

this capacity. However, it was not

intended to be used as a system to

classify the inherent productivity of the

site.

Soil Series

The most widely used land classification

system in the South is the soil mapping

approach utilizing the concept of the soil

series. A soil series consists of soils

that are essentially alike in all major

profile characteristics (Brady, 1974).

This system is used by the USDA Soil

97
Conservation Service in the national soil

survey. This is a continuing program under

which the soils of the entire United

States will eventually be mapped. The

concept of the soil series has been widely

adopted as the basis for mapping forest

soils. Many forest products companies have

mapped or are mapping their land using

this approach (Broerman, 1978; Campbell,

1978). In some cases, standard SCS soil

series are employed in the mapping

process, while in others "in house" soil

series are developed.

SELECTING AN APPROPRIATE LAND

CLASSIFICATION SYSTEM

There is no one, single land

classification system that is best suited

to all situations. What works successfully

in one region may be completely inadequate

in another region, even within the same

company or organization.

The essential unit of silviculture is

the stand (Smith, 1986). Therefore, the

first requirement of a appropriate land

classification system, is that the spatial

scale of the land classification system

must match the spatial scale of the stand.

As Smith (1986) states: "The tendency to

treat large groups of dissimilar stands as

if they conformed to a uniform,

hypothetical average should be studiously

avoided." The logical conclusion of this

is that land classification systems

developed for large-scale, regional

applications will probably have very

little utility to the silviculturalist in

the much smaller world of the stand.

The appropriate land classification

system for silvicultural applications

depends on both the intensity of

silviculture practiced and the specific

land base in question.

Intensitv of Mananement

In an intensively managed forest,

individual stands as small as one acre may

be recognized; whereas under a more

extensive management system the same

forest may contain stands no smaller than

several hundred acres. These two

situations will require different land

classification systems. An extremely

detailed land classification system will

be needed if intensive silviculture is to

be practiced in a region with a very

heterogeneous landscape. However, this

same system may be inappropriate and

unnecessarily expensive where more

extensive silvicultural systems were

practiced.

In many areas of the South, industrial

forest management is progressing from the

idea of the regulated forest to the

domesticated forest (Stone, 1975). In the

domesticated forest, management inputs are

high and technological innovations are

incorporated into silvicultural practices.

This high input forestry is synonymous

with high cost forestry. Economic

considerations require that these high

input silvicultural systems be extremely

site specific in order to obtain the high

yields necessary to justify the costs.

Therefore, as the transition to the

domesticated forest occurs, there is a

need for more detailed information from

land classification systems.

In the domesticated forest, site quality

is no longer a fixed quantity; it may be

improved or degraded. Therefore, land

classification systems must address not

only the inherent productivity of each

site, but also the potential to affect

site productivity through silvicultural

manipulations. Fragile sites must be

recognized and treated gently to avoid

site degradation. Sites where the inherent

productivity can be greatly enhanced must

also be delineated so that the appropriate

silvicultural treatments can be applied to

capture this potential productivity.

hit Region and Landscape

IIeteroaeruziQ

The physiographic region where the

silviculturalist works has a large impact

on the selection of an appropriate land

classification system. In the mountainous

regions of Virginia, North Carolina,

Kentucky, West Virginia and Tennessee,

factors such as parent material, aspect,

slope, and elevation vary tremendously and

strongly affect forest productivity and

management. Therefore, these features need

to be incorporated into a land

classification system for this region

(Smalley, 1980). In contrast, in the

flatwoods of the Coastal Plain in Georgia

and Florida, the landscape is flat and the

soils are derived from marine deposits.

Differences in drainage class, soil

texture, and subsurface horizon formation

strongly affect productivity and forest

management. These features change

dramatically with an elevation change of

less than two or three feet. In this

region, a land classification system must

recognize these more subtle features.

INTEGRATING A LAND CLASSIFICATION SYSTEM

INTO THE SILVICULTURAL DECISION PROCESS

Nearly every silvicultural decision can

benefit from the information provided by

a land classification system. In practice,

even when a formal land classification

system is not in place, most silvicultural

decisions probably utilize some implicit
form of land classification. The tract

manager who recognizes an area as "A good

piece of dirt that really grows trees" or

a "Wet spot that gets boggy with just a

little rain" has a land classification

system that may be as good or better than

one generated from a computer. However,

this type of personal, implicit land

classification system is rapidly

overwhelmed by the complexities of

silviculture in the domesticated forest. A

formal ecological land classification

system can provide the framework for sound

silvicultural decisions in this complex

environment.

The first step in integrating a land

classification system into the

silvicultural decision process is to

involve those who are going to use the

system in the development process. There

needs to be open, detailed and continuous

communication among the field foresters

who will utilize the information, those

conducting the actual land classification

in the field, and the research or

technical group who developed the system.

This will result in a much improved

system. Field foresters are much more

likely to utilize a system that they have

confidence in because they had input into

and helped develop it. Involving the field

foresters should also improve and actually

speed up the classification process

because the field mappers can draw on the

accumulated knowledge and experience of

those who work day-to-day on the land.

Likewise, feedback from both the

classifiers and foresters in the field,

allows the developers to correct flaws in

the system and fine tune it to the needs

of a particular area or group.

A successful land classification system

also needs to store the data in an

accessible manner and present it in a

clear and flexible form. Foresters and

land managers usually need their

information “yesterday”. Therefore, they

need to have ready access to the data in a

form they can use. Unwieldy maps that are

hard to work with or difficult to

interpret will soon be gathering dust in

the back of the closet or relegated to the

bottom shelf of the bookcase. The

information needs of foresters also change

rapidly. One day the land classification

data may need to be interpreted relative

to forest fertilization decisions. The

next, it may be needed to decide on road

locations, harvest boundaries or site

preparation prescriptions.

Incorporating a land classification

system into a geographic information

system (GIS) is the best way to meet

requirements of accessibility and

flexibility. With land classification

combined in a GIS, the planning and

organization of complex silvicultural and

forest management operations in the

domesticated forest becomes possible and

practical.

Combining a land classification system

into GIS also provide a mechanism to

retain the accumulated knowledge and

experience of land managers. The days when

a forester spent the majority of a career

on one tract of land are ending. As

foresters move from one area to another, a

considerable period of time may be spent

learning the new area. During this time,

productivity and efficiency are reduced.

However, incorporating data and

observations from previous managers into a

GIS based land classification system would

allow a forester to learn quickly what has

and has not worked in the past. This can

serve as an intelligent starting point

from which to proceed.

From a silvicultural standpoint, a

combined GIS/land classification system

should be followed by the development of

a silvicultural decision support system.

The objective of such an "expert" system

would be to provide quantitative

information on the silvicultural options

available. Ideally, it should permit the

evaluation of integrated silvicultural

systems rather than just marginal

treatment, For example, rather than just

determine which is the "best"

fertilization treatment, it should

evaluate combinations of site

preparation, fertilization, thinning, and

perhaps competition control to determine

what is the "best" overall combination of

treatments. The goal is not to replace

field foresters with a computer model,

but rather to provide them with the

information they require to do their job

more effectively.

APPLICATIONS OF LAND CLASSIFICATIONS

SYSTEMS

Silvicultural decisions can not be based

solely on an ecological land

classification system. Obviously, other

factors such as stand conditions strongly

influence silvicultural decisions.

However, an appropriate land

classification system can be used as a

starting point to guide most silvicultural

decisions. In this section, a few examples

of silvicultural decisions that rely

heavily on information that a land

classification system can provide will be

presented. Numerous other examples exist

which could have also been given.

.S-=ies Selection

Guiding the selection of the best

species to plant on a given site is

99
perhaps the most common use a land

classification system in forestry. The

problems resulting from planting a species

"off site" have been recognized for

decades. In many cases, selection of the

appropriate species is relatively straight

forward process; i.e. longleaf pine is

planted on the sand hills in the Coastal

Plain. The problem becomes more complex

when several species perform equally well

or an exotic species is introduced into a

region. However, it is with these less

straight forward decisions that a land

classification is most needed.

A decision related to species selection,

is the deployment of genetically improved

material resulting from a tree breeding

program. In this case, a land

classification system can be used to

deploy disease resistant genotypes to high

hazard areas, or fast growing genotypes to

the best sites. In some companies, family

block plantings have been established

where specific families are matched to

certain sites. This level of refinement

requires a very detailed land

classification program.

Site Preparation

Because of the strong influence soil

type has on the need for certain site

preparation treatments, this is another

silvicultural decision that is simplified

when a land classification system is in

place. For example, in the Lower Coastal

Plain, bedding on wet sites is required to

achieve adequate survival and growth. In

other regions, certain sites require

subsoiling to break up compacted soil

horizons. The productivity of some fragile

sites can be severely degraded by

intensive mechanical site preparation.

f . . .

Herbicide Aoolicatuan

Many herbicides used in forestry are

soil active compounds. Their efficacy

varies with factors such as texture and

organic matter content of the soil.

Information on these properties from a

land classification system can be used to

help select which chemical to use and the

appropriate application rate. An

ecological land classification system can

also provide information of the types and

densities of competing vegetation that

will likely exist on a site.

using the CRIFF soil groups (Pritchett and

Comerford, 1983). In other regions,

fertilization recommendations may be based

on soil series or even site quality

classes (Peterson and Gessel, 1983). All

of these systems for identifying sites

that will respond to fertilization can be

incorporated into a land classification

system.

HarvestinaSvstems

The appropriate system for harvesting

timber can usually be determined from a

land classification system. Factors such

the suitability of a site for dry or wet

weather logging, machine or hand felling,

ground or aerial skidding can all be

evaluated. The best location for logging

roads can also be determined. A land

classification system used in this manner

can ensure that the harvesting system

selected will be the most economic and

will minimize disturbance of the site.

Numerous examples of the use of a land

classification system in the interface

between silviculture and forest management

also exist. Two examples illustrate some

of these applications.

.

ihventorv and Harvest Declslons

Forest stands often include a range of

soils and site quality classes. The

ability to break larger stands into

smaller, more uniform areas will increase

the precision of inventory systems. A land

classification can be used to stratify

stands for this purpose. If sufficient

variability exists, large stands can be

broken into smaller units, which can then

be managed more effectively. A land

classification system can also help with

harvest scheduling. Area and adjacency

constraints in the harvest schedule can be

addressed effectively using a land

classification system.

Pronertv Taxation

Much of the forest land in the South is

appraised for tax purposes on the basis of

productivity. An accurate land

classification system can be used to

support or refute the appraised value of

forest land. Site quality evaluations from

a detailed land classification system will

often be accepted by local tax assessors.

.

Eertilisation r .

CONCLUSIONS

Forest fertilization is another

silvicultural treatment that can be based

on soil properties. To maximize the return

from fertilization, landowners must be

able to locate the most responsive sites.

In the Coastal Plain, effective

fertilization recommendations can be made

Ecological land classifications can be

extremely useful in the silvicultural

decision process. Nearly every

silvicultural decision requires the type

of information a land classification

system can provide.The need for this

100
information becomes more acute as the

intensity of management increases.

However, not all land classification

systems are appropriate for this purpose.

Appropriate land classification systems

must match the spatial scale of the stands

being managed. The intensity of

silviculture and the physiographic region

the classification system is to be used in

are also critical factors affecting

selection of an appropriate land

classification system.

LITERATURE CITED

Bockheim, J.D., ed. 1984. Proceedings:

Forest land classification: Experiences,

problems, perspectives. Madison, WI:

University of Wisconsin, Department of

Soil Science. 276 pp.

Brady, Nyle, C. 1974. The nature and

properties of soils. 8th Edition. New

York: Macmillan Publishing Co., Inc. 639

PP.

Broerman, F.S. 1978. Site classification -

Union Camp Corporation. In. Balmer,

W.E., ed. Proceedings Soil Moisture --

Site Productivity Symposium; November l3, 1977; Myrtle Beach, SC: pp. 64-73.

Campbell, Robert, G. 1978. The

Weyerhaeuser land classification system.

In. Balmer, W.E., ed. Proceedings Soil

Moisture -- Site Productivity Symposium;

November l-3, 1977; Myrtle Beach, SC:

PP. 74-81.

Carmean, W.H. 1975. Forest site quality

evaluation in the United States.

Advances in Agronomy 27:209-269.

Coile, T.S.; and Schumacher, F.X. 1964.

Soil-site relations, stand structure,

and yields of slash and loblolly pine

plantations in the southern United

States. Durham, NC: T. S. Coile, Inc.

296 pp.

Fisher, R.F.; and Garbett, W.S. 1980.

Response of semimature slash and

loblolly pine plantations to

fertilization with nitrogen and

phosphorus. Soil Science Society of

America Journal 44:850-854.

Lemmon, Paul E. 1970. Grouping soils on

the basis of woodland suitability. In:

Youngberg, Chester T.; and Davey,

Charles B., eds. Tree Growth and Forest

Soils. Corvallis, OR: Oregon State

University Press. pp. 413-426.

Williams, T.M.; and Gresham, C.A., eds.

1988. Predicting consequences of

intensive forest harvesting on long term

productivity by site classification.

Proceeding IEA/BE Project CPC-10

Workshop. Georgetown, SC: Clemson

University, Baruch Forest Science

Institute.

Perterson, C.E.; and Gessel, S.P. 1983.

Forest fertilization in the Pacific

Northwest: Results of the regional

forest nutrition research project. In:

Zahner, Robert. 1957. Field procedures for

soil-site classification of pine land in

south Arkansas and north Louisiana.

Occasional Paper 155. New Orleans: U. S.

Department of Agriculture, Forest

Service, Southern Forest Experiment

Station. 17 pp.

Ballard, Russell; and Gessel, Stanley,

P. eds. I.U.F.R.O. Syposium on forest

site and continuous productivity: 1982

August 22-28; Seattle, WA. Gen. Tech.

Rep. PNW-163. Portland, OR: U.S.

Department of Agriculture, Forest

Service, Pacific Northwest Forest and

Range Experiment Station. pp. 365-369.

Pritchett, W.L.; and Comerford, N.B. 1983.

Nutrition and fertilization of slash

pine. In: Stone, E.L. ed. The managed

slash pine ecosystem. Gainesville, FL:

University of Florida, School of Forest

Resources and Conservation. pp. 69-90.

Saviello, T. 1979. Northeast soil-based

decision model. Research Letter No. 35.

Bangor, ME: International Paper Co.

Smalley, Glendon, W. 1980. Classification

and evaluation of forest sites on the

western Highland Rim and Pennyroyal.

General Technical Report SO-30. New

Orleans, LA: U. S. Department of

Agriculture, Forest Service, Southern

Forest Experiment Station. 120 pp.

Smalley, Glendon, W. 1982. Classification

and evaluation of forest sites on the

Mid-Cumberland Plateau. General

Technical Report SO-38. New Orleans, LA:

U. S. Department of Agriculture, Forest

Service, Southern Forest Experiment

Station. 58 pp.

Smith, D.M. 1986. The practice of

Silviculture. 8th Edition. New York:

John Wiley & Sons. 527 pp.

Stone, E.L. 1975. Soil and man's use of

forest land. In: Bernier, B.; and

Winget, C.H. eds. Forest soils and

forest land management. Quebec: Les

Presses De L'Universite Laval. pp. l-9.

Wickware, G.M.; and Stevens, W.C., eds.

1986. Proceedings: Site classification

in relation to forest management. Joint

Forestry Research Committee Publication

O-P-14. Sault Ste. Marie, Ontario:

Forestry Canada.

101
SITE EVALUATION FOR COMMERCIALLY IMPORTANT

SOUTHERN HARDWOODS:

A PRACTICAL FIELD METHODl/

James B. Bakerll

Abstract.--This presentation provides a method of site

evaluation for 14 commercially important hardwoods by incorporating the physical, moisture, nutrient, and aeration

properties of a soil, as well as soil-site factors that influence these properties, into a site quality rating. The

site evaluation technique also (1) identifies soil factors

that limit tree growth, (2) provides a basis for possible

soil improvement treatments, and (3) allows for comparisons

in productivity of hardwoods on a range of sites.

Dr. Baker's presentation was based on two previously published manuscripts. For more information contact Dr. Baker or see:

Baker, James B.; Broadfoot, W. M. 1979. A practical field method of site evaluation for commercially important southern hardwoods. Gen.

Tech. Rep. SO-26. New Orleans, LA. U.S.

Department of Agriculture, Forest Service,

Southern Forest Experiment Station. 51 PP.

Harrington, Constance A.; Casson, Bettina M.

1986. SITEQUAL - A user's guide. Computerized site evaluation for 14 southern

hardwood species. Gen. Tech. Rep. SO-62.

New Orleans, LA. U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 13 p.

11 Presented at the Ecological Site Classification Symposium, Charlotte, NC, January 7-9,

1991.

21 Research Forester, U.S Department of

Agriculture, Forest Service, Southern Forest Experiment Station, Monticello, AR.
MAPPING / GIS APPLICATIONS

IN CLASSIFICATION SYSTEMS

DYNAMIC DIGITAL DATABASES: THE KEY

TO PRACTICAL LAND CLASSIFICATION SYSTEMS?-'

James L. Smit&'

Abstract.--It is clear that strategies for categorizing the

ecological landscape will be increasing in importance in the coming

years. The temporal and spatial variability of the elements of the

landscape make a timely and flexible data base a necessity for developing and implementing these strategies. Remote sensing and geographic

information systems are the foundations of such a dynamic, digital

data base. Recent developments in these fields will be chronicled,

planned activities for the near future described, and some thoughts

on the long term situation in these important technical support areas

put forth.

Keywords: Geographic information systems, satellite imagery,

resource management.

INTRODUCTION

I see the purpose of this, the first presentatation of the session, as an introductory one.

It is my job to set the table for the speakers

who will follow. Thus, I will confine myself to

a non-specific discussion of the present stateof-the-art of advanced data gathering technologies (i.e. remote sensing) and information

production tools (geographic information systems

(GIS)). Although specific application situations may be mentioned from time-to-time, they

are not the crux of this particular presentation. I leave that task to the exemplary slate

of speakers who come later.

THE CONTEXT

My first task is to set the context for the

presentation, and for the major topics of the

presentation. We all need to understand on what

foundation we are standing. The presentation

will deal with remote sensing and geographic

information systems. Within remote sensing, the

emphasis will be upon digital, remotely sensed

imagery acquired by satellite platforms, and

within GIS, the capabilities of such systems and

how they may be utilized in a land classification

I/ Presented at the Ecological Land ClassificatTon Symposium, Charlotte, NC, January 7-9,

1991.

2/ Associate Professor, Department of Forestry,

VirTjinia Polytechnic Institute and State

University, Blacksburg, Virginia 24061-0324.

situation. While these topics will be discussed

separately, and independently to a great degree,

they must be thought of as parts of a whole,

for neither reaches its greatest potential

without the other. We cannot call it a true

symbiotic relationship, for remote sensing would

survive without GIS, and GIS would survive

without remote sensing. However, each improves

the other and thus it is a question of prosperity

and growth. I will use an analogy to illustrate.

We can regard the development and use of a land

classification system as a task, or job, much

as we regard the driving of a nail as a task to

be performed when building a wall. This task

requires the use of the appropriate tool, which

in the case of the nail is the hammer. When we

examine the tool, we can break it down into its

relevant parts and determine the importance of

these parts. First, we have the head of the

hammer. It is the mass and velocity of the

hammer head which actually performs the task of

driving the nail. However, the hammer head

would be much more difficult to use without a

way to give it direction and speed. Thus, the

second constituent part, the handle, actually

makes the hammer head more efficient at its task,

although a nail can be driven in many instances

without it depending on the resistance of the

material to be nailed. The handle thus is not

always required, but makes the use of the

hammer head more efficient and more widespread

in its potential application. Finally, let's

not forget the operator. A hammer without an

operator lays on the floor next to the nail and

does nothing.

105
How is a hammer like remote sensing and GIS? I

would argue that they are very much alike, and

here is how. Recall that our task is to implement and use a land classification system in the

process of natural resource management, rather

than drive nails to build a wall. Our tool in

this case, like the hammer, is composed of constituent parts. The part of the tool that

actually does the work, i.e., the hammer head, is

data, for it is this data that actually drives

the system and produces the result. One way to

obtain data is remote sensing! After the data

base has been constructed using remote sensing

and other means, we use a GIS to grip that data

and produce information. The GIS is used to

control and manipulate the data, i.e., give the

hammer head speed and direction. However, the

GIS itself does no work; its purpose is to make

the decision process more efficient, and perhaps

through these efficiencies, more complete.

Lastly, the natural resource professional uses

the GIS to direct the appropriate data in the

appropriate form to the task at hand, much as

the carpenter must skillfully direct a hammer.

The GIS/remote sensing hammer has no value

without a trained and competent operator, for it

cannot produce the desired result, a decision,

by itself.

REMOTE SENSING

Remember the parts of our hammer. Let's talk

first about the head; the part that does the work.

In our analogy, the head of the mammer is the

data. We must have up-to-date data of sufficient

quality to achieve our goals, whatever they may

be. The question then becomes how we will

acquire the necessary data. Data can come from a

variety of sources, for instance, field inventory, aerial photography, existing data from

other sources, and satellite imagery. It is

important to note here that although field work

will not be covered explicitly in this presentation, it is an integral part of any natural

resource data collection endeavor. Rarely will

the remotely sensed data sources stand by themselves without some field verification. Although

we will be talking about great advances in

technology today, they still require some groundpounding even in the best situations. Let's

briefly talk about aerial photography and

existing data sources, and then discuss satellite

imagery in more detail.

Aerial photography has become the neglected

stepchild of remote sensing in many ways. The

advent of digital scanners and satellite platforms has made the study of aerial photography

a minor science to funding agencies. I consider

this to be a great mistake because we have never

utilized aerial photography to its fullest

potential. There are a number of different

photographic systems available (film type, camera

type, filter), and the information they contain

at different scales, seasons, etc. has never been

fully explored. I urge you to remember aerial

photography when you need to obtain resource

information. Do not fall into the unnecessary

technology trap every time!

Another thing to always remember is that other

people and organizations are also in the

business of acquiring information about natural

resources. The U.S. federal government must be

the largest archive of resource information in

the world, and all of that information is

available to you. The questions you must answer

are whether the quality and form of that information meets your requirements. Never assume

that it does, or that it does not. In most

cases, federally-acquired resource data is

extremely inexpensive, and there is no way you

could acquire similar information for that price

in any other way. However, all data has

limitations, and you must match your needs to

those limitations. Let's examine one example,

digital elevation models, more closely.

I have chosen to discuss digitial elevation

models as an example of existing data sources

because they seem to be of particular importance

to those involved in land classification. In

addition, DEM's as they are often called (or

DTM's), illustrate the characteristics of existing data sources well. A DEM can be described

as a computer-readable (digital) model of surface

terrain features, i.e., we have stored the

elevations above sea level for a number of

known locations on the earth's surface. Values

important for land classification can be derived

from a DEM, such as slope gradient, aspect,

slope position and slope shape. We have even

related volume in natural stands to topographic/

land form values derived from a DEM with some

success. Although not required in theory,

the most common form of a DEM is a square or

rectangular matrix of elevation values called an

altitude matrix. Note here that there are

other ways to store elevation data, such as

triangulated irregular networks (TIN) which have

advantages and disadvantages. However, the

altitude matrix is pervasive, and is the

structure for DEM data obtained from the U.S.

Geological Survey. An individual DEM covers

the same area as a USGS 7 l/2 minute topographic

quadrangle, and can be referenced by the name

of the quad. The resolution of this, the most

common DEM, is 30 meters. This can be taken to

mean that surface features less than 30

meters in size may be lost, or at least washed

out in the data set. Elevations are recorded

to the nearest meter, unless the contour interval of the associated quad is less than five

feet, in which case the elevations are recorded

to the nearest foot. These DEM's are not yet

available everywhere, but where they are, they

are very inexpensive. The purchase cost is as

little as $7 each if more than six are ordered

at one time. This data is far from perfect; it

is limited in resolution; it is limited in

availability; it contains occasional large

elevation errors (blunders); it is really of

unknown precision. However, I have found the

data useful, and cost-effective, but caution

is advised. Like all data acquired by someone

else, it does not always fulfill my needs. If

it is misused, it is not their fault, it is

mine!

106
Remember that we are talking about data base

construction, and have previously mentioned field

work, aerial photography, and existing data

sources. I want to spend a bit more time on

satellite imagery, because it seems to be a

focus of the papers that follow. Civilian

satellite remote sensing has been around since

1972, and much time, effort and money has been

spent learning how to acquire it and how to use

it. I would say that great progress has been

made. There are two major systems available to

us, SPOT and Landsat TM. SPOT is a French

system, with two modes of operation. The fi,rst

mode is visible panchromatic, which has a single

spectral band with IO meter resolution. The

second mode of operation is multispectral, which

has three spectral bands (green, red and nearinfrared) at 20 meter resolution. SPOT also is

a pointable platform, which gives it the

capacity to decrease the number of days which

elapse between successive coverages, and to produce stereo satellite images. The cost for a

60km x 60km scene (0.8 million acres) in digital

;;;i;t is approximately $2000 (0.2 cents per

. Landsat Thematic Mapper (TM) is an

American system. The sensor has seven bands

(blue, green, red, near-infrared, mid-infrared

and thermal), and a resolution of 30 meters.

The cost for a 185km x 179km scene (7.7 million

acres) in digital form is approximately $4000

(0.05 cents per acre).

All sensor systems are different, and each has

its advantages and disadvantages. It is thus

difficult to make general statements about

satellite imagery and its characteristics.

However, I do believe that we can make general

observations about the advantages of satellite

imagery as a concept. The first advantage is

one of synoptic coverage, that is, affordable

information for a large area. The costs quoted

above are indicative of the situation. If

resource information for a large area is

needed, satellite imagery may be the only economical choice. Second, satellites provide

repetitive coverage of these large area. In

most cases, several images of a region can be

acquired.each year, which can be an important

advantage if we need to monitor short term change

in the resource. The third advantage is the

ability to select spectral bands for different

tasks. Humans can only sense visible light,

aerial photography is largely limited to visible

light and near-infrared wavelengths, but

mechanical scanners can sample from essentially

the entire electromagnetic spectrum. We can

pick and choose wavelengths as needed, and combine the most important spectral bands for each

task. Fourthly, the digital nature of the data

allows us to utilize a variety of enhancement

algorithms, which can make the basic data much

more informative. Finally, the digital nature

of the data makes it a natural component of an

integrated, dynamic resource data base, which

by nature must reside in some sort of computer

environment.

Now that we have covered the characteristics of

the basic systems, we need to talk a bit about

the trends in satellite imagery and digital image

processing. First, I see no major changes in

the design or operations of SPOT or TM (assuming

the American TM program survives the experiment

of quasi-commercialization). SPOT 3, scheduled

for launch sometime after 1993, will likely add

a mid-infrared band to its multispectral mode.

Many vegetation "scientists" have been clamoringforthis addition to the sensor. In the next

year or so (supposedly) the next in the series

of Landsat platforms, Number 6, will be launched

with the Enhanced Thematic Mapper (ETM). This

sensor is basically the same as TM, with a coregistered 15 meter panchromatic band. Also,

look for the launch of Canada's RADARSAT, and

the European Space Agency's ERS-1, both of

which have RADAR sensors on board. Don't forget

the Ikm resolution Advanced Very High Resolution

Radiometer (AVHRR), which will be discussed

further in a paper during this session. Japan

also has a little known satellite called Marine

Observation System which has some terrestrial

capabilities. As you can see, there are a

variety of platforms and sensors available, not

just SPOT and TM. That is one of the trends in

satellite imagery -- more specialized platforms

and sensors will be available in the future,

making the remotely sensed data more useful for

a variety of applications. It is also obvious

that another trend is improving spatial resolution. While smaller and smaller pixel sizes

are not a panacea, most practitioners have

stated that an increase in the spatial resolution

of satellite image data would make it more

useful. The resolution of satellite image data

is now similar to high altitude aerial

photography, but it still is not comparable to

large and medium scale photography used by

resource managers on a day-to-day basis for

stand level management. A third trend is evident as well, that the number of bands available will increase, and they will be more

specifically designed and selected for particular

purposes. Fourth, and I believe an important

one, the cost of processing digital data will

decrease. A good image processing system can

S now be purchased for a few tens of thousands of

dollars, as opposed to a few hundreds of thousands

of dollars a decade ago. A fifth trend is that

of product flexibility. In years past, satellite data had to be purchased for an entire

scene. SPOT and EOSAT (the parent of TM) both

offer sub-scene products, which, although more

costly per acre, may be more cost-effective.

A negative trend is the increasing cost of the

raw satellite data. An MSS tape from Landsat

used to cost $200, and the same aerial coverage

by TM costs $4000. I expect that the rate of

increasing data costs will continue at worst,

or at best, slow over the coming year. Both of

these organizations are profit-making enterprises, and must increase their price as costs

increase.

107
As I combine all these trends together in my

mind, the overriding trend is that the cost/

benefit ratio for satellite imagery is improving

tremendously. It is more costly to acquire, but

it is also more useful, and more easily used.

Be mindful of my earlier warning about all data

sources. Satellite data has a place in resource

planning, but it will not do everyting. It

has a place in the overall strategy of decision

making but it is only one part of the hammer

head.

GEOGRAPHIC INFORMATION SYSTEMS

As I stated earlier, GIS and remote sensing are

intricately connected. This connection comes

from the fact that GIS is predominantly an information manager, and thus there must be information present for it to be useful. Increasing

complexity of the decision making situations,

and increasing management costs are requiring

that we be more clever and efficient in acquiring

and utilizing information. Voile', the GIS. If

you remember our analogy, the GIS is the handle

of the hammer; the part that allows us to use

the hammer head more efficiently. GIS is our

window to the data base, our data base handle

if you will, and it can also add to the data

base through built-in spatial analysis functions.

Although no definition of GIS is accepted by

everyone, I have my own way of defining it. My

definition goes to the purpose of GIS. Note

that every feature in the landscape, roads,

streams, forests, fields, soils, etc., has both

a location and a set of characteristics

associated with that location. In other words,

there are two kinds of information, spatial

(locational), and attribute (characteristics).

A GIS is a way of storing and manipulating

spatial information and attribute information,

and explicitly linking these two disparate data

types. Spatial and attribute information can be

manipulated individually, or in tandem, depending on the task at hand. Although all GIS's

have different ways of accomplishing the job,

all are attempting to efficiently tell us

where we have'what and what is next to what.

-that therZ$nothinsnherently di777Zult

about what the GIS does. -Everything-a GIS does

could be done by an appropriately trained human

given the data and enough time. A human can

overlay maps; a human can do neighborhood analyses; a human can look up information in the data

base. However, the speed of the computer means

it can usually do the job much faster. This

speed allows us to either do the same job in

less time, ordomore things in the same amount

of time. That is the advantage of GIS.

When GIS's are discussed, the first argument

that usually arises is whether raster or vector

data structures are best. I won't pursue that

argument in detail, but I will comment. First,

raster structures, such as DEM's and satellite

images have advantages and disadvantages.

Vector structures, which are more similar to

traditional looking resource maps, have advantages and disadvantages. Some data is inherently

raster, and some is inherently vector. Why

can't we have both? Instead of deciding

between them, let's have it all. The GIS of the

future should use raster when it is appropriate,

and vector when it is appropriate. The raster

vs vector argument is no argument at all. Let's

move on to more important matters.

An important concept to understand when dealing

with GIS's is the "theme" or layered data base

concept. In a data base, each individual type

of spatial information is stored as a separate

map. For instance, roads of all types are in

one map; roads become a theme in the data

base. A set of attributes is attached to the

map, giving us the connection between locations

and characteristics. There are many other

themes, hydrography (streams, lakes, etc.),

soils, ownership lines, etc., all stored individually with their associated attributes. A

satellite derived land use/land cover map can

be a theme. A DEM can be a theme. What I am

describing is a dynamic, compartmentalized,

integral data base. When a decision needs to

be reached, the appropriate themes are selected,

and combined if necessary. This theme concept

gives the data base more flexibility, and a

more dynamic character. Let's use a classification example to illustrate. Suppose we want

to classify the area according to aspect,

stratigraphy, soil series and current vegetation. Each of these four factors would be

captured and stored as a separate theme, and

spatially combined (overlayed) to form the

classification. If an error is found in one of

the themes, or if a map (theme), such as current

vegetation, needs to be updated, only the

changes are made to those themes. The themes

are recombined, and a new up-to-date classification is produced. Think of how remote sensing

can be used in theme development. Think of how

the timeliness of satellite imagery can help us

update certain themes., That is what I mean by

a dynamic, digital data base. Dynamic means

current. Digital means accessible.

There are many useful analysis functions built

into most GIS. These may be very helpful in a

land classification system, and provide another

justification for GIS in such situations. I

will categorize the main spatial analysis

functions into two groups, neighborhood analysis

and boolean combinations (overlay). In

neighborhood analysis, distance to features

can be calculated, proximity of buffer zones

automatically generated, and such things as

viewsheds determined. I believe that the

importance of these functions to resource

management are fairly obvious. How far is it

from a particular point to that structure, or

from this road to the stream. Create a new

management unit that includes all areas within

500 meters of a lake. If I allow the lessee

108
to place a well-head at this point, is there

any place on the trail above where it could be

seen? These are all examples of neighborhood

operators. I described boolean combinations

(also called map overlay) in the paragraph

above when I discussed how individual themes

can be merged, or joined to produce new information. There are at least five kinds of map

overlay, such as union and intersection, and

ways forusing map overlay in management.

situations are too numerous to list.

In summary, suffice it to say that GIS's are

powerful tools for resource management.

Their greatest power is in integration, whereby

various types of data can be combined to produce

the information needed for decision-making.

Remember, a GIS is only as good as the data that

was entered into it. A GIS cannot make the

quality of your data better. It cannot give

data more precision or resolution. It can

make the data more useful, by making it easier

to access. A GIS can perform spatial analyses

which are becoming more and more important as

environmental concerns become prevalent. A

GIS can, I believe, make the decision-making

process more efficient, and thus, more complete.

SUMMARY

The technologies for acquiring and using resource

information have come a long way in the last

IO-20 years. We can do things today that were

only dreamed of by our predecessors. Remote

sensing and GIS can be very helpful, but we must

recognize their limitations. Remember that our

goal is to make better decisions concerning our

important natural resources. Wherever technologies can be used to accomplish that end, they are

appropriate. Where technologies do not help

accomplish that end, they are not appropriate.

It is up to you to decide how and when to

utilize technology. Do not blame misuses of

technology on inanimate data or computers.
USING A GEOGRAPHIC INFORMATION SYSTEM

TO CLASSIFY FOREST PRODUCTIVITY

IN NORTHWESTERN CALIFORNIA’

John A. Brockhaus

Lawrence Fox III

Nancy D. Tosta’

Abstract--Classification of forest vegetation were combined with ecological zone

location, elevation, slope and aspect within the framework, of a geographic

information system to predict forest land productivity over a very large area.

Forest vegetation cover classes were provided by Landsat MSS data through

computer classification. Ecological zones were compiled from indicator species and

local climate data. Elevation, slope and aspect were obtained from DMA digital

terrain data. All data layers were registered within a raster based geographical

information system. The overall accuracy performance of the model was found to

range from 74 percent to 86 percent. depending upon the location within the study

area.

Keywords: Landsat, GIS, forest productivity, ecozone, terrain.

INTRODUCTION

Federal, state, and local agencies have been

mapping prime agricultural lands for several years.

Most of this work has focused on the identification

of lands for production of cattle and crops.

Recently the United States Forest Service has

undertaken several projects nationwide to examine

techniques for mapping forest land units having

various levels of productivity. Three productivity

classes were chosen: (1) prime timberland, capable

of producing 85 or more cubic feet of wood per acre

per year; (2) non-prime timberland, producing

between 50 and 85 cubic feet of wood per acre per

year; and (3) non-forest land, producing less than

50 cubic feet of wood per acre per year.

The purpose of this project was to evaluate the

potential of combining Landsat, topographic, and

ecological zone data within the framework of a

geographic information system (GIS) for classifying

and mapping forest land productivity. Specific

objectives were to: (1) Develop a georeferenced data

base for mapping timberland productivity; (2)

develop and apply linear stepwise discriminant

analysis (LSDA) models for classifying prime, non-

‘Presented at the Ecological Land Classification

Conference held in Charlotte, NC on January 7-

9,1991.

*Research Associate, College of Forestry, North

Carolina State University; Professor, Forestry Dept.,

Humboldt State University; and Director of

Geographic Information Systems, Teale Data Center,

State of California.

prime, and non-forest land; (3) assess the accuracy

of the models in predicting the three categories of

forest land productivity.

BACKGROUND

Forest managers and researchers have used site

index as a reliable measure of forest land

productivity for many years (Spurr and Barnes,

1973). Site index is an expensive and inconvenient

variable to measure when conducting assessments

of forest productivity over large land areas.

Aerial photographs have been used to estimate

productivity based on vegetation, topography, and

soils (Choate, 1961). Photographs have greatly

reduced the survey time needed when compared to

ground sampling techniques. However, the

inventory continues to be labor intensive for large

areas.

Previous research indicated that spectral

reflectance patterns, developed from Landsat data

through computer classification, could provide

vegetation cover information over large areas at

lower costs than when using aerial photography

(Fox et al., 1983). Productivity has been shown to

be poorly correlated with unclassified Landsat

spectral data (Tom and Miller, 1980). Reliable

estimates of forest productivity may be obtained,

however, when these data are classified and

integrated with digital topographic information.

With this in mind, it was decided to use classified

Landsat data‘in order to compress the unclassified

spectral data set from approximately 60 digital

values in four channels to only 14 to 15 classes of

land cover. Previous experience indicated that

110
information critical to mapping levels of productivity

would probably be contained in the classified data

(Fox et al., 1983).

Ecological zone maps had been used previously

to incerase the detail of a Landsat classification

(Fox and Mayer, 1981). These zones were defined

to represent significant climatic and vegetative

regions throughout an area. There was interest in

the value of eco-zones for predicting productivity,

compared to the value of topographic data and

Landsat vegetation classes for making predictions.

METHODS

This work was performed in two separate study

areas (Figure 1) using slightly diferent data sets for

each area. The northern study area (Humblodt

County) represents a conifer forest region known to

be highly productive, yet containing small regions of

non-forest use. The southern area (Mendocino

County) contains a greater range of conifer forest

productivity and considerable non-forest acreage.

both areas are mountainous with elevations ranging

from sea level to 7000 feet.

Figure l--Locational map of the study site.

Classifying the productivity of forest land with

LSDA models required the definition of a categorical

response variable (productivity classes) and a set of

predictor variables (Landsat vegetation classes,

ecological zone classes, and topographic variables).

The goal of the analysis was to linearly combine the

predictor variables so as to best classify timberland

into one of the three productivity classes.

The following predictor variables were made

available for use by the discriminant analysis

models: vegetation cover as determined from

classified Landsat data; elevation, percent slope, and

apsect class defined by digital elevation data; and

ecological zones (eco-zones) in Humboldt County

only, as determined from existing map sources (Fox

and Mayer, 1981). These variables were selected

for this study based on two criteria: (1) that

variables selected were probably highly correlated

with productivity based on previous studies; and (2)

variables could be obtained from satellite image

data, digital elevation data, or published maps

rather than from sample surveys or airphoto

interpretation.

A supervised Landsat classification of vegetation

cover was available for Humboldt County. It was

developed for a previous project using guided

clustering to select spectral statistics from trianing

areas and a maximum likelihood algorithm for final

classification (Fox and Mayer, 198 1). This

supervised classification of portions of two Landsat

scenes contained 14 categories tailored to forest

communities including, in two cases, classes of

species:

Redwood forest

Douglas-fir forest

Dominant Douglas-fir/broadleaf

Dominant broadleaf/Douglas-fir

Mixed conifer forest

Dominant coniferlbroadleaf

Broadleaf forest

Broadleaf Savannah

Brush land

Dominant brush/conifer regeneration

Agriculture

Grassland

Bare soil

Other

By contrast, an unsupervised classification was

available for Mendocino County. The unsupervised

analysis was completed as part of a previous

statewide land classification project and was based

on a resampled mosaic of Landsat scenes (TostaMiller and Peterson, 1980). It contained 15

categories of general land cover as well as very

generalized forest types such as conifer and

broadleaf classes:

Conifer forest

Dominant conifer/broadleaf

Dominant broadleaf/conifer

Broadleaf forest

Open conifer

Open broadleaf

Brushland

Open shrub

Agriculture

Grassland

Bare soil

Rock

Water

Urban

Other

These two methods of classification provided

contrasting data sources and represented vastly

different levels of technical work.

111
Topography was described by three variables:

elevation, percent slope, and aspect. These

variables were derived from Defense Mapping

Agency (DMA) digital terrain data provided by the

U.S. Geological Survey. Elevation and percent slope

were treated as continuous variables while aspect

was categorized into eight compass directions; north,

northeast, east, southeast, south, southwest, west,

and northwest.

Six eco-zones were included in the analysis of

Humboldt County only (Figure 2). These zones

were defined to stratify significant changes in forest

composition and local climate, and yet still be

generalized enough to form a manageable number

of zones. Coast redwood (Seuuoia semnervirens

Endl.) is the major timber species in zone one, the

moist coastal zone. Zone two, the moist interior,

is dominated by Douglas-fir (Pseudotsuaa menziesii

Carr.), tanoak (Lithocarnus densiflorus Rehd.), and

Pacific madrone (Arbutus menziesii Pursh.). Zone

three, the dry interior, is dominated by Douglas-fir

and California black oak (Quercus kelloggii Newb.).

Broadleaf species (tanoak and Pacific Madrone)

dominate the forest cover of zone four, the south

interior. Zone five, the south coast, is characterized

by Douglas-fir. Zone six, coastal spruce, represents

a small area dominated by sitka spruce (Picea

sitchensis Carr.).

The predictor variables (Landsat class, eco-zone,

elevation, slope, aspect) were geographically

registered and encoded into a raster based GIS

(Smith and Blackwell, 1980). The grid cell size was

100 metres by 100 metres (2.47 acres). A small

training data set consisting of 19,382 grid cells Was,

systematically sampled from both study areas. The

total population consisted of 1,855,572 grid cells, or ‘. -. -. 4583,262 acres. This was determined to be the

largest sample obtainable, given constraints of time

and cost. The sampled data was used to develop

the predictive equations of the LSDA model.

0 10 20

mi 10s

Figure l--Locator map for the six ecozones used in

the Humboldt County study area. The names of

the zones are (1) Moist Coastal, (2) Moist Interior,

(3) Dry Interior, (4) South Interior, (5) South Coast,

.and (6) Coastal Spruce.

The training data set contained the values of the

predictor variables and the productivity classes.

‘1

Values for the predictor variables were obtained

I

,

from the data base containing the training data set. I

Data characterinzing the productivity classes of the

training grid cells were acquired from soil

vegetation maps which had been geometrically

registered to the data base (U.S. Forest Service,

1961). Forest productivity classes used in this

study were not printed on these maps. However,

Douglas-fir and Ponderosa Pine (Pinus ponderosa

Dougl.) site classes (ranges of site index) were

printed for each forest vegetation type identified on

these maps. These site index values were

transformed into forest productivity classes using

U.S. Forest Service conversion tables.

Discriminant functions were developed from the

training data with the Statistical Analysis System

(SAS) software and BMDP program 7M at the state

of California’s Teale Data Center. Predictor

variables were treated differently depending on

whether they were categorical or continuous data.

Categorical predictor variables were transformed as

the statistical theory used to develop LSDA assumes’

continuous predictor variables. Landsat class, ecozone, and aspect class were treated as if each class

were a separate variable and coded as zero or one

depending on the presence or absence of the class.

For example, Landsat class one (redwood forest)

was defined as its own variable, having a value of

one if the grid cell was in this class and a value of

zero if not. This has been shown to be a legitimate

method of coding categorical predictor variables in’

LSDA and regression (Hand, 1981; Brockhaus et al.,

1989). Percent slope and elevation were not

transformed, as they were already continuous

variables.

Predictor variables were selected by the computer

program in a stepwise manner to maximize

discrimination between productivity classes.

Variables which did not provide statistically

significant discrimination at the 0.05 level of

probability were not included in the final equations.

The discriminant functions, developed from the

training data, were used to classify the entire

geographical data data base into the three

productivity classes: prime, non-prime, and nonforest. The VICAR-IBIS image processing and

‘raster based GIS software package was used for

this step (Smith and Blackwell, 1980). Finally,

thematic maps of each study site were printed from

the grid cell data base at a scale of l:lOO,OOO.

RESULTS AND DISCUSSION

Seventeen predictor variables were selected for

inclusion into the discriminant model for Humboldt

County while 18 were selected for Mendocino

County. The selected set of predictor variables in

Humboldt County consisted of eight Landsat

vegetation classes, four eco-zone classes, four aspect

classes, and percent slope. Predictor variables

112
selected for Mendocino County included twelve

Landsat vegetation classes, four aspect classes,

percent slope, and elevation.

Landsat vegetation classes were found to be more

significant in determining site quality in Mendocino

than in Humboldt County. The inclusion of ecozone boundaries in Humboldt County corresponded

to vegetation type boundaries. Aspect class

variables were selected first in both counties,

reflecting their value in determining these three

levels of productivity. The majority of the Landsat

classes included in the models were either nonforest or broadleaf forest classes because conifer

classes were not closely associated with productivity.

The presence of broadleaf and non-forest vegetation

influenced the model toward predicting a low

productivity class. The species specific conifer

classes, provided by the Humboldt Landsat

classification, did not contribute significantly to the

model.

The final classification maps provided a visual

indication of the geographic distribution and extent

of each productivity class. These maps also provide

a site specific indication of productivity. Area

summaries were compiled by county.

Humboldt County was shown to be 74.7 percent

prime forest and 24.5 percent non-forest. The nonprime class was shown to occupy 0.8 percent of the

the counties land area, sharply lower than that

reported by the 1968 U.S. Forest Survey, which

indicated that 12 percent of the land area is nonprime (Oswald, 1968). The non-prime area reported

here generally agrees with the published site classes

of the sample of grid cells taken from the soil

vegetation maps to develop the discriminant models

(1.6 percent non-prime). The 1968 Forest Survey

estimate of prime forest land was 14.9 percent

lower than the estimate reported here in terms of

land area classified and the non-forest area was 5.2

percent lower than what was estimated in this

study.

Area summaries for Mendocino County agreed

well with the Forest Survey. The area of prime

forest land reported here was 9.3 percent lower

than the Forest Survey estimate; non-prime, 13.3

percent higher; and non-forest 3.7 percent higher.

These discrepancies are acceptable considering

differences in the methods used by the U.S. Forest

Service.

Site specific accuracy checks were made in both

counties using a sample of the training data in

Mendocino County and an independent test data set

in Humboldt County. Overall classification accuracy

was reduced mainly by errors in the non-forest

productivity class. A large number of non-forest

grid cells were classified as prime in both counties.

Many of these errors probably occurred because grid

cells in the rangeland and agricultural areas on the

Soil-vegetation maps were defined as non-forest land

on those maps (i.e., the ground truth was nonforest). However, these areas did contain small

groves of conifer forest, often larger than one grid

cell in area. If a forested grid cell was in a highly

productive eco-zone and aspect class, it was

probably classed as prime, producing a classification

error according to the evaluation rules used here.

One might argue that many rangeland areas can

become productive forest once trees are allowed to

invade the site.

The cost of the project was $220,000 or 4.8 cents

per acre. The supervised Landsat classification

(Humboldt County) was approximately four times

more expensive than the unsupervised (Mendocino

County). Map production and land area summary

costs accounted for a third of the project budget.

The project had research objectives which included

software development that added to the cost of the

inventory. An operational project of this scale could

be done for approximately $150,000.

CONCLUSIONS

Merging vegetation-cover classes derived from

Landsat data, topographic parameters derived from

DMA terrain data, and published eco-zone maps

into a statistical model effectively produced three

land-cover classes for mapping forest productivity.

Eco-zone classes contributed to classification

accuracy and reduced the number of vegetation

cover classes required for prediction of forest

productivity classes. Vegetation cover classes were

also shown to be significant to the model. The non-.

forest classes such as grass or brush were more

highly correlated with productivity than were the

conifer forest classes. Non-forest classes were

provided in equal detail by both the unsupervised

Landsat classification and the supervised

classification. However, the supervised classification

was four times more expensive than the

unsupervised. The most valuable contribution from

this process is the thematic map. The “in place”

information by maps is not available from a sample

survey.

ACKNOWLEDGEMENTS

The financial support of the U.S. Forest Service

and the California Department of Forestry under

Interagency Agreement No. 3-080-078 is gratefully

acknowledged. A special thanks is also given to

Dynamic Graphics Inc. of Berkeley, California for

the in-kind contribution of software development

and map production.

113
LITERATURE CITED

Brockhaus, J. A., M. Campbell, R. Bruck, and S.

Khorram. 1989. Analysis of forest decline in the

southern Appalachians with TM data. In

proceedings of the 1989 ASPRS/ACSM Annual

Convention, pp. ‘227 - 236.

Choate, G. A. 1961. Estimating Douglas-fir site

quality from aerial photographs. Research Paper

45. Pac. N.W. Forest and Range Exp. Sta.

Portland, Oregon, 26 p.

Fox, L., and K. E. Mayer. 1981. Using ecological

zones to increase the detail of Lanclsat

classifications. Proceedings of the Fall Technical

Meeting, American Society of Photogrammetry and

Remote Sensing. San Francisco. pp. 113-124.

Fox, L., K. E. Mayer, and A. R. Forbes. 1983.

Classification of forest resources with Landsat data.

J. of Forestry 83:283-287.

Hand, D. J. 1981. Discrimination and

Classification. John Wiley, N.Y. pp. 96-97.

Oswald, D. D. 1968. The timber resources of

Humboldt county, California. Resource Bulletin

PNW-26. Pac. N.W. Forest and Range Exp. Sta.

Portland, Oregon. 33 p.

Oswald, D. D. 1972. Timber resources of

Mendocino and Sonoma Counties, California.

Resource Bulletin PNW40. Pac. N.W. Forest and

Range Exp. Sta. Portland, Oregon. 76 p.

Smith, A. Y., and R. J. Blackwell. 1980.

Development of an information data base for

watershed monitoring. Photogrammetric

Engineering and Remote Sensing 46:1027-1038.

Spurr, S. H., and B. V. Barnes. 1973. Forest

Ecology. Ronald Press, N.Y. pp. 247-256.

Tom, C. H., and L. D. Miller. 1980. Forest site

mapping and modeling. Photogrammetric

Engineering and Remote Sensing. 46: 1585-1596.

Tosta-Miller, N., and D. Peterson. 1980. Forest

multiple-value assessment and land-cover

classification of California. Proceedings of the 14

Int. Symp. on Remote Sensing of Environment. San

Jose, Costa Rica. Environmental Research Institute

of Michigan. Ann Arbor, Michigan.

U.S. Forest Service. 1961. Soil-vegetation maps of

California. Resource Bulletin PSW-13. Pac. S.W.

Forest and Range Exp. Sta. Berkeley, California.

6 P.

Zinke, P. J. 1961. Forest site quality as related to

soil nitrogen content. Trans. 7th Int. Congress Soil

Sci. 3:411-418.

114
CLASSIFYING FOREST PRODUCTIVITY AT DIFFERENT SCALES'

Robin Lambert Graham2

Abstract.--Spatial scale is an important consideration in

evaluating, using, or creating forest productivity classifications. Scale affects the accuracy of the classification

and the data requirements for constructing a classification.

These points are illustrated using three classifications at

different spatial scales.

Keywords: Spatial scale, spatial variability

INTRODUCTION

Spatial scale is an important consideration

when evaluating, using, or constructing forest

productivity classifications. First, the factors

which dominate spatial variability in forest

productivity are scale dependent. For example,

within a stand, spatial variability in

productivity is dominated by microsite

differences; within a national forest such as the

Cherokee National Forest, spatial variability is

dominated by topography and land-use history

(e.g., years since harvest); within a large region

such as the Southeast, spatial variability is

dominated by climatic patterns. Second,

classifications developed at different spatial

scales are often used for different purposes. For

example, stand-level classifications are often

keys or rules used in the field to judge the

quality or potential of a site. National-forest

classifications are often presented as maps or

tables and may be used in forest land planning.

Regional classifications may be maps or tables and

may be used to quantify or predict resource

availability. These scale-related differences in

controlling factors and purposes will affect both

the methods and the data used to develop

classifications. In this paper, I will illustrate

these points by describing and comparing three

forest productivity classifications, each

developed for a specific purpose at a specific

scale. My objective is not to argue for or

'Presented at the Ecological Land

Classification: Applications to Identify the

Productive Potential of Southern Forests

Symposium, Charlotte, NC, January 7-9, 1991.

2Research Staff, Environmental Sciences

Division, Oak Ridge National Laboratory,

P.O. Box 2008, Oak Ridge, TN 37831-6038.

against any of these particular classifications

but rather to heighten awareness of the critical

role that spatial scale plays in the use and

development of forest productivity

classifications.

EXAMPLES OF DIFFERENT CLASSIFICATIONS AT DIFFERENT

SPATIAL SCALES

Stand Scale (Tens of Hectares)

Existing methods of evaluating future stand

productivity at a site generally rely on evidence

of past tree growth, for example, site index.

Such classification methods, although reliable and

conservative, are less useful if (1) the future

stand will be a different genotype than existed

there formerly, (2) there are no data on past tree

growth at the site, or (3) the site has been

changed in some fundamental way since the previous

stand. These empirical methods also provide

little insight into the factors that control

productivity. More mechanistic approaches are

possible and as an example of a stand-level forest

productivity classification, I will describe a

classification method designed to explore the

factors which control potential stand

productivity. The classification relies on a

mechanistic model of stand productivity and was

developed at Weyerhaeuser Company in the early

1980s (Graham et al. 1985, Graham et al. 1986).

Method

This classification example is a mechanistic

model which predicts daily net stand productivity

over 1 year. The model is driven by daily

climatic data-rainfall, net incoming radiation,

average air temperature, average wind speed, and

average daytime relative humidity. The site is

defined by soil water holding capacity and total

amount of nitrogen in the trees and the forest

115
floor. The stand is characterized by

17 parameters among which are leaf area index,

optimal canopy light use efficiency, and ratio of

leaf biomass to total living tissue. The model

predicts nitrogen concentration in the canopy,

water uptake, soil water depletion, gross primary

productivity, and net primary productivity. It

does not predict stand growth because it does not

distribute the stand net productivity among

different types of tissue (e.g., wood, bark, or

branches). Details about the model can be found

in Graham et al. 1985.

The model relates potential site productivity

to site environment with ten fundamental equations

which:

1. define the effect of climate on net turnover

rate of nitrogen,

2. allocate site nitrogen to stand tissue,

3. predict the mass of living tissue in the stand,

4. predict the amount of sunlight absorbed by the

canopy,

5. predict the effect of temperature on light use

efficiency,

6. predict the maximum gross productivity of the

canopy with no water restrictions,

7. predict the effect of temperature on

respiration rates,

8. predict the maximum net productivity of the

canopjr with no water restrictions,

9. calculate the ratio of actual

evapotranspiration to potential

evapotranspiration, and

10. calculate maximum net productivity with water

restrictions.

Results

The model has been used to compare potential

productivity of typical sites in the northwest,

southeast, and central south; to determine optimal

leaf area; and to predict the impact of warmer,

drier climates on stand productivity (Graham et

al. 1985, Graham et al. 1986). Figure 1,

generated from multiple model runs, illustrates

the comparative effects of different climatic

variables at two sites. The model shows how

different climatic variables limit productivity at

different times in the year and how interactions

between different climatic variables can depress

productivity more than the most limiting variable

could by itself.

The model is useful for making interregional

comparisons of potential stand productivity, for

comparing soil influences within a region, or for

suggesting research directions. The model is not

useful for predicting current forest productivity.

Furthermore, the intensive climatic data needed to

run the model, which are only available at very

limited locations, will probably limit its use for

mapping potential forest productivity.

Regional Scale (Millions of Hectares)

Existing methods of evaluating or mapping

current regional forest productivity generally

rely on extensive and intensive forest surveys.

(a) ’ ’

0.32

’m.S~rti,AR~ ’ ’ ’

0 OPTIMAL m TEMPERATURE

0.28 0 ACTUAL A PRECIPITATION

V RADIATION 0 RELATIVE HUMIDITY

0.24

3 0.18

f

r” 0.12

E 0.08

6 0.04

52a 0

k

3

-0.04

3 0.24

8

2 0.20

g 0.18

0.12

0.08

0.04

0

-0.04 I I I I I I I I I I 1

J F M A M J J A S 0 N D

MONTH

Figure l.--Model predictions on the effect of site

climate on forest productivity. Initially, the

climate was set to year-round ideal conditions:

Air temperature = 20°C; daily rainfall - 8 mm;

Solar radiation - 280 watts/mz; and relative

humidity - 90 percent. Each climatic variable was

then relaxed to actual values, while the other

variables were kept ideal. Finally all the

climatic variables were set to their actual

values. The actual variable values came from

weather records of a typical year.

This method is extremely valuable, but it is time

consuming and expensive. Satellite imagery is

extensive, can be acquired at frequent intervals,

and is sensitive to canopy characteristics such as

greenness and water content-characteristics which

are functionally related to productivity (Tucker

and Sellers 1986). For these reasons satellite

imagery holds promise for classifying forest cover

and forest productivity over large regions

(Iverson et al. 1989a). As an example of a

regional-scale forest-productivity classification,

I will use a mapping approach based on satellite

imagery. The method relies on Landsat Thematic

Mapper (TM) data, NOAA Advanced Very High

Resolution Radiometer (AVHRR) data, and limited

plot-based, ground truth on forest productivity.

The method was developed in the late 1980s with

funding from NASA and has been applied to two U.S.

regions and evaluated using Tennessee Valley

116
Authority (TVA) and U.S. Forest Service (USFS)

forest inventory data (Iverson et al. 1988,

Iverson et al. 1989b).

Methods

The method used is outlined in Figure 2.

First, a regression model predicting forest

productivity on the basis of pixel TM band values

is developed using ground-based forest plot

productivity values and their corresponding

TM band values.3 The TM band data should be from

a phenological period in which there is large

contrast between forested pixels and unforested

pixels. For hardwood-dominated regions this

occurs either in late spring when forests are

leafed out and agricultural fields are still

largely bare or in early fall when forests are

still leafed out but agricultural fields have been

harvested and pastures are starting to dry out. A

minimum of 30 plots is needed, and more are

desirable. The plots should capture the range of

productivity rates likely to be seen in the

region. Ideally, the plots should be scattered

across the entire land area captured in the TM

image although practically this is probably not

possible. Once the regression model has been

developed, it is applied to all the forested

pixels within the TM image to create a map of

forest productivity. The resolution and extent of

this map is of course the same as the TM image,

that is pixels 30-meters square, covering an area

no greater than 160 km by 160 km (the size of a

single TM scene). An entire scene is not needed,

but at least a quarter of a scene is desirable.

Once the TM-scale productivity map is

generated, it is used as "ground truth" to develop

a second regression model predicting forest

productivity on the basis of AVHRR band values.

(An AVHRR pixel is 1.1 kilometers square and

encompasses about 1300 TM-sized pixels.) To do

this, an AVHRR image taken during the same

phenological period as the TM image is overlaid on

the TM-based productivity map. The forestproductivity pixel values falling within a single

AVHRR pixel are extracted, and the overall forest

productivity value associated with the AVHRR pixel

is calculated by summing the productivity values

of all forested pixels and dividing by the total

number of TM-sized pixels within the AVHRR pixel.

Nonforested TM pixels are included in the

calculation so that the resultant productivity

value is forest production per unit of land as

opposed to forest production per unit forested

land. About 100 AVHRR pixels are needed and

should be scattered over regions on the map with

3A pixel is a picture element and is the basic

land area unit in a satellite image. The size of

the pixel will determine the sharpness of the

image. The smaller the pixel size, the sharper

the image. In satellite imagery, each pixel is

associated with one or more band values,

reflecting the amount of light given off by the

surface area (represented by the pixel) within a

given band of wavelengths.

Develop TM model of forest

productivity using TM band

values and plot measures of

forest productivity

JI

TM model of forest productivity

Apply TM model to entire TM

scene to create landscape map

of forest productivitv

Landscape map of forest productivity

#I

AVHRR model of forest productivity

Figure 2.--Outline of regional land classification

method using nested TM and AVHRR images.

variable forest cover and forest productivity so

that the selected AVHRR pixels encompass all

situations observed on the scene. The forest

productivity values associated with the AVHRR

pixels are regressed against the AVHRR pixel band

values to develop the regression model predicting

forest production. The final step is to take the

full AVHRR image, stratify out pixels that clearly

contain no forest (such as water), and apply the

AVHRR regression model to all the pixels' band

values to create a AVHRR-scale map of forest

productivity. Such a map will typically cover an

area containing millions of hectares because a

single AVHRR scene can be up to 2400 kilometers on

a side. A more complete description of the

methods are given in Iverson et al. 198913, Iverson

et al. 1988, and Cook et al. 1989.

Results

These methods were applied to an AVHRR and a TM

quarter scene centered over the Great Smoky

Mountain National Park. About a hundred forest

production plots located in mostly mixed hardwood

forests within the Park were used for the initial

forest-productivity ground truth (Cook et al.

198913, Iverson et al. 1988). The regression

equation relating forest productivity to TM band

value had an r2 of only 0.27 (n = 111, p < 0.0001).

The high significance but low rz of the model means

that the model can predict the expected median

forest productivity over large areas (>lOO pixels)

117
with a high degree of accuracy (+/- ca.

10 percent); however, its ability to predict the

forest productivity of any one pixel is poor (Cook

et al. 1989). Consequently, the map, while not

appropriate for depicting forest productivity at a

fine scale, was appropriate for developing the

AVHKR forest productivity regression. The

equation relating forest productivity to AVHKK

band values had an r2 of 0.51 (n - 99, p < 0.0001).

This equation was applied to the band values of

the AVHRR pixels to create a map of forest

productivity with a spatial resolution of

1.1 kilometers square. To display the results and

compare them with TVA estimates of forest

productivity, the AVHKR pixel values were averaged

and multiplied by county area to produce countylevel estimates of forest productivity. Table 1

compares TVA county-level estimates of forest

productivity with the AVHRR predictions.4 Because

the model was developed initially in terrain

dominated by hardwood forest, it predicted

productivity better in counties with such

Table l.--The correlation and significance of

correlation between AVHRR county estimates of

forest productivity and TVA county estimates of

total forest productivity and of hardwood forest

productivity.

County N TVA total forest TVA hardwood forest

grouping

r P< r P<

All counties

Total 168 0.52 0.0001 0.62 0.0001

By state

GA 4 9 0.72 0.0001 0.80 0.0001

KY 1 9 0.76 0.0001 0.96 0.0001

N C 3 2 0.78 0.0001 0.91 0.0001

S C 1 7 0.55 0.0228 0.62 0.0076

TN 5 5 0.73 0.0001 0.85 0.0001

VA 1 3 0.66 0.0134 0.88 0.0001

Counties where >40 nercent of the forest

is hardwood

Total 7 8 0.68 0.0001 0.91 0.0001

By state

GA 1 0 0.86 0.0013 0.88 0.0008

KY 12 0.79 0.0021 0.92 0.0001

NC 17 0.82 0.0001 0.93 0.0001

TN 2 9 0.74 0.0001 0.96 0.0001

VA 1 0 0.75 0.0119 0.99 0.0001

4Because the original productivity data from the

ground-based plots were not in the same units as

the TVA estimates, the AVHKK results were compared

through correlation.

118

conditions. Likewise, the AVHRK productivity

estimates correlated much more closely with

hardwood annual growth increment than with total

annual growth increment.

This method is not a substitute for the

statistically rigorous forest surveys undertaken by

TVA and the USFS. However, such surveys are not

possible in all parts of the world, nor is it

possible to repeat them at short time intervals.

The method does appear to capture the spatial

variability of forest productivity over large areas

and, therefore, is useful for evaluating largescale within-region differences in forest

productivity. The method would appear to hold

promise for monitoring changes in regional or

global patterns in forest productivity but probably

not for quantifying forest resources. This

satellite-classification approach is thus

complimentary to traditional forest surveys, which

are-impractical for global monitoring but very

useful for predicting regional resources.

Subcontinental Scale (Billions of Hectares)

The development of new forests can sequester

significant amounts of atmospheric CO,.

Consequently, afforestation has been proposed as

one means of reducing carbon dioxide buildup in the

atmosphere. Because forests have many

environmental and economic benefits apart from CO,

mitigation, afforestation is appealing. The degree

to which tree planting can mitigate

CO, buildup depends in part on the area of land

available and suitable for forests. As an example

of a very-large-scale forest productivity

classification, I will use a forest productivity

classification designed to identify the amount and

general location of land in Sub-Saharan Africa

suitable for afforestation with industrial tree

plantations. The classification was developed to

assist the African Bureau of the U.S. Agency for

International Development in evaluating the impact

of different land-use strategies on greenhouse gas

emissions (Graham et al. 1990).

Methods

At a continental scale, land availability for

afforestation with industrial plantations is

defined by climatic conditions, general soil

fertility, and current land use. Climatic

conditions and soil fertility will control the

productivity or viability of plantations, whereas

current land use identifies locations without

forest cover. (Current land use will also affect

the likelihood of adoption of plantation forestry

although this was not considered explicitly in this

classification.) To gather data on

intercontinental variations in these variables,

continental maps and a Geographic Information

System (GIS) were used. The GIS extracted from the

maps at regular 0.4-degree intervals information

about soil order, annual rainfall, country

identity, and current land use. This information

was used to create a data base with about

9000 observations, each observation representing a

single point on the continent and containing the

map-extracted information. The soil order
information was converted to a fertility index

based on the agricultural capability information

for each soil order. A simple function was then

developed to calculate an index of potential

plantation productivity for each point, based on

site departures from ideal annual rainfall and on

soil conditions. A threshold productivity index

value above which plantations were viable was used

to identify those points with soil and climate

conditions suitable for plantation forestry. The

land use at points capable of supporting plantation

forestry was then examined to determine how much of

the land at each point was not currently in

forests. This information was then summed within

countries to develop country statistics on land

area available for afforestation with industrial

plantations. Details on the method are found in

Graham et al. 1990.

Results

The approach identified 196 million unforested

hectares within Sub-Saharan Africa capable of

supporting at least moderately productive

plantation forestry. If a more-stringent

productivity threshold was used, only 62 million

hectares are available. Cameroon, Ethiopia,

Madagascar, Mozambique, Nigeria, Tanzania, and

Zaire were identified as countries with more than

10 million hectares of land currently not forested

but suitable for plantation forestry. Assuming

each of the 162 million hectares was planted and

managed as an industrial plantation, this land-use

conversion would represent a permanent one-time

sequestering of about 9.1 billion metric tons of

carbon (Graham et al. 1990). Current fossil fuel

carbon emissions are about 5.1 billion metric tons

per year.

This classification approach is useful for

systematically evaluating the continental potential

for afforestation. The simplicity of the approach

is a function of the paucity of continental data

bases and information about this region. The

approach could be improved using a more complex

productivity index function. The method is

appropriate for large-scale analyses of regions for

which information on land use and land capability

are sketchy. It is not appropriate for actually

siting plantations because many other factors need

to be considered such as labor pool, nearness of a

market, or cost of capital.

DISCUSSION

Spatial scale will constrain the accuracy and

information content of a classification. As

classifications expand, that is, cover larger and

larger areas, accuracy at any one location tends to

decrease. However, new information on the spatial

patterning of productivity and perhaps the causes

of the patterning become available. For example,

neither the satellite-based classification nor the

continental classification would be appropriate

tools for predicting the site productivity of a

given hectare. Rather, they provide generalized

information about the spatial pattern of forest

productivity, something a local classification

method could not do.

The scale of the classification will also

dictate the type of data needed to develop the

classification. Classifications that are specific

to a location will often be keyed to the local

variables that most affect productivity, such as

soil texture or depth and topographic position.

Climate is often ignored because it is presumed to

be uniform within the area of consideration.

Classifications that are extensive or are intended

to be applicable to many regions, such as the three

classifications presented in this paper, may need

to include climate or a surrogate for climate.

Extensive classifications can be developed

either bottom-up or top-down; each way has its

advantages and disadvantages. Bottom-up

classifications created by aggregating the results

of many local observations are probably more

believable and accurate. However, the bottom-up

approach is severely constrained by the need to

have uniform information coming from all the local

sites. For example, different countries may have

different definitions of soils, climate, forest

production, vegetation classes, etc., and/or

different methods for measuring or evaluating any

or all of these variables. Aggregation of the

information into a common system may therefore be

extremely difficult if not impossible. Top-down

classifications, such as the regional and

continental classifications presented in this

paper, are also constrained by the need for uniform

spatially extensive information. Satellite data

are valuable in this regard and are often the only

data available with such qualities, particularly in

remote areas. Unfortunately, satellite data cannot

directly measure either productivity or the

variables driving productivity such as soil

quality, canopy cover, or moisture availability.

Satellite data can only be correlated to some of

these variables. Thus, any resultant

classification is highly dependent on the strength

of these correlations.

In summary, spatial scale is an important

component of forest productivity classifications.

Understanding how spatial scale may constrain the

utility of a classification is important for using

classifications wisely and for developing useful

classifications.

REFERENCES

Cook, Elizabeth A.; Iverson, Louis R.; Graham,

Robin L. 1989. Estimating forest productivity

with thematic mapper data and biogeographical

data. Remote Sensing of the Environment

28:131-141.

Graham, Robin L.; Perlack, Robert D.; Prasad,

Anantha M. G.; Ranney, Jack W.; Waddle,

Daniel B. 1990. Greenhouse gas emissions in SubSaharan Africa. ORNL Technical Report 6640, Oak

Ridge National Laboratory, Oak Ridge TN. 135 pp.

[Available form Oak Ridge National Laboratory,

Oak Ridge, TN]

119
Graham, R.L.; Fox, T.R.; Dougherty, P.M.. 1986.

Multiple stress factors: The potential roles of

system models in assessing the impact of

multiple stresses on forest productivity. In:

Hennessey, Thomas C.: Dougherty, Phillip M.;

Kossuth, Susan V.; Johnson, Jon D., eds. Stress

physiology and forest productivity. Proceedings

of the physiology working group technical

session. Society of American Foresters National

Convention; 1985 July: Fort Collins, CO: Boston:

Martinus Nijhoff Publishers. pp. 151-169.

Graham, Robin L.; Farnum, Peter; Timmis, Roger;

Ritchie, Gary A. 1985. Using modeling as a tool

to increase forest productivity and value. In:

Ballard, R.; Farnum, P.; Ritchie, G. A.; Winjum,

J. K., eds. Forest potentials: productivity and

value, proceedings of the 4th Weyerhaeuser

Science Symposium; 1984 August: Tacoma, WA: pp.

101-130. [Available from Weyerhaeuser Company,

Tacoma, WA]

Iverson, Louis R.; Graham, Robin Lambert; Cook,

Elizabeth A. 1989a. Applications of remote

sensing to forested ecosystems. Landscape

Ecology 3(2):131-143.

Iverson, L. R.; Cook, E. A.; Graham, R. L. 1989b.

A technique for extrapolating and validating

forest cover across large regions: Calibrating

AVHRR data with TM data. International Journal

of Remote Sensing 10(11):1805-1812.

Iverson, L. R.; Cook, E. A.; Graham, R. L.; Olson,

J. S.; Frank, T. D.; Ke, Y. 1988. Interpreting

forest biome productivity and cover utilizing

nested scales of image resolution and

biogeographical analysis. Final report to NASA

Thematic Working Group. pp 135. [Available from

NASA Headquarters, Washington, DC]

Tucker, C. J.; Sellers, P. J. 1986. Satellite

remote sensing of primary production.

International Journal of Remote Sensing

7(11):1395-1416.

ACKNOWLEDGEMENTS

The author wishes to thank her co-workers on

developing the three classifications (their many

names are found in the references),

Helga Van Miegrot and Lynn Wright for their

thoughtful reviews, and Carole Kappelmann for her

careful typing. Oak Ridge National Laboratory is

managed by Martin Marietta Energy Systems under

contract DE-AC05-840R214000 with the U.S.

Department of Energy. This article is publication

no. 3636 of the Environmental Sciences Division,

Oak Ridge National Laboratory.

120
LANDFORMS, SOILS, AND FOREST GROWTH:

IDENTIFICATION AND INTEGRATION

WITH GEOGRAPHIC INFORMATION SYSTEM&

R. David Hammer/

Abstract.--A definition of soil system is provided which is

suitable for integrating forest productivity, soil, and

climatic data using geographic information systems. Users

of geographic information systems and digital elevation

models are urged to apply these technologies in ways to increase man's knowledge of the structure and function of

forest ecosystems.

Keywords: digital elevation models, landforms, soil survey,

soil spatial variability, site index

INTRODUCTION

Forest site productivity has received considerable attention in this country for seven

decades. That another symposium addresses the

topic indicates the persistence of unanswered

questions and the interest in obtaining results.

Tacitly implied is the human frustration which results when solutions are sought and not found.

Past efforts in forest site productivity will

be reviewed with emphasis on the soil resource.

The perspective from which the soil has been

studied will be examined. Space constraints restrict development of certain themes. For example,

tree nutrition will not be discussed. This omission is not from unawareness of the importance of

the topic, but is a realization that cursory treatment would serve no useful purpose. Some important concepts will be presented with minimal

justification. Key references will be provided so

the reader can retrace concepts and past debates.

Finally, a conceptual framework will be presented which could be used in conjunction with

digital elevation models (DEM) and geographic

information systems (GIS) as research tools for

l/ Presented at the symposium "Ecological Land

Classification: Applications to Identify the

Productive Potential of Southern Forests,

Charlotte, NC, January 7-9, 1991.

/ Assistant Professor of Soil Science, 144

Mumford Hall, University of Missouri, Columbia, MO

65211.

description and study of forest ecosystems. Hopefully the offered approach will foster a more

holistic framework for evaluating forest soil

systems. The goal is to stimulate thought, to

broaden perspectives, to integrate concepts, and

to open dialogue.

GEOGRAPHIC INFORMATION SYSTEMS AND DIGITAL

ELEVATION MODELS

Defining GIS and DEM

Detailed descriptions of GIS and DEM will not

be given, because definitions reflect the perspectives and applications of the definers. The

emphasis will be upon potential applications of

these technologies to evaluate and inventory

forest site productivity. Burrough's (1986)

definition of GIS as a “. . .set of fools for collecting, storing, retrieving at will, transforming,

and displaying spatial data from the real world

for a particular set of purposes." will be used.

Digital elevation models are "Any digital representation of the continuous variation of relief

over space. . .I' (Burrough, 1986). Other recent

textbooks describing GIS and DEM include Raper

(1989), Estes (1990), and Tomlin (1990).

Current applications of GIS and DEM

Geographic information systems and DEM (usually

standard U.S. Geological Survey 30 m DEM) are

commonly components in land use planning and resource inventory. Hammer et al., (in press) have

reviewed current applications. The most common

use of GIS technology has been production of

special use maps from existing data bases. This

121
is an excellent use of GIS technology, however the

potential of GIS as a research tool should not be

overlooked. New technology and techniques traditionally have been the avenues to advances in

scientific knowledge (Cline, 1961).

An example of producing new maps from existing

data is Hendrix and Price's (1986) use of slope,

aspect, and soil data in combination with a

regionally-developed site index equation in central

Vermont. The three layers of information were

combined in GIS. A site index regression equation

was used to develop potential site index maps from

the soil, slope, and aspect information. The

Universal Soil Loss Equation (USLE) was used with

the slope, aspect and soils data to predict soil

erosion. This use of GIS produced potential site

index maps and soil erosion maps which would have

been difficult to obtain with other methods. The

data produced were new data, because they previously

did not exist. The GIS technology is well suited

for combining data in this way.

However, this application raises important

questions. How accurate and precise were the

original data layers? How accurate and precise,

then, are the final maps? The sources of the slope

and aspect data were not provided, so the precision

and accuracy are unknown. The heterogeneity of

soil survey mapping units will be discussed. Without knowledge of the source and precision of the

baseline data, the final results are subject to

doubt. The weaknesses of the USLE are widely known.

Placing layers of imprecise or inaccurate data

one upon the other will not create more precise or

more accurate information. The level of risk in

applying such maps is unknown. The potential

exists to compound error at a rate not previously

possible. When provided to the uninformed, without

cautionary warnings regarding possible deficiencies,

such information offers the opportunity for enormous

error. Failure resulting from application of these

data could result in the rejection of a technology

with great potential benefits, not because the

technology was flawed, but because the data were.

Careful, detailed field verification is required

for maps produced from multiple layers of data

within GIS.

Discussion will now focus upon past forest site

productivity and soil research. Following each

section, potential ways of using GIS to integrate

data and concepts will be presented.

FOREST SITE PRODUCTIVITY

Important factorsThe topic is complex. Forest productivity results from the interactions of: 1) the genetic

potential of the crop and its competitors;

2) forest management practices -- which run the

gamut from planting through stand management and

harvest techniques; 3) site properties which

affect tree growth -- including soils, stratigraphy,

geology, hydrology, geomorphology, and past site

history; 4) climate, including seasonal distribution

of energy and water; and 5) unplanned perturbations,

including man's impact upon global climate.

The variety of site- and crop-specific interactions emerging from this mosaic is bewildering.

General truisms are few, and the need for systemspecific research is probably greater than

acknowledged. Perhaps the poultice lies within

perspective, technology, and methodology.

Perspectives

Perspective is an important component in any of

man's activities. As Cline (1961) observed in his

silver anniversary address to the Soil Science

Society of America, "The picture is not the same to

all who work in the science, for it is composed of

knowledge . . . and different men know, or think

they know, different things." Cline's subsequent

comments implied that established scientific

inertia is only slowly overcome.

The perspective presented herein is strongly

influenced by the author's awareness that soils are

important components of ecosystems. No component

of an ecosystem, physical or biological, can be

understood as an individual entity. The system

must be studied before the integrated functioning

of the parts can be adequately understood,

Soil site investigations

No comprehensive review of site quality and

forest productivity has been published since

Carmean (1975), although Stone's (1978) discussion

of soil moisture regimes is important. Carmean's

review supported Coil'e (1952) observation. Site

quality is a function of the "quantity and quality"

of the rooting volume. The challenge is defining

"quantity and quality" in a meaningful way for the

selected specie(s).

Henderson et al., (1990) suggested that a soilbased productivity index (PI) offered a conceptual

framework meaningful for evaluating soil properties

to assess the quantity and quality of the rooting

volume. The PI concept has important implications

for many forest ecosystems. The concept is discussed in detail elsewhere (Henderson et al., 1990;

Huddleston, 1984).

Traditional site index studies have focused upon

tree height as a function of landscape features and

soil properties. Often a single soil pit was used

to obtain soil data subsequently regressed against

tree height. Powers (1986) suggested that this

approach was naive because the soil variance,

temporal and spatial, is unknown. Statistical

rigor of many of these studies left much to be desired (Stone, 1978; McQuilkin, 1976). Traditional

attempts to correlate yield of plants with soil

properties have focused upon identifying a few

"key" static soil properties, without regard to

processes important in the genesis or maintenance

of the soil attributes (Wilding and Drees, 1983).

Such studies often ignored those soil/site

attributes affecting water-supplying (author's

emphasis) capacity (Henderson et al., 1990).

122

----
Rather, the static measurement of soil waterholding capacity has been the focus of attention.

Powers (1986) predicted that advances in forest

site productivity would be insignificant until soil

water relationships have been addressed adequately.

Another major limitation to soil-site studies is

in application of results. Graphs and equations

resulted from these investigations, whereas maps

and inventories are the tools for forest management

and planning (Stone, 1978).

The technologies of GIS and DEM have important

implications for these approaches in evaluating

forest site productivity. For example, Moore and

Nieber (1989) used 10 m DEM with GIS to relate nonpoint sources of pollution to topographic factors.

They also predicted times and amounts of relative

soil wetness at various locations within the

landscape.

With GIS, landform patterns can be identified

and representative landforms selected. Forest

vegetation can be studied using remotely-sensed

data during different times of the year. Periods

of stress induced by climate, pests, o'r pathogens

offer ideal times to relate stand vigor (compared

with remotely sensed data) to topographic features

(identified with GIS).

Soil variance could be correlated to landforms

with GIS and used with a PI to assess causal properties affecting tree growth within soils and

landscapes. The PI could be used with GIS to

develop productivity mapping units independent of

soil taxonomic constraints. The integration of

soils and landscapes could be very precise using

GIS and DEM.

THE SOIL RESOURCE

The dynamic soil

The soil resource, the basis for forest tree

growth, is a complex, dynamic body which has received inadequate and shallow (literally) treatment

in most forest productivity studies. Too frequently, soil analyses have focused upon the upper

fraction of the solum and have ignored the genetic

soil horizons. Soil horizons reflect both current

and past pedogenic processes and are the key to

understanding the chemical, physical, mineralogical,

and biological interactions within the soil landscape (Simonson, 1959; Nikiforoof, 1959).

Point locations or infrequent transects are the

usual sources of our data. Those who have

recognized the dynamic nature of the soil system

generally have focused upon the forest floor as

the source of most temporal variability in the

soil system.

Hammer et al. (1991) used factor analysis to

determine the relative proportions of soil variance

accounted for by soil chemical and physical properties in the three upper genetic soil horizons

within a forested ultisollinceptisol landscape.

The B horizons cation levels and pH extracted

34.7% of the variance. Color and thicknesses of

subsurface horizons accounted for another 16.0% of

the variance. The A horizon physical and chemical

properties (horizon thickness, organic matter content, and cation concentrations) accounted for

27.0% of the extracted variance. Most of the soil

variance was associated with subsoil properties.

The importance of the subsoil for forest tree

growth generally has been overlooked. Comerford's

(1984) review of the role of subsoils in plant

nutrition relied heavily upon agronomic research.

Subsoil has received the most attention when it has

contained physical or chemical limitations to root

growth (Loftus, 1971). The importance of lateral

subsoil water movement in sloping landscapes requires much more attention before the soil can be

understood as a dynamic system.

The soil system is a continuum in which the important variables do not all change at the same

rate or in the same direction. Nikiforoff (1959)

lamented the tendency of many investigators to regard the soil as a static, innate object rather

than a dynamic chemical, physical, and biological

system. Further, he predicted that soil research

would not regain the impetus enjoyed during the

birth of the science until focus shifted from

classification to morphology and the causal mechanisms of soil development. Some progress has been

made in this arena, but taxonomy continues to receive disproportionate emphasis. Plant root-soil

interactions under natural conditions require much

study.

Potential applications of GIS are endless for

identifying patterns of geomorphic and stratigrapti

influences on soil variability. Patterns and distribution of land surface shape could be correlated

to geologic and stratigraphic features. Seasonal

distribution of soil water could be modeled, then

compared to soil distribution. Water distribution

with landscapes could be integrated with soils and

productivity using GIS and DEM.

Soil survey and soil taxonomy

Soil survey mapping units frequently are components of forest management plans. The inadequacies of the soil series as a predictor of site

quality are well documented (Farnsworth and Leaf,

1965; Van Lear and Hosner, 1967; Shetron, 1972;

Esu and Grigal, 1979). Jones (1969) concluded

that "soil series alone are too heterogeneous

ecologically to serve as a basis for evaluating

timber productivity. . ." Carmean's classic study

(1967) showed that topographic features were more

accurate predictors of black oak (0. velutina Lam.)

site index than soil series. The inadequacies of

the soil series are compounded when uninitiated

users assume that delineations upon soil survey

sheets represent pure units similar to the model

pedon described in the survey.

.Grigal (1984) itemized problems with soil survey

as: 1) lack of purity in mapping units; 2) lack

of coincidence of soil survey boundaries with maps

based on non-soil properties; 3) coarse mapping

scale; 4) failure to match soil series to landfornm;
5) lack of continuity within soil series concepts;

and6) the failure of Soil Taxonomy (Soil Survey

Staff, 1975) as a model to construct soil maps.

Many of these problems are interrelated. For example, important small landforms frequently cannot

be identified at the prevalent mapping scale. The

failure of soil survey users to precisely define

the needs of the survey to the Soil Conservation

Service results in a general product which has

limited application for specific needs.

Soil survey field procedures are not well-suited

to coll.ecting the kinds of data necessary for quantitative descriptions of the soil, particularly in

forested lands. The problem is long-standing. The

most extensive mapping unit in older soil surveys

probably is the "rough rocky" delineation commonly

used to indicate forested uplands in surveys

published prior to tmplementation of the current

Soil Taxonomy (Soil Survey Staff, 1975). The

agricultural bias in soil classification persists

today.

The soil surveyor's primary interest is in naming

and delineating soil mapping units. Soil individuals

are separated on the basis of taxonomic criteria

which may not reflect soil properties important to

tree growth. Mapping scale (usually 1:24,000) does

not permit separations of units smaller than 2 ha.

Landforms and their associated soils often cannot

be identified on field sheets at this scale.

Grigal (1984) described a situation in Minnesota

in which soils formed in lacustrine materials both

in narrow valleys and in broad lakebeds were classified as the same soil series in spite of microclimate differences important to forest tree growth.

The author observed a similar situation in western

Washington. The soils were loamy-skeletal, mixed,

mesic Andic Xerochrepts moderately deep to compact

glacial till. The soils occupied different landforms, but were classified into the same series.

Over 8,000 ha of the soils were on undulating till

plains in the Puget Trough. Another 12,000 ha of

the soil were on colluvial toeslopes of the Cascade

Mountains. Douglas-fir (Pseudotsuga menziesii

(Mirb.) France) site index on 50 year curves

averaged 7 m higher on the toeslopes. The difference

was ultimately attributed to subsurface water movement resulting from summer snowmelt from the summit,

over 1700 m above the toeslope. The till plain

landforms did not have a source of supplemental

soil water. The soils ultimately were taxonomically

separated. In retrospect, classifying the toeslope

soils as xeric was a mistake.

The soil surveyor does not collect soil variability data in a format suitable for most land users.

Field notes and transect information, when collected,

are not incorporated into the soil survey. Soil

variability information is presented empirically as

the "range of characteristics" associated with soil

series. This format does not allow extrapolation

of soil variability information across the range

of the mapping units..

Young et al. (in press) addressed this problem.

They recommended that transects be systematically

established within representative field sites for

all mapping units on the legend. Soil properties

important for perceived land use, regardless of

their taxonomic significance, would be measured and

recorded. Transect data, subsequently presented in

tabular format, would include means, ranges, standard deviations, and confidence intervals. In

Missouri, procedures are being developed to retain

all field notes after a survey is completed. Ultimately, the notes will be transcribed and

included with the statewide soil data base.

Site index data in soil surveys is presented as

means for individual mapping units. Ranges and

standard deviations are lacking. The site index

curves used to obtain data are cited infrequently,

and the source of the data often are unavailable

for the reader. Often the published site index

data are from a regional data base and may have

been obtained in adjoining states. Problems associated with site index have been well documented

(Bull, 1931; Carmean, 1975; Grigal, 1984).

Matching soil properties to landforms with GIS

and DEM is an arena deserving much attention. Landform description and distribution of landforms

within landscapes are exercises for which GTS and

DEM seem ideally suited. Algorithms must be developed for this purpose, but Moore and Nieber (1989)

have demonstrated the potential.

Hammer (1986) used stem analysis to examine tree

growth within landforms. Height of yellow-poplar

(Liriodendron tulipifera L.) and white oak (Quercus

alba L.) were shown to increase downslope as soil

depth and water-supplying capacity increased

(Figure 1). With GIS and DEM, the within-landform

variance in soils and tree growth could be integrated and extrapolated across regions, increasing

precision of productivity estimates and identifying

sites suitable for species or intensive management.

;;

‘i

f

20

s

8 1 0

i i

I

Index Age _

Relative Position of Trees on Slope

I I I I I I I

0 10 20 30 40 50 60 70 80

AGE (years)

Figure 1. Height of yellow-poplar as a function

of Landscape position on the mid-Cumberland Plateau.

Growth curves were derived from stem analysis (from

Hammer, 1986).

124
RELATIONSHIPS OF LANDFORMS TO SOILS AND TREE GROWTH

The importance for tree growth of landform characteristics such as surface shape, slope length,

and aspect are well documented (Carmean, 1965 and

1975; Einspahr and McComb, 1951; Graney, 1977;

Smalley, 1967; Hannah, 1968; Ike and Huppuch, 1968).

Landformsfrequently are components of site classification schemes. Indeed, several papers in this

symposium have addressed the topic. Rowe (1984),

Smalley (1984), and Grigal (1984) advocated the

importance of landforms in forest site assessment.

Soil development involves accumulation of parent

materials and horizonation which expresses the

dominant soil-forming processes (Simonson, 1959).

Landform features influence soil development because

landform shape and internal composition are influenced by the forces which deposit the parent

materials (Daniels et al., 1971). Landform shape

and composition both influence the rate and volume

of water movement into and through the soil

(Huggett, 1975). The seasonal distribution of water

is the driving force of soil genesis (Crowther,

1953). The implications of these interactions for

tree growth are obvious. Dan and Yaalon (1968)

coined the term "pedomorphic surface" to describe a

"landscape in which soils and relief are genetically

and evolutionarily interdependent."

Soil series and mapping units must be mare closely correlated to geomorphic surfaces. Daniels et al.

(1971) and Ruhe (1975) have predicted that future

efforts to identify soil units with reduced variability will rely more heavily upon geomorphology.

Typically, soil-landscape studies used line

transect sampling (Huddleston and Riecken, 1973);

Evans and Franzmeier, 1986, Pregitzer et al., 1983).

Patterns of soil distribution related to geomorphic

surfaces have been revealed. Variances and rates

of change of soil properties within landforms, and

the relationships of those changes to geomorphic

surfaces, remain unknown.

Crowther (1953) urged a move from taxonomic considerations of soil genesis to a focus on internal

relationships of soils with their landforms. He

foresaw a search for "general relationships" as

distribution of soils in relation to axes representing as many factors, both quantitative and

qualitative, as can be observed and tested."

Crowther's statement could be regarded as a justification for multivariate statistical analyses of

soil-landform relationships.

Hammer (1986) used a 10 m grid on three midCumberland Plateau landtypes to sample soil properties taken with 2.5 cm diameter cores. Discriminant

analyses was used to classify individual soil cores

using 35 chemical and physical properties representing the three uppermost genetic horizons.

Discriminant analysis classified all soil cores into

the correct landtypes. Cannonical correlation revealed a distinct clustering of soils into groups by

respective landtypes (Figure 2). The cannonical

loading scores revealed the soil properties most

responsible for the clustering.

0 Summit

9

7

5

3

1

I

-1 t

-9 ’

I I I I I I I I

-8 -6 -4 -2 0 2 4 6 8

CANNONICAL VARIATE TWO

Figure 2. Distribution of soil profiles in three

forested mid-Cumberland Plateau landtypes. The

data from 35 soil properties representing 132

soil cores were analyzed with cannonical discrimination (from Hammer, 1986).

Three soil variables -- AB horizon color and

thickness and extractable acidity of the Et horizon

- - caused separations along cannonical variate 1.

The discriminators along cannonical variate 2 were

A horizon thickness and Ca, Mg, and K levels from

A, AB, and Bt horizons. Results showed that relatively few soil properties can be used to distinguish landforms within a landscape, but that the

entire soil profile must be considered. The important soil discriminators probably would change

with changes in the soil-landscape environment.

More research is needed in this arena.

THE SOIL SYSTEM -- DEFINITIONS AND PERSPECTIVE

Defining the soil system

Soil scientists have approached pedological

research from a variety of perspectives. Dijkerman

(1974), Yaalon (1975), Huggett (1975) and Smeck

(1983) provide important reviews. A major focal

point is the definition of soil system. Scientists

do not agree upon the boundaries and structure of

defined soil systems. Several definitions exist.

All are limiting in some way, particularly for use

as the conceptual basis for forest site productivity.

Daniels et al. (1984) defined a soil system as

11 . . .a recurring group of soils that occupies the

landscape from the interstream divide to the stream.

The soils that make up these systems usually occupy

specific landscape positions as a result of the

internal soil environment produced by the interaction of stratigraphy, hydrology, geomorphology

and climate." This definition provides lateral

boundaries, but does not define a lower boundary.

Vegetation is omitted, and climate is implied only

through its effect on the soils that occupy the

landscape and its effect on hydrology. The definition of landscape as it is applied here, is not

clear. Ruhe (1969) defined landscape as the

portion of the land surface which can be seen in a

125
single view. Ruhe's experience in prairie ecosystems obviously influenced this definition.

Jenny's (1980) conceptual soil unit, the

"tessera", is a ":Landscape element of arbitrary

cross section." When vegetation is included, the

system is an "ecotessera." Depth limits are not

defined, but climate is included.

A more precisely defined model is Huggett's

(1975) "valley basin." This system is bounded by

the soil surface, the watershed, and the weathering

front at the base. The system is thermodynamically

open and can be viewed as a unit cell within a basin

network drainage system. Huggett's model was developed from the recognition that the "flux of

solids, colloids and solutes within and across the

landscape is . . . organized within the framework

of. . . system units." Notably absent is the vegetation component. A limitation of this system in

soils of the southeastern United States is the lower

boundary. The author has observed highly weathered

landscapes in which the lower boundary of weathering

extends for tens of meters. Saprolite can be extremely deep, particularly in landforms receiving

lateral throughflow.

A conceptual soil-landscape model for forest

ecosystems

The best elements of the above models can be

combined to produce anaesthetically pleasing

conceptual soil system. The lateral boundaries

would be the watershed divides. The unit cell could

be watersheds bounding streams of any order. The

"unit cell" could be chosen to represent local first

order drainage basins representative of local

geologic and stratigraphic conditions (i.e. deep

loess adjacent to the Missouri River) or higher

order stream basins representative of major land

resource areas (Figure 3). The size of Huggett's

"unit cell" would be a function of objective. The

watershed (unit cell) boundaries could be identified

with GIS and DEM.

Figure 3: Hypothetical soil system bpundaries.

The figure is a three-dimensional surface contour

generated with GIS from 10 m DEM data. The small

unit on the right represents a first order watershed. The larger unit represents a third order

watershed.

The lower boundary of the system would be the

maximum rooting depth of perennial vegetation.

Locating this boundary would require investigating

the entire rooting volume of the soil. Stratigraphic discontinuities would be components of the

model if plant roots cross the discontinuity.

Vegetation and climate would be components of

the system. Climate could be described and defined by objective, but should include seasonal

distribution of water, potential evapotranspiration and solar energy input.

Structural elements within the cell would be

individual pedomorphic surfaces, stratigraphy,

and geology. All of the components of the system

could be modeled or monitored as layers within a

GIS system.

APPLICATIONS OF GIS AND DEM IN LANDSCAPE ANALYSIS

A field test of slope class maps from DEM and GIS

Hanmeret al. (1990) established 10 m test grids

in 16 ha fields at two locations in Atchison

County, MO. Slope was field-measured with a

clinometer for each of the approximately 1400 10 m

cells in each field. lope class maps were

produced with ARCINEO d software using standard

U.S.G.S. 30 m DEM and 10 m DEM produced from

aerial photography. Field-measured slope classes

were compared cell-by-cell with slope class maps

from the 30 m and 10 m DEM and with the slope

classes from the cooperative soil survey.

Filtered 10 m DEM produced the most accurate

and precise slope class maps across the range of

slopes in the study fields. Within each site, the

soil survey correctly classified between 30% and

40% of the area, 30 m DEM correctly classified

about 25%, and filtered 10 m DEM correctly classified between 53% and 59% of the area (Figure 4).

Filtered 10 m DEM correctly classified at least a

O-2 3-6 6-9 10-1415-25 >25

SLOPE CLASS

(percent)

Figure 4. Percentages of area correctly classfied into respective slope classes using three

methods. Methods are standard soil survey

(survey), generated from 30 m DEM with GIS (30 m),.

and generated from filtered 10 m DEM with GIS

(lO.m(F2)).
portion of each slope class represented in both

fields, while the soil survey represented only

dominant slope classes. The 30 m DEM did not

identify the O-2% or >25% slope class in the west

area (Figure 5). The patterns of slope class distribution within fields were most accurately repsented by 10 m filtered DEM (Figure 5).

60

SURVEY 30 M 10 M lO/(Fl:lO/(F2)

METHOD

Figure 5. Percentage of total area of two study

fields correctly classified into respective slope

classes using several methods--soil survey

(survey), 30 m DEM (30 M), 10 m DEM (10 M), and

10 m DEM filtered once and filtered twice,

respectively (lo/F1 and lO/F2).

The precision of GIS produced special use maps

cannot be expected to exceed the precision of baseline data. Standard U.S.G.S. 30 m DEM have a

vertical accuracy with 7 to 15 m standard deviation

(Elassal and Caruso, 1984). Field measurements

showed the 10 m DEll used in Atchison County had

1.4 m standard deviation. The finer grid of the

10 m DEM allowed a more detailed protrayal of the

subtle relief in the study area. Concavities and

convexities on the ridges and backslopes were

observed and their slope classes were measured

(Figure 6).

Figure 6. . Slope class maps produced with several

methods. Study site was a 17 ha field in Atchison

County, MO.

A . Three-dimensional surface contour

of a 16 ha field. Produced from 10 m

DEM with GIS. Total relief in the field

was 37 m.

B. Field measured slope

classes. Each cell is

10 m square.

Figure 6. (continued)

SLOPE CLASS LEGEND

c l

O-2%

q 3-5%

6-Y%

10 - 14 %

15 - 25 %

Greater than 25 %

C. Slope class map D . Slope class map

produced in soil survey. produced with GIS and

30 m DEM.

E . Slope class map

produced with GIS and

filtered 10 m DEM.

Other applications of GIS and DEM for assessing

site quality

A second project in Missouri is being conducted

to test the hypothesis that computer-generated

slope and aspect maps from 10 m DEM can be used to

increase the speed and precision of field soil

survey activities. A second hypothesis is that

soil variance, once established, can be extrapolated across similar geomorphic surfaces with

GIS.

127
Representative landforms will be identified with

GIS. The representative landforms will be intensively sampled and soil properties important to

water retention and movement and to plant root

distribution will be measured. The variance of

soil properties will be determined for specific

geomorphic surfaces. Soil raxonomic units will be

determined on the basis of the intensive sampling.

The measured variance will then be extrapolated

to similar landscapes using GIS. Randomly selected

geomorphic surfaces will be sampled to analyze

the goodness of fit of the extrapolated data.

Computer cartigraphic techniques will be used to

integrate mapping units with geomorphic surfaces.

The soil survey will contain soil data in tabular

form. Means, variances, standard deviations, and

confidence intervals of specific soil properties

will be included in the tables. Mapping unit

composition will be statistically evaluated on the

basis of limiting and non-limiting inclusions.

Should the Boone County exercise be successful,

similar techniques will be employed in the soil

survey of approximately 20 counties in the Ozark

Uplift area of Missouri. These counties contain

approximately 5.3 million ha of dissected, mostly

forested topography. Plans are underway to

eliminate the traditional county-by-county administrative structure and to map by geophysical

provinces determined by geology, geomorphology,

vegetation, climate, and land use, The objective

of the effort would be to use GIS and DEM to enhance the quality of the survey effort, with

specific emphasis on evaluating forest site

productivity.

Much of this area is remote. The use of GIS to

identify representative landscape patterns would

be important. Survey crews could intensively

sample those areas with easy access, and GIS could

be used to extrapolate the information across remote areas. Random field sampling would be used to

verify and test the limits of the extrapolated

data.

The major land uses in this region are recreation, forestry, and pasture for forage. The soillandscape information would be collected with these

uses in mind. The survey would be enhanced with

several watershed studies in which representative

geomorphic surfaces within watersheds would be

instrumented to measure seasonal distribution of

water within landscape elements. A range of watersheds would be selected to represent the major land

resources in the region. Watersheds would be

selected on the basis of their topographic features,

soils and quality and composition of timber stands.

Stem analysis would be used to correlate forest

productivity to soil-landform units. The plans for

this activity are being discussed by state and

federal agencies including the Soil Conservation

Service (SCS), U.S. Forest Service (USFS), the

Missouri Department of Conservation (MDC) and the

Missouri Department of Natural Resources (MDNR).

The project would involve acquisition of 10 m or

15 m DEM for the entire area.

The DEM would be generated from high resolution

satellite imagery. If this effort were successful,

the entire survey would be on a digital data base

which would include slope-aspect information

generated from the DEM. Soil survey uskrs would

then have access to only the data they needed, and

would be able to obtain custom made special use

maps for the area of interest. The MDNR and SCS

would jointly administer the data base.

CONCLUSIONS

Soils and forest site productivity are highly

correlated with landtype (geomorphic surfaces) and

stratigraphy. Traditional efforts to estimate site

productivity have suffered from a lack of precision

in quantifying the soil resource and relating soil

properties to species-specific site requirements.

The technologies of GIS and DEM offer the opportunity to more closely weld soil units to landtypes and to construct soil system databases which

contain the kinds of data useful for prediction,

planning, and management. The potential of GIS

and DEM as research tools should not be overlooked

in the haste to embrace their cartographic and

data base management attributes.

LITERATURE CITED

Bull, H. 1931.

determining s

plantations.

43:1-28.

The use of polymorphic curves in

ite qua1ity in young red pine

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Burrough, P.A. 1986. Principles of geographical

information systems for land resources assassment. Oxford: Clarendon Press. 194 pp.

Carmean, W.H. 1965. Black oak site quality in

relation to soil and topography in southeastern

Ohio. Soil Science Society of America

Proceedings 29:308-312.

Carmean, W.H. 1975. Forest site quality evaluation in the United States. Advances in Agronomy

27 :209-258.

Cline, M.G. 1961. The changing model of soil.

Soil Science Society of America Proceedings

25 ~442-446.

Coile, T.S. 1952. Soil and the growth of forests.

Advances in Agronomy 4:329-398.

Comerford, N.B.; Kidder, G.; Mollitor, A.V. 1984.

Importance of subsoil fertility to forest and

non-forest plant nutrition. In: Stone, E.L.,

ed. Forest Soils and treatment impacts.

Proceedings, Sixth North American Forest Soils

Conference, Knoxville, TN: pp. 381-404.

Crowther, E.M. 1953. The skeptical soil chemist.

Journal of Soil Science 4:107-122.

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Dan, J.; Yaalon, D.H. 1968. Pedomorphic forms

and pedomorphic surfaces. In: Ninth International Congress of Soil Science Transactions;

Vol. 4, Adelaid, Australia: Elsevier

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northeastern Iowa. Journal of Forestry

49:719-723.

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elevation models. U.S. Geological Survey

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Estes, J.S. 1990. Geographic information systems:

an introduction. Englewood Cliffs, NJ:

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Esu, I.E.; Grigal, D.F. 1979. Productivity of

quaking aspen. Soil Science Society of

America Journal 43:1189-1192.

Evans, C.V.; Franzmeier, D.P. 1986. Saturation,

aeration, and color patterns in a toposequence

of soils in north-central Indiana. Soil

Science Society of America Journal 50:975-980.

Farnsworth, C.E.; Leaf, A.L. 1965. An approach

to soil-site problems: Sugar mapel-soil

relations in New York. In: Youngerberg, C.T.,

ed. Forest soil relationships in North America.

Proceedings Second North American Forest Soils

Conference, Corvalis, OR: University Press.

Graney, D.L. 1977. Site index prediction for red

oaks and white oaks in Boston Mountains of

Arkansas. Research Paper SO-139. U.S. Depart;

ment of Agriculture, Forest Service, Southern

Forest Experiment Station.

Grigal, D.F. 1984. Shortcomings of soil surveys

for forest management. In: Bockheim, J.G.,

ed. Proceedings Symposium Forest Land

Classification: Experience, Problems,

Perspectives: 1984 March, Madison, WI:

pp. 148-164.

Hammer, R.D.. 1986. Soil variability, soil water,

and forest tree growth on three forested

Cumberland Plateau landtypes. Knoxville, TN:

University of Tennessee. 319 pp. Dissertation.

Hammer, R.D.; Astroth, J.H.; Henderson, G.S.;

Young, F.J. (In press) Applications of

geographic information systems and digital

elevation models for soil survey and land use

planning. In: Special Publication, Spatial

variability and map units for soil surveys.

American Society of Agronomy, Madison, WI.

Hammer, R.D.; Philpot, J.W.; Maatta, J.M. 1991.

Applying principal component analysis to soillandscape research: Quantifying the subjective.

In: Proceedings 1990 Kansas State University

Conference on Applied Statistics in Agriculture,

Manhattan, KS.

Hannah, P.R. 1968. Estimating site index for

white and black oaks in Indiana from soil and

topographical factors. Journal of Forestry

66:412-417.

Henderson, G.S.; Hammer, R.D.; Grigal, D.F. 1990.

Can measurable soil properties be integrated

into a framework for characterizing forest

praductivity? In: Gessel, S.P. (and others)

ed. Sustained productivity of forest soils.

Proceedings Seventh North American Forest Soils

Conference, Vancouver, BC: pp. 137-154.

Hendrix, W.D.; Price, J.W. 1986. Application of

geographic information systems for assessment

of site index and forest management constraints.

In: Proceedings, Geographic Information

Systems Workshop. 1986 April; Atlanta, GA:

pp. 262-272.

Huddleston, J.H. 1984. Development and use of

soil productivity ratings in the United States.

Geoderma 32:297-317.

Huddleston, J.H.; Riecken, F.F. 1973. Local

soil-landscape relationships in western Iowa:

I . Distributions of selected chemical and

physical properties. Soil Science Society of

America Proceedings 37:264-270.

Huggett, R.J. 1975. Soil landscape systems: A

model of soil genesis. Geoderma 13:1-22.

Ike, A.F.; Huppuch, C.D. 1968. Predicting tree

height growth from soil and topographic site

factors in the Georgia Blue Ridge Mountains.

Georgia Forest Research Council Research Paper

p. 54.

Jenny, H. 1980. The soil resource origin and

behavior. New York, NY: Springer-Verlag.

377 pp.

Jones, J.R. 1969. Review and comparison of site

evaluation methods. Research Paper RM-51.

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Rocky Mountain Forest Experiment Station.

Loftus, N.S. 1971. Yellow-poplar root development on Hartsells subsoils. Research Note

so-131. U.S. Department of Agriculture Forest

Service, Southern Forest Experiment Station.

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assessment of soil erosion and nonpoint source

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pollution. Journal of Minnesota Academic

Science 55:18-25.

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Powers, R.F. 1987. Predicting growth responses

to soil management practices: Avoiding

"future shock" in research. In: Future

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Science Society of America Golden Anniversary

Papers, New Orleans, LA: Soil Science Society

of America, Madison, WI. pp. 391-403.

Pregitzer, K.S.; Barnes, B.V.; Lemme, G.D. 1983.

Relationship of topography to soils and

vegetation in an upper Michigan ecosystem.

Soil Science Soaiety of America Journal

47:117-123.

Raper, .I. (ed). 1989. Three dimensional applications in geographical information systems.

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Limitations of the use of vegetation. In:

Bockheim, J.G., ed. Proceedings Symposium

Forest Land Classification: Experience,

Problems, Perspectives: 1984 March, Madison, WI:

pp. 132-147.

Ruhe, R.V. 1975. Geomorphology. Boston, MA:

Houghton Mifflin, Co.

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theory of soil genesis. Soil Science Society

of America Proceedings 23:152-156.

Smalley, G.W. 1967. Soil-site relations of

upland oaks in north Alabama. Research Note

SO-64, U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station.

Smalley, G.W. 1984. Landforms: A practical

basis for classifying forest sites in the

interior uplands. In: Proceedings Twelth

Annual Hardwood Symposium. Hardwoods Research

Council, Cashiers, NC. pp. 92-112.

Soil Survey Staff. 1975. Soil taxonomy: A basic

system of soil classification for making and

interpreting soil surveys. Agricultural

Handbook 436. Washington, DC; U.S. Department

of Agriculture. 754 pp.

Stone, E.L. 1978. A critique of soil moisturesite productivity relationships. In:

Balmer, W.E., ed. Proceedings Soil MoistureSite Productivity, Atlanta, GA: U.S. Department

of Agriculture, Forest Service. pp. 377-387.

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and cartographic modelling. Englewood Cliffs, NJ:

Prentice-Hall. 249 pp.

Van Lear, D.H.; Hosner, J.F. 1967. Correlation

of site index and soil mapping units poor for

yellow-poplar in southwest Virginia. Journal

of Forestry 65:22-24.

Wilding, L.P.; Drees, L.R. 1983. Spatial

variability and pedology. In: Wilding, L.P.

(and others) ed. Pedogenesis and soil taxonomy:

I . Concepts and interactions. New York, NY:

Elsevier. pp. 83-113.

Young, F.J.; Maatta, J.M.; Hammer, R.D. (in press)

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130
POSTER PRESENTATIONS

SAMPLING MAP UNITS TO CHARACTERIZE FOREST SITE QUALITY1

Charles J. Everett and John H. ThorpZ

Abstract.--Eleven soil map units which can be identified and

delineated using conventional soil survey procedures were

characterized as to loblolly pine (Pinus taeda L.) procluctivity. Four study sites, each 4 hGe=size and

containing four permanent 0.04 hectare measurement plots,

were established for every map unit studied. For eight of

the eleven map units, there is greater than 80% probability

that estimated mean site index (age 15) is within f0.65

meters of the actual population mean. In addition, loblolly

pine height growth curves were constructed for six soil map

units by destructive stem analysis of 32 plantation-grown

trees per map unit. Geologic landform and soil drainage

class had significant effects upon curve shape.

INTRODUCTION

Most soils of the South Carolina Lower

Coastal Plain are derived from Quaternary age

marine and fluvial sediments. Five depositional environments are recognized: deltaic

plains, barrier island and bar hills, shelf

plains, marsh plains, and valley systems.

Soils formed in former marsh plains are the

most productive, while soils in old barrier

islands and bar hills are generally the least

productive.

Post-depositional weathering is also a

factor influencing forest site quality.

Sediments found further inland at relatively

high elevations on the Wicomico and Penholoway terraces are considerably older than the

Talbot, Pamlico, and Princess Anne terraces

found closer to the coast. Soils derived

from older sediments tend to be acid and

infertile due to weathering, regardless of

the original character of the sediment.

Soils derived from young sediments, however,

may be fertile.

Where Quaternary deposits are thin, the

older sediments of Tertiary age influence

soil properties. Deposits of phosphate rock

are associated with the base of the

1 Paper presented at Ecological Land Classification: Applications to Identify the

Productive Potential of Southern Forests

Symposium, Charlotte, NC, January 7-9,

1991.

' Research Soil Scientist and Soil Classification Scientist, Westvaco Forest Science

Laboratory, Summerville, SC.

Quaternary sediments , particularly where in

contact with the top of the Cooper Marl.

Morphologically similar soils have been shown

to differ greatly with respect to site quality depending on whether or not phosphate rock

was present at a shallow depth (Ellerbe and

Smith, 1963).

FIELD PROCEDURES

This research project attempts to characterize the site quality of selected soil

mapping units in the context of intensive

culture of loblolly pine. These soil map

units are special integrations of geomorphic

lanclform, depositional environment, landscape

position, understory vegetation, and fertility. In relation to the USDA-SCS Soil Taxonomy, many of our soil map units are phases of

soil series designed for site-specific intensive forestry.

A total of 218 loblolly pine plantations

were identified as candidate study sites that

had the following characteristics: planted

between 1968 and 1974, bedded, adequately

drained if wet, phosphorus fertilized if

needed (based on soil test), and neither

thinned nor burned hot enough to damage

trees.

For each soil map unit studied, four pine

plantations were randomly selected. A soil

map of each pine stand was drawn, and a four

hectare study area was randomly located within the map unit delineation being investigated. Four permanent 0.04 hectare measurement plots were randomly placed within each

four hectare study site. Measurement plots

were not placed on windrows, within 40 meters

of a major ditch, within 20 meters of a road,

nor within 10 meters of a map unit boundary.

133
ob

I

ob

I

cd

t

I

Range Mean * S.E.

Map unlls ordered low to N* rite hdex

Figure 1. Site quality by map unit. Mean site indices of map units labelled

with the same letter are not significantly different (P = 0.05).

Site index at age 15 was calculated for each

plot using average height of all dominant and

codominant trees, plantation age, and a single set of site index curves.

For six soil map units, stem analysis was

performed on 32 trees (four trees per plot;

two plots per location). These trees were

within four rows of the measurement plot, had

a height within one standard deviation of the

mean height of all dominant and codominant

trees on the measurement plot, and were free

of major physical, insect, or fungal damage.

Annual height measurements were taken at the

end of the summer flush. Disks were cut to

verify height-age pairs with a ring count.

RESULTS AND DISCUSSION

Nested analyses of variance were used to

identify sources of variation at three levels: (1) map units; (2) locations within map

units; and (3) plots within locations. In

Figure 1, similar letters indicate no significant difference among map unit mean site

indices according to Student-Newman-Keuls

multiple range test (P = 0.05). For eight of

the eleven map units studied, there is

greater than 80% probability that estimated

mean site index (age 15) is within to.65

meters of the actual population mean. This

level of accuracy is considered satisfactory

for management decision-making.

To identify polymorphic height growth patterns the stem analysis data were first standardized. All height data for a map unit

were multiplied by a constant, which set the

map unit's mean height at age 13 equal to

100% relative height. Height growth curves

were then described mathematically using a

modification of the Chapman-Richards model.

Differences in height growth curve shape were

evaluated using F-tests comparing full versus

reduced regression models for standardized

data.

Height growth patterns for Map Units A and

I were not significantly different (P =

0.05), nor did growth curve shapes differ

significantly for Map Units F and K. The

soils of Map Units A and I are similar in

texture and drainage class, but occur on

different geologic terraces. The soils of

Map Units F and K occur adjacent to one

another in the same landscape, but differ by

one drainage class. All other comparisons

indicated significant differences (P < 0.05)

in height growth curve shape among map units.

If two general families of site index curves

are desired, A-I and D-F-J-K would be the

best groupings of the map units studied. The

two groups of map units differ in that A and

I are coarser textured than D, F, J, or K.

CONCLUSIONS

Map unit mean site indices can be estimated with a high degree of accuracy. For eight

of the eleven map units studied, there is

greater than 80% probability that estimated

mean site index (age 15) is within to.65

meters of the actual population mean. Significant differences (P < 0.05) in map unit

mean site indices were detected.

Height growth patterns for loblolly pine

plantations vary significantly by map unit.

In this study, six soil map units exhibited

four statistically distinct growth curve

shapes. Two general groups of height growth

patterns were related to the subsoil texture

of their underlying soils.

LITERATURE CITED

Ellerbe, C. M. and G. E. Smith, Jr. 1963.

Apparent influence of phosphate marl on site

index of loblolly pine in the Lower Coastal

Plain of South Carolina. Journal of Forestry

61: 284-286.

134
PLACING "MAN" IN REGIONAL LANDSCAPE CLASSIFICATION:

USE OF FOREST SURVEY DATA TO ASSESS HUMAN INFLUENCES

FOR SOUTHERN U.S. FOREST ECOSYSTEMS'

Victor A. Rudis and John B. Tansey2

Abstract. --Information from plots surveyed by U.S.

Department of Agriculture, Forest Service, Forest

Inventory and Analysis (FIA) units provides a basis for

classifying human-dominated ecosystems at the regional

scale of resolution. Attributes include forest stand

measures, evidence of human influence, and other

disturbances. Data from recent FIA surveys suggest that

human influences are common to selected forest areas of

the Southern United States and that these influences need

to be addressed in regional ecological land

classification and productivity estimation.

Keywords: disturbances, forest productivity, harvest,

management, ownership.

INTRODUCTION

Identifying the best areas to grow trees or to

maintain viable forest ecosystems is clearly

useful for regional planners, conservation groups,

tax assessors, and private landowners. The best

forest areas, however, often are defined relative

to surveys of limited areas and are not easily

discerned from existing vegetation or local stand

conditions.

At the stand level, a simple method to classify

a forest stand's productive capacity is to

estimate the biomass of interest in relation to

its age. However, partial disturbances in many

forests reduce the use of this technique to

undisturbed stands. At the landscape level, stand

history of extensive forested areas is rarely

known. In addition, periodic disturbances,

historic settlement patterns, land use practices,

and forest fragmentation vary widely.

In the Southern United States, nearly all

forests have been cut at some time. Historic

1

Presented at the Symposium on Ecological

Land Classification: Applications to Identify the

Productive Potential of Southern Forests,

Charlotte, NC, January 7-g. 1991.

2

Research Forester, Forest Service, U.S.

Department of Agriculture, Southern Forest

Experiment Station, Starkville. MS 3975q-OgO6; and

Forester, Forest Service, U.S. Department of

Agriculture, Southeastern Forest Experiment

Station, Asheville, NC 28802-2680. respectively.

settlement and land-clearing practices have

eliminated much of the natural forest vegetation.

In forested areas, disturbances such as commercial

harvesting, livestock grazing. prescribed fire,

noncommercial firewood cutting, and land-clearing

activities continue to influence species

composition of remaining stands. Adjacent

nonforest land uses, operability for timber

harvesting, relative access for multiple uses, and

relative remoteness all contribute to the mosaic

of forest cover at the landscape level.

Data are available to conceptualize the

regional importance of these features. Information

is derived from the forest survey data base

maintained and updated by the U.S. Department of

Agriculture, Forest Service, Forest Inventory and

Analysis (FIA) units. Disturbances and uses are

inferred from prevailing land ownership, adjacent

land uses, physical features, and evidence of

human activities in forested areas. Field

observations and calculated attributes are

described in FIA field manuals and data base

documents.

RESULTS AND CONCLUSIONS

Forest land dominates in selected areas of the

southern Coastal Plain, Appalachian Mountains,

and Interior Highlands. Forest cover has been

eliminated or reduced in major urban counties, and

along the lower Mississippi River floodplain and

other important agricultural regions (figure 1).

More than 30 percent of the forested land has been

harvested since the last survey of about a decade

earlier. Commercial harvest activity is highest

135
Figure 1. --Percent forest area by county,

1982-1989 surveys, Southern United States.

along the southern Coastal Plain and lowest in

southern Florida, the lower Atchafalaya River

Basin in Louisiana, and western Virginia (figure

2).

In addition to the data illustrated here,

related information is available on the regional

distribution of ownership (Rosson and Doolittle

1987). fire occurrence (Rudis and Skinner, in

press), harvest activities (McWilliams 1989), and

remote areas (Rudis 1986). Trends in forest type,

ownership, and area are discussed by Alig and

others (1986), while numerous State-level forest

resource assessments provide additional statistics

on timber productivity and disturbance

characteristics (e.g., Bechtold and others 1990,

Rudis 1988).

The information above summarizes a more

extensive presentation depicting the distribution

of human influences in Southern United States

forests. Only a portion of the data is illustrated

in this report. The extent and distribution of

forested areas and related disturbances suggest

that human influences are important in selected

areas of the Southern United States and that these

influences need to be incorporated in regional

ecological classification and in the estimation of

forest productivity.

LITERATURE CITED

Alig, Ralph J.; Knight, Herbert A.; Birdsey,

Richard A. 1986. Recent area changes in

southern forest ownerships and cover types.

Res. Pap. SE-260. Asheville, NC: U.S.

Department of Agriculture, Forest Service,

Southeastern Forest Experiment Station. 10 p.

Bechtold, William A.; Brown, Mark J.; Sheffield,

Raymond M. 1990. Florida's forests, 1987.

Resour. Bull. SE-110. Asheville, NC: U.S.

Figure 2. --Percent forest area with recent

commercial harvest activity by county, 1982-1989

surveys, Southern United States.

Department of Agriculture, Forest Service,

Southeastern Forest Experiment Station. 83 p.

McWilliams, William H. 1989. Residual pine

stocking after harvest on private timberland: a

summary for six Southern States. Res. Pap.

SO-252. New Orleans, LA: U.S. Department of

Agriculture, Forest Service, Southern Forest

Experiment Station. 12 p.

Rosson, James F., Jr.; Doolittle, Larry. 1987.

Profiles of Midsouth nonindustrial private

forests and owners. Resour. Bull. SO-125. New

Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment

Station. 39 p.

Rudis, Victor A. 1986. Emerging patterns in the

distribution of roadless forested areas in the

Midsouth. In: Kulhavy. David L.; Conner,

Richard N.. eds. Wilderness and natural areas

in the Eastern United States: a management

challenge: Symposium proceedings: 1985 May

13-15; Nacogdoches, TX. Nacogdoches, TX:

Stephen F. Austin State University, Center for

Applied Studies, School of Forestry: 265-270.

Rudis, Victor A. 1988. Nontimber values of east

Texas timberland. Resour. Bull. SO-139. New

Orleans, LA: U.S. Department of Agriculture,

Forest Service, Southern Forest Experiment

Station. 34 ,p.

Rudis, Victor A.; Skinner, Thomas V. [In press].

Fire's importance in South Central U.S.

forests: distribution of fire evidence, In:

Nodvin, Stephen C., ed. Fire and the

environment: ecological and cultural

perspectives: an international symposium; 1990

March 20-24; Knoxville, TN. Asheville, NC: U.S.

Department of Agriculture, Forest Service,

Southeastern Forest Experiment Station. 15 p.

136
TIMBERLAND DOWNTOWN?:

SOUTHERN FOREST RESOURCES ALONG THE URBAN-RURAL CONTINUUMl/

Christopher E. DeForest, Thomas G. Harris, Jr., Frederick W. Cubbage, and Arthur C. Nelson21

Abstract.--An Urban-Rural Continuum provides a simple, practical way to classify forest resources and estimate the size and extent of

the urban forest. Metropolitan counties contain over 26 percent of the Southeast’s timberland acreage-about 28 million acres--and over

26 percent of its standing sawtimber and softwood and hardwood growing stocks. A ten-point Urban-Rural Continuum which codes

counties from most highly urbanized to most rural allows even more precise stratification of timber resources.

Keywords: Continuum codes, urban forest, timberland, timber availability, land-use planning.

INTRODUCTION

We used forest inventory data and Urban-Rural Continuum

Codes to calculate what portion of the Southeast’s forest

resources lie within the “urban forest,” narrowly ti

expansively defined. Assigning “timber management”

probabilities to zones of the urban forest would enable foresters

to estimate how much of the inventoried forest will actually be

harvested and retained as timberland. Similarly, urban foresters

and rural planners can use the codes to identify, assess, and

manage the “urban forest,” writ small or large.

DATA

Countv Classification

We used the USDA Economic Research Service’s UrbanRural Continuum Codes (Butler 1990) for counties in the

southeastern US. The Census Bureau has divided all US

counties into metropolitan and non-metropolitan counties;

metropolitan counties make up Metropolitan Statistical Areas

(MSAs). The Urban-Rural Continuum further divides metro

counties into four codes (0 to 3), and non-metro counties into

six codes (4 to 9), as defined below.

Forest Statistics

We used the most current forest survey Resource Bulletins

from the USDA’s Southeastern and Southern Forest Experiment

Stations. Alabama data is from 1990; Florida from 1987/88;

Georgia from 1989; South Carolina from 1986187; North

Carolina from 1984 and 1990; and Virginia from 1985/86. The

Resource Bulletins include data on the number of acres of

timberland in each county; the standing sawtimber volume; and

the softwood and hardwood growing stock volumes. The same

Bulletins provided data on timber growth and removals.

l/Presented at the Ecological Land Classification: Applications to

Identify the Productive Potential of Southern Forests

symposium, Charlotte, NC, January 7-9, 1991.

2lResearch Coordinator, Professor, and Associate Professor,

School of Forest Resources, University of Georgia, Athens GA;

and Associate Professor, Graduate City Planning

ProgramCollege of Architecture, Georgia Institute of

Technology, Atlanta GA.

URBAN-RURAL CONTINUUM CODES (from Butler 1990)

Code Metro counties:

0 Central counties of metro areas of 1 million population or

more

1 Fringe counties of metro areas of 1 million or more

2 Counties in metro areas of 250,000 to 1 million

3 Counties in metro areas of fewer than 250,000

Nonmetro counties: ’ 4 Urban oonulation of 20.000+. adiacent to a metro area

5 Urban &pohulation of 2d,OOO+; not adjacent

6 Urban population of 2,500-19,999, adjacent to a metro area

7 Urban population of 2,500-19,999, not adjacent

8 Completely rural or ~2,500 urban population, adjacent to a

metro area

9 Completely rural or ~2,500 urban population, not adjacent

toametroarea

METHODS

Cou Q Codes

Wi used MapMaker software to display counties according to

their Urban-Rural Continuum Codes, as displayed in this

symposium’s poster session. MapMaker allowed us to lump

different county codes together, to show the cumulative effects

of broadening the “urban” areas beyond the central urban

counties. Counties coded 0 through 3 comprise the Metropolitan

Statistical Areas (MSAs).

. . Fo est Qatm~s

We set up Quattro spreadsheets for each southeastern state and

entered the county-level data on timberland acreage, standing

sawtimber volume, and softwood and growing stock volumes.

(We combined hardwood and softwood species to arrive at

standing sawtimber volume). Another column had each

county’s Urban-Rural Continuum code.

s

We then sorted the krest s;tistics spreadiheets?y UrbanRural county code, to calculate the percent of the Southeast’s

forest resources in each code, and in groups of codes

encompassing broader notions of “urban”. We also compared

timber growth to timber removals in all southeastern wunties,

focusing on counties within MSAs.

137
RESULTS

Highly urban counties account for little of the Southeast’s forest

resources; but the “urban forest” becomes a mificant

c o ponent as one broadens the definition of urban. Table 1

prlents the portion of the Southeast’s forest resources within

the single most urban counties (coded 0), the two most urban

groupings (coded 0 or l), the three most urban, and so on.

Recall that counties coded 0 to 3 comprise Metropolitan

Statistical Areas; the MSAs contain 28 million acres of the

Southeast’s timberland, or about 26%. The MSAs also have

approximately 26% of the Southeast’s standing sawtimber

volume (softwood and hardwood combined), and approximately

26% of the Southeast’s softwood growing stock and hardwood

growing stock volume.

We also found that timber growth exceeded timber removals in

116 of the 153 metro counties in the Southeast, or 76% (four

Florida counties and one Virginia county lacked timber growth

and removal data). In the other 37 counties, timber removals

exceeded net annual growth.

CONCLUSIONS

Is the Urban-Rural Continuum a useful way to classifi the

productive potential of forests? YES, especially as a quick first

cut on where to--and not to--put effort into evaluating the

biolopical productive potential.

Foresters should note that timber availability probably parallels

the Urban-Rural Continuum. Timberland in the urban core

probably won’t be harvested; timberland in the suburbs or

exurbs may or may not be. Timber removals there may result

from land clearing and conversion of timberland to “higher and

better” uses. Foresters relying on forest survey statistics may

choose to reduce their own estimates of current and future forest

resources accordingly.

Urban foresters and planners can classify and manage the

urban forest along the Urban-Rural Continuum. It may help

them anticipate public demands on urban--and sprawling

suburban--forests, where amenity preferences may outweigh

commodity production. The Continuum--and a suitably

expansive vision of the urban forest--could guide land-use

planning by state and local governments.

DIRECTIONS FOR RESEARCH

1) Test the hy

the Urban-Rural?

thesis that actual timber availability parallels

Continuum:

Analyze timberland values along the Continuum

Analyze changes in land-use and cover type in urban

areas

Locate counties with tree protection ordinances or local

logging regulations

Add “management objectives” query to forest survey

data form, focusing on small forest landowners

2) Based on timber removal and timberland conversion trends

along the Continuum, estimate correction factors for true

availability of forest resources for harvest.

3) Integrate Continuum method with other classification

schemes. To identify possible timberland purchases or mill

sites, perhaps first winnow out land in counties considered “too

urban’~, then apply an ecologically-based method for identifying

biological productivity potential.

4) Arrange Conservation Reserve Program (tree planting) data

along the Urban-Rural Continuum and predict future timber

supplies.

Acknowledgements: The authors wish to thank research

assistants Charles 0. Bailey, Jr., Samuel C. Carlton, and

Xiaowen Tao of the School of Forest Resources for their help.

This study was funded in part by the Georgia Forestry

Commission and the School of Forest Resources.

Lrl?ERATuREcITED

Butler, Margaret A. April 1990. Rural-urban continuum codes

for metro and nonmetro counties. Washington DC: U.S.

Department of Agriculture Economic Research Service,

Agriculture and Rural Economy Division. Staff Report No.

9028. 38 pp.

Table l.--Percentage of Southeastern USd timberland acreage, standing sawtimber volume, and softwood and hardwood growing stock

within each Urban-Rural Continuum Codebland groups of codes. Percentages are listed singly for each county code, and cumulatively

beginning with counties coded 0 (the most highly urbanized counties). Counties coded 0 through 3 comprise Metropolitan Statistical

Areas (MSAs).

URBAN-RURAL CONTINUUM CODE

Metro Counties

0 1 2 3

Timberland area

Percent

Cumulative % E

Sawtimber Volume

Percent

Cumulative % :*oo

Softwood Growing Stock

Percent 1.0

Cumulative % 1 .O

Hardwood Growing Stock

Percent

Cumulative % 2:

2.4 13.9

3.0 17.0 2;:;

3.1 14.2

4.1 18.3 2:::

2.6 3.6 14.682 268:;

3.6 13.7

4.4 18.1 2::;

Nonmetro Counties

5 6 7 8 9 Total

3::; 24.4 59.4 78.9 19.5 90.2 11.4 100% 9.8 100%

354:; 23.9 59.8 78.7 18.9 89.7 11.0 10.3 100% 100%

354:: 24.5 60.0 79.2 19.2 90.5 11.3 lZ% 100%

364:: 23.9 60.2 77.1 16.9 88.4 11.3 100% 11.6 100%

a/ Alabama, Florida, Georgia, South Carolina, North Carolina, and Virginia

b/ adapted from Butler (1990)
S. J. TajchmanL'

Abstract.--Spatial variation of site factors in a forested

Appalachian watershed is considered. Within forest types,

specific site parameters can be defined for partial areas

of irregular shape randomly distributed in the watershed.

A three-dimensional analysis of landscapes and statistical

approach to forest land classification are proposed.

Keywords: Topography, site factors, spatial variation.

Foresters attempt to relate the productive

capacity of forest land to site factors such as

topography, soil, climate, and others. In the

central Appalachians the best sites are usually

found on lower slopes, north and east aspects,

and on gentle concave-shaped slopes; poorer

sites usually are found on narrow ridges and

upper slopes, on south and west aspects, and on

steep convex-shaped slopes.

Site evaluations can be regarded as point

observations and their extrapolation into

characteristic landscape units was considered

by Carmean (1970). With this in mind, spatial

distributions of important site factors in the

forested Appalachian watershed were obtained

(Boyles, 1983; Minton and Tajchman, 1990;

Tajchman and Wiant, 1983; Tajchman et al., 1988).

A detailed description of the study area is given

by Tajchman (1981).

The results suggest, that traditional evaluations and classifications of forest land may

appear as idealized models missing the actual

conditions. They show that within forest types,

specific site parameters, e.g., thickness of

soil horizons, stoniness, soil moisture, above

ground biomass, can be defined for partial areas

of irregular shape randomly distributed in the

watershed. This can be seen in Fig. 1 based on

data for the A-horizon thickness.

A detailed description of all forest lands

does not seem to be feasible, however statistical

characterstics of site factors based on a detailed

analysis of selected landscapes could be obtained

for defined geographic regions. "Partitioning of

L'Professor of Forest Meteorology,

Division of Forestry, West Virginia University,

Morgantown, WV 26506.

Figure l.--The distribution of the actual differences between plot values and the average value

of A-horizon thickness for the entire watershed.

Partial areas characterized by positive or negative signs are separated by isolines representing

the average for the whole catchment (X = 8.8 cm).

The numbers represent average values and standard

deviations of A-horizon thickness in centimeters

for partial areas of the catchment. Adopted from

Boyles (1983).

139
landscapes into logical units based on sound

physiographic or ecological principles" ( Symposium Objective) should be based on a quantitative

analysis.

Topography and other site factors can be

defined in the X, Y, Z - coordinate system

(Tajchman, 1975, Fig. 2).

J

Figure Z.--Forest lands can be analysed in the

X, Y, Z - coordinate system. They can be subdivided into triangular segments regarded as

elements of planes. Computed topographic parameters, and other properties and processes,

for each triangle can be assigned to the

coordinates of its gravity center. This permits

spatial, temporal, and multidimensional analysis

of forest lands, and delineation of sites showing

specific properties. The above diagram is after

Tajchman (1975).

LITERATURE CITED

Boyles, R.L. 1983. Analysis of stoniness and

rockiness in a forested catchment in north

central West Virginia. Morgantown: West

Virginia University. 142 pp. Thesis.

Carmean, W.H. 1970. Site quality for eastern

hardwoods. Inr The silviculture of oaks and

associated species. U.S.F.S. Res. Pap. NE434, 27 pp.

Minton, C.M. and S.J. Tajchman. 1990. Soil

moisture characteristics of forested slopes

in the central Appalachians. Forestry Notes,

W.V. Univ. Agric. For. Exp. Stn., Vol. 14.

In press.

Tajchman, S.J. 1975. On calculating the horizon

limitation and the short wave radiation income

for a mountainous area. Rivista Italiana di

Geofisica, Vol. 1 (Speciale): 174-180.

Tajchman, S.J. 1981. On computing topographic

characteristics of a mountainous catchment.

Can. J. For. Res., ll(4): 768-774.

Tajchman, S.J. and H.V. Wiant, Jr. 1983. Topography and biomass characteristics of a

forested catchment in northern Appalachians.

For. Ecol. Manage., 5: 55-69.

Tajchman, S.J., Harris, M.H. and Townsend, E.C.

1988. Variability of the radiative index of

dryness in an Appalachian watershed. Agric.

For. Meteorol., 42: 199-207.

The next stage should involve the corresponding

analysis of the processes of forest growth,

including a wide spectrum of mass and energy

exchanges. Economic value, susceptibility to

disease and weather hazards, insect manifestation,

position of roads and logging areas could be also

considered. Since forests cover a part of the

landscape, classification of forest lands should

be linked to the classifications of remaining

areas.

140
CLASSIFICATION AND EVALUATION OF THE NATCHEZ TRACE STATE FOREST, STATE RESORT PARK,

AND WILDLIFE MANAGEMENT AREA FOR TIMBER AND WILDLIFE HABITA&/

Glendon W. Smalley:/, Kenderick S. Arneyzl,

Lorenda A. Sharberzl, and Hart W. Applegatefti

ABSTRACT

The 45,000-acre Natchez Trace State Forest,

Park, and Wildlife Management Area (NTSF) began

as a land reclamation project of the Resettlement

Administration in 1935. The State of Tennessee

acquired ownership in 1955. Smoother ridges,

moist bottoms, and some slopes were cleared, rowcropped, and pastured resulting in extensive sheet

and gully erosion of the fragile soils. Mixed oak

forest dominate the uplands that were not cleared;

loblolly pine plantations occupy the former gullied

cropland. Bottomland hardwoods occur in the wet

bottoms; some ponded areas support alder thickets.

Most of the erosion has been controlled. Upland

soils are derived from a 2 to 3-foot cap of loess

and/or the underlying loamy and clayey unconsolidated Coastal Plain sediments.

A land classification system has been

developed as part of the overall land management

planning process. The landscape was divided into

25 landtypes based on differences in geology,

topography, soils, vegetation, and stream order.

-11 Poster presented at the Symposium on

Ecological Land Classification: Applications to

Identify the Productive Potential of Southern

Forests, Charlotte, NC, January 7-9, 1991.

-2/Principal Soil Scientist (retired), USDAForest Service, Southern Forest Experiment Station

and Consultant--Forest Land Classification and

Evaluation, Rt. 1, Box 541, Sewanee, TN 37375.

q/Staff Forester and Wildlife Biologist

respectively, Tennessee Wildlife Resources Agency,

Ellington Agricultural Center, P. 0. Box 40747,

Nashville, TN 37204.

61 Assistant State Forester, Tennessee

Division of Forestry, 701 Broadway, Nashville,

TN 37203.

Landtypes are described in terms of nine elements

(geographic setting, dominant soils, parent

material, solum thickness, surface soil texture,

internal soil drainage, relative soil water

supply, soil fertility, and vegetation). Each

landtype is evaluated in terms of productivity

(site index and mean annual cubic growth) and

desirability (most desirable, acceptable, and

least desirable) of selected hardwoods and

conifers for timber production, and for suitability

as wildlife habitat. Also, each landtype is rated

for five problems (plant competition, seedling

mortality, equipment limitations, erosion hazard,

and windthrow hazard), that can affect forest

management operations. The resulting landtype map

is one element of the physical and biological

information about NTSF that is stored on an ESRI

(Environmental Systems Research Institute)

Geographic Information System.

The land classification system permits the

intensive study of the relationships between

forest plant communities and the landscape units.

The ultimate goal of such a study is the capability

of predicting which community(s) grow on each

unit and to ascertain the successional pathways

resulting from various forest cuttings. Once

plant-landscape relationships are known, land

managers can easily and economically determine

wildlife habitat parameters.

Although the land classification was developed

just for NTSF, it is applicable to an estimated 1

to 2 million acres in West Tennessee, northeast

Mississippi, and northwest Alabama.

Key words: forest land classification, site

productivity, erosion, timber management,

wildlife habitat, Upper East Gulf Coastal Plain,

West Tennessee.

141
SOIL/SITE INFLUENCES ON FOREST GROWTH WITHIN THREE

PHYSIOGRAPHIC REGIONS OF TENNESSEE. 1/

P. Alan Mays and Elizabeth R. Smith 2/

Abstract.--Most often, landform classification systems

attempt to rate forest productivity based on data that are

not site specific, Remeasurement data from permanent

forestry plots were used to evaluate one system developed

by the U.S. Forest Service. Considerable agreement exists

between the observed and reported annual growth rates for

selected hardwood species on most landtypes within the study

areas.

Keywords: annual growth rate, landtype, hardwoods

INTRODUCTION

Many attempts have been made to rate forest

productivity and site limitations based on landform classification systems. Most often, the

development of these systems depended on data which

were not site specific. In this study, data

collected from 282 Continuous Forestry Inventory

(CFI) plots within three physiographic regions of

Tennessee were used to evaluate one such system

developed by the U.S. Forest Service (Smalley

1980, 1984) for the classification and evaluation

of sites in the interior uplands.

Three study sites were selected to represent the

forests of the Tennessee Valley Region based on

availability of remeasurement data and physiographic location. The sites selected include:

(a) Emory River Land Company (ERLC) is a private

landholding locateci in the Cumberland Mountains of

eastern Tennessee. Soils have formed in interbedded sandstone, siltstone and shale. ERLC has

the greatest relief with elevations ranging from

323 m to 957 m above sea level. Rainfall averages

148 cm annually. Disturbance at this site has been

primarily due to fire and logging (Smith 1990).

(b) Land Between the Lakes (LBL) is a national

demonstration area managed by TVA and is located

at the interface of the Western Highland Rim and

I/Presented at the Ecological Land Classification

Symposium, Charlotte, NC, January 7-9, 1991.

/Environmental Scientist and Forest

Biometrician/Ecologist, respectively, Tennessee

Valley Authority, Norris, TN.

Coastal Plain Provinces in Tennessee and Kentucky.

Soils have formed in cherty limestone and both

gravelly and clayey Coastal Plain sediments. Many

upland areas have been covered with a blanket of

loess. Annual precipitation totals 131.5 cm. The

greatest amount of disturbance at LBL is due to

logging.

(c) Wayne County (WC) is also located in the

Western Highland Rim and Coastal Plain Provinces in

southwest Tennessee and is comprised of mostly

private landholdings. Soil parent materials are

similar to those of LBL but include the thick beds

of cherty limestone of the Fort Payne Formation.

Climate and past site disturbance is also

comparable to that of LBL.

Within each site, plots were chosen for

reinventory and collection of additional data based

on species composition, tree size, topography,

soils, and the absence of significant recent

disturbance. Plots were natural, second-growth

stands of mixed hardwood species with diameter

distributions that consisted of primarily large

trees (greater than or equal to 28 cm dbh). Soils

were described from opened pits in each plot.

Annual basal increment was estimated using remeasurement data. Species' productivity was compared

with the expected productivity from Smalley's

system using paired t-tests.

RESULTS

The majority of all CFI plots were easily

adapted to the landtype classification scheme

established by the U.S. Forest Service for these

regions. A limited number of plots at LBL and WC

were placed into landtypes that are more commonly

found in other subregions of the Western Highland
Rim. Over sixty percent of all plots within each

site were concentrated on four distinct landtypes -

narrow ridges, colluvial sideslopes (both north and

south aspect), and concave footslopes/stream

bottoms.

Agreement between the observed and reported

annual growth rates for selected hardwood species

was greatest at the ERLC plots in the Cumberland

Mountains, where only yellow-poplar growth rates

were significantly less on the colluvial north

sideslopes and stream bottom landtypes. Disagreements in annual growth rates at LBL were. confined

to upland oaks in essentially the same landtypes.

The WC plots exhibited the greatest differences

between growth rates of the three sites. Upland

oaks and yellow-poplars on the three most dominant

landtypes had significantly different annual growth

rates. It should be noted that the WC site had

fewer plots per landtype and appeared to have

experienced more recent disturbance than the ERLC

and LBL sites.

Total mean annual growth rates were much higher

at the ERLC site due to higher moisture availability

and a greater proportion of pines, which exhibited

higher growth than associated hardwood species.

Total mean annual growth increased along a moisture

gradient that extended from narrow ridges into the

footslope/stream bottom landtypes. Total annual

growth rates for the WC site ranged from 16.4 cubic

feet per acre for the narrow ridges to 53.4 cubic

feet per acre for the stream bottom plots. By

comparison, the annual growth rates at the Cumberland Mountains plots were 35.6 cubic feet per acre

on narrow shale ridges and 57.9 cubic feet per acre

in the stream bottoms. Total annual growth rates

on those plots at the ERLC site which had a

significant number of pines could exceed 80.0 cubic

feet per acre.

SUMMARY

Smalley's land classification system appears to

be a suitable method for estimating potential

productivity for forest stands within the regions

of our study. Such a system may be useful for

assessing trends in current growth, as well as

providing a gauge with which to measure future

changes in growth and species composition.

LITERATURE CITED

Smalley, Glendon W. 1980. Classification and

evaluation of forest sites on the Western

Highland Rim and Pennyroyal. U.S. Dep. Agric.

For. Serv. Gen. Techn. Rep. SO-30, 120 p. South

For. Exp. Stn., New Orleans, LA.

Smalley, Glendon W. 1984. Classification and

evaluation of forest sites in the Cumberland

Mountains. U.S. Dep. Agric. For. Serv. Gen.

Tech. Rep. SO-50, 84 p. South. For. Exp. Stn.,

New Orleans, LA.

Smith, Elizabeth R. 1990. Temporal and spatial

variability in tree growth response to climate.

Knoxville, TN: The University of Tennessee. 363 p.

Ph.D. Dissertation.

143
SCS FOREST/SOIL DATABASE OF NORTH CAROLINA"

Albert Coffeg'

Keywords: Site index, soil map units, soil profile

description.

Utilization of soil map units is a logical way

to classify forest lands based on their potential

productivity. The USDA Soil Conservation Service

(SCS) uses soil as the base element for determining

site index. Other macro-elements that influence

site index include climate, slope, slope position,

aspect, drainage, and elevation. Micro environmental factors, not always easily identified, also

play a role in forest productivity. Micro environmental factors are not recorded for determining SCS

site index.

SCS maintains a National Forest/Soil Database.

It is a collection of site index plots collected

on specific soil series throughout the nation.

This database enables the user to analyze different

macro-elements using soil as the base. On some

sites, soil is the most identifiable and important

element. On these sites, site index based just on

the soil is uniform and closely defined. On othes

sites, soil, used with a combination of other

macro-elements, more closely defines the site.

This database is used to assign site indices in

published soil surveys, soil interpretations for

the SCS Technical Guide, and for establishing new

soil series. Soil surveys and the Technical Guide

are used by SCS conservationists, foresters, and

landowners for conservation planning, land use

decisions, and species selection. Information from

the Forest/Soil Database is available from SCS

staff foresters within each state.

Recent refinements in soil mapping, particularly

in the mountains, will make soil map units better

indicators of site productivity. These improvements

include expanded recognition of parent material,

aspect, elevation, soil temperature, and rainfall.

In addition, the use of 1 inch = 1000 feet photography allows for delineation of more detailed map

I/Poster paper presented at the symposium on

Ecological Land Classification: Applications to

Identify the Productive Potential of Southern

Forests, Charlotte, NC, January 7-9, 1991.

/Albert Coffey is Forester, Soil Conservation

Service, U.S. Department of Agriculture, Raleigh,

NC.

units than can be done with conventional scale

photographs such as 1 inch = 2000 feet.

In North Carolina site index data collection by

SCS was begun in 1956 when a plot of shortleaf pine

(Pinus echinata Mill.) was collected on Pacolet

soil in Wilkes County. Since then 1,790 plots have

been collected across the state on 203 soil series.

Foresters and soil scientists from SCS, North Carolina Forest Service, U.S. Forest Service, and North

Carolina Division of Soil and Water Conservation

have collected plot data.

Data is collected on form SCS-ECS-005 and includes

plot location in state plane coordinates which are

digitized from 7.5 minute United States Geological

Survey (USGS) topographic maps. Elevation is also

determined from USGS maps. Precipitation at each

plot is determined from rainfall charts for North

Carolina, or more site-specific data is used if it

is available. Slope, slope length, and aspect

(azimuth) are recorded. Upper, middle, and lower

slope positions are also shown.

A detailed soil profile description is prepared

by a soil scientist for each site index plot.

Having a profile description allows the plot data

to be placed in the correct soil series if the

series names are changed during subsequent field

reviews or during correlation. The profile description includes identification and depth of horizons

and any of the features described for horizons

listed in the National Soils Handbook.

Site index is collected on five trees of the same

species in each plot if suitable trees are found.

Trees that exhibit tight rings or abnormal growth

patterns are not used. Only dominant and co-dominant trees are measured since the objective is to

determine potential soil productivity. Other data

recorded includes diameter, height, inches of radial

growth in the last 10 years, understory abundance,

canopy density, and a listing of as many as 12

understory and ground plants. Scientific plant

name symbols are used in data recording.

Standard deviation can be calculated from the SCS

Forest/Soil Database from soil and any combination

of data collected when enough plots are available.

The SCS standard for statistical accuracy is a

coefficient of variation (C.V.) of eight or less.
After the data is collected, it is checked,

edited, and entered by computer into the National

Forest/Soil Database. Information can be retrieved

by any of the fields or combinations of fields on

form SCS-ECS-005. Tables of data by county, state,

or major land resource area are available.

Acknowledgments: The author thanks Sara A. Browning,

Forest Service, U.S. Department of Agriculture; and

James L. Robinson, Soil Conservation Service, U.S.

Department of Agriculture, for contributing data

and suggestions.

LITERATURE CITED:

USDA Soil Conservation Service. 1980. National

Forestry Manual.
POTENTIAL PRODUCTIVITY OF LOBLOLLY PINE PLANTATIONS

IN THE SOUTHEASTERN UNITED STATE&’

S.R. Colbert and H.L. Allen;’

Realized levels of productivity (stemwood

production per unit area per year) for pine

plantations in the Southeastern United States

are substantially lower than potential. Even in

fully stocked stands, existing growth rates

rarely represent a site’s productive cacacity;

growth is typically reduced to a level at which

an adequate supply of the limiting resource can

be maintained to assure proper: physiological

function.

Classification of forest lands based on

potential productivity requires knowledge of the

ecophysiological processes (e.g., the amount of

solar radiation intercepted, the photosynthetic

efficiency of the canopy, the consumption of

fixed carbon to stemwood), as well as the

climatic and site factors (e.g., availability of

light, water, and nutrients, and inherent levels

of heat, CO2, and 02)’ affecting these

processes.

Over the past 25 years, productivity of

southern pines has been significantly increased

through application of silvicultural treatments

such as drainage, fertilization, weed control,

cultivation, thinning, pest management, and

genetic improvement, The North Carolina State

Forest Nutrition Cooperative (NCSFNC) has 15

regionwide studies comprising over 375

installations designed to examine silvicultural

options to improve loblolly pine productivity

during stand regeneration and in established

stands. The goal of this research effort is to

increase the production efficiency of southern

pine stands through the silvicultural

manipulations of site resources.

Results from a poorly drained clay soil in

South Carolina illustrate the potential response

from cultural treatments at time of stand

establishment. Ten-year height and stand volume

responses of loblolly pine to bedding,

1’ Presented at Ecological Land

Classification: Applications to

Identify the Productive Capacity of

Southern Forests, Charlotte, NC, January

7-9, 1991.

21 The authors are Associate

Director, and Associate Professor of

Forestry and Director, respectively, North

Carolina State Forest Nutrition Qodperative, X

State.University, Raleigh, NC 27.695-8008.

d~~‘~:~“~:30~r-’

nd weed control averaged 14 ft

respectively, over the control

Treatment effect: on

.

annual height growth were

most pronounced during the first four years of

growth, although incremental gains continued

through 10 years. The largest response was due

to bedding, followed by fertilization, and weed

control. Annual height growth for the

bedding+fertilizer+weed control treatmfnt

culminated at approximately 4.0 ft yr after 8

years.

Six-year volume responses to N+P

fertilization over a wide range of existing 3

graying conditions range9 fr”T 569 to 911 ft

ac- and averaged 687 ft ac . Strong

incremental gains were observed during the

fifth and sixth years following fertilization;

additional incremental gains appear probable for

future years on these sites. Of a host of

initial stand, site, foliage, and climatic

variables screened& analyses indicate that stand

leaf area, fascicle weight, and foliar N

concentrations were the most highly correlated

with response. In contrast, there was no

correlation between response and stand basal

area.

Seven years after treatment, net volume

growth response to thinning for a 21-year-old

loblolly p3ne

was 370 ft ac

siand on the Cumberland Pla

for all trees and 338 ft 5

eau-1

a c

for crop trees. The combination of fertilization

;I: ;ir;$;gf:,c;z,,ed net volume growth

over the control for all

trees and crop trees, respectively. Crop tree

diameter growth response for this study

increased to a maximum at age 7 of 0.65 inches

for the fertilization+thinning treatment, 0.35

inches for thinning alone, and 0.21 inches for

fertilization alone.

These data demonstrate that southern pine

productivity is not fixed--silvicultural

treatments can dramatically affect the

availability and allocation of soil water and

nutrient resources to enhance growth and yield.

Forest managers have two strategies for

improving pine productivity: 1) accelerate

individual tree growth to shorten the time it

takes for crop trees to achieve or regain full

site occupancy and/or 2) increase the maximum

leaf area attained at full site occupancy.

The amount, display, and duration of leaf

area or foliage biomass largely determines the

quantity of radiation intercepted by forest
canopies. In addition, patterns of carbon

fixation, transpiration, and aboverground

respiration within forest stands are closely

associated with foliage mass or surface area. A

strong, positive, and linear relationship

existed between stemwood growth and leaf area

index in an analysis of three of the Regionwide

13 installations. The ability to intercept

radiation has been hypothesized as a major

determinant of productivity in forest stands.

Theoretical analyses indicate that maximum

aboveground productivity of southern pine stands

;“y;s m-2 be achieved at projected leaf areas of

. . Leaf areas of all but the most

productive southern pine stands are far below

this maximum--values of 1.5 to 3.0 are commonly

measured. Low nutrient availability, water

stress, high temperatures, and elevated ozone

levels have been implicated as factors causing

suboptimal leaf area index and reduced light

interception, resulting in diminished

productivity.

Traditional stocking measures based on basal

area and number of stems are measures of past

performance and may not always provide an

adequated measure of current or potential

productivity. Leaf area represents a measure of

site occupancy that integrates tree size, stand

density, and site resource supply. While

foliage surface area is strongly related to

stocking in young stands, we hypothesize that

leaf area is independent of basal area in fully

stocked stands after canopy closure. Under

these conditions, leaf area can have a wide

range of values depending on site quality and

climatic limitations. The relationship between

1987 cohort leaf surface area and basal area

supports this hypothesis.

Effective stand management requires knowledge

of the factors limiting leaf area and whether

these limitations can be ameliorated. Maximum

site occupancy should be redefined as the leaf

area index sustainable given fixed site

resources (i.e., those that cannot be

manipulated silviculturally). By calculating

the difference between a stand’s current and

maximum potential leaf area, forest managers

should be able to predict the magnitude and

duration of response to fertilization and other

silvicultural practices.

Clearly, the productivity potential of the

southern pine region is not presently being

realized. Application of state of the art

silvicultural treatments from plantation

establishment could increase site index

(dominant hkg&ht at age 23) and dean annual

increment (MA ) by as m ch as 10 to 15 ft and

150 to 200 ftJ ac-1 yrSY respectively , on most

sites. These gains are iossible while

maintaining or actually decreasing regeneration

costs. For established Stand?, MAI could be

increased by over 100 ft ac yr .

Major limitations impeding the application of

existing technology are: 1) uncertainty

concerning the magnitude, probability, and

economic value of response; 2) lack of knowledge

necessary to develop the appropriate fertilizer

prescription; 3) inadequate capital for

silvicultural treatments due to uncertainty of

the long-term supply and value of wood; and 4)

infrastructure barriers that slow the acceptance

or implementation of new tehcnologies. To

overcome these limitations we must better inform

forest managers of the opportunities and provide

sufficient evidence to increase their confidence

that these potentials can be realized.

147
GIS AND LAND CLASSIFICATION:

INTEGRATED DECISION SUPPORT FOR

INDUSTRIAL FOREST MANAGEMENT&'

Brent J. Keeferz/

Abstract: This poster presentation examines several

practical examples of applying land classification to

industrial forest management problems. These

application areas include environmental regulation,

timber inventory, fertilization and herbicide

recommendations, and harvest size planning. Our

experiences overwhelming conclude that GIS is

essential to using land classification information.

Keywords: wetlands, fertilization, clear-cut, inventory

The only way to effectively and fully use

land classification information is to input this

information into a geographic information system

thereby providing the capabilities to organize,

manipulate, analyze, display and create

information in such a way that responsible and

accurate decisions can be made. ITT Rayonier

Inc., Southeast Forest Resources launched the

development of a GIS 2 years ago using pc-based

ARC/INFO and then moved to SUN workstation

ARC/INFO 8 months ago. The displayed maps

represent several practical applications that

involve analyzing or interpreting land

classification information.

Potential Wetlands Classification:

As forest industry continues to come under

fire from environmental regulation, GIS provides

an invaluable tool to assess the impact of

potential environmental regulations. It also

permits us to develop management plans which

consider multiple objectives including

environmental concerns. One poster presented

demonstrated three different wetlands

interpretations based on the Clean Water Act,

U.S. Fish and Wildlife Service, and the

Interagency Federal Manual on wetlands. Each

definition of wetlands resulted in more acreage

of managed pine plantations being classed as

wetlands. This particular map was used to show

the need for a more narrow and more reasonable

&/Presented at the Ecological Land

Classification Application To Identify The

Productive Potential Of Southern Forests,

Charlotte, NC, January 7-9, 1991

ZIGIS Coordinator, ITT Rayonier Inc.,

Southeast Forest Resources, Fernandina Beach, FL

148

,

r- ,,_. __~-- , , -

definition of wetlands. We are also able to

map areas where endangered or environmentally

sensitive plant and wildlife species occur on

our lands. We can then monitor the impacts of

our activities and develop appropriate

management plans.

Timber Inventory:

An application currently being evaluated

is to use the differences in soil types to

assist in creating sampling strata for timber

cruising. Although a stand of timber has been

managed over it's rotation as a homogeneous

unit, in actuality it may not be homogeneous

due in part to site quality differences. When

the stand is cruised for volume estimates at

harvest, these in-stand differences must be

recognized and the statistical sample adjusted

to account for them. Currently, our foresters

often divide a stand into strata based upon

observation of timber differences from the

field or aerial photography.

This GIS and soil mapping project may

allow us to stratify the stand by using

changes in soil type under the stand.

Clearcut Size Reduction:

ITT Rayonier has recognized the impact

that large, contiguous acreages of clearcut

forestland have on the public's image of

industrial forestry. Consequently, we are

attempting to reduce clearcut size and

implement a 2-3 year period of "green-up"

between adjacent clearcuts. Soil types can

often be used as a logical method for breaking

up a large stand into several smaller cutting

groups that can then be scheduled for harvest

2-3 years apart.
Fertilization Recommendations:

ITT Rayonier is involved in a very active

fertilization program, but often selection of

the stands which will benefit most from

fertilization is a difficult and time consuming

task. Currently the decision process involves

field observations of each potential

fertilization site. With the GIS, we are able

to overlay the soil classification layer with

the timber stand layer and then query the

resulting unioned layer to select stands for

fertilization. The criteria for fertilization

include stand age, soil type, site quality and

past fertilization history.

In conclusion, GIS provides the means

necessary to integrate multiple sources of

land classification information. A decision

support system is thereby created which

supplies many alternatives to the decision

maker. The system is not the decision maker,

but rather the synthesizer of hundreds of

alternatives into several of the best

alternatives from which the decision maker can

choose.

*U.S. GOVERNMENT PRINTING OFFICE: 1991-534-675 149
‘I ” I ,) ,._, .....l,.--r,. n--“..,--, ,..../”-~I_--. llvl,-l.l- _-,-
r

-- -__-_________-_I_____-_-_- -

1

Mengel, Dennis L.; Tew, D. Thompson, eds. 1991. Proceedings of i

a symposium: Ecological land classification: applications to identify i

the productive potential of southern forests. Gen. Tech. Rep. SE68.

Asheville, NC: U.S. Department of Agriculture, Forest Service, f

Southeastern Forest Experiment Station. 149 pp. i

Eighteen papers representing four categories-Regional Overviews;

Classification System Development; Classification System Interpretation;

!

Mapping/GIS Applications in Classification Systems-present the state 1

of the ‘art in forest-land classification and evaluation in the South. In I

addition, nine poster papers are presented. I