Integrating Ecosystem Sampling,

Gradient Modeling, Remote Sensing,

and Ecosystem Simulation to Create

Spatially Explicit Landscape

Inventories

Robert E. Keane

Matthew G. Rollins

Cecilia H. McNicoll

Russell A. Parsons

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-92

June 2002
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Abstract

Keane, Robert E.; Rollins, Matthew G.; McNicoll, Cecilia H.; Parsons, Russell A. 2002. Integrating

ecosystem sampling, gradient modeling, remote sensing, and ecosystem simulation to

create spatially explicit landscape inventories. RMRS-GTR-92. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 61 p.

Presented is a prototype of the Landscape Ecosystem Inventory System (LEIS), a system for

creating maps of important landscape characteristics for natural resource planning. This system

uses gradient-based field inventories coupled with gradient modeling remote sensing, ecosystem

simulation, and statistical analyses to derive spatial data layers required for ecosystem management. Field data were collected in two large (more than 10,000 km2

) study areas along important

environmental gradients using modified ECODATA methods. A multilevel database was used to

derive response variables for predictive landscape mapping from the ECODATA database. Linkage of gradient models with remote sensing allows a standardized, flexible, detailed, and comprehensive classification of landscape characteristics. Over 40 spatially explicit variables were derived for each study area using existing spatial data, satellite imagery, and ecosystem simulation.

This spatial database (the LEIS GIS) described landscape-scale indirect, direct, and resource

gradients and provided predictor variables for multivariate predictive landscape models. Statistical

programs and GIS were used to spatially model several landscape characteristics as a proof of

concept for the LEIS. These proof-of-concept products were: (1) basal area, (2) western redcedar

habitat, and (3) fuel models. Output maps were between 65 percent and 90 percent accurate when

compared to reference data from each study area. Main strengths of the LEIS approach include:

(1) a standardized, repeatable approach to sampling and database development for landscape

assessment, (2) combining remote sensing, ecosystem simulation, and gradient modeling to create predictive landscape models, (3) flexibility in terms of potential maps generated from LEIS, and

(4) the use of direct, resource, and functional gradient analysis for mapping landscape characteristics.

Keywords: gradient modeling, remote sensing, geographic information systems, ecosystem simulation, predictive landscape mapping, ecosystem management

The use of trade or firm names in the publication is for reader information and does not imply

endorsement by the U.S. Department of Agriculture of any product or service.
The Authors

Robert E. Keane is a Research Ecologist with the USDA Forest Service, Rocky Mountain Research

Station at the Fire Sciences Laboratory, P.O. Box 8089, Missoula, MT 59807; Phone (406) 329-4846,

FAX (406) 329-4877, e-mail: rkeane@fs.fed.us. Since 1985, Keane has developed various ecological

computer models for the Fire Effects Project for research and management applications. His most

recent research includes the synthesis of a First Order Fire Effects Model; construction of mechanistic

ecosystem process models that integrate fire behavior and fire effects into succession simulation;

restoration of whitebark pine in the Northern Rocky Mountains; spatial simulation of successional

communities on the landscape using GIS and satellite imagery; and the mapping of fuels for fire behavior

prediction. He received his B.S. degree in forest engineering in 1978 from the University of Maine,

Orono; his M.S. degree in forest ecology from the University of Montana, Missoula, in 1985; and his

Ph.D. degree in forest ecology from the University of Idaho, Moscow, in 1994.

Matthew G. Rollins is an Ecologist with the USDA Forest Service, Rocky Mountain Research Station

at the Fire Sciences Laboratory, P.O. Box 8089, Missoula, MT 59807; Phone (406) 329-4960, FAX

(406) 329-4877, e-mail: mrollins@fs.fed.us. Since 1993, Rollins has worked with GIS, image analysis,

and both empirical and mechanistic modeling for characterizing landscapes for broad scale studies

and assessments. His research has involved spatial validation of the carbon and hydrologic cycles in

the BIOME-BGC ecosystem process model, direct comparison of lightning-caused fire occurrence and

lightning occurrence databases, and evaluation of 20th-century fire patterns in two large Rocky Mountain

wilderness areas using digital fire atlases. He received his B.S. degree in wildlife biology in 1993 and

M.S. degree in forestry in 1995 from the University of Montana, Missoula. Working at the Laboratory of

Tree-Ring Research with Thomas Swetnam and Penelope Morgan (University of Idaho) he received

his Ph.D. degree in watershed management from the University of Arizona in 2000.

Cecilia H. McNicoll is an Ecologist and Wildlife Biologist with the Pike & San Isabel National Forest

and Comanche & Cimarron National Grasslands, Leadville, CO. She received her B.S. degree in natural

resource management from the University of Nevada, Reno in 1982, and her M.S. degree in forestry

from the University of Montana in 1992.

Russell A. Parsons is a GIS Specialist with the USDA Forest Service, Rocky Mountain Research

Station at the Fire Sciences Laboratory, P.O. Box 8089, Missoula, MT 59807. Phone (406) 329-4872,

FAX (406) 329-4877, e-mail: rparsons@fs.fed.us. Russ has worked in GIS and remote sensing since

1997. He received his B.S. degree in forestry in 1992 from the University of California, Berkeley, and

his M.S. degree in 1999 from the University of Idaho, Moscow. Russ has worked as a fire monitor in

Sequoia and Kings Canyon National Parks in California, and served as an agroforestry extensionist

volunteer in the Peace Corps in Ecuador from 1995 to 1997.

Acknowledgments

This project was a joint effort between the USDA Forest Service, Rocky Mountain Research Station’s

Fire Sciences Laboratory and the Northern Region, and NASA (National Aeronautics and Space

Administration-ARS-0000-178). This project was started with funding from the Northern Region Ecology

Program and completed with funding from NASA Research Grant AFRR-0000-0175. We recognize Wendel

Hann, USDA Forest Service, and Michele Wasienko-Holland, Lolo National Forest, USDA Forest Service

for their critical role in this project. Other USDA Forest Service employees we thank are Dan Leavell,

Kootenai National Forest, Pat Green, Nez Perce National Forest, Colin Hardy, Bob Burgan, James Menakis,

Don Long, Janice Garner, Kirsten Schmidt, Scott Mincemoyer, and Todd Carlson, Fire Sciences Laboratory,

Rocky Mountain Research Station. We also thank Joseph White, Baylor University; Peter Thornton, University

of Montana; and Kathy Schon, Brian Paulson, Myron Holland and John Pierce, of Missoula, MT for their

valuable help during this effort.
Contents

Introduction .......................................................................................................................................... 1

Background........................................................................................................................................... 2

Gradient Modeling........................................................................................................................ 2

Remote Sensing and Image Processing ........................................................................................ 4

Integration of Gradient Modeling and Remote Sensing ............................................................... 5

Study Areas ........................................................................................................................................... 6

Methods ................................................................................................................................................ 7

Field Sampling Methods............................................................................................................... 9

LEIS Database ............................................................................................................................ 14

Ancillary Spatial Data (LEIS GIS) ............................................................................................. 15

Simulated Spatial Databases....................................................................................................... 15

Remote Sensing and Image Classification ................................................................................. 16

Gradient Analysis and Modeling ................................................................................................ 17

Demonstration of LEIS............................................................................................................... 18

Accuracy Assessment ................................................................................................................. 18

Results ................................................................................................................................................ 19

Field Sampling and LEIS Databases .......................................................................................... 19

Landscape Mapping and Accuracy Assessment ......................................................................... 24

Discussion........................................................................................................................................... 26

Field and Ancillary Data ............................................................................................................. 27

Spatial Data ................................................................................................................................ 33

Remote Sensing/Image Processing............................................................................................. 33

Simulated Spatial Databases....................................................................................................... 33

Gradient Analyses and Modeling ............................................................................................... 34

Limitations .................................................................................................................................. 35

Potential Applications ................................................................................................................. 35

General Ecology ......................................................................................................................... 36

Fire Behavior and Effects ........................................................................................................... 36

Wildlife ....................................................................................................................................... 36

Vegetation ................................................................................................................................... 36

Summary and Conclusions ................................................................................................................. 36

References .......................................................................................................................................... 36

Appendix A—Data contained in the ECODATA database ................................................................. 42

Appendix B—Parameter lists for GMRS-BGC—the ecosystem simulation model used to

generate primary ecophysiological gradients. .............................................................................. 46

ii
USDA Forest Service RMRS-GTR-92 1

Introduction

Successful, scientifically based ecosystem management

requires multiple-scale, spatially explicit inventories of important landscape characteristics (Jensen and Bourgeron

1993). Extensive and comprehensive Geographic Information Systems (GIS) databases of landscape composition,

structure, and function are essential for credible analyses

and enlightened planning (Murray and Snyder 2000). The

quality of these data relies on efficient, economical, and

ecologically based land system inventories (Hann and others 1988). Mapped landscape attributes are critical to many

planning efforts because they may be used in comprehensive statistical analyses and simulations to evaluate trends

and patterns, compare management alternatives, and ensure

conservation of important ecosystem components and processes over broad scales (Miller 1994). However, many land

management agencies lack comprehensive spatial data of

important ecological elements critical for effective land

management planning (Quigley and others 1996).

In general, traditional inventory efforts were designed

for specific land-use projects (for example, timber and grazing) applied at the forest stand level. These inventories are

limited in application because they do not document spatial

dependencies of inventory elements across a landscape. Few

describe ecosystem attributes continuously across an entire

landscape (that is, wall-to-wall coverage) or landscape characteristics that are unrelated to direct resource concerns (for

example, microclimate). Traditional stand-based inventories also tend to disregard small vegetation communities,

such as riparian stream bottoms and seeps, that can contain

critical ecosystem processes or elements, such as high productivity or rare plants, within the landscape. Finally, most

inventories were designed for the sampling of only one ecosystem element (for example, timber inventory) and this

design is not always optimal for describing other elements

(for example, fuel loadings, hiding cover, productivity).

Conventional inventory techniques are not sufficient for

ecosystem management and planning for several reasons

(Quigley and others 1996). First, comprehensive landscape

planning requires descriptions of all ecosystems, not just

forests. Second, commonly used timber inventories confine

the majority of measurements to tree attributes; but many

other characteristics, such as undergrowth species, fuel loadings, disturbance histories, and habitat suitability, are needed

for ecosystem analyses. Third, in general, traditional inventory techniques fail to capture zones of transition between

adjacent ecosystems (ecotones), which are important to considerations of the movement of organisms, resources, and

disturbance across landscapes. Fourth, timber inventory data

are difficult to use as ground reference when creating maps

from remotely sensed data because of incompatible sampling designs and scales (Lachowski and others 1995). Additionally, sampling intensity is traditionally designed by

minimizing variance in timber volume, which may be inappropriate for the collection of other ecosystem inventory

data. Future inventories must sample all important ecosystem characteristics using designs that balance sampling cost

with information quality, and at the same time, the inventories must provide field reference for remote sensing projects

to extrapolate these ecosystem characteristics over large

space and long time scales.

A system is needed that integrates sampling, analysis,

and mapping to efficiently quantify important landscape

attributes across multiple spatial and temporal scales. Products of such a system should include the spatial databases

essential for quantitative ecosystem management. Presented

here is a prototype of the Landscape Ecosystem Inventory

System (LEIS), a mapping system that integrates extensive

ecological sampling with remote sensing, ecosystem simulation, and multivariate cartographic modeling to create spatial data for ecosystem management. The primary objective

of LEIS is to develop cost-effective methods that rapidly

generate spatial inventories of ecosystem characteristics at

relatively broad scales (for example, entire National Forests). The system would economically prepare thematic data

layers portable to GIS that comprehensively depict a wide

variety of ecological properties of a landscape in a spatial

domain. The system uses the physiographic, spectral, environmental, and ecological gradients that describe ecosystem processes and conditions across landscapes as the foundation for a mapping system. These gradients are then used

Integrating Ecosystem Sampling, Gradient Modeling,

Remote Sensing, and Ecosystem Simulation to Create

Spatially Explicit Landscape Inventories

Robert E. Keane

Matthew G. Rollins

Cecilia H. McNicoll

Russell A Parsons
2 USDA Forest Service RMRS-GTR-92

to generate maps of ecosystem characteristics at various

scales for ecosystem management planning or “real-time”

situations, such as input for fire behavior predictions.

Background

Gradient Modeling

Gradient analyses provide a powerful means to describe

and classify ecological communities in terms of spatial and

temporal environmental gradients (Kessell 1976a, 1979).

First introduced by Ramensky (1930) and Gleason (1926,

1939), gradient analysis has been improved and refined in

the United States by Bray and Curtis (1957) and Whittaker

(1967, 1975). It is often defined as a quantitative description of the distribution of a plant species along one or more

environmental gradients, such as elevation or precipitation

(fig. 1). Traditionally, ecologists have used the composition

and abundance of plant species to identify the environmental gradients important for classifying vegetation. Complex

numerical techniques such as ordination, principal components analysis, reciprocal averaging, and canonical correspondence analysis have given ecologists the ability to identify and describe the ecological gradients that directly and

indirectly affect plant composition (Gauch 1982; ter Braak

1987). Once key gradients are identified, they can then be

mathematically represented in a gradient model to predict

changes in species composition across a landscape (Gotz

1992; Kessell 1979).

Gradient modeling has seen limited use in natural resource management (see Gosz 1992; Kessell 1976b, 1979)

because gradient analyses are data intensive and require

detailed knowledge of complex mathematical tools and

highly variable species-environment relationships (Gauch

1982; Kessell 1979; ter Braak 1987). Franklin (1995) extensively reviewed this subject area and mentions that most

of the easily measured environmental gradients are often

the secondary or indirect factors influencing vegetation composition. Primary or direct ecological factors are often inferred from these surrogate or secondary gradients. For example, changes in species composition by elevation, one of

the most important gradients in the Western United States,

are actually a result of changes in temperature and precipitation with altitude (Kessell 1979; Muller 1998).

Austin and Smith (1989) define three types of environmental gradients, which provide a useful taxonomy for discussion. Indirect gradients, such as slope, aspect, and elevation, have no direct physiological influence on plant

dynamics. Relationships to vegetation pattern are likely to

be location specific. Direct gradients, such as temperature

and humidity, have direct physiological impact on vegetation. Neither of these gradients are consumed by vegetation. On the other hand, the energy and matter used or consumed by plants such as light, water, and nutrients define

resource gradients. Direct and resource gradients are important for mapping vegetation and ecosystem characteristics because they fundamentally define the potential species niche, yet they have rarely been used in natural resource

planning (Austin 1984; Austin and others 1983). Müller

(1998) adds spatial and temporal dimensions to Austin and

Smith’s (1989) three gradient types, and then introduces a

fourth type: functional gradients. Functional gradients describe the response of the biota to the three gradient types.

Included in this gradient category would be biomass, fuels,

Figure 1—An example of a simple gradient analysis. The distribution of ponderosa pine across gradients of elevation and vapor

pressure deficit differs from that of subalpine fir. Ponderosa pine grows at lower elevations and can tolerate drier conditions.

Bell-shaped curves represent the expected normal distribution of the two types over gradients of elevation and precipitation.

LEIS capitalizes on many of these direct, indirect, and functional gradients to improve the accuracy of maps representing a

wide variety of ecosystem characteristics.
USDA Forest Service RMRS-GTR-92 3

and leaf area index (Müller 1998). Most gradient models use

indirect spatial gradients to describe and map vegetation-environment relationships (see Kessell 1979). But the next generation of gradient models will incorporate direct, resource, and

functional gradients to more accurately map ecosystem characteristics (Austin and Smith 1989; Franklin 1995).

Many studies have described or mapped plant communities using environmental variables in a gradient-based

approach (see Goodchild and others 1996). Patten (1963)

used climate, soils, topography, and geology to map vegetation patterns in the Madison Range of Montana. Iverson

and Prasad (1998) mapped the regional distribution of 80

Eastern United States tree species using climate parameters.

Gradient analysis was used in Glacier National Park to predict timberline shifts from topography and disturbance

(Brown 1994; Habeck 1969). Allen and Peet (1990) used

several ordination techniques to perform a gradient analysis of the Sangre de Cristo range in Colorado to understand

and predict plant species distributions from soil, climate,

and topography gradients. Gosz (1992) used gradient analysis to detect species change in Central Rocky Mountain forests of Colorado. Davis and Goetz (1990) used topography,

geology, and simulated clear-sky radiation to predict the

distribution of live oak in California. An expert systems

approach was used to map species composition from topographical conditions using a rule-based method (Twery and

others 1991). Temperature and precipitation maps were used

to map regions that would be suitable for plant species in

Africa (Booth and others1989). Emmingham (1982) describes how to use ecological indexes based on climate and

soil moisture to predict species distribution. The preceding

efforts were limited in that they pertain only to vegetation community analysis and this focused on indirect, as opposed to

direct, gradient analysis, which tends to limit the utility of the

final model for predictive landscape mapping (Gauch 1982).

Synthesis of the results of gradient analyses into a prognostic gradient model is difficult because of many interrelated factors. Many gradient analysis projects use ordination to identify environmental gradients, and since most ordination techniques use only species’ canopy cover as independent variables to identify gradients, it is difficult to accurately create a predictive environmental equation from

resultant ordination axes (Gauch 1982). Ordination axes often represent the integration of several environmental factors, making it difficult to evaluate the relative contribution

of each factor to the gradient signal represented by the axes.

New mathematical techniques allow the integrated analysis

of environment and species composition (ter Braak 1987),

but characterizing major gradients from species composition is still tenuous because of the complex role of genetics,

disturbance, and succession (see fig. 1). And many environmental gradients that influence the vegetation dynamics are

still unknown or difficult to characterize across a landscape

(Whittaker 1967), or the set of gradients that influence

ecosystems many be entirely different from one landscape to

another. For example, elevation might influence vegetation in

one landscape while soils may govern vegetation in another.

Perhaps the biggest barrier in developing a prognostic

gradient model is an accurate spatial description of existing

conditions to quantify successional gradients. It is important to know the current state of ecosystem development to

predict important ecological conditions. For example, Keane

and others (1998a,b) found that adding stand structure and

composition to a model containing slope, aspect, and elevation increased classification accuracy of fuel maps by 10 to

30 percent. Remote sensing provides an efficient tool to

quantify current ecological conditions relative to the development gradients used for predictive purposes. The gradient modeling approach, combined with remote sensing, has

the potential to be the most flexible ecosystem inventory

and mapping tool for this new era in ecosystem management (Ahern and others 1982; Davis and others 1991;

Franklin 1995; Ohmann and Spies 1998).

Some studies have predicted or mapped ecosystem characteristics other than vegetation using direct gradient approaches. Wildlife species distribution maps were developed

using bioclimatic mapping, statistical analysis, and GIS

modeling (Aspinall 1992; Pereira and Itami 1991). Potential natural vegetation was mapped using topography, soils,

and climate for United States rangelands (Jensen and others

2001), Swiss forests (Brzeziecki and others 1993), and California ecosystems (Walker and others 1993). Fire regime

was modeled from new environmental and vegetation attributes using a gradient approach (Barton 1994; Rollins and

others, in review; Romme and Knight 1981). The study presented in this report is based on the theoretical concept of

traditional gradient analysis but widens the scope to use direct gradients of process variables with multivariate regression (or related methods) to map not only plant community

distributions, but also many other ecosystem properties that

are important for landscape assessment, such as productivity, fuels, fire regime, and forage potential.

Recent ecosystems research has identified a group of

processes-oriented, ecophysiological variables that govern

vegetation dynamics across several scales that can be used

as direct, resource, and functional gradients (see Austin 1987;

Waring and Running 1998). For example, Hall and others

(1992) argue that observed patterns of distribution and abundance in plants and animals in space and time are a direct

result of species-specific energy costs and gains along many

functional and resource gradients. Klopatek and others

(1998) described the patterns of carbon fluxes (decomposition, standing biomass, litterfall) across environmental gradients in semiarid ecosystems. Primary productivity gradients were used to assess changes in vegetation diversity in

arctic tundra (Williams and Rastetter 1999). Nixon (1995)

describes how forest productivity can be predicted from soil

moisture and nutrient fluxes. However, ecophysiological

variables, such as evapotranspiration and net primary productivity, are difficult and costly to measure across large

land areas because they require specialized equipment and

intensive sampling over long periods.

Ecosystem simulation models can now quantify these

ecophysiological processes in a spatial and temporal domain
4 USDA Forest Service RMRS-GTR-92

(Neilson and Running 1996; Running and Hunt 1993;

Thornton 1998). Jones (1971) identifies several climatebased mechanistic gradients that may be simulated to predict ecosystem variables. Bugmann (1996) used a gap model

to study species distribution relationships along complex

ecological gradients in Switzerland. Running (1994) validated his FOREST-BGC model across a climatic gradient

in Oregon and his results demonstrate the utility of using

process models to describe direct mechanistic gradients.

Milner and others (1996) then used FOREST-BGC coupled

with a climate model to create a biophysical soil-site model

for predicting timber productivity in Montana. This simulation technology paves the way for a new generation of gradient modeling that can mechanistically quantify the important direct, functional, and resource gradients influencing vegetation to map landscape characteristics. These

mechanistic gradients, computed from a combination of field

sampling, computer simulation, and remotely sensed data,

provide powerful mapping tools for ecosystem management

projects (Franklin 1995; Greer 1994).

There are many advantages of using direct gradient modeling over other mapping schemes. First, gradient approaches

are preferable to expert system approaches where decision

rules are based on opinions rather than empirical data (Morgan and others 2001). Second, gradients are often scale independent, flexible, and portable (Franklin and Woodcock

1997; Gosz 1992; Whittaker 1975). If gradients are similar

in lands outside the sampled areas, the landscape models

may be extrapolated to unsampled areas. Third, some gradients are static and do not change over time (topography,

for example) so repeated sampling is not necessary. Relationships of ecological characteristics to direct environmental gradients probably won’t change in the near future, but

the spatial distribution of direct gradients will change. So

simulations of future climates may be used with gradient

models to compute distributions of future vegetation assemblages (Linder 2000). Fourth, expensive gradient-based field

databases have long-term value since they were collected

to quantify gradients as well as current land conditions.

Gradient predictive algorithms may be modified and refined

as additional land areas are sampled and more environmental measurements are taken. Fifth, vegetation-gradient relationships will enable resource managers to explore new aspects in landscape ecology and will provide context for understanding the effect of human activities on complex ecological interrelationships and landscape patterns (Müller

1998; Nixon 1995).

As mentioned, a major shortcoming of most gradient

modeling approaches is that the results describe potential

rather than existing conditions. Quantification of successional pathways and factors that control successional trajectories is costly and enigmatic using gradient modeling,

especially considering the detail needed for ecosystem management projects. Moreover, existing conditions mapped

using a gradient model are often dictated by coarse spatial

resolution of mapped gradients. Abrupt changes in the biota

at smaller scales are difficult to quantify using coarsely

mapped gradients. For example, riparian communities and

fine-scale successional changes are difficult to map using a

gradient approach because most GIS layers lack the detail

to identify environmental factors that regulate these communities. Therefore, the real strength of gradient modeling

lies in its ability to describe the potential for areas to contain or possess a particular ecosystem characteristic or category, such as cover type or basal area, and to describe these

variables continuously across landscapes. Examination of

how ecosystem characteristics overlap, with special attention to transition zones or ecotones, may yield valuable insight for assessing the movement of organisms (for example,

the migration of weeds), the flow of resources (for example,

water or nitrogen), or the spread of disturbance (fire, for

example) across landscapes. This information may also be

used to narrow the range of possibilities for classifications

of remotely sensed images to increase accuracy and provide context for existing maps.

Remote Sensing and Image Processing

Image processing of remotely sensed data (for example,

satellite imagery) offers a cost-effective, but less accurate,

alternative to extensive photointerpretation for describing

existing conditions across landscapes (Jensen 1986;

Lachowski and others 1995; Verbyla 1995). However, the

spectral, spatial, and temporal resolution of some remotely

sensed imagery products might be inappropriate for describing certain important ecosystem characteristics, such as fuel

loadings, biomass, and fire regime (Keane and others 2000).

Traditional remote sensing techniques using canopy dominants for community classifications may be inefficient for

predicting composition and coverage of other, less dominant species (Sagers and Lyon 1997). The accuracy of satellite-derived maps is often quite low when classified attributes (categories) are designed for land management applications rather than optimally matched to spectral response

patterns (Keane and others 1998b; Verbyla 1995). The process of classifying remotely sensed imagery is as much an

art as a science; it is not always standardized or repeatable.

Achieving high imagery classification accuracy often requires an extensive and costly field sampling effort, or a

compromise in the utility of the mapped classification to

natural resource management.

New image processing technologies now allow the addition of biophysical and plant demography gradients to improve classification accuracy (Ahern and others 1982; Foody

1999). However, this requires extensive knowledge of the

relationships between site and vegetation conditions across

the classified landscape. This is exactly the type of ecological information used in traditional models of vegetation

structure and composition known as gradient models that

were discussed earlier (Kessell 1979).

There are major limitations to using only passive optical

remote sensing products (based on light reflectance) to construct maps of ecological characteristics. Conventional remote sensing relies on the spectral reflective properties of a
USDA Forest Service RMRS-GTR-92 5

stand to consistently predict locations of other similar stands

(Jensen 1986). Unfortunately, light reflection is not always

an accurate or consistent variable to use in the spatial prediction of biotic elements across landscapes. Many physical factors limit the predictive ability of satellite imagery,

including shadow, atmospheric distortion, composite pixels, species similarity, sensor inadequacies, light scattering,

and sensor resolution (Verbyla 1995). It is common for two

very different vegetation communities to have the same spectral signature. The mathematics and statistics used to “train”

spectral distributions to predict vegetation characteristics

in supervised approaches are complex and may be limiting

because they assume a normal distribution of error (Jensen

and Qiu 1998; Knick and others 1997).

Many remote sensing efforts have combined environmental analysis with conventional image processing to create

maps of existing vegetation or land cover (see Davis and

others 1991). Topographical variables, derived from Digital Elevation Models (DEMs), have long been used to stratify

or augment image classification procedures for mapping

vegetation attributes (see Cibula and Nyquist 1987; Fahsi

and others 2000; Lieffers and Larkin-Lieffers 1987, for examples). Miller and Golden (1991) used physiography and

Landsat Multispectral Scanner (MSS) imagery to map forest site classifications. Topography, geographic zones, and

Landsat MSS imagery with ground data were used to map

forest productivity in northwestern California (Fox and others 1985). Georeferenced ecological field data coupled with

kriging and satellite imagery were used to analyze ecological patterns at landscape scales in South Carolina (Michener

and others 1992). Bolstad and Lillesand (1992) used soils

and terrain to map forest vegetation in Wisconsin, but He

and others (1998) improved on their methods by integrating

Forest Inventory and Analysis (FIA) plot inventory data with

GIS layers of regional ecosystem classification, climate, and

soils to map dominant species in northern Wisconsin. Shao

and others (1996) used potential vegetation types derived

from soils and topography to refine a cover type classification from satellite imagery for a natural reserve in

China. A major problem with many of these efforts is

that the field reference data were not collected along the

same environmental gradients used as predictors in the

classification process.

Many spatial inventories for natural resource planning

are based on classified satellite imagery that describes distributions of vegetation communities across the landscape

(Bain 1989; Bolstad and Lillesand 1992; Schowengerdt

1983). These communities are often described by the dominant plant species (Verbyla 1995). Land management will

typically assign a myriad of ecosystem attributes to each

mapped vegetation community category to map other resource-oriented characteristics on the landscape (Bain 1989;

Greer 1994). As a result, errors in the spectral classification

are compounded with errors resulting from attribute assignment to yield maps that do not always portray a true spatial

representation of ecological components (Foody and Curran

1994). Moreover, many ecosystem attributes can be

unrelated to the dominant species community type (see

Foody and Curran 1994; Waring and Running 1998). For

example, coarse woody debris loading can be the same for

young forests as old forests, depending on disturbance history (Brown and Bevins 1986). An alternative to assigning

ecosystem characteristics to remotely sensed vegetation

types is to treat satellite spectra as predictor variables in a

database of many potential predictor variables rather than

the sole source of information for mapping landscape characteristics. Simple derivatives from satellite imagery can

define reflectance gradients that are very useful for predicting landscape composition, structure, and function.

Integration of Gradient Modeling and

Remote Sensing

Some recent mapping and image classification efforts

illustrate the power of formally melding environmental information with satellite imagery to develop better ecological maps. Michener and others (1992) combined GIS, field

data, and spatial statistics to construct an effective tool for

exploring oyster population dynamics. Ohmann (1996) demonstrates how regional plot data can be linked to environmental gradients derived from climate models and digital

maps to derive information relevant to forest planning and

policy. Ohmann and Spies (1998) used those same methods

to identify regional gradients from extensive field data to

characterize woody species composition in Oregon. They

were able to develop a conceptual model of species environment relations at the regional scale, which in conjunction with remote sensing can be used to accurately map forest species. Ahern and others (1982) linked gradient analysis and spectral data to predict forest species distributions

in the North Cascades Mountains in Washington, U.S.A.

Many unique aspects of the study described in this report distinguish it from those studies presented above. First,

this study formally integrated a comprehensive gradientbased field inventory sampling system with remote sensing

and ecosystem simulation to improve the mapping process.

Second, most of the previous studies were concerned with

creating only one product—a vegetation map—while this

study presents a system that can be used to map a wide variety of ecological attributes. Third, some maps created from

LEIS are made up of probability surfaces. That is, maps

represent the probability of any area on the map to possess

a specific characteristic (for example, fuel model 10). This

is in contrast to other mapping efforts where final output

spatial data were presented as nominal or ordinal categories. This allows the end users of the output maps from LEIS

to determine their own strategies for classifying ecological

characteristics, thus maximizing the utility of the final spatial data layers for a wide variety of applications. A fourth

unique aspect is that LEIS has many more environmental

gradients to predict ecosystem characteristics than most other

studies, and many of these gradients are ecophysiological

direct or functional gradients. This contrasts with Kessell

(1979) who used only seven indirect gradients to predict
6 USDA Forest Service RMRS-GTR-92

vegetation and fuels in Glacier National Park. Fifth, the primary ecophysiological gradients that dictate ecosystem properties, such as productivity, evapotranspiration, and leaf area

index, are formally integrated into this gradient modeling

approach. Sixth, this study melds new remote sensing technology and ecosystem simulation software with field sampling and conventional remote sensing to allow a more fundamental spatial characterization of the gradients that control ecosystems. Last, this report details methods and protocols for implementing LEIS on any land area.

Study Areas

The Kootenai River study area (KRSA) on the Kootenai

National Forest in northwestern Montana (fig. 2a) and

Salmon River study area mostly on the Nez Perce National

Forest in central Idaho (fig. 2b) are the two large (10,300

and 11,000 km2, respectively) and diverse regional landscapes selected for this study. These landscapes are bounded

by the Hydrologic Unit Code watershed delineation at the

4th code level (Seaber and others 1987). They were selected

because they are quite different in topography, geology, and

vegetation, yet they are representative of surrounding land

areas. In addition, there is an estimated 10 to 20 percent

overlap in environmental gradients across the two study areas, which allows for expansion of ecological gradients

across study watersheds.

The Kootenai study area (fig. 2a) is bounded by Canada

to the north, the Whitefish Range to the east, the Yaak River

watershed to the west, and Clark Fork River watershed to

the south. Climate is mostly modified maritime with mild,

wet winters and warm, dry summers (Finklin 1987). The

study area is a productive Northern Rocky Mountain landscape containing hemlock (Tsuga heterophylla) and cedar

(Thuja plicata) forests at low elevations on moist to wet

sites (northerly aspects and stream bottoms). Mixed conifer

forests of Douglas-fir (Pseudotsuga menziesii), western

larch (Larix occidentalis), lodgepole pine (Pinus contorta),

grand fir (Abies grandis), and, to some extent, western white

pine (Pinus monticola) dominate the productive

midelevation zones. Lower subalpine areas usually consist

of subalpine fir (Abies lasiocarpa), spruce (Picea

engelmannii and glauca), mountain hemlock (Tsuga

mertensiana), and lodgepole pine. Upper subalpine forests

are mostly whitebark pine (Pinus albicaulis), subalpine fir,

spruce, and small amounts of alpine larch (Larix lyallii).

Permanent shrub and herblands are present at the highest

elevations (greater than 2,000 m). A great portion of

Figure 2—The Kootenai River (KRSA; A) and

Salmon River (SRSA; B) study areas in

northwestern Montana and central Idaho

of the Northern Rocky Mountains.
USDA Forest Service RMRS-GTR-92 7

forested lands (approximately 40 percent) on the Kootenai

study area has been logged in the recent past (1950 to

present). Large, stand-replacement fires occurred somewhat

infrequently on this study area prior to Euro-American settlement (pre-1900) (Arno 1980).

The Salmon River study area (SRSA; fig. 2b) encompasses lands east of Grangeville, ID, mostly on the Nez

Perce, and somewhat on the Payette, National Forests. Climate is xeric maritime with warm, mild winters and hot,

dry summers (Finklin 1988). Lower elevations are mostly

grassland communities of bluebunch wheatgrass, shrubland

communities, and mixed ponderosa pine (Pinus ponderosa)

forests and savannahs. Middle elevations comprise primarily ponderosa pine, Douglas-fir, ponderosa pine, and grand

fir forests. Upper elevations are dominated by lodgepole

pine, subalpine fir, Engelmann spruce, and whitebark pine

(Cooper and others 1991). This study area has also been

extensively logged in the mid and high elevations, but not

as extentsively as the KRSA. Fires were quite common in

the low- and mid-elevation forests pre-1900 with fire

intervals ranging from 5 to 30 years (Arno 1980). These

fires were often surface or understory burns with an occasional stand-replacement event.

There is an estimated 10 to 20 percent overlap in environmental gradients across the two study areas. Bunchgrass types in the KRSA are found mostly on lowland

areas around Eureka, MT, while bunchgrass communities comprised the majority of area in the SRSA grassland types. Similar potential vegetation types across the

areas occur in the grand fir (ABGR) and subalpine fir

(ABLA) forests. Both study areas have timberline and

alpine communities. Dry, Douglas-fir and ponderosa pine

forests are also common in both study areas (Cooper and

others 1991).

Methods

The LEIS consists of many integrated components

(fig. 3). Gradient-based sampling methods were designed

to obtain comprehensive, process-based inventories of

Figure 3—A diagram of the Landscape Ecosystem Inventory System. Boxes in the center represent the steps to create the

ECODATA database, the LEIS GIS, and landscape planning maps. Initial and derived GIS layers (left side) are used as

independent variables to predict maps of landscape and eco-system characteristics (right side) for landscape assessments

and ecosystem management.
8 USDA Forest Service RMRS-GTR-92

Table 1—Data contained in the LEIS GIS for each study area. Data were either obtained from existing sources, or derived

using simulation programs developed specifically for the LEIS. Variables were compiled as Arc/Info grids and used as

predictors in landscape models of basal area (m2

/ha), western redcedar habitat, and fire behavior fuel models 5, 8,

and 10.

Layer name Description Source Year

Geographic features

CARTO Cartographic feature files (roads, Nez Perce and Kootenai National Forests 1989

 trails, streams, etc.)

LTA Land type association USDA 1997 1998

DEM Digital elevation model USGS 2001 2001

SLOPE Slope derived from DEM in percent USGS 2001 2001

ASPECT Direction of exposure in azimuths USGS 2001 2001

CURVE Relative concavity/convexity Derived (ESRI 1998) 2001

PLAN_CURVE Curvature in the direction of slope Derived (ESRI 1998) 2001

PSAND Percent of sand in soil SCS 1991 1991

PSILT Percent of silt in soil SCS 1991

PCLAY Percent of clay in soil SCS 1991

SDEPTH Depth to bedrock Derived (Zheng and others 1996) 2001

Satellite imagery

REFLC1 TM5 at-sensor reflectance, band1 Derived (Marham and Barker 1986) 2001

REFLC2 TM5 at-sensor reflectance, band2 Derived (Marham and Barker 1986) 2001

REFLC3 TM5 at-sensor reflectance, band3 Derived (Marham and Barker 1986) 2001

REFLC4 TM5 at-sensor reflectance, band4 Derived (Marham and Barker 1986) 2001

REFLC5 TM5 at-sensor reflectance, band5 Derived (Marham and Barker 1986) 2001

REFLC7 TM5 at-sensor reflectance, band7 Derived (Marham and Barker 1986) 2001

PCA1 Principle component #1 of TM5 bands Derived (ERDAS 1999) 2001

PCA2 Principle component #2 of TM5 bands Derived (ERDAS 1999) 2001

PCA3 Principle component #3 of TM5 bands Derived (ERDAS 1999) 2001

BRIGHT Kauth-Thomas transform of TM5 bands Derived (Kauth and Thomas 1976) 2001

GREEN Kauth-Thomas transform of TM5 bands Derived (Kauth and Thomas 1976) 2001

WET Kauth-Thomas transform of TM5 bands Derived (Kauth and Thomas 1976) 2001

LAI Leaf Area Index (m2

/m2) Derived (Nemani and others 1993)

MNDVI Modified Normalized Difference Vegetation Index Derived (Nemani and others 1993)

Weather

TMAX_1km Average annual maximum temperature (∞C) (Thornton and others 1997; ICBEMP) 1996

PPT_1km Average annual precipitation (m) (Thornton and others 1997; ICBEMP) 1996

PET Average annual potential evapotranspiration (m) Derived (WXGMRS) 2001

PPT Average annual precipitation (cm yr-1) Derived (WXGMRS) 2001

SRAD Average annual daily solar radiation (kJ m-2 day-1) Derived (WXGMRS) 2001

TAVE Average annual average temp. (∞C) Derived (WXGMRS) 2001

TDEW Average annual dewpoint temp. (∞C) Derived (WXGMRS) 2001

TMIN Average annual minimum temp. (∞C) Derived (WXGMRS) 2001

TMAX Average annual maximum temp. (∞C) Derived (WXGMRS) 2001

TSOIL Average annual soil temp. (∞C) Derived (WXGMRS) 2001

VPD Average annual vapor pressure deficit (mbar) Derived (WXGMRS) 2001

Ecophysiological variables

NPP Net primary productivity (kg C m-2) Derived (GMRS-BGC) 2001

NEP Net ecosystem production (kg C m-2) Derived (GMRS-BGC) 2001

ER Ecosystem respiration (kg C m-2) Derived (GMRS-BGC) 2001

AR Autotrophic respiration (kg C m-2) Derived (GMRS-BGC) 2001

MR Maintenance respiration (kg C m-2) Derived (GMRS-BGC) 2001

OUTFL Outflow (kg H2O m-2) Derived (GMRS-BGC) 2001

ecosystem characteristics for each study area at various spatial and temporal scales. A remote sensing/image processing protocol was designed to map general ecosystem categories and spectral characteristics over the study areas. Environmental and ecological simulation models were used toprovide descriptions of direct, functional, and resource gradients for each area. Finally, the spatial data layers (hereafter referred to as the LEIS GIS) (table 1) were linked with

the field data to create a comprehensive, multivariate mapping system for generating maps characterizing landscape
USDA Forest Service RMRS-GTR-92 9

components for broad-scale assessments and landscape management. The structure, design, and example applications

of LEIS are presented in detail in this paper; the actual implementation and utility of LEIS will depend on the adopting

agency or organization. The next sections describe the methods used to develop each of these components as a demonstration of the capabilities of the LEIS.

Field Sampling Methods

A hierarchically structured, relevé-based, sampling design was developed to inventory important ecosystem characteristics across each study area (Hann and others 1988;

Jensen and others 1993; Keane and others 1990). Data collection emphasized sampling ecosystem processes to ensure

adequate coverage of important ecological gradients, to provide data for parameterizing simulation models, and to provide context for interpreting ecosystem conditions and dynamics. The field sampling was designed with three main

emphases: (1) to serve as reference data for classification

of satellite imagery, (2) to provide initialization and parameterization data for ecological simulation models, and (3)

to obtain a wide variety of information along environmental gradients to serve as potential response variables in predictive statistical landscape models.

Plot sampling locations were based on the distribution

of ecosystem processes across both study areas at multiple

scales (Gillison and Brewer 1985; Quigley and others 1996);

however, it was difficult to spatially describe ecosystem

processes for both study area landscapes before sampling.

For example, mapping areas of high and low productivity,

or spatially delineating fire history without first reconnoitering the area would have been untenable with mid-1990s

technology. Maps of ecosystem processes over large areas

are rare and extremely difficult to compile (Running and

others 2000). As a result, landscape and ecosystem diversity in the LEIS sampling strategy was represented using a

set of environmental surrogates mapped prior to sampling

and easily identified in the field. Spatial data based on ecosystem simulation, GIS modeling, and expert systems were

used to describe the distribution of these environmental surrogates (presented below). We assumed that the surrogate

variables selected for sample stratification in this study

would adequately represent the myriad of other ecological

processes that potentially influence ecosystem characteristics. These ecosystem process-based stratifications were

implemented at four spatial scales for stratification of the

study areas—study areas, subbasins, plot polygons, and

macroplots.

One satellite image was purchased for each 4th code HUC

boundary. Areas within this boundary, but falling outside

the boundaries of the Landsat Thematic Mapper 5 (TM5)

scene, were excluded from the analyses presented here. A

large area in the north of the Upper Salmon River HUC

consisted of private land with mixed agriculture. This area

was excluded from this analysis because of limited sampling on private land. These two limitations reduced the

final study areas to 10,100 and 11,000 km2 for the KRSA

and SRSA, respectively.

Both study areas were divided into units called subbasins

based on watershed delineations at sixth-level Hydrologic

Unit Codes (HUC) (Seaber and others 1987) (see fig. 4, 5,

and 6). Approximately 10 percent of these subbasins were

selected for sampling based on accessibility, diversity of

ecosystem processes, and geographical distribution. Accessibility was assessed from road and trail GIS data layers.

Distributions of regional ecosystem processes were assessed

using surrogate data from coarse-scale climate, geomorphology, and hydrology GIS data layers developed for the Interior

Columbia Basin Ecosystem Management Project (ICBEMP)

Scientific Assessment (Quigley and others 1996,

www.ICBEMP.gov). Average annual precipitation and average annual temperature maps (1 km2 resolution) simulated

from extensive weather station data represented climate for

determining which subbasins were to be sampled (Thornton

and others 1997). These data were combined to provide an

index of the variability of climate across each study area

(Denton and Barnes 1998) (fig. 4, 5). Physiography was

mapped using regional delineations of subsections (Bailey

1995) and landtype associations as created by Nesser and

Ford (1997) for the ICBEMP. Soils were described from

State Soil Geographic Database (STATSGO) data layers

(Soil Conservation Service 1991). Spatial combinations of

these climate and physiographic data served as surrogates

for approximating the distribution of ecosystem processes

related to landscape composition, structure, and function

(Booth and others 1989; Clark 1989).

The selection of subbasins for sampling presented some

special challenges. The short time frame and limited resources for this study precluded remote area (roadless) sampling, and only allowed the sampling of a few subbasins per

study area (5 to 10 percent of the total area). Therefore,

subbasins were identified for sampling according to the following criteria. Each subbasin was assigned a climate category, a dominant physiographic type, and a dominant soils

type. There were approximately 10 to 20 unique combinations of these three environmental classifications in each

area. Next, transportation data (roads and trails) were used

to qualitatively identify subbasins with suitable accessibility. Sub-basins without suitable road access were removed

from consideration. From the remaining landscapes, we randomly selected 7 to 12 subbasins in each study area to represent biophysical gradients (combinations of climate, physiography, and soils) across the extent of each study area.

Plot polygons were hierarchically nested under subbasins

and defined areas having uniform biological and environmental conditions within subbasins. The primary purpose

for delineating plot polygons was to identify the extent of

area to be described by sampling at the macroplot level.

The entire subbasin was not delineated to the plot polygon

level; only homogeneous areas within subbasins that were

represented by macroplots were delineated. There is a oneto-one correspondence between plot polygons and

macroplots (fig. 5). A relevé-based, gradsect approach was
10 USDA Forest Service RMRS-GTR-92

used to locate, select, and delineate plot polygons sampled

in this study (Austin and Heyligers 1989; Gillison and

Brewer 1985; Mueller-Dombois and Ellenburg 1974;

Whittaker 1967). Gradsect sampling is gradient-directed

sampling of ecosystem attributes focused on problems of

inadequate representation of important but small ecological

settings (riparian stream bottoms, for example), while minimizing survey costs (Austin and Heyligers 1989; Jensen and

Bourgeron 1993). Gradsects were deliberately selected to

contain the strongest environmental gradients to optimize

the database for representing influences of biophysical conditions on ecosystem dynamics and landscape characteristics.

In this study, we used elevation, aspect, and potential

vegetation type (PVT; coarse-scale habitat types) as primary

criteria for gradsect location within subbasins. A PVT describes a specific biophysical setting that supports a unique

and stable climax plant community (Arno and others 1985;

Cooper and others 1991; Jensen and Bourgeron 1993; Pfister

and others 1977). Cover type and structural stages were also

used to identify important successional gradients for sampling (Keane and others 1996a). The cover type classification was taken from the Interior Columbia Basin Ecosystem Management Project (Keane and others 1996a), which

was based on the Society of American Foresters (SAF) and

Society of Range Mangers (SRM) cover type classifications

(Eyre 1980; Shiflet 1994). A process-based structural stage

classification developed by O’Hara and others (1996) was

used to characterize stand development (see Oliver and

Larson 1990). Combinations of potential vegetation type,

500-m elevation classes, 90∞ aspect classes (0–90∞, 90–180∞,

180∞–270∞, and 270∞–360∞), cover type, and structural stage

were used as guides for landscape polygon delineation. Climate classes were not used because they could not be identified in the field and the climate pixel resolution was too

coarse (1 km2

) (Keane and others 2000).

Aerial photos, digital orthophoto quads, and 7.5-minute

topographical maps were used to detect major changes in

the above criteria within sampled subbasins in the field. Plot

polygons were distributed to represent the range of conditions within each subbasin. Matrix worksheets and field

maps of PVT by elevation, aspect class, existing vegetation, and structural stage were used to balance sampling

across major biophysical and disturbance gradients within

each subbasin. Representativeness of the macroplot locations was qualitatively determined by how well the area represented the gradients to be sampled, while taking into account many ecosystem properties such as local topography,

disturbance history, and community composition. Boundaries for plot polygons were digitized using orthophoto

quadrangles, topographic maps, and field observations as

Figure 4—Detail of the Kootenai River study area (KRSA) with sampled subbasins highlighted in red. Colors represent climate

indices used to stratify the study area for gradient-based sampling. We show maps from the KRSA in this and the following

two figures for brevity. The same delineations were used for the Salmon River study area (SRSA).
USDA Forest Service RMRS-GTR-92 11

Figure 5—A closeup of two of the sampled subbasins in the KRSA. Plot polygons, delineated to represent variability within

subbasins, are displayed along with corresponding macroplots.
12 USDA Forest Service RMRS-GTR-92

Figure 6—A closeup of landscape polygons in the KRSA. Landscape polygons represent ecologically distinct units within the

study areas. Wall-to-wall coverage of landscape polygons was used to extrapolate simulated weather and ecophysiological

variables over the entire extent of each study area. The inset shows the distribution of landscape polygons over the entire

KRSA.
USDA Forest Service RMRS-GTR-92 13

Table 2—List of all the databases contained in the ECODATA field database.

Database level Database name Description File name

1. Field data General data General site and vegetation information GF.DB

Location linkage Geographical information LL.DB

Disturbance history Record of all disturbance events DH.DB

Plant composition Species cover and height by size class PC.DB

Downed woody Fuel information DW.DB

Tree data Individual tree measurements TD.DB

Disease and insects Insect and pathogen information DI.DB

Optional data Ecosystem and biophysical information OP.DB

2. Summarized data Fuels Computed fuel loadings and duff depths DW.DBS

Tree and stand data Computed stand and tree characteristics TD.DBS

3. Parameter data BGC parameters Ecophysiological parameters for BGC BGC.MIP

BGC initialization Initial inputs for BGC from the SCOOP program BGC.MIP

WX initialization Inputs and parameters for WXGMRS program WX.MIP

4. Simulated data BGC output file Average annual output from BGC BGC.SIM

WX output file Summarized simulated weather from WXGRMS WX.SIM

guides. The delineated plot polygons had to be at least

0.004 km2 (1 acre) in size.

Macroplots were the finest sampling units and were established within each delineated plot polygon (fig. 5). It was

assumed that ecological conditions within a macroplot were

representative of the ecological conditions of the entire plot

polygon (Mueller-Dombois and Ellenburg 1974). A relevé

approach was again used to locate the macroplot in a representative area within the boundaries of the delineated polygon. Representativeness was qualitatively evaluated from a

wide assortment of environmental and biological conditions

including disturbance history, slope position, and tree and

fuel distribution (Keane and others 1998b, 2000; Kessell

1979). This task was quite easy as polygon homogeneity

was already high because the plot polygon boundaries were

already defined to minimize spatial variability in biophysical conditions. Macroplots were established at least 50 m

from any edge representing a distinct boundary between

cover types or structural stages. A wooden stake, labeled

with plot number and date, was driven into the ground to

mark the center of the representative portion of the polygon. In the future, these plots could be permanently located

with a long-term monument (rebar, for example) to allow

for monitoring of changing conditions.

Although the modified gradsect-relevé approach used in

this study has many limitations, it is the only approach that

ensures that wide ranges of ecosystem gradients are sampled

given the limited sampling budget. In contrast, stratified

random sampling describes the distribution of existing conditions using abundant plots that are difficult and costly to

install and measure. Stratified random sampling allows comprehensive analysis using parametric statistics with tests for

significance, but it does not always capture variability in

the processes that control vegetation demography, namely

fire and succession (Austin and Gaywood 1994). Our relevé

approach emphasizes the representativeness of a sample in

terms of landscape composition, structure, and function

rather than statistical validity. Stratified random sampling

is costly, resource intensive, and sometimes difficult to

implement. Traditional stratified sample design focuses on

minimizing the variance in one variable of interest, while

other variables are not addressed. A major limitation of the

gradsect-relevé approach, however, is that it tends to be subjective and potentially biased. It requires extensive knowledge

of landscape characteristics and disturbance dynamics. This

knowledge often differs across sampling crews or across

geographic areas (Keane and others 1998a,b, 2000).

Macroplots were circular and approximately 0.04 ha. The

size of the macroplot was adjusted upward to 0.08 ha in

forests where trees were large (diameter more than 50 cm)

and slopes were steep (greater than 20 percent). Comprehensive and standardized ECODATA methods were used to

sample ecological characteristics at the macroplot (Keane

and others 1990; Hann and others 1988; Jensen and others

1993). ECODATA consists of a wide variety of sampling

methods, plot forms, databases, and analysis programs that

may be integrated to design specific inventory and analysis

applications. Several ECODATA sampling methods were

combined and modified to create a gradient-based sampling

inventory for each macroplot. Details of the sampling procedures are presented in the ECODATA handbook (Hann and

others 1988) and will not be discussed here, but an overview

of collected data is included next and presented in table 2.

Biophysical parameters were evaluated at each macroplot

using the ECODATA General Form (GF) and methods.

Measured variables included elevation, aspect, slope, soil

characteristics, and Potential Vegetation Type (PVT). Geographical position was recorded using a global positioning

system. Cover and height of all vascular and nonvascular

(mosses and lichens) plant species were estimated using the

Plant Composition (PC) methods. Replicated microplot sampling techniques were not employed in this study, as the

objective was to characterize ecological settings for

mapping (inventory), rather than to quantitatively describe
14 USDA Forest Service RMRS-GTR-92

plant composition for comparison purposes (monitoring).

Diameters, heights, ages, growth rates, and crown dimensions were estimated for all trees within a macroplot boundary using the Tree Data (TD) methods. The forest floor was

described using the ECODATA Downed Woody (DW) procedures recording fuel loadings, and fuel, duff, and litter

depths. Ecophysiological measurements were taken using

specialized methods developed for this study and recorded

on the Optional (OP) form. These data included leaf area

index (LAI) measured with a LAI-2000 (LI-COR 1992),

leaf longevity by tree species, soil water holding capacity,

and qualitative estimation of the fire regime. ECODATA

Disturbance History (DH) methods (Hann and others

1988) were used to evaluate recent evidence of insects,

diseases, grazing, harvesting, and fire activity for each

macroplot.

All measurements were entered into databases using specialized data entry programs (Hann and others 1988). Exhaustive data checking programs were then employed to

evaluate each data value for range violations and illogical

combinations (Keane and others 1990). Intermediate databases were checked against plot forms for further quality

control and assessment. Checked data were then summarized and exported to ASCII text files for input to various

database analysis software and simulation programs using

specialized computer programs specially developed for this

study (and discussed later).

Permanent macroplots were established in four representative plot polygons in two subbasins in each study area to

temporally describe important ecosystem processes. Several important ecosystem processes were measured at various time intervals on these plots to quantify simulation model

parameters and understand the temporal dynamics in process classifications. Litterfall, soil respiration, and decomposition rates were among the more important variables

measured at hourly, daily, and monthly time steps for parameterizing and verifying ecosystem simulations. Plot polygons for permanent plots were selected based on the climate, soil, and landform as mentioned above. One permanent macroplot was established in four topographic locations (north- and south-facing, low- and high-elevation settings) in the two selected subbasins per study area. Permanent macroplot locations were also selected so that major

potential vegetation types and cover types were represented

in each study area.

Seven litter-fall traps were placed in a box-like pattern

within each permanent macroplot. Organic material that fell

into the traps were sorted and weighed by the following categories: (1) needles, (2) twigs (0 to 0.25 inches diameter),

(3) small branchwood (0.25 to 1 inches diameter),

(4) branchwood (1 to 3 inches diameter), (5) logs (3+ inches

diameter), (6) other—cones and reproductive parts, undergrowth leaves (deciduous leaves), grass. Leaf area index

was measured with a LAI-2000 plant canopy analyzer

(LI-COR 1992) each time the littertraps were emptied (on a

monthly basis). Three soil respirometers were installed at

each permanent macroplot to measure soil respiration

(Toland and Zak 1994). Respirometers were 2-foot diameter,

2-foot long plastic containers with the bottoms removed.

Containers were driven into the ground until they were about

1 to 2 inches into mineral soil. Then, a canister of soda lime

of known weight was placed in the container and the top

secured. After 24 hours, the soda lime was removed and

weighed to determine respiration. Soil respirometer measurements were done coincidentally with littertrap measurements.

LEIS Database

A hierarchically structured database was designed to organize the complex information and different data types used

in the LEIS (fig. 7; table 2). Data collected in the field occupy the top of the database structure. These are actual measurements of ecosystem characteristics and represent the

most accurate and defensible data in the database. These

data are the foundation of the predictive landscape modeling in LEIS and were stored in the ECODATA format (Keane

and others 1990). Computer programs were then developed

to summarize the information in the ECODATA database to

occupy the next level of the LEIS database. For example,

tree density (trees ha-1), basal area (m2 ha-1), and stand age

(years) were computed from the individual tree measurements

stored in the ECODATA Tree Data (TD) field database.

The ECODATA database provides a solid foundation for

LEIS, but is not the only source of data included in the final

predictive landscape analysis. Many other important ecosystem attributes have predictive value but are too costly or

difficult to sample over large areas. For example, average

annual precipitation and evapotranspiration are important

climate variables that dictate plant dynamics and demography

(Anderson and others 1998; Woodward 1987); however,

meaningful spatially explicit measurements of these attributes

require specialized equipment and years of sampling, which

Figure 7—Levels in the hierarchal organization of the LEIS

database. As levels proceed farther from the field database

they become more removed from reality.
USDA Forest Service RMRS-GTR-92 15

would be prohibitive in a management-oriented, inventorybased sampling effort. Instead, this study quantifies those

important ecosystem characteristics using extrapolation techniques and simulation modeling.

Simulation model input and parameter data occupy the

third level in the LEIS database structure (fig. 7). These data

were computed from the field and summary databases to

create the input parameters and initialization files required

by the set of simulation models used in this study. Simulations were run at both the macroplot and landscape polygon

(described below) levels. In this implementation of LEIS,

we simulated ecosystem processes for all polygons across

the study area. These polygons differed from plot polygons

in that they were created from satellite imagery or stand

maps and were delineated across the entire study area (wallto-wall coverage). Simulations at the macroplot level were

used during the model parameterization phase to ensure that

simulations logically represented biophysical and ecological characteristics and gradients.

The last and lowest level in the LEIS database contains

simulated data, which are summarized outputs from three

simulation models discussed in the next section. These simulated direct and resource gradients are one of the main characteristics that set the LEIS approach apart from other gradient modeling studies.

Ancillary Spatial Data (LEIS GIS)

A key component of predictive landscape analysis is the

availability and quality of spatial data used to represent predictor variables across the entire study areas (Franklin 1995).

Electronic maps quantifying the spatial distribution of important direct, functional, and resource gradients are essential to the implementation of LEIS. These data layers were

obtained or derived from several sources, using several

methods (table 1). Other data layers were created using the

simulation models described below. From this point on, the

spatial database (GIS layers) used in LEIS is referred to as

the LEIS GIS.

All spatial databases contained in the LEIS GIS are listed

in table 1. Data layers from the Interior Columbia River

Basin Project were used in the hierarchical sampling stratification of the study areas as described in previous sections.

Ancillary data (for example, hydrography and transportation data) were obtained from the Kootenai National Forest

for the KRSA and the Nez Perce National Forest for the

SRSA. Topographic variables were derived from DEMs

obtained from the National Elevation Database (USGS

2001). Data layers for elevation, aspect, slope, profile curvature, and planform curvature were derived from DEMs

using standard GIS techniques (ESRI 1998; USGS 1987).

Profile curvature is the curvature of an area in the direction

of the slope. It is calculated as the second derivative of the

surface; that is, it represents the slope (or rate of change) of

the slope (ESRI 1998; Moore and others 1991). Convex

profile curvature is indicative of shoulder slopes, and

concave curvature is characteristic of foot slopes. Planform

curvature is the curvature along the contour of a slope; it is

perpendicular to profile curvature. Convex planform curvature is characteristic of nose slopes or buttes and concave

planform curvature characteristic of head slopes or cirques.

In all analyses, aspect was linearly transformed to distance

from northeast (45∞) (Beers and others 1966) to linearize

the “circular” distribution of aspect. High and low transformed values of aspect represent northerly directions (for

example, 0∞ = 360∞). By linearly transforming aspect we

could include aspect as a continuous variable opposed to a

discrete classified variable in statistical analyses.

Soil depth and soil texture data (used in simulation modeling) were derived from DEMs, STATSGO soils data, and

hydrological modeling (Beven and Kirkby 1979; SCS 1991;

Zheng and others 1996). Methods for deriving and compiling simulated and remotely sensed gradients are described

in the next two sections.

Simulated Spatial Databases

Three ecosystem simulation programs were used to represent mechanistic environmental gradients in this project.

Annual output data from each program were summarized

for each landscape polygon and then compiled as separate

data layers in the LEIS GIS. For example, mean annual precipitation for each landscape polygon was calculated from

20 years of daily weather simulated by the DAYMET program (described in the next paragraph). Another example:

average annual net primary productivity for each landscape

polygon was calculated from GMRS-BGC using data derived from the ECODATA database and DAYMET weather

output for each landscape polygon. Each simulation model

was parameterized using data representing site characteristics and ecophysiological rates and constants, the majority

of which were taken from the field database. Model parameters that were not sampled during the field campaigns were

derived from the literature or existing databases (Keane and

others 1996b) (table 2; appendix B). Each landscape polygon was assigned a parameter list for initialization of the

simulation model (DAYMET, WXGMRS, and GMRSBGC). Once the modeling was completed, outputs from each

model were joined back to the corresponding landscape

polygon to create maps for the LEIS GIS. See appendix B

for parameter lists for GMRS-BGC by landscape polygon

type.

Weather was computed for each landscape polygon using the DAYMET program developed by Thornton and others (1997). DAYMET is a sophisticated spatial implementation of the MTCLIM model originally developed by

Hungerford and others (1989) and improved by Running

and Thornton (1996). Daily weather values of maximum

and minimum temperature, relative humidity, precipitation,

and solar radiation are calculated across each study area

using physiographic relationships and adiabatic lapse rates

to extrapolate 20 years of weather data from over 300

weather stations in and around the study area (Thornton

1998). Output from DAYMET was used as input for
16 USDA Forest Service RMRS-GTR-92

WXGMRS and GMRS-BGC to create other simulated databases. This model required extensive parameterization for

each study area, but the final accuracies were within 1 ∞C

and less than 1 ∞C bias.

The program WXGMRS was used to summarize the

daily weather sequences computed by DAYMET into integrated measures of local weather/climate for each landscape

polygon. WXGMRS uses the five DAYMET weather variables to summarize and simulate other important weatherrelated variables that may represent useful predictive direct

gradients, such as potential evapotranspiration, soil water

potential, and vapor pressure deficit (see table 1 for list of

WXGMRS outputs). While these variables were not used

in this implementation of the LEIS, WXGMRS also computes daily fire danger indices for each landscape polygon using procedures detailed in Albini (1976) and Anderson (1982).

Important biogeochemical ecosystem dynamics for each

landscape polygon were simulated using GMRS-BGC, a

modification of the ecosystem simulator BIOME-BGC developed by Running and Hunt (1993) and Thornton (1998).

GMRS-BGC simulates fluxes of various carbon, nitrogen,

and water pools at the stand- and landscape-level using

mechanistic ecophysiological process relationships. It is a

“Big Leaf” model where stand conditions are represented

by the various carbon pools (Running and Coughlan 1988).

Input and output routines in Thornton’s (1998) version of

BIOME-BGC were the only functions modified to create

the GMRS-BGC variant used in this study. A suite of AMLs

and C++ routines were used to create the GMRS-BGC input parameter and initial condition files for each landscape

polygon. Then, GMRS-BGC was executed for 250 years to

allow the initial conditions to equilibrate with input weather

data, which was cycled every 20 years. Results for six simulated daily biogeochemical variables (table 1) were summarized across the next 100 years of simulation for each

landscape polygon and written to the LEIS GIS database.

Simulated variables represent direct, resource, and functional

gradients used to predict spatial landscape characteristics

across each study area. In addition to each landscape polygon on the landscape, the three simulation models were run

for every macroplot, and output was joined with the field

data to create an overall LEIS tabular database with both

collected and simulated variables for each macroplot. Simulations for each macroplot aided in data exploration but were

not used in the predictive landscape analyses. To ensure

consistency, variables simulated for each landscape polygon (with wall-to-wall coverage for each study area) were

used as independent variables in the final statistical mapping algorithms.

Remote Sensing and Image Classification

Landsat-Thematic Mapper 5 satellite images (scenes)

were obtained for each study area for dates in 1995. These

imagery data were used in two ways. First, standard image

classification approaches were used to directly delineate

landscape polygons, an additional landscape unit that was

used in spatial extrapolation of WXGMRS and GMRS-BGC

output (see next sections and fig. 6). Landscape polygons

differ from plot polygons in that each study area had wallto-wall coverage for landscape polygons. Plot polygons, on

the other hand, represented homogeneous areas within

sampled subbasins (6th-code HUCs) that were represented

by macroplots (fig. 5). There was a one-to-one correspondence between macroplots and plot polygons and a one-tomany correspondence between macroplots and landscape

polygons (fig. 6).

We conducted unsupervised classifications (cluster analysis of the spectral data) to determine how well the ECODATA

database would serve to delineate the entire landscape into

landscape polygons. Database queries were built to define

unique ecological classes within the ECODATA database.

For example, plots with a large proportion of western

redcedar and associated vegetation were assigned to the

western redcedar landscape polygon class. A reference database was compiled where every plot was assigned to one

of several ecologically distinct classes. This database was

implemented as a signature (or training) database in a fuzzy

classification/fuzzy convolution routine within the ERDAS

IMAGINE image processing software to create complete

coverage of landscape polygons in each study area (fig. 6;

table 3) (ERDAS 1999). We included elevation and aspect

data to improve these classifications (Fahsi and others 2000).

The resulting landscape polygons served as the simulation

units for DAYMET, WXGMRS, and GMRS-BGC.

Overall accuracies for the landscape polygon coverages

(table 3) for each study area were 62 percent (Kˆ = 0.56) for

the KRSA and 42 percent (Kˆ = 0.39) for the SRSA.

The second use for satellite data in LEIS was direct

integration of raw reflectance and derived spectral transformations into gradient-based, statistical analysis. This

Table 3—Initial classification schemes used in classifying

satellite imagery. Note that the only difference is the

inclusion of a separate class for western larch (LAOC)

in the KRSA. To investigate different methods for

ecologically delineating the study areas, the

classifications were further separated using amount of

biomass (structure) in the KSRA and by aspect in the

SRSA.

Kootenai River Salmon River

study area study area

1 ABGR 1 ABGR

2 ABLA 2 ABLA

3 HERB 3 HERB

4 LAOC 4 PICO

5 PICO 5 PIPO

6 PIPO 6 PSME

7 PSME 7 SHRUB

8 SHRUB 8 THPL

9 THPL 9 Cloud/snow

10 Cloud/snow 10 Water

11 Water 11 Rock/developed

12 Rock/developed
USDA Forest Service RMRS-GTR-92 17

maximized the utility of spectral data and derivatives as additional predictors for landscape characteristics that may be

difficult to remotely sense using traditional approaches. Atsensor reflectance, spectral principle components (PCA1,

PCA2, and PCA3), Kauth-Thomas transformations

(BRIGHT, GREEN, WET) (ERDAS 1999; Kauth and Thomas 1976), Modified Normalized Difference Vegetation

Index (MNDVI) (Nemani and others 1993), and Leaf Area

Index (LAI) were all treated as important gradients in the

final predictive landscape model (table 1). At-sensor

reflectances (raw spectral information about the landscape)

were calculated from the TM5 scenes based on algorithms

in Markham and Barker (1986). These algorithms converted

the digital numbers contained in the raw imagery to reflectance units (mW (cm2 sr mm)-1) and corrected for sun angle

at the time of image acquisition. Using at-sensor reflectance

values, we derived spectral principle components and KauthThomas transformations for each TM5 scene. These transformations are examples of ways to mathematically distill

the information contained in several bands (for example,

the spectral bands of a TM5 scene) down to three or four

data layers that contain the majority of the information in

the original raw data (ERDAS 1999; Jensen 1986). The

Kauth-Thomas transformation differs from principle components transformation in that it has been “tuned” so that

the output represents the brightness, greenness, and wetness of the landscape. Leaf Area Index and MNDVI were

derived using measurements contained in the ECODATA

database and spectral band ratios using the methods described in Nemani and others (1993).

Landsat Thematic Mapper scenes are readily available

to land management agencies, but TM data do not always

generate useful ecosystem-based maps (Bolstad and

Lillesand 1992; Keane and others 1998a,b, 2000; White and

others 1995). Imagery products that are unavailable to land

management agencies because of high cost, high data storage requirements, or limited license distribution were not

considered in this study.

Gradient Analysis and Modeling

In this study, the gradient model is not a set of mathematical algorithms implemented as a computer program.

Instead, the LEIS gradient model is composed of: (1) the

suite of integrated hierarchical spatial and tabular databases

created for this study that represent the distribution of important predictive environmental gradients (tables 1 and 2),

and (2) the statistical steps for creating predicted landscape

maps for specific study areas. The LEIS gradient model is

dynamic in structure. A wide variety of multivariate statistical analyses may be used to develop empirical predictive

algorithms from the spatial and tabular databases. The

ECODATA database provides information to be used as

dependant or response variables. Data layers in the LEIS

GIS (table 1) serve as potential independent or predictor

variables in the multivariate modeling. Predictive algorithms

(the resultant statistical models) are used together with

spatial predictors in the LEIS GIS to compile maps of landscape characteristics.

This generalized approach allows the greatest flexibility

in gradient model development but at the cost of limited

implementation. Development of the empirical predictive

algorithms requires extensive expertise in statistical analysis, ecological interrelationships, and database management,

so implementation of LEIS in other areas may require specialized personnel. However, these protocols can be easily

adjusted or formulated to generate new predictive equations

for new areas or new applications. And they can be refined

and modified as additional field data or gradient GIS layers

become available. The protocols may be easily implemented

into standard statistical software so that local statistical experts are not needed, but this will require additional funding.

Many statistical techniques may be used to develop the

empirical algorithms for landscape characteristics from the

environmental gradient information in the LEIS databases.

First, standard statistical summary techniques, correlation

analysis, and/or Classification And Regression Trees

(CART) (Breiman and others 1984) may be used to explore

the information in the database and the predictive value of

all appropriate variables, to determine possible relationships

between and across gradients, and to reduce the list of independent variables included in the final predictive models.

Scatterplot matrices and multidimensional graphs of important environmental variables may be created to identify and

select the most powerful predictive variables for various

ecosystem characteristics (Mueller-Dombois and Ellenburg

1979; Sokal and Rohlf 1995).

Ordination, detrended correspondence analysis and canonical correspondence analysis are additional methods of

indirectly investigating gradients that control ecosystem

characteristics using vegetation composition and structure

(Gauch 1982; Kessell 1979). However, we only used these

tools to classify vegetation, not to map the classified categories. Some of the vegetation classifications in ECODATA

needed refinement to more accurately describe and predict

vegetation communities. Therefore, we used the techniques

mentioned above to validate and then refine the ECODATA

vegetation classification keys and categories. Leavell (2000)

used these techniques along with the LEIS database to generate a new vegetation classification for the Kootenai National Forest using attributes in the LEIS database.

We used scatterplot matrices, statistical summaries, and

CART in this study to investigate relationships in the LEIS

GIS database. Several multivariate statistical analysis techniques were employed to create predictive algorithms. General linear modeling was used when the dependent variable

was continuous, such as basal area (m2 ha-1). When dependent variables were binary (for example, presence/absence

of fuel model 5), log-linear modeling and logistic regression techniques were employed to create predictive landscape models. Hosmer-Lemshow goodness of fit (Hosmer

and Lemshow 1989) and Receiver Operating Characteristic

Curves (Metz 1978) were used to assess fit of logistic modes.

Curve-fitting procedures, neural networks, or General
18 USDA Forest Service RMRS-GTR-92

Additive Models could also prove to be useful for creating

predictive landscape maps in LEIS (Austin 1984; Austin

and others 1994; De’ath 1999); however, because of limitations in time and computing power we were unable to thoroughly investigate the many potential methods for predictive landscape modeling. Ongoing research focuses on using CART, General Additive Models, and polynomous Logistic Regression to create predictive landscape maps of fuels

and fire regime in the two study areas.

Demonstration of LEIS

The ability of LEIS to create maps useful for ecosystem

management was demonstrated for both study areas. Although the databases and analyses mentioned next could be

used to create many resource-based maps, only three were

selected for mapping on both study areas to prove that LEIS

is a viable approach to predictive landscape mapping. The

three maps were selected because they have direct application to many current forest-planning issues.

Basal Area—A map of basal area (m2 ha-1) may be especially useful to timber use, restoration efforts, and other land

management projects. A map depicting potential timber basal

area can allow for the identification of areas of harvestable

timber resources. Current inventory techniques quantify this

variable, but at great cost and time requirements. This map

was created to evaluate its ability for assisting timber harvest planning rather than implementation. Basal area was

computed from ECODATA tree information for each

macroplot. Correlation analysis was used to identify those

LEIS variables that have the ability to predict basal area.

The most significant variables were then used in a general

linear model used to predict basal area for each study area.

We developed the models using stepwise iteration, using

minimum improvement in R2 as a threshold for the final

model. Model fit was assessed using individual parameter

estimates, confidence intervals, and model R2. Accuracy of

the final continuous surface was assessed using the regression techniques described later.

Western Redcedar Distribution—A map depicting the

distribution of specific forest or nonforest plant species

would be useful in many phases of resource management

that focus on particular species, including threatened and

endangered plant species or species with high economic

value. We arbitrarily chose western redcedar, but any species represented in the study area could potentially be

mapped. If the species is not in the overstory or is quite

rare, and index may be developed using measured parameters from the ECODATA database. This index of potential

habitat would then be used as a response variable in the

final predictive landscape mapping. For each plot we determined whether western redcedar was present or absent based

on species composition lists. These data were converted into

a binomial variable (presence/absence of western redcedar)

and used as a response variable in a stepwise logistic

regression model for each study area. Independent variables

were drawn from the LEIS GIS (table 1), variables meeting

the criteria for entry to the model were plotted to test for

colinearity. Final models were corrected for prior probabilities using classification tables. Receiver operating characteristic curves (ROC curve) (Metz 1978) and model fit were

determined using maximum-likelihood analysis and the

Hosmer and Lemshow Goodness of Fit Test (Hosmer and

Lemshow 1989).

Fuel Model—Maps of the spatial arrangement of fuels

are a main requirement for successful fire management using new tools such as the FARSITE fire spread model. These

maps are particularly difficult to create because traditional

remote sensing approaches fail to discern subtle differences

in fuel configuration based on spectral data alone (Keane

and others 1998a,b, 2000). Based on National Fire Danger

Rating System fuel model assignments (Anderson 1982) in

the ECODATA database, we constructed three separate logistic regression modes for each study area that predicted

the potential for any cell on the landscape to be fuel model

5, fuel model 8, or fuel model 10 (the three predominant

fuel models in forested areas of northern Idaho and northwestern Montana). We mapped these fuel models because

they represented the largest proportion of forested fuel models in the study areas. Logistic regression models were constructed using methods identical to the models created for western redcedar distribution described in the previous section.

Accuracy Assessment

Accuracy assessment was performed on all levels of data

and maps. Computed and simulated data were compared

against the plot measurements when possible (LAI, for example). Model input parameters were compared with field

measurements from the eight permanent process-based

macroplots described in an earlier section. Accuracy of most

ancillary data layers was also determined from the macroplot

information (Congalton 1991). For instance, Keane and others (1998a) found the DEM for the Selway-Bitterroot Wilderness complex had an average error of 15 m. Topographic

variables surveyed at each macroplot were plotted against

the DEM and DEM derivatives to assess the accuracy of

the topography data used as landscape-scale direct gradients and as inputs to each of the models that simulated direct and resource gradients.

Accuracy of the three output maps for each study area

(gradient modeling and image processing) was computed

using hierarchical techniques presented in Keane and others (1998a,b, 2000), which were based on methods presented

in Congalton (1991), Mowrer and others (1996), and

Congalton and Green (1999). Initial testing, validation, and

verification of existing and developed spatial data layers

involved overlaying the layer in question with the

ECODATA plot data and comparing measured values with

the predicted landscape maps (Hyyppa and others 2000).

Accuracy assessment procedures differed by the type of map:

(1) categorical maps (maps that portray discrete, nominal

classification categories), and (2) continuous maps (polygon values measured using continuous data scales).
USDA Forest Service RMRS-GTR-92 19

Accuracy assessments of categorical maps were accomplished using the methodologies presented in Congalton

(1991), Woodcock and Gopal (1992), and Gopal and Woodcock (1994). Twenty percent of macroplot data were held

out from model development and used in assessing accuracy of the final maps. Omission and commission errors were

computed for each map category, and a final accuracy was

estimated using the KHAT statistic (Congalton 1991;

Congalton and Green 1999; Mowrer and others 1996). The

KHAT statistic describes agreement between classified data

and reference data and adjusts overall accuracy to account

for the uneven distribution of plot data across classification

categories (Congalton 1991; Congalton and Green 1999).

Accuracy of continuous maps, such as elevation, aspect, and

slope, were computed using a regression approach similar

to that used by Keane and others (1998b). Observed values

for each polygon (that is, plot data) were regressed with the

predicted values (that is, polygon assignments) from the

maps using a linear, least-squares regression (Sokal and

Rohlf 1995).

Results

Field Sampling and LEIS Databases

Over 900 macroplots were measured by eight to 10 crews

of two people each over the 20 subbasins selected in both

study areas during the 1995 field season. It took more than

3 hours for a crew of two people to measure the many ecological variables on each forested macroplot. Measurements

requiring extensive expertise such as fire regime characterization and insect and disease surveys were performed by a

select group of four highly trained people to ensure consistency in estimations. All data were entered into appropriate

ECODATA databases and reviewed for quality before analysis. Graphical/statistical analysis showed distributions of

plots were similar to distributions of elevation and existing

vegetation across each study area (fig. 8 and 9). However, it

appeared graphically that plots in the KSRA might have been

unevenly weighted to elevations between 750 and 1,000 m

and to elevations between 1,450 and 1,750 m in the SRSA

Figure 8—Distribution of plots (black bars) and distribution of elevation (gray bars) for each study area. Results for two-sample

Kolmogorov-Smirnov (K-S) tests for unique distributions are shown. The distribution of elevation represented by the macroplots

is indicative of the distribution of elevation over each study area.
20 USDA Forest Service RMRS-GTR-92

(fig. 8). Plots in the SRSA appeared to misrepresent the proportions of herb and cedar cover types found in the study

areas. There were more plots in the herb cover type than

warranted by the proportion of the study area in the herb

cover type. In contrast, there were fewer plots in the western redcedar cover type than warranted by the proportion of

the area in this type (fig. 9).

Each database presented in table 1 was created using

computer programs developed specifically for this project.

Each program scanned the data, and if critical fields were

missing for a macroplot due to mistakes during sampling or

by data entry crews, the programs entered a missing value

(a –9999 was used for this project) for the summarized or

simulated data field. Some plots had so many missing or

bad values that they were eliminated from the database. As

a result, only 926 plots (525 on the Lower Salmon and 486

on the Kootenai study area) were kept in the LEIS database.

Computer programs of UNIX computer instructions (that

is, shell scripts) and Arc Macro Language routines (ESRI

1998) were written to execute each computer program and

simulation model in a sequence that would result in the creation of all databases in less than 4 hours. This allowed quick

and efficient updates of the LEIS database as new field data

became available and as quality control was implemented.

The final format of the raw and summarized database was

general enough to allow compilation in any of a number of

existing or ancillary database and statistical software packages.

We explored structure and information content in all LEIS

databases using scatterplot matrices (fig. 10, 11, and 12).

This suggested that several of the spatial data layers in the

LEIS GIS were strongly correlated, not surprising since

several variables represented by certain layers served as

inputs for other layers (average temperature, TAVE, and

elevation, ELEV, for example). Instead of removing collinear parameters from the database from the beginning, we

went ahead and created models using the full database. We

Figure 9—Distribution of plots (black bars) and

distribution of dominant overstory (gray bars)

for each study area.
USDA Forest Service RMRS-GTR-92 21Figure 10—The distribution of satellite reflectance derivatives over at-sensor reflectance in the near infrared. Near-infrared reflectance (NIR) or TM Band 4 is a measure of

biomass. Principal component (PC) 1 and NIR were more correlated in the KRSA than the SRSA. Whereas PC 2 and NIR were more cocorrelated in the SRSA than the

KRSA. This scatterplot “matrix” approach was used to determine whether or not there were independent variables that were highly correlated. Correlated independent

variables were removed from the final predictive landscape models based on p-values of the parameter estimates.
22 USDA Forest Service RMRS-GTR-92Figure 11—The distribution of weather parameters over elevation. Elevation is commonly used in gradient modeling as an indirect gradient that represents temperature and

precipitation. Temperature, vapor pressure deficit, potential evapotranspiration, and dewpoint were highly intercorrelated in each study area. For this reason, we removed

all but the most important of these variables from the analysis.
USDA Forest Service RMRS-GTR-92 23 Figure 12—Distribution of ecophysiological parameters over elevation. In general, elevation and ecophysiological variables were unrelated. Net ecosystem production (NEP), net primary productivity (NPP), and ecosystem respiration (ER) all describe rates and status of the carbon cycle. Outflow is water received as precipitation but not evaporated or transpired.
24 USDA Forest Service RMRS-GTR-92

removed the correlated independent variables with the highest P-value if they remained in the model after stepwise selection. As a result, elevation (ELEV) failed to enter any of

the regression models for either study area. This indicated

that simulated direct gradients, such as average annual

vapor pressure deficit (VPD) and average annual incident

solar radiation (SRAD), were more important than topographic variables representing indirect gradients.

Landscape Mapping and Accuracy Assessment

Accuracies of spatial data in the LEIS GIS used as independent variables and output maps were evaluated by

comparing maps with values measured at each macroplot.

Measured topographic variables matched well with values

derived from the DEM (fig. 13 and 14). Predicted soil depth

varied from 0 to 1.5 m, and leaf area index varied from

Figure 13—Topographic parameters measured at each macroplot on the KRSA compared with values extracted from the DEM

and DEM derivatives. R2 is a measure of the variability within the data described by a linear fit. M is the slope of the regression

line. The slope of this line is 1.0 for a perfect one-to-one correspondence between measured values and the DEM. Y0 is the

value on where the regression line intersects the y-axis; this value would be 0 in the case of one-to-one correspondence

between measured values and the DEM.
USDA Forest Service RMRS-GTR-92 25

1.0 to 4.5 m2 m–2. Values were plotted against measured

values from each macroplot (fig. 15 and 16).

Overall, derived and simulated values representing direct and resource gradients such as ecosystem respiration

(ER), net ecosystem production (NEP), SRAD, and VPD,

were most important in the final landscape models. In each

case, elevation was less important than variables traditionally represented by elevation in ordination-based gradient

analysis (TAVE or PRECIP, for example). This suggests that

simulation modeling of direct gradients provided significantly more information than indirect gradients for these

predictive landscape models.

General linear models predicted basal area well (fig. 17;

table 4). Predicted basal area values varied from 0 to 192 m2

ha-1 in the KRSA and from 0 to 219 m2 ha-1 in the SRSA.

The Durban-Watson Statistic, a measure of independence

of residuals, indicated that autocorrelation was not a problem in the empirical models predicting basal areas; an

important concern as each of the predictor variables was

based on spatial data. Predicted basal area values fit well

with basal area measured at each macroplot (fig. 13). Spectral (TM5) predictor variables (for example, Reflectances,

MNDVI, LAI) were important in general linear models

from each study area (table 6). Ecophysiological variables

(for example, NEP and ER) were most important in predicting basal area distribution in the KRSA, and weather

variables more important in the SRSA. Measures of

biomass (LAI and MNDVI) and soil depth (SDEPTH) were

positively related to basal area in both final models

(table 6).

Figure 14—Topographic parameters measured at each macroplot on the SRSA compared with values extracted from the DEM

and DEM derivatives. See caption for figure 9 for description of R2 , M, and Y0

.
26 USDA Forest Service RMRS-GTR-92

Maps of the distribution of western redcedar were 88 and

95 percent accurate in the KRSA and SRSA, respectively

(fig. 18). KHAT scores indicated that predicted western

redcedar distribution was significantly improved over chance

agreement between predicted and reference data that were

not included in the model-building database. HosmerLemshow goodness of fit tests and ROC curves indicated

that the final logit models fit the data quite well (fig. 19;

tables 5 and 6). In the KRSA, maintenance respiration and

incident solar radiation were the most important parameters

in predicting distribution of western redcedar. Spectral and

physiographic gradients were most important in the final

model for the SRSA.

Accuracies of fuel model maps ranged from 65 percent

for fuel model 5 in the KRSA to 84 percent for fuel model

10 in the SRSA (fig. 20). Overall accuracies were quite high

while KHAT statistics were quite low because we applied

the KHAT statistics to binomial maps rather than multiple

class maps. While a chi-square may be more appropriate

for these two-way classification tables, the traditional chisquare does not account as well as KHAT for uneven proportions in the response variable. Hosmer-Lemshow goodness of fit tests and ROC curves indicated that the final logit

models fit the data well (fig. 19; tables 5 and 6). KauthThomas greenness (GREEN) from the TM5 imagery was a

good predictor of fuel model in the KRSA while spectral

information and incident solar radiation (SRAD) were important predictorsin the SRSA. In both study areas, autotrophic

respiration was an important predictor of fuel model 10.

Discussion

Clearly, integration of remote sensing, simulation modeling, and direct gradient analysis provided an efficient and

successful approach for developing maps for broad-scale

assessments and ecosystem management. The ability of remote sensing and ecosystem simulation to portray subtle

changes in landscape characteristics coupled with the

Figure 15—Predicted versus measured leaf area index (m2 leaf area per m2 ground area). Maps are of LAI predicted using

satellite imagery along with the modified normalized vegetation index method from Nemani and others (1993). Both models

underpredicted LAI.
USDA Forest Service RMRS-GTR-92 27

ability of gradient modeling to predict geographic distributions of biotic communities can enable land managers to

quickly construct ecological maps of project areas for use

in land management planning. Gradient modeling also

allows spatially explicit descriptions of important processes

on the landscape, which are important in monitoring change

over time. The findings of this study may be used to develop an automated system that will create maps of

ecosystem characteristics for any area using combinations

of field inventories, remotely sensed digital data, existing

and derived spatial data, and gradient analysis.

Field and Ancillary Data

Field sampling strategies emphasized the collection of

data that best represented landscape patterns and ecosystem

processes across each of these broad study areas. We feel

that the main goals of the sampling efforts were achieved:

(1) The ECODATA database served as reference for the ecological classification of satellite imagery to

landscape polygons, (2) field data provided the appropriate

information for the initialization and parameterization data

for simulation programs (that is, WXGMRS and GMRSBGC), and (3) the ECODATA database provided a wide

variety of information that served as potential response

variables in predictive landscape models. The ECODATA

database was portable and easily manipulated using common spreadsheet, database, and GIS software. In future applications, combinations or indices of variables from the

database could represent more complex response variables

such as forage status or rare plant habitat. For example, an

index combining stand composition and structure data with

Figure 16—Predicted versus measured soil depth (m). Soil depth was developed using summary statistics of soil depth from the

ECODATA database, the DEM, and a hydrologic model. Low correspondence between measured and predicted values may

result from inadequate sampling methods for accurately representing the variability of soil depth at these broad scales. The

model underpredicted soil depths in the KRSA. Overall the distribution of soil depth seems reasonable, with shallow soils on

steep slopes and deeper soils in valley bottoms and broad flat areas.
28 USDA Forest Service RMRS-GTR-92

Table 4—Results of general linear modeling of basal area (m2 ha-1) using data from the LEIS GIS.

KRSA SRSA

R2 = 0.408, D-W statistic = 1.4 R2 = 0.470, D-W statistic = 1.4

Parameter Estimate P-Value Parameter Estimate P-Value

Intercept –23120 0.0603 Intercept 6695.69374 0.0006

REFLC1 766388 0.0336 REFLC4 3983.58311 <0.0001

REFLC2 141.59805 0.0006 LAI 37.27595 <0.0001

REFLC3 0.39031 0.0077 WET 16.38068 0.0004

REFLC5 –26.62651 0.0181 PCAC2 –5918.53028 <0.0001

REFLC6 37.90413 0.0315 PCAC3 –3885.11360 0.0012

WET –0.20748 0.0088 SDEPTH –0.05229 0.0191

PCAC2 –11.32196 <0.0001 PSAND 0.68969 0.0285

NDVI 5.86468 0.0012 EFFPPT 5.47525 0.0007

SDEPTH 209.22240 <0.0001 TDAY 209.18574 0.0038

CURVE 170.07526 0.0003 RH –1.16220 0.0065

PLAN_CURVE –0.13953 0.0331 PET –2.48401 0.0093

PET 141.59805 0.0006 SRAD 0.00758 0.0452

ER 37.90413 0.0315

OUTFLOW –0.20748 0.0088

Figure 17—Predicted versus measured basal area for each study area. General linear models fit the data quite well. Basal area

varied from 0 to 192 m2 ha-1 in the KRSA and from 0 to 219 m2 ha-1 in the SRSA. Maps represent basal area across each

study area.
USDA Forest Service RMRS-GTR-92 29

fire return interval could serve as a response variable in a

predictive landscape model of fire regimes. Additional

macroplots would certainly have improved the power and

scope of the LEIS databases, but this would have been

costly and time consuming. A comprehensive sampling

program, such as Forest Inventory Analysis (FIA) or Forest Health Monitoring (FHM), is needed to ensure accurate

and abundant field data are available for gradient analysis.

A main limitation to the gradient-relevé approach used

in LEIS is that plot locations are subjectively determined at

the time of sampling. This is at least partially mitigated,

however, by stratifying sample location using physiographic

and climatic data. The success of this stratification largely

depends on the quality and availability of these processdriven, broad-scale data, which are becoming increasingly

available at regional to continental scales.

The launch of the Terra Satellite (http://terra.nasa.gov)

has ushered in a new era for natural resource mapping.

Imagery from Terra provides daily global coverage of parameters that could be used in the initial LEIS sampling

stratification. The MODIS (Moderate Resolution Imaging Spectroradiometer) sensor on the Terra platform is

linked to complex software that will generate extensive

maps of ecosystem variables such as net primary production and evapotranspiration every 2 days and over the

course of a growing season at 1 km2 resolution. The National Elevation Database (USGS 2001) provides standardized 30-m DEMs for the entire United States, and an

updated version of STATSGO soil texture and soil

depth data will be available nationwide by 2002

(www.ftw.nrcs.usda.gov/stat_data.html). The DAYMET

database (www.daymet.org), once available, will provide

summaries of an 18-year daily record of temperature, precipitation, and solar radiation at a 1-km resolution for

the continental United States. Once the MODIS,

STATSGO, and DAYMET products are available, these

Figure 18—Probability surfaces for the presence of western redcedar. Maps represent the output of logistic regression models

using presence/absence of western redcedar as a response variable and spatial data in the LEIS as independent variables.
30 USDA Forest Service RMRS-GTR-92 Figure 19—Receiver operating characteristic (ROC) curves for logistic models from the KRSA and SRSA. ROC curves show tradeoffs between sensitivity and specificity for statistical models. The closer the line is to the left side and top of the graph, the better the model fit. The value C represents the area under the curve and describes how well the model fits the data. A C of 1 is a perfect fit and a C of 0.5 indicates complete lack of fit.
USDA Forest Service RMRS-GTR-92 31

Table 5—Logistic regression parameters and estimates for

the Kootenai River study area.

KRSA

Western redcedar

Model G2 = 100.4, d.f. = 12, P < 0.0001

H-L X2 = 3.58, P = 0.89

Overall map accuracy = 88 percent KHAT = 0.50

Parameter Estimate P-Value

Intercept –887.3 0.0042

REFLC1 92357.8 0.0022

REFLC3 233630 0.0025

BRIGHT –31.2966 0.0025

GREEN 66.6583 0.0024

WET –53.4234 0.0024

PCAC3 240337 0.0025

CURVE –3.4932 0.0083

SRAD –0.00149 <0.0001

MR –47.5166 0.0002

OUTFL –0.0696 0.0005

Fuel model 5

Model G2

 = 59.07, d.f. = 9, P < 0.0001

H-L X2 = 8.73, P = 0.36

Overall map accuracy = 65 percent KHAT = 0.14

Parameter Estimate P-Value

Intercept 44.2917 <0.0001

REFLC5 53.7473 00.1614

GREEN 0.1088 <0.0001

SLOPE 0.0344 0.0151

ASPECT 0.00406 0.0132

AR –145.4 0.0064

TAVE –2.5818 <0.0001

ER 42.0271 0.0251

MR 47.7271 0.0029

KRSA

Fuel model 8

Model G2

 = 36.04, d.f. = 6, P < 0.0001

H-L X2 = 5.31, P = 0.72

Overall map accuracy = 70 percent KHAT = 0.26

Parameter Estimate P-Value

Intercept 8.9307 0.2442

REFLC4 411.2 0.0071

REFLC6 –257.0 0.0118

GREEN 0.2908 0.0023

MNDVI 2.0850 0.0224

PET 0.9543 0.0007

Fuel model 10

Model G2 = 85.51, d.f. = 13, P < 0.0001

H-L X2 = 6.45, P = 0.60

Overall map accuracy = 69 percent KHAT = 0.23

Parameter Estimate P-Value

Intercept –390.2 0.0483

REFLC1 40343.6 0.0356

REFLC3 101994 0.0373

BRIGHT –13.6326 0.0372

GREEN 28.7847 0.0389

WET –23.2919 0.0381

PCAC3 103183 0.0406

ASPECT –0.00367 0.0136

PSAND –0.1037 0.0442

CURVE –2.2395 0.0091

AR 106.2 0.0105

Table 6—Logistic regression parameters and estimates for

the Salmon River study area.

SRSA

Western redcedar

Model G2 = 166.6, d.f. = 16, P < 0.0001

H-L X2 = 0.89, P = 0.34

Overall map accuracy = 95.5 percent KHAT = 0.577

Parameter Estimate P-Value

Intercept 2694.0 0.0053

REFLC2 –410.1 0.0068

REFLC4 55.6042 0.0163

SLOPE 0.1263 0.0337

ASPECT 0.0162 0.0321

PCLAY –0.8122 0.0174

PLAN_CRV 14.0670 0.0007

Fuel model 5

Model G2 = 54.36, d.f. = 35, P < 0.0001

H-L X2 = 2.71, P = 0.95

Overall map accuracy = 84 percent KHAT = 0.32

Parameter Estimate P-Value

Intercept –2.6141 <0.0001

REFLC3 –68.1064 <0.0001

PCAC1 13.9427 <0.0001

SLOPE 0.0418 <0.0001

SRSA

Fuel model 8

Model G2

 = 36.41, d.f. = 2, P < 0.0001

H-L X2 = 10.93, P = 0.20

Overall map accuracy = 74 percent KHAT = 0.03

Parameter Estimate P-Value

Intercept 7.6486 0.0004

REFLC5 –5.9546 0.0003

ARAD –0.1436 0.0002

Fuel model 10

Model G2

 = 86.89, d.f. = 6, P < 0.0001

H-L X2 = 4.12, P = 0.84

Overall map accuracy = 84 percent KHAT = 0.44

Parameter Estimate P-Value

Intercept 23.7219 0.0003

REFLC5 –10.4344 <0.0001

ARAD –0.3719 0.0001

RH –0.0750 0.0064

ER 720.1 0.0046

NEP 711.1 0.0050
32 USDA Forest Service RMRS-GTR-92

Figure 20—Anderson fire behavior fuel models 5, 8, and 10. Colors represent combinations of probability of presence/absence

of each fuel model. Fuel model 5 is assigned blue, fuel model 8 green, and fuel model 10 red. The inset RGB color model

defines mixtures of colors. Dark areas represent unclassified areas. In the SRSA, dark areas in the western half of the study

area are likely fuel model 1, grass/shrub lands, which was not included in this map of fuels.
USDA Forest Service RMRS-GTR-92 33

data will provide excellent sources of data for broad-scale

landscape stratification and could potentially replace most

of the complex ecosystem simulation conducted for this

LEIS prototype.

Cluster or discriminant analysis could have provided a

more objective method for stratifying the study areas by

unique ecological characteristics (Blaszczynski 1997;

Hessburg and others 2000; Nathan and McMahon 1990).

Raw temperature and precipitation values (without

classification into categories), along with topography (elevation, aspect, and slope), soils (percent sand, silt, and clay),

and geology could have been used as independent variables

to obtain landscape/ecosystem cluster classes (Hessburg and

others 2000). Then, one or more landscapes could have been

randomly selected for sampling from each cluster class depending on accessibility. Keane and others (2000) used a

similar method to select landscapes to sample for fuel mapping on the Gila National Forest. This alternative would allow the quantitative inclusion of many more landscape descriptors (for example, texture from satellite imagery) and

provide a less subjective means of selecting landscapes to

sample (Blaszczynski 1997). It was not employed in this

study because of limited time and lack of comprehensive,

wall-to-wall, process-based data prior to field sampling.

Spatial Data

A wide variety of spatial data— existing, derived, and

simulated—were used in this demonstration of LEIS. Error

propagation within and across layers certainly affected

results, but the effects of accumulated error were at least

partially reduced because accuracy assessment used

macroplot information not included in model building. Digital Elevation Models from the National Elevation Database

proved to be a better source of topographic data than older,

coarser DEMs (see fig. 9 and 10) (Keane and others 1998a).

Accuracies for several of the modeled surfaces in the LEIS

GIS were determined by comparison with information from

the ECODATA database.

Remote Sensing/Image Processing

Landscape polygons (the polygons used for wall-to-wall

simulations of weather and ecophysiological predictor variables) (fig. 6) were classified using spectral data from TM5

imagery and information about topography, stand structure,

and upper, middle, and low-level dominant vegetation. In

general, the landscape polygon classification for the KRSA

yielded more satisfactory results. Overall accuracy for the

KSRA landscape polygon classification was 62 percent

(KHAT = 0.56). Overall classification accuracies were low,

probably as a result of using too many classes in each classification scheme (table 3). It is important to note, however,

that a map based on classified imagery was not intended as

an end result of LEIS. Rather, the landscape polygon

classifications only provided the “modelable units” for the

simulation models WXGMRS and GMRS-BGC. In future

implementations of the LEIS methodology, care should be

taken to assure that spectral information discriminates sufficiently between different types of landscape polygons. For

example, satellite imagery sometimes cannot discriminate

between Douglas-fir and grand fir cover types. These

forests are similar with regard to their spectral signature,

which is what a satellite measures. Adding information

about elevation or aspect or both may improve cover type

classification.

Objectively determining the proper spatial and floristic

scale for ecosystem simulation is difficult and may require

an iterative approach to ensure that the accuracy of the classified map is acceptable and the ecological parameterizations

are meaningful. For example, it is difficult to accurately

parameterize subtle ecological differences between western redcedar and mesic grand fir forests; biogeochemically

these forest types are quite similar because they contain similar species. Future implementations of LEIS may require

that similar types such as these be collapsed into a single

category, such as “general mesic forests.” In general, as more

classes (types of ecologically distinct units) are included in

the classification, the classification accuracy will decrease.

If classification accuracy is quite low it may become necessary to group ecological units that are biogeochemically

similar. Aggregating types will potentially simplify ecological parameterization and increase map accuracy; however,

aggregation may reduce the overall meaningfulness of the

simulated data layers. Finding balance between map accuracies and the floristic scale of the ecophysiological

parameterizations is a necessity.

Simulated Spatial Databases

Maps of leaf area index were created for two reasons:

(1) for parameterization of the ecological simulation model,

and (2) to serve as predictor variable in the statistical mapping process. Leaf area index was mapped based on algorithms from Nemani and others (1993). This method uses a

combination of red, near infrared, and mid-infrared satellite measured reflectance to estimate leaf area index over

broad areas. Addition of information from mid-infrared reflectance corrects for the effects of understory vegetation

(Nemani and others 1993; White and others 1997). Leaf area

index was calculated for every macroplot using allometric

equations and the ECODATA Tree Data database. One satellite-based model for LAI was created using the combined

LAI estimates from each macroplot and the TM5 scenes for

each study area. Macroplot LAI varied from 0.0 to 7.0 in

each study area, while modeled LAI varied from 0.5 to 4.0

in the KRSA and from 1.5 to 4 in the SRSA (fig. 15). Mapped

values of LAI were reasonable for forests of the Northern

Rocky Mountains (Nemani and others 1993; White and others 1997). The low accuracy in LAI mapping may be the

result of a number of factors. First, the sampling strategy

for LEIS had to represent a variety of landscape gradients,

of which LAI was only one. As a result, the sampling density may not have been sufficient to accurately map LAI for
34 USDA Forest Service RMRS-GTR-92

these broad study areas. Second, actual measurements of

LAI from the LAI 2000 often differ from values derived

allometrically from tree data because of nonrandom selfshading and other problems with instrumentation (White and

others 1997). Allometric equations are determined for specific areas, and the validity of exporting these equations to

other areas is unknown. White and others (1997) developed

methods to combine direct measurements of LAI with values for LAI derived from allometric equations to improve

mapping accuracies. Combining measured and estimated

LAI values and an adjusted sample design for LAI coupled

with the mid-infrared correction used here could improve

the accuracies of mapped LAIs, which represent a key input parameter for both WXGMRS and GMRS-BGC.

Mapped soil depth was used for (1) model parameterization (it is used in calculating water storage for every landscape polygon), and (2) as a predictor variable in the statistical mapping process. Soil depth was modeled for each study

area using the methods of Zheng and others (1996), which

is largely based on STATSGO data (Soil Conservation Service 1991) and the TOPMODEL contributing area topographic algorithm (Beven and Kirkby 1979). The algorithm

was parameterized using maximum soil depth data from

STATSGO and mean and mode soil depths recorded in the

ECODATA database. Soil depth was estimated for each

macroplot based on small soil pits and from measuring exposed soil profiles where available near macroplots (road

cuts, for example). This method of estimating soil depth for

each macroplot is probably insufficient for supporting accurate, broad-scale mapping of soil depth. However, the

surfaces of soil depth appeared to represent the probable

spatial distribution of soils quite well (fig. 16). That is, valley bottoms and wide, flat areas had the deepest soils, while

steep slopes and sharp ridges had shallow soils. Accuracy

of modeled LAI and soil depth was calculated from point

data for parameters that are difficult to measure efficiently

for points, and nearly impossible to measure over broad areas because they have high spatial variabilities. Accuracies

presented are likely the worst-case scenarios because they

compare point estimates with predicted maps across two

broad study areas. A statistically rigorous assessment of the

true accuracy would need to involve a separate sample set,

tailored to represent the variability of the parameters in question (Stehman 2001). Practical constraints limited the extent to which we could collect a separate database for accuracy assessment in this study.

Macroenvironmental and biogeochemical variables were

simulated for each study area using DAYMET, WXGMRS,

and GMRS-BGC. Parameters for initializing these models

were derived from the ECODATA database and assigned to

landscape polygons derived from satellite imagery. A major

limitation of LEIS is the complexity and difficulty of parameterizing these models over broad areas. As stated

above, new data sources such as the DAYMET weather

archive (www.daymet.org) and the MODIS sensor (http:/

/terra.nasa.gov) may provide the data necessary for LEIS,

making extensive ecosystem simulation unnecessary. The

MODIS LAI, net primary productivity, and incident

photosynthetically active radiation data will be useful for

validating existing local models.

Gradient Analyses and Modeling

Overall, general linear modeling and logistic regression

worked well for creating predictive landscape models.

Both general linear modeling and logistic regression are

characterized by assumptions that relationships between

predictor and response variables are monotonic, that error is normally distributed, and that samples are independent. These assumptions are most likely violated in

the implementation of LEIS presented here. However, the

final maps rather than the statistical models are the main

product of LEIS. Final accuracy assessments of map products were conducted using points separate from the modelbuilding database and using techniques completely separate from model building. This minimizes the importance

of violating statistical assumptions with regard to the validity of the final maps. Nevertheless, this violation of assumptions in modeling methodology may affect the repeatability of this implementation of LEIS. Many advanced statistical and mathematical techniques are available for this

type of predictive landscape mapping. In future implementations of LEIS, Classification and Regression Trees,

General Additive Models, and principal curves offer powerful modeling techniques with fewer assumptions. These

methodologies are not presented here because one of our

goals was to show that relatively simple statistical

techniques could result in fairly accurate predictive landscape models.

Multivariate methods employed here allow resulting spatial data output from LEIS to be provided as probability

surfaces. These surfaces could be “sliced” up or classified

in some fashion to create a discrete map of the ecosystem

characteristic in question. For example, areas with probabilities over 0.5 were assigned presence values. For example,

in the final map for fuel model 10, pixels with probabilities

over 0.5 were assigned fuel model 10. This was necessary

for accuracy assessment purposes. In other implementations

of LEIS the probability for determining presence/absence

of specific ecosystem characteristics may be different. For

example, if a map were being created of the distribution of

a rare plant, different probabilities could be examined to

assess best and worse case scenarios for the amount of available habitat. Probability surfaces of ecosystem characteristics are probably not as useful for “real-time” management

applications as discrete maps. However, probability surfaces

are valuable for a number of reasons:

1. Ecosystem characteristics are not discretely distributed. On real landscapes ecological characteristics vary

continuously, with zones of transition between adjacent

types. In fact, it could be argued that ecological communities are so fuzzy that a discrete map misrepresents reality.

2. Probability surfaces can be used to parameterize error

models (Goodchild 1996), or to create maps of ecosystem
USDA Forest Service RMRS-GTR-92 35

characteristics that incorporate uncertainty (Franklin

1995; Woodcock and Gopal 1992). By presenting probabilistic maps, the uncertainty or fuzziness inherent on

real landscapes is explicitly incorporated into landscape

planning and assessment.

3. Providing the final maps as probability surfaces allows different interpretations in terms of creating thematic maps. What one person determines important in

terms of a probability threshold may differ significantly

from what another person thinks. In these cases, both

maps could easily be produced and compared using a

probability surface.

Accuracies of the maps created from the LEIS demonstration do not indicate limited usefulness. The primary use

of maps created from LEIS is for planning rather than implementation, and spatial data for planning does not need to be

highly accurate or precise (Jensen and Bougeron 1993).

Often, the most important information obtained from planning maps is relative trends in space, which predictive landscape models based on gradients can capture quite well

(Franklin 1995; Kessell 1979).

Limitations

There are many shortcomings in the LEIS design that

could affect the quality and value of mapped ecosystem

characteristics. First, not all the environmental gradients that

affect ecosystem dynamics are properly sampled and represented in this implementation of LEIS. It would be nearly

impossible to completely represent the entire range of environmental conditions that influence ecosystem structure and

composition (Kessell 1979). The maps that are created from

LEIS are only as good as the data used to create them. If

maps of rare plant species or habitats are desired, it is essential that these habitats are represented in the field database. Factors that define the plant’s demography must be

represented spatially as independent variables in the final

predictive models.

There are many ways to improve the predictive ability

of LEIS. Less rigorously collected field data, such as FIA

(Forest Inventory and Analysis), FHM (Forest Health Monitoring), and historical ECODATA plot data, can be included

in the field database or used for accuracy assessment. Missing data will necessitate an adaptive approach when using

these “legacy” field databases. Parameters in the simulation models could be quantified using actual field measurements. For example, the specific leaf area is an important

parameter that can be easily quantified by species for different geographic regions. Simulation models could be improved by including the most recent modeling techniques

and ecophysiological research. Extensive testing, validation,

and sensitivity analysis could also improve results. Additional GIS layers that spatially describe new ecosystem characteristics, such as canopy cover, could be added to improve

the extrapolation of the gradients across the landscapes. And

most important, other simulation models could be added to

LEIS to compute additional management-oriented variables.

For example, the set of ECOPAC programs (Keane and others

1990) could be integrated into LEIS to compute important items

such as wildlife hiding cover and thermal cover, forage quality

and quantity, and fire hazard.

The Landscape Ecosystem Inventory System is not a finished product ready for transfer to land management. Obviously, LEIS needs further development to be seamlessly

integrated as a landscape inventory and analysis tool.

Statistical methods for creating the gradient-based predictive equations need to be standardized and implemented into

an easy and efficient computer program that creates maps

from the equations. Gradient modeling and remote sensing

protocols need to be clearly presented in great detail so that

others can easily follow these procedures to create useful

ecological maps. A comprehensive sampling, database storage, and analysis system needs to be refined so that the field

and simulated data are easily obtained. Key GIS layers need

to be refined and maintained to capture current research technology, field data improvements, and management and natural disturbance activities. The steps to transfer LEIS to land

management, although extensive, are relatively easy to complete with additional funding because many of the suggested

improvements already exist. This report presents the prototype of LEIS to demonstrate that such a system can be an

important and integral inventory and management tool.

Potential Applications

Future applications of spatial data generated from LEIS

are as numerous and diverse as the variables stored in the

databases. For example, landscape structure and composition maps could be valuable in prioritizing long-term fire

and fuels management projects for entire National Forests.

Landscape metrics such as fractal dimension, patch size,

and core areas can be computed from these maps and then

used in assessments of wildlife habitat suitability. Disturbance regimes, such as fires or floods, could be directly classified on a landscape using mapped gradients. Because the

entire suite of parameters that describe the carbon and hydrologic cycles are simulated for the LEIS GIS, they could

be used to investigate the implications of management activity on future productivity or water quality or both. Initial

conditions to many ecosystem simulation models can be

mapped with this system allowing the projection of future

landscape dynamics as a consequence of management actions. Fuel-model and fuel-loading maps can be generated

for fire behavior simulations for planning and “real-time”

applications (that is, wildland fire). Fuel loading maps can

also be used to predict fire effects such as smoke and tree

mortality. Stand attributes influencing animal species distributions, such as snag density, hiding cover, and thermal

cover, can be delineated spatially using the LEIS system.

Mapping of threatened and endangered species habitats is

easily accomplished by explicitly defining the gradients

important for their distribution. Timber volume can be

coarsely mapped to provide information as to harvest

schedules.
36 USDA Forest Service RMRS-GTR-92

Here are just a few plausible uses of the spatial data layers that can be generated once the Landscape Ecological

Inventory System is in place:

General Ecology

1. Landscape structure and composition are easily described using generated vegetation community spatial information.

2. Ecological processes, such as NPP, fire regimes, or

potential insect outbreaks, can be spatially mapped either as direct gradients or from imagery.

3. Input parameters and conditions to many ecosystem

simulation models can be mapped with this LEIS allowing the prediction of landscape dynamics as a consequence of management policies.

Fire Behavior and Effects

1. Fuel-model class and fuel-loading maps can be generated for fire behavior simulation using spatial fire

spread models such as FARSITE. These predictions can

be done in real-time or for planning or projections purposes. Fire behavior officers (FBOs) can use these maps

to evaluate fire attack strategies.

2. Departure in fire frequency from historical ranges of

variability can be mapped using a derived map of historical

fire regimes along with 20th century fire occurrence data.

3. Smoke generation and dispersal can be predicted once

these maps are generated using smoke models such as

FOFEM and PUFF.

Wildlife

1. Structural characteristics of the landscape can be directly mapped using LEIS. Fractal dimension, patchiness,

fragmentation, corridors, and other landscape attributes

can be assessed.

2. Stand attributes influencing animal species, such as

snag density, hiding cover and thermal cover, can be delineated spatially.

3. “Gap” type analyses can be done for most wildlife

species using gradient analyses techniques that emphasizes gradients affecting fauna instead of flora.

Vegetation

1. LEIS is a quick, efficient, and cheap method of generating vegetation maps for forest planning and management.

2. Mapping of threatened and endangered species habitats is easily accomplished by explicitly defining the

“niche” of these species on the mapped gradients.

3. Restoration activities can be prioritized using GIS

overlay techniques with mapped vegetation layers and

other process layers such as fire regimes, insect and disease regimes, and so on.

4. Timber characteristics such as volume, productivity,

or density can be coarsely mapped to provide information as to harvest schedules.

Summary and Conclusions

The Landscape Ecosystem Inventory System (LEIS)

combines hierarchical, gradient-based sampling with remote

sensing, ecosystem simulation, and multivariate cartographic

modeling to produce a wide variety of maps for use in progressive ecosystem management. This report presents a prototype implementation of LEIS that illustrates the utility of

this combined approach for creating spatial data layers useful for landscape assessments and natural resource management.

Existing data layers were used to stratify two large study

areas on the Kootenai and Nez Perce National Forests for

the purpose of sampling 926 plots along ecophysiological

gradients. Field data were used to derive additional variables for each plot, to create an ecological classification for

each study area, to parameterize simulation models, and to

derive response variables for predictive landscape models.

Satellite imagery was used, along with field data, to create

classified maps representing ecological units for each study

area. Simulation models were used to derive weather and

ecophysiological parameters for each ecological unit, resulting in a synoptic database with environmental, physiographic, and biophysical data layers. An extensive GIS was

compiled including physiographic, spectral, environmental,

and ecophysiological data layers to serve as potential predictor variables in predictive landscape models (table 1).

These data were used in predictive landscape models of basal

area (m2 ha-1), presence/absence of western redcedar, and

presence/absence of fuel models 5, 8, and 10. Results indicated that maps were fairly accurate, and that the LEIS was

potentially useful for creating maps for use in landscape

planning and ecosystem management.

The Landscape Ecosystem Inventory System has many

aspects that are similar to other approaches to predictive

landscape mapping (see Franklin 1995 for an overview),

but it is unique in that it integrates gradient-based sampling,

remote sensing, ecosystem simulation, and multivariate statistical analyses. Perhaps the most unusual aspect of LEIS

is its ability to map a wide variety of ecosystem characteristics using one comprehensive spatial database. This, along

with the recent availability of comprehensive topographic,

soils, climate, and ecophysiological data, makes LEIS an

excellent tool for spatially quantitative ecosystem management.

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Williams, M.; Rastetter, E. B. 1999. Vegetation characteristics and

primary productivity along an arctic transect: implications for

scaling up. Journal of Ecology. 87: 885–898.

Woodcock, C.; Gopal, S. 1992. Accuracy assessment of the

Stanislaus forest vegetation map using fuzzy sets; proceeding

of the fourth biennial remote sensing applications conference;

1991 April; Orlando, FL. 17 p.

Woodward, F. I. 1987. Climate and plant distribution. Cambridge:

Cambridge University Press. 174 p.

Zheng, D.; Hunt E. R., Jr.; Running, S. W. 1996, Comparison of

available soil water capacity estimated from topography and

soil series information. Landscape Ecology. 11(1): 3–14.
42 USDA Forest Service RMRS-GTR-92

Appendix A—Data contained in the ECODATA database

Stand variables

Variable name Type Description Source

AGE Num Average age of stand (yr) Calculated

ALD Num Average log diameter (cm) Calculated

AVEDBH Num Average DBH across all trees (cm) Calculated

AVEHT Num Average height of all trees (m) Calculated

BAREA Num Live stem basal area (m2

/ha) Calculated

DDBH Num Dead tree average DBH (cm) Calculated

DHGT Num Dead tree average height (m) Calculated

DLDEPTH Num Duff/litter depth (cm) Measured

DOMDBH Num Dominant DBH (cm) Measured

DOMHGT Num Dominant layer average height (m) Calculated

DOMHT Num Height of dominant layer (m) Measured

DUFFD Num Duff and litter depth (cm) Measured

LAGE Num Live tree average age (yr) Calculated

LBA Num Live basal area (m2

/ha) Calculated

LDBH Num Live tree average DBH (cm) Calculated

LHGT Num Live tree average height (m) Calculated

LOGDIA Num Average log diameter (cm) Calculated

MAXAGE Num Maximum age of stand (yr) Measured

SAPAREA Num Live sapwood area (m2/ha) Calculated

SAPPH Num Sapling density (sap/ha) Calculated

STAGE Num Structural stage Measured

STALL Num Tall shrub cover (percent) Measured

TLARGE Num Large tree cover (percent) Measured

TPH Num Trees density (trees/ha) Measured

TPOLE Num Medium tree cover (percent) Measured

TREE Num Total tree cover (percent) Measured

TSAP Num Sapling tree cover (percent) Measured

TSEED Num Seedling tree cover (percent) Measured

Ecophysiological variables

Variable name Type Description Source

AHD Num Average annual absolute humidity (ug/m3) Simulated

AR Num Autotrophic respiration (kgC/m2) Simulated

ARAD Num Average annual solar radiation (kw/m2

) Simulated

CRAD Num Average annual corrected rad (J/m2/day) Simulated

CROOTC Num Coarse root carbon (kgC/m2) Simulated

ER Num Ecosystem respiration (kgC/m2) Simulated

ET Num Evapotranspiration (kgH2O/m) Simulated

FBPROD Num Forb dry weight prod. (kg/m2) Simulated

FROOTC Num Fine root carbon (kgC/m2) Simulated

FRPROD Num Fern dry weight production (kg/m2) Simulated

GCSH Num Canopy conductance sensible heat (m/sec) Simulated

GLSH Num Leaf conductance sensible heat (m/sec) Simulated

GPP Num Gross primary productivity (kgC/m2) Simulated

GPROD Num Gramminoid dry weight prod. (kg/m2

) Simulated

GR Num Growth respiration (kgC/m2) Simulated

HR Num Heterotrophic respiration (kgC/m2) Simulated

LAI Num Leaf area index all sided (m2/m2) Measured

LAID Num Overstory LAI (m2 m-2) Measured

LAIU Num Understory leaf area index (m2 m-2) Measured
USDA Forest Service RMRS-GTR-92 43

LEAFC Num Leaf carbon (kgC/m2) Calculated

LEAFLONG Num Average leaf longevity (yr) Measured

LR Num Litter respiration (kgC/m2) Simulated

MPROD Num Moss and lichen production (kg/m2) Simulated

MR Num Maintenance respiration (kgC/m2) Simulated

NEP Num Net ecosystem production (kgC/m2) Simulated

NPP Num Net primary productivity (kgC/m2) Simulated

OUTFLOW Num Outflow (kgH2O/m) Simulated

PET Num Average annual potential ET (m) Simulated

PLAI Num Leaf area index projected (m2/m2) Measured

PSI Num Soil water potential (MPa) Estimated

RDEPTH Num Free rooting depth (m) Estimated

SPROD Num Shrub dry weight production (kg/m2) Simulated

SR Num Soil respiration (kgC/m2) Simulated

STEMC Num Stem carbon (kgC/m2) Simulated

TPROD Num Tree production (kg/m2) Simulated

VMC Num Soil volumetric water content (m3/m3) Simulated

VPD Num Average annual vapor press deficit (mbar) Simulated

WHC Num Water holding capacity (mH2O m-1 Soil) Estimated

ECODATA variables

Variable name Type Description Source

ANIMAL Char Animal disturbance severity (code) Measured

DSIZE Char Existing vegetation dead life form size class (code) Measured

EROSTAT Char Erosion status (code) Measured

EROTYPE Char Erosion type (code) Measured

GMRSID Num Plot ID number (none) Measured

KEYID Char ECODATA Key ID 15 character (none) Measured

LAT Char Latitude (dd) Measured

LONG Char Longitude (dd) Measured

MECH Char Mechanical disturbance code (code) Measured

NITEMS Num Number of entries in stand table Measured

PHASE Char Indicator spp.—site/phase Measured

PLA Num Plot location accuracy (m) Measured

PLM Char Plot location method (code) Measured

PLOTPOS Char Plot position (code) Measured

PVTREF Char Potential vegetation reference (code) Measured

RADIUS Num Plot radius (m) Measured

SCLASS Char User spectral class Measured

SELD Num Overstory LAI error (m2

 m-2) Measured

SELU Num Understory LAI error (m2 m-2) Measured

SPFEAT Char Special features (code) Measured

UTME Num UTM easting (m) Measured

UTMN Num UTM northing (m) Measured

UTMYR Num UTM year (yrs) Measured

UTMZ Num UTM zone (code) Measured

WIDTH Num Plot width (m) Measured

WILD1 Char Wildlife evidence (code) run Measured

Physical variables

Variable name Type Description Source

ASPECT Num Aspect (degree) Measured

DNE Num Distance from NE (1–80 ̊) Measured

EHORZ Num East horizon angle (percent) Measured

ELEV Num Elevation (m) Measured
44 USDA Forest Service RMRS-GTR-92

HPS Char Horizontal plot shape (code) Measured

HUC5 Num HUC1-5 unit codes Measured

HUC6 Num HUC sixth code label Measured

LANDF Char Geomorphic landform (code) Measured

PMAT Char Parent material (code) Measured

SDEPTH Num Soil depth (m) Measured

SHORZ Num South horizon angle (percent) Measured

SLOPE Num Slope (percent) Measured

VPS Char Vertical plot shape (code) Measured

WHORZ Num West horizon angle (percent) Measured

FORM Char Formation (code) Measured

Cover type variables

Variable name Type Description Source

BARESOIL Num Ground cover—bare soil (percent) Measured

BVEG Num Ground cover—basal vegetation (percent) Measured

FERN Num Fern and allies cover (percent) Measured

FORB Num Forb cover (percent) Measured

GRAM Num Gramminoid cover (percent) Measured

GRAVEL Num Ground cover—gravel (percent) Measured

DUFF Num Ground cover—litter and duff (percent) Measured

INDSPP1 Char Indicator species 1—series Measured

INDSPP2 Char Indicator species 2—habitat type Measured

INDSPP3 Char Indicator species 3—phase Measured

LFORM Char Existing vegetation lifeform (code) Measured

LCC Char Existing vegetation canopy cover code (code) Measured

LLDOM1 Char Lower layer dominant species 1 (code) Measured

LLDOM2 Char Lower layer dominant species 2 (code) Measured

LOGCOV Num Downed woody log cover (percent) Measured

LSIZE Char Existing vegetation. live lifeform size class (code) Measured

MLDOM1 Char Middle layer species dominant 1 (sp.) Measured

MLDOM2 Char Middle layer dominant 2 (sp.) Measured

MLICH Num Moss and lichen cover (percent) Measured

MOSS Num Ground cover—moss and lichens (percent) Measured

ROCK Num Ground cover—rock (percent) Measured

SHRUB Num Total shrub cover (percent) Measured

SLOW Num Low shrub cover (percent) Measured

SMID Num Mid shrub cover (percent) Measured

ULDOM1 Char Upper layer species dominant 1 (code) Measured

ULDOM2 Char Upper layer species dominant 2 (code) Measured

TVLARGE Num Very large tree cover (percent) Measured

WATER Num Ground cover—water (percent) Measured

WOOD Num Ground cover—wood (percent) Measured

Fire variables

Variable name Type Description Source

BI Num Burning index Calculated

ERC Num Energy release component (Btu/ft2) Calculated

FDEPTH Num Integrated fuel depth (m) Calculated

FEFM Num Fire effects fuel model Calculated

FIRE Char Fire disturbance severity code (code) Calculated

FMODEL Num Fire behavior fuel model (code) Calculated

FUELD Num Integrated fuel depth (m) Calculated

IC Num Ignition component (probability) Calculated

IR Num Reaction intensity (Btu/ft) Calculated
USDA Forest Service RMRS-GTR-92 45

MC1 Num Moisture content 1-hour wood fuel (percent) Calculated

MC10 Num Moisture content 10-hour wood fuel (percent) Calculated

MC100 Num Moisture content 100-hour wood (percent) Calculated

MC1000 Num Moisture content 1,000-hour wood (percent) Calculated

MSF Num Mixed-fire regime return interval (yr) Calculated

NLSF Num Non-lethal underburn interval (yr) Calculated

SC Num Spread component (ft/min) Calculated

SRF Num Stand-replacement fire interval (yr) Calculated

BI_SUM Num Burning index threshold (days above threshold) Calculated

ERC_SUM Num Energy release component threshold (days above threshold) Calculated

IC_SUM Num Ignition component (days above threshold) Calculated

KBDI Num Keetch-Byram Drought Index (index) Calculated

W1 Num 1-hr loading (0–0.25 inches) (kg m-2) Measured

W10 Num 10-hr loading (0.25–1 inches) (kg m-2) Measured

W100 Num 100-hr loading (1–-3 inches) (kg m-2) Measured

W1000 Num 1,000-hr loading (3+ inches) (kg m-2) Measured

W10001 Num 1,000-hr loading (3+ inches) LDC 1 (kg m-2) Measured

W10002 Num 1,000-hr loading (3+ inches) LDC 2 (kg m-2) Measured

W10003 Num 1,000- hr loading (3+ inches) LDC 3 (kg m-2) Measured

W10004 Num 1,000- hr loading (3+ inches) LDC 4 (kg m-2) Measured

W10005 Num 1,000-hr loading (3+ inches) LDC 5 (kg m-2) Measured

W1000R Num 1,000-hr loading (3+ inches) rotten (kg m-2) Measured

W1000S Num 1,000-hr loading (3+ inches) sound (kg m-2) Measured

Weather variables

Variable name Type Description Source

DAYL Num Average annual day length (seconds) Simulated

DDAY Num Average annual degree days (deg day) Simulated

DSR Num Average annual days since rain (days) Simulated

DSS Num Average annual days since snow (days) Simulated

EFFPPT Num Average annual effective precipitation (cm/yr) Simulated

PPT Num Average annual precipitation (cm/yr) Simulated

RH Num Average annual relative humidity (percent) Simulated

SNOW Num Average annual snow water depth (cm) Simulated

SRAD Num Average annual daily radiation (J/m2/day) Simulated

SWABS Num Average annual absorbed SW radiation (kJ/m2/day) Simulated

SWTRANS Num Average annual transmit SW radiation (kJ/m2/day) Simulated

TAVE Num Average annual average temp ( ̊C) Simulated

TDAY Num Average annual daytime temp ( ̊C) Simulated

TDEW Num Average annual dewpoint temp ( ̊C) Simulated

TMAX Num Average annual maximum temp ( ̊C) Simulated

TMED Num Medium tree cover (percent) Simulated

TMIN Num Average annual minimum temp  ̊C) Simulated

TNIGHT Num Average annual nighttime temp ( ̊C) Simulated

TSOIL Num Average annual soil temp ( ̊C) Simulated
46 USDA Forest Service RMRS-GTR-92 Appendix B—Parameter lists for GMRS-BGC—the ecosystem simulation model used to generate primary ecophysiological gradients. ABGR (Grand fir) 1 Parameter name Description Value Units Source STATSGO, DEM, SDEPTH Soil depth Varies m Hydrologic model SAND Percent sand Varies Percent STATSGO SILT Percent silt Varies Percent STATSGO CLAY Percent clay Varies Percent STATSGO ELEV Elevation Varies Meters DEM LATITUDE Latitude Varies Decimal degrees GPS ALBEDO Fraction of incident radiation reflected by surface 0.800000 None Keane and others 1996b ATMN Atmospheric nitrogen deposition 0.000400 kg N m-2 yr-1 Waring and Running 1998 SNOWWATER Default for amount water in snowpack 16.000000 kg/m2 Keane and others 1996a SOILWATER Initial soil saturation 0.500000 m3 H O m 2 -3Soil Keane and others 1996a SLA Specific leaf area 30.000000 None Measured, satellite imagery SUNSHADE Ratio of shaded to sunlit leaves 3.000000 None Keane and others 1996a PLAR All-sided to projected leaf area ratio 2.200000 None Waring and Running 1998 CIC Canopy water interception coefficient 0.100000 kg m-2 LAI-1 Diameter-1 Keane and others 1996a K Light extinction coefficient 0.500000 None Waring and Running 1998 LEAFNRUB Fraction of leaf N in rubisco 0.120000 Ratio Waring and Running 1998 TOPT Optimum temperature for stomatal conductance 25.000000 ∞C Waring and Running 1998 TMAX Maximum temperature for stomatal conductance 40.000000 ∞C Waring and Running 1998 LWP1 Leaf water potential: start of conductance reduction –0.200000 Mpa Waring and Running 1998 LWP2 Leaf water potential: complete conductance reduction –1.400000 Mpa Waring and Running 1998 VPD1 Vapor pressure deficit: start of conductance reduction 500.000000 Pa Waring and Running 1998 VPD2 Vapor pressure deficit: complete conductance reduction 2000.000000 Pa Waring and Running 1998 GMAX Maximum leaf-scale stomatal conductance 0.008000 m s-1 Waring and Running 1998 GCUT Leaf-scale cuticular conductance 0.000050 m s-1 Waring and Running 1998 GBOUND Leaf-scale boundary layer conductance 0.080000 m s-1 Waring and Running 1998 LEAFON Yearday leaves on 0.000000 Julian day Keane and others 1996a LEAFOFF Yearday leaves off 0.000000 Julian day Keane and others 1996a LEAFEXP Days it takes for 50 percent expansion 21.000000 # Days Keane and others 1996a LEAFDROP Days it takes for 50 percent drop 21.000000 # Days Keane and others 1996a LEAFTURN Annual leaf turnover fraction 0.111111 None Keane and others 1996a MORT Annual whole plant mortality fraction 0.001000 None Keane and others 1996a
USDA Forest Service RMRS-GTR-92 47 LEAFCN C:N ratio of leaves 35.000000 None Waring and Running 1998 LITCN C:N ratio of litter 80.000000 None Waring and Running 1998 FROOTCN C:N ratio of fine roots 35.000000 None Waring and Running 1998 STEMCN C:N ratio of stem 50.000000 None Waring and Running 1998 LITLAB Leaf litter labile proportion 0.300000 None Waring and Running 1998 LITCELL Leaf litter cellulose proportion 0.500000 None Waring and Running 1998 LITLIG Leaf litter lignin proportion 0.200000 None Waring and Running 1998 FROOTLAB Fine root labile proportion 0.250000 None Waring and Running 1998 FROOTCELL Fine root cellulose proportion 0.500000 None Waring and Running 1998 FROOTLIG Fine root lignin proportion 0.250000 None Waring and Running 1998 WOODCELL Dead wood cellulose proportion 0.500000 None Waring and Running 1998 WOODLIG Dead wood lignin proportion 0.500000 None Waring and Running 1998 RLC Proportion C allocated to fine roots vs. Leaf carbon 0.600000 None Waring and Running 1998 CSC Proportion C allocated to coarse roots vs. stems 0.300000 None Waring and Running 1998 STOREC Fraction of C pool to hold over for next year 0.100000 None Waring and Running 1998 STOREN Fraction of N pool to hold over for next year 0.100000 None Waring and Running 1998 MAXLEAFC Peak growing season leaf carbon 0.050000 kg C/m2 Waring and Running 1998 BEGNPP Starting proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998 ENDNPP Ending proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998 LEAFC Leaf carbon 45.26776 kg C/m2 Calculated from field data FROOTC Fine root carbon 20.08995 kg C/m2 Calculated from field data SAPC Sapwood carbon 0.284010 kg C/m2 Calculated from field data STEMC Stem carbon 0.284010 kg C/m2 Calculated from field data CROOTLC Live coarse root carbon 8.440642 kg C/m2 Calculated from field data CROOTDC Dead coarse root carbon 8.440642 kg C/m2 Calculated from field data WOODC Woody debris carbon 2.217250 kg C/m2 Calculated from field data LITLC Labile litter carbon 0.084227 kg C/m2 Calculated from field data LITUC Unshielded litter carbon, cellulose 0.168453 kg C/m2 Calculated from field data LITSC Shielded litter carbon, cellulose 0.252680 kg C/m2 Calculated from field data LITGC Litter carbon, lignin 0.336906 kg C/m2 Calculated from field data SOILFC Soil carbon, fast microbial recycling pool 0.200899 kg C/m2 Calculated from field data SOILMC Soil carbon, medium microbial recycling pool 0.200899 kg C/m2 Calculated from field data SOILSC Soil carbon, slow microbial recycling pool 0.200899 kg C/m2 Calculated from field data SOILRC Soil carbon, recalcitrant (slowest) 1.406296 kg C/m2 Calculated from field data LITN Litter nitrogen, labile pool 0.021057 kg N/m2 Calculated from field data SOILN Soil nitrogen, mineral pool 0.020090 kg N/m2 Calculated from field data
48 USDA Forest Service RMRS-GTR-92 ABLA (Subalpine fir) 2 Parameter name Description Value Units Source STATSGO, DEM, SDEPTH Soil depth Varies m Hydrologic model SAND Percent sand Varies Percent STATSGO SILT Percent silt Varies Percent STATSGO CLAY Percent clay Varies Percent STATSGO ELEV Elevation Varies Meters DEM LATITUDE Latitude Varies Decimal degrees GPS ALBEDO Fraction of incident radiation reflected by surface 0.800000 None Keane and others 1996b ATMN Atmospheric nitrogen deposition 0.000400 kg N m-2 yr-1 Waring and Running 1998 SNOWWATER Default for amount water in snowpack 16.000000 kg/m2 Keane and others 1996a SOILWATER Initial soil saturation 0.500000 m3 H O m 2 -3Soil Keane and others 1996a SLA Specific Leaf Area 25.000000 None Measured, satellite imagery SUNSHADE Ratio of shaded to sunlit leaves 3.000000 None Keane and others 1996a PLAR All-sided to projected leaf area ratio 2.200000 None Waring and Running 1998 CIC Canopy water interception coefficient 0.100000 kg m-2 LAI-1 Diameter-1 Keane and others 1996a K Light extinction coefficient 0.500000 None Waring and Running 1998 LEAFNRUB Fraction of leaf N in rubisco 0.100000 Ratio Waring and Running 1998 TOPT Optimum temperature for stomatal conductance 25.000000 ∞C Waring and Running 1998 TMAX Maximum temperature for stomatal conductance 40.000000 ∞C Waring and Running 1998 LWP1 Leaf water potential: start of conductance reduction –0.300000 Mpa Waring and Running 1998 LWP2 Leaf water potential: complete conductance reduction –1.300000 Mpa Waring and Running 1998 VPD1 Vapor pressure deficit: start of conductance reduction 500.000000 Pa Waring and Running 1998 VPD2 Vapor pressure deficit: complete conductance reduction 2000.00000 Pa Waring and Running 1998 GMAX Maximum leaf-scale stomatal conductance 0.006000 m s-1 Waring and Running 1998 GCUT Leaf-scale cuticular conductance 0.000050 m s-1 Waring and Running 1998 GBOUND Leaf-scale boundary layer conductance 0.080000 m s-1 Waring and Running 1998 LEAFON Yearday leaves on 0.000000 Julian day Keane and others 1996a LEAFOFF Yearday leaves off 0.000000 Julian day Keane and others 1996a LEAFEXP Days it takes for 50 percent expansion 21.000000 # Days Keane and others 1996a LEAFDROP Days it takes for 50 percent drop 21.000000 # Days Keane and others 1996a LEAFTURN Annual leaf turnover fraction 0.153846 None Keane and others 1996a MORT Annual whole plant mortality fraction 0.001000 None Keane and others 1996a LEAFCN C:N ratio of leaves 35.000000 None Waring and Running 1998 LITCN C:N ratio of litter 80.000000 None Waring and Running 1998 FROOTCN C:N ratio of fine roots 35.000000 None Waring and Running 1998 STEMCN C:N ratio of Stem 50.000000 None Waring and Running 1998 LITLAB Leaf litter labile proportion 0.300000 None Waring and Running 1998 LITCELL Leaf litter cellulose proportion 0.500000 None Waring and Running 1998
USDA Forest Service RMRS-GTR-92 49

LITLIG Leaf litter lignin proportion 0.200000 None Waring and Running 1998

FROOTLAB Fine root labile proportion 0.250000 None Waring and Running 1998

FROOTCELL Fine root cellulose proportion 0.500000 None Waring and Running 1998

FROOTLIG Fine root lignin proportion 0.250000 None Waring and Running 1998

WOODCELL Dead wood cellulose proportion 0.500000 None Waring and Running 1998

WOODLIG Dead wood lignin proportion 0.500000 None Waring and Running 1998

RLC Proportion C allocated to fine roots vs. Leaf carbon 0.600000 None Waring and Running 1998

CSC Proportion C allocated to coarse roots vs. stems 0.300000 None Waring and Running 1998

STOREC Fraction of C pool to hold over for next year 0.100000 None Waring and Running 1998

STOREN Fraction of N pool to hold over for next year 0.100000 None Waring and Running 1998

MAXLEAFC Peak growing season leaf carbon 0.050000 kg C/m2 Waring and Running 1998

BEGNPP Starting proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998

ENDNPP Ending proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998

LEAFC Leaf carbon 59.56010 kg C/m2 Calculated from field dataFROOTC Fine root carbon 32.31973 kg C/m2 Calculated from field data

SAPC Sapwood carbon 0.375043 kg C/m2 Calculated from field data

STEMC Stem carbon 0.375043 kg C/m2 Calculated from field data

CROOTLC Live coarse root carbon 14.030007 kg C/m2 Calculated from field data

CROOTDC Dead coarse root carbon 14.030007 kg C/m2 Calculated from field data

WOODC Woody debris carbon 0.864850 kg C/m2 Calculated from field data

LITLC Labile litter carbon 0.146636 kg C/m2 Calculated from field data

LITUC Unshielded litter carbon, cellulose 0.293272 kg C/m2 Calculated from field data

LITSC Shielded litter carbon, cellulose 0.439908 kg C/m2 Calculated from field data

LITGC Litter carbon, lignin 0.586545 kg C/m2 Calculated from field data

SOILFC Soil carbon, fast microbial recycling pool 0.323197 kg C/m2 Calculated from field data

SOILMC Soil carbon, medium microbial recycling pool 0.323197 kg C/m2 Calculated from field data

SOILSC Soil carbon, slow microbial recycling pool 0.323197 kg C/m2 Calculated from field data

SOILRC Soil carbon, recalcitrant (slowest) 2.262381 kg C/m2 Calculated from field data

LITN Litter nitrogen, labile pool 0.036659 kg N/m2 Calculated from field data

SOILN Soil nitrogen, mineral pool 0.032320 kg N/m2 Calculated from field data

HERB (Herbaceous vegetation) 3

Parameter name Description Value Units Source

STATSGO, DEM,

SDEPTH Soil depth Varies m Hydrologic model

SAND Percent sand Varies Percent STATSGO

SILT Percent silt Varies Percent STATSGO

CLAY Percent clay Varies Percent STATSGO

ELEV Elevation Varies Meters DEM

LATITUDE Latitude Varies Decimal degrees GPS
50 USDA Forest Service RMRS-GTR-92 ALBEDO Fraction of incident radiation reflected by surface 0.800000 None Keane and others 1996b ATMN Atmospheric nitrogen deposition 0.000400 kg N m-2 yr-1 Waring and Running 1998 SNOWWATER Default for amount water in snowpack 16.000000 kg/m2 Keane and others 1996a SOILWATER Initial soil saturation 0.500000 m3 H O m 2 -3Soil Keane and others 1996a SLA Specific leaf area 25.000000 None Measured, satellite imagery SUNSHADE Ratio of shaded to sunlit leaves 2.000000 None Keane and others 1996a PLAR All-sided to projected leaf area ratio 3.200000 None Waring and Running 1998 CIC Canopy water interception coefficient 0.100000 kg m-2 LAI-1 Diameter-1 Keane and others 1996a

K Light extinction coefficient 0.350000 None Waring and Running 1998

LEAFNRUB Fraction of leaf N in rubisco 0.120000 Ratio Waring and Running 1998

TOPT Optimum temperature for stomatal conductance 25.000000 ∞C Waring and Running 1998

TMAX Maximum temperature for stomatal conductance 40.000000 ∞C Waring and Running 1998

LWP1 Leaf water potential: start of conductance reduction –0.200000 Mpa Waring and Running 1998

LWP2 Leaf water potential: complete conductance reduction –1.500000 Mpa Waring and Running 1998

VPD1 Vapor pressure deficit: start of conductance reduction 500.000000 Pa Waring and Running 1998

VPD2 Vapor pressure deficit: complete conductance reduction 2000.000000 Pa Waring and Running 1998

GMAX Maximum leaf-scale stomatal conductance 0.008000 m s-1 Waring and Running 1998

GCUT Leaf-scale cuticular conductance 0.000050 m s-1 Waring and Running 1998

GBOUND Leaf-scale boundary layer conductance 0.080000 m s-1 Waring and Running 1998

LEAFON Yearday leaves on 0.000000 Julian day Keane and others 1996a

LEAFOFF Yearday leaves off 0.000000 Julian day Keane and others 1996a

LEAFEXP Days it takes for 50 percent expansion 21.000000 # Days Keane and others 1996a

LEAFDROP Days it takes for 50 percent drop 21.000000 # Days Keane and others 1996a

LEAFTURN Annual leaf turnover fraction 0.330000 None Keane and others 1996a

MORT Annual whole plant mortality fraction 0.001000 None Keane and others 1996a

LEAFCN C:N ratio of leaves 35.000000 None Waring and Running 1998

LITCN C:N ratio of litter 90.000000 None Waring and Running 1998

FROOTCN C:N ratio of fine roots 35.000000 None Waring and Running 1998

STEMCN C:N ratio of stem 50.000000 None Waring and Running 1998

LITLAB Leaf litter labile proportion 0.300000 None Waring and Running 1998

LITCELL Leaf litter cellulose proportion 0.500000 None Waring and Running 1998

LITLIG Leaf litter lignin proportion 0.200000 None Waring and Running 1998

FROOTLAB Fine root labile proportion 0.250000 None Waring and Running 1998

FROOTCELL Fine root cellulose proportion 0.500000 None Waring and Running 1998

FROOTLIG Fine root lignin proportion 0.250000 None Waring and Running 1998

WOODCELL Dead wood cellulose proportion 0.500000 None Waring and Running 1998

WOODLIG Dead wood lignin proportion 0.500000 None Waring and Running 1998

RLC Proportion C allocated to fine roots vs. Leaf carbon 0.600000 None Waring and Running 1998

CSC Proportion C allocated to coarse roots vs. stems 0.300000 None Waring and Running 1998

STOREC Fraction of C pool to hold over for next year 0.100000 None Waring and Running 1998

STOREN Fraction of N pool to hold over for next year 0.100000 None Waring and Running 1998

MAXLEAFC Peak growing season leaf carbon 0.050000 kg C/m2 Waring and Running 1998
USDA Forest Service RMRS-GTR-92 51

BEGNPP Starting proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998

ENDNPP Ending proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998

LEAFC Leaf carbon 0.079218 kg C/m2 Calculated from field data

FROOTC Fine root carbon 0.125717 kg C/m2 Calculated from field data

SAPC Sapwood carbon 0.007549 kg C/m2 Calculated from field data

STEMC Stem carbon 0.007549 kg C/m2 Calculated from field data

CROOTLC Live coarse root carbon 0.025408 kg C/m2 Calculated from field data

CROOTDC Dead coarse root carbon 0.025408 kg C/m2 Calculated from field data

WOODC Woody debris carbon 0.689920 kg C/m2 Calculated from field data

LITLC Labile litter carbon 0.024578 kg C/m2 Calculated from field data

LITUC Unshielded litter carbon, cellulose 0.049156 kg C/m2 Calculated from field data

LITSC Shielded litter carbon, cellulose 0.073734 kg C/m2 Calculated from field data

LITGC Litter carbon, lignin 0.098312 kg C/m2 Calculated from field data

SOILFC Soil carbon, fast microbial recycling pool 0.012572 kg C/m2 Calculated from field data

SOILMC Soil carbon, medium microbial recycling pool 0.012572 kg C/m2 Calculated from field data

SOILSC Soil carbon, slow microbial recycling pool 0.012572 kg C/m2 Calculated from field data

SOILRC Soil carbon, recalcitrant (slowest) 0.088002 kg C/m2 Calculated from field data

LITN Litter nitrogen, labile pool 0.006144 kg N/m2 Calculated from field data

SOILN Soil nitrogen, mineral pool 0.001257 kg N/m2 Calculated from field data

LAOC (Western larch) 4

Parameter name Description Value Units Source

STATSGO, DEM,

SDEPTH Soil depth Varies m Hydrologic model

SAND Percent sand Varies Percent STATSGO

SILT Percent silt Varies Percent STATSGO

CLAY Percent clay Varies Percent STATSGO

ELEV Elevation Varies Meters DEM

LATITUDE Latitude Varies Decimal degrees GPS

ALBEDO Fraction of incident radiation reflected by surface 0.800000 None Keane and others 1996b

ATMN Atmospheric nitrogen deposition 0.000400 kg N m-2 yr-1 Waring and Running 1998

SNOWWATER Default for amount water in snowpack 16.000000 kg/m2 Keane and others 1996a

SOILWATER Initial soil saturation 0.500000 m3 H O m 2 -3Soil Keane and others 1996a

SLA Specific leaf area 25.000000 None Measured, satellite imagery

SUNSHADE Ratio of shaded to sunlit leaves 3.000000 None Keane and others 1996a

PLAR All-sided to projected leaf area ratio 2.200000 None Waring and Running 1998

CIC Canopy water interception coefficient 0.100000 kg m-2 LAI-1 Diameter-1 Keane and others 1996a

K Light extinction coefficient 0.500000 None Waring and Running 1998

LEAFNRUB Fraction of leaf N in rubisco 0.100000 Ratio Waring and Running 1998

TOPT Optimum temperature for stomatal conductance 25.000000 ∞C Waring and Running 1998
52 USDA Forest Service RMRS-GTR-92 TMAX Maximum temperature for stomatal conductance 40.000000 ∞C Waring and Running 1998

LWP1 Leaf water potential: start of conductance reduction –0.300000 Mpa Waring and Running 1998

LWP2 Leaf water potential: complete conductance reduction –2.000000 Mpa Waring and Running 1998

VPD1 Vapor pressure deficit: start of conductance reduction 500.000000 Pa Waring and Running 1998

VPD2 Vapor pressure deficit: complete conductance reduction 2000.00000 Pa Waring and Running 1998

GMAX Maximum leaf-scale stomatal conductance 0.006000 m s-1 Waring and Running 1998

GCUT Leaf-scale cuticular conductance 0.000050 m s-1 Waring and Running 1998

GBOUND Leaf-scale boundary layer conductance 0.080000 m s-1 Waring and Running 1998

LEAFON Yearday leaves on 0.000000 Julian day Keane and others 1996a

LEAFOFF Yearday leaves off 0.000000 Julian day Keane and others 1996a

LEAFEXP Days it takes for 50 percent expansion 21.000000 # Days Keane and others 1996a

LEAFDROP Days it takes for 50 percent drop 21.000000 # Days Keane and others 1996a

LEAFTURN Annual leaf turnover fraction 0.102564 None Keane and others 1996a

MORT Annual whole plant mortality fraction 0.001000 None Keane and others 1996a

LEAFCN C:N ratio of leaves 35.000000 None Waring and Running 1998

LITCN C:N ratio of litter 90.000000 None Waring and Running 1998

FROOTCN C:N ratio of fine roots 35.000000 None Waring and Running 1998

STEMCN C:N ratio of stem 50.000000 None Waring and Running 1998

LITLAB Leaf litter labile proportion 0.300000 None Waring and Running 1998

LITCELL Leaf litter cellulose proportion 0.500000 None Waring and Running 1998

LITLIG Leaf litter lignin proportion 0.200000 None Waring and Running 1998

FROOTLAB Fine root labile proportion 0.250000 None Waring and Running 1998

FROOTCELL Fine root cellulose proportion 0.500000 None Waring and Running 1998

FROOTLIG Fine root lignin proportion 0.250000 None Waring and Running 1998

WOODCELL Dead wood cellulose proportion 0.500000 None Waring and Running 1998

WOODLIG Dead wood lignin proportion 0.500000 None Waring and Running 1998

RLC Proportion C allocated to fine roots vs. leaf carbon 0.600000 None Waring and Running 1998

CSC Proportion C allocated to coarse roots vs. stems 0.300000 None Waring and Running 1998

STOREC Fraction of C pool to hold over for next year 0.100000 None Waring and Running 1998

STOREN Fraction of N pool to hold over for next year 0.100000 None Waring and Running 1998

MAXLEAFC Peak growing season leaf carbon 0.050000 kg C/m2 Waring and Running 1998

BEGNPP Starting proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998

ENDNPP Ending proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998

LEAFC Leaf carbon 2.629579 kg C/m2 Calculated from field data

FROOTC Fine root carbon 1.565557 kg C/m2 Calculated from field data

SAPC Sapwood carbon 0.168376 kg C/m2 Calculated from field data

STEMC Stem carbon 0.168376 kg C/m2 Calculated from field data

CROOTLC Live coarse root carbon 4.105540 kg C/m2 Calculated from field data

CROOTDC Dead coarse root carbon 4.105540 kg C/m2 Calculated from field data

WOODC Woody debris carbon 0.347900 kg C/m2 Calculated from field data

LITLC Labile litter carbon 0.118906 kg C/m2 Calculated from field data

LITUC Unshielded litter carbon, cellulose 0.237811 kg C/m2 Calculated from field data
USDA Forest Service RMRS-GTR-92 53

LITSC Shielded litter carbon, cellulose 0.356717 kg C/m2 Calculated from field data

LITGC Litter carbon, lignin 0.475622 kg C/m2 Calculated from field data

SOILFC Soil carbon, fast microbial recycling pool 0.156556 kg C/m2 Calculated from field data

SOILMC Soil carbon, medium microbial recycling pool 0.156556 kg C/m2 Calculated from field data

SOILSC Soil carbon, slow microbial recycling pool 0.156556 kg C/m2 Calculated from field data

SOILRC Soil carbon, recalcitrant (slowest) 1.095890 kg C/m2 Calculated from field data

LITN Litter nitrogen, labile pool 0.029726 kg N/m2 Calculated from field data

SOILN Soil nitrogen, mineral pool 0.015656 kg N/m2 Calculated from field data

PICO (Lodgepole pine) 5

Parameter name Description Value Units Source

STATSGO, DEM,

SDEPTH Soil depth Varies m Hydrologic model

SAND Percent sand Varies Percent STATSGO

SILT Percent silt Varies Percent STATSGO

CLAY Percent clay Varies Percent STATSGO

ELEV Elevation Varies Meters DEM

LATITUDE Latitude Varies Decimal degrees GPS

ALBEDO Fraction of incident radiation reflected by surface 0.800000 None Keane and others 1996b

ATMN Atmospheric nitrogen deposition 0.000400 kg N m-2 yr-1 Waring and Running 1998

SNOWWATER Default for amount water in snowpack 16.000000 kg/m2 Keane and others 1996a

SOILWATER Initial soil saturation 0.500000 m3 H O m 2 -3Soil Keane and others 1996a

SLA Specific leaf area 25.000000 None Measured, satellite imagery

SUNSHADE Ratio of shaded to sunlit leaves 3.000000 None Keane and others 1996a

PLAR All-sided to projected leaf area ratio 2.200000 None Waring and Running 1998

CIC Canopy water interception coefficient 0.100000 kg m-2 LAI-1 Diameter-1 Keane and others 1996a

K Light extinction coefficient 0.500000 None Waring and Running 1998

LEAFNRUB Fraction of leaf N in rubisco 0.100000 Ratio Waring and Running 1998

TOPT Optimum temperature for stomatal conductance 25.000000 ∞C Waring and Running 1998

TMAX Maximum temperature for stomatal conductance 40.000000 ∞C Waring and Running 1998

LWP1 Leaf water potential: start of conductance reduction -0.300000 Mpa Waring and Running 1998

LWP2 Leaf water potential: complete conductance reduction -1.460000 Mpa Waring and Running 1998

VPD1 Vapor pressure deficit: start of conductance reduction 500.000000 Pa Waring and Running 1998

VPD2 Vapor pressure deficit: complete conductance reduction 2000.000000 Pa Waring and Running 1998

GMAX Maximum leaf-scale stomatal conductance 0.006000 m s-1 Waring and Running 1998

GCUT Leaf-scale cuticular conductance 0.000050 m s-1 Waring and Running 1998

GBOUND Leaf-scale boundary layer conductance 0.080000 m s-1 Waring and Running 1998

LEAFON Yearday leaves on 0.000000 Julian day Keane and others 1996a

LEAFOFF Yearday leaves off 0.000000 Julian day Keane and others 1996a

LEAFEXP Days it takes for 50 percent expansion 21.000000 # Days Keane and others 1996a

LEAFDROP Days it takes for 50 percent drop 21.000000 # Days Keane and others 1996a
54 USDA Forest Service RMRS-GTR-92 LEAFTURN Annual leaf turnover fraction 0.222222 None Keane and others 1996a MORT Annual whole plant mortality fraction 0.001000 None Keane and others 1996a LEAFCN C:N ratio of leaves 35.000000 None Waring and Running 1998 LITCN C:N ratio of litter 90.000000 None Waring and Running 1998 FROOTCN C:N ratio of fine roots 35.000000 None Waring and Running 1998 STEMCN C:N ratio of stem 50.000000 None Waring and Running 1998 LITLAB Leaf litter labile proportion 0.300000 None Waring and Running 1998 LITCELL Leaf litter cellulose proportion 0.500000 None Waring and Running 1998 LITLIG Leaf litter lignin proportion 0.200000 None Waring and Running 1998 FROOTLAB Fine root labile proportion 0.250000 None Waring and Running 1998 FROOTCELL Fine root cellulose proportion 0.500000 None Waring and Running 1998 FROOTLIG Fine root lignin proportion 0.250000 None Waring and Running 1998 WOODCELL Dead wood cellulose proportion 0.500000 None Waring and Running 1998 WOODLIG Dead wood lignin proportion 0.500000 None Waring and Running 1998 RLC Proportion C allocated to fine roots vs. Leaf carbon 0.600000 None Waring and Running 1998 CSC Proportion C allocated to coarse roots vs. stems 0.300000 None Waring and Running 1998 STOREC Fraction of C pool to hold over for next year 0.100000 None Waring and Running 1998 STOREN Fraction of N pool to hold over for next year 0.100000 None Waring and Running 1998 MAXLEAFC Peak growing season leaf carbon 0.050000 kg C/m2 Waring and Running 1998 BEGNPP Starting proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998 ENDNPP Ending proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998 LEAFC Leaf carbon 0.2440310 kg C/m2 Calculated from field data FROOTC Fine root carbon 0.1095940 kg C/m2 Calculated from field data SAPC Sapwood carbon 0.199237 kg C/m2 Calculated from field data STEMC Stem carbon 0.199237 kg C/m2 Calculated from field data CROOTLC Live coarse root carbon 3.350678 kg C/m2 Calculated from field data CROOTDC Dead coarse root carbon 3.350678 kg C/m2 Calculated from field data WOODC Woody debris carbon 2.893450 kg C/m2 Calculated from field data LITLC Labile litter carbon 0.134112 kg C/m2 Calculated from field data LITUC Unshielded litter carbon, cellulose 0.268224 kg C/m2 Calculated from field data LITSC Shielded litter carbon, cellulose 0.402336 kg C/m2 Calculated from field data LITGC Litter carbon, lignin 0.536448 kg C/m2 Calculated from field data SOILFC Soil carbon, fast microbial recycling pool 0.109594 kg C/m2 Calculated from field data SOILMC Soil carbon, medium microbial recycling pool 0.109594 kg C/m2 Calculated from field data SOILSC Soil carbon, slow microbial recycling pool 0.109594 kg C/m2 Calculated from field data SOILRC Soil carbon, recalcitrant (slowest) 0.767158 kg C/m2 Calculated from field data LITN Litter nitrogen, labile pool 0.033528 kg N/m2 Calculated from field data SOILN Soil nitrogen, mineral pool 0.010959 kg N/m2 Calculated from field data
USDA Forest Service RMRS-GTR-92 55

PIPO (Ponderosa pine) 6

Parameter name Description Value Units Source

STATSGO, DEM,

SDEPTH Soil depth Varies m Hydrologic model

SAND Percent sand Varies Percent STATSGO

SILT Percent silt Varies Percent STATSGO

CLAY Percent clay Varies Percent STATSGO

ELEV Elevation Varies Meters DEM

LATITUDE Latitude Varies Decimal degrees GPS

ALBEDO Fraction of incident radiation reflected by surface 0.800000 None Keane and others 1996b

ATMN Atmospheric nitrogen deposition 0.000400 kg N m-2 yr-1 Waring and Running 1998

SNOWWATER Default for amount water in snowpack 3.000000 kg/m2 Keane and others 1996a

SOILWATER Initial soil saturation 0.300000 m3 H O m 2 -3Soil Keane and others 1996a

SLA Specific leaf area 20.000000 None Measured, satellite imagery

SUNSHADE Ratio of shaded to sunlit leaves 3.000000 None Keane and others 1996a

PLAR All-sided to projected leaf area ratio 2.200000 None Waring and Running 1998

CIC Canopy water interception coefficient 0.100000 kg m-2 LAI-1 Diameter-1 Keane and others 1996a

K Light extinction coefficient 0.500000 None Waring and Running 1998

LEAFNRUB Fraction of leaf N in rubisco 0.080000 Ratio Waring and Running 1998

TOPT Optimum temperature for stomatal conductance 25.000000 ∞C Waring and Running 1998

TMAX Maximum temperature for stomatal conductance 40.0000000 ∞C Waring and Running 1998

LWP1 Leaf water potential: start of conductance reduction –0.400000 Mpa Waring and Running 1998

LWP2 Leaf water potential: complete conductance reduction –1.650000 Mpa Waring and Running 1998

VPD1 Vapor pressure deficit: start of conductance reduction 500.000000 Pa Waring and Running 1998

VPD2 Vapor pressure deficit: complete conductance reduction 2000.000000 Pa Waring and Running 1998

GMAX Maximum leaf-scale stomatal conductance 0.006000 m s-1 Waring and Running 1998

GCUT Leaf-scale cuticular conductance 0.000050 m s-1 Waring and Running 1998

GBOUND Leaf-scale boundary layer conductance 0.080000 m s-1 Waring and Running 1998

LEAFON Yearday leaves on 0.000000 Julian day Keane and others 1996a

LEAFOFF Yearday leaves off 365.000000 Julian day Keane and others 1996a

LEAFEXP Days it takes for 50 percent expansion 21.000000 # Days Keane and others 1996a

LEAFDROP Days it takes for 50 percent drop 21.000000 # Days Keane and others 1996a

LEAFTURN Annual leaf turnover fraction 0.187617 None Keane and others 1996a

MORT Annual whole plant mortality fraction 0.001000 None Keane and others 1996a

LEAFCN C:N ratio of leaves 35.000000 None Waring and Running 1998

LITCN C:N ratio of litter 90.000000 None Waring and Running 1998

FROOTCN C:N ratio of fine roots 35.000000 None Waring and Running 1998

STEMCN C:N ratio of stem 50.000000 None Waring and Running 1998

LITLAB Leaf litter labile proportion 0.300000 None Waring and Running 1998

LITCELL Leaf litter cellulose proportion 0.500000 None Waring and Running 1998
56 USDA Forest Service RMRS-GTR-92 LITLIG Leaf litter lignin proportion 0.200000 None Waring and Running 1998 FROOTLAB Fine root labile proportion 0.250000 None Waring and Running 1998 FROOTCELL Fine root cellulose proportion 0.500000 None Waring and Running 1998 FROOTLIG Fine root lignin proportion 0.250000 None Waring and Running 1998 WOODCELL Dead wood cellulose proportion 0.500000 None Waring and Running 1998 WOODLIG Dead wood lignin proportion 0.500000 None Waring and Running 1998 RLC Proportion C allocated to fine roots vs. Leaf carbon 0.600000 None Waring and Running 1998 CSC Proportion C allocated to coarse roots vs. stems 0.300000 None Waring and Running 1998 STOREC Fraction of C pool to hold over for next year 0.100000 None Waring and Running 1998 STOREN Fraction of N pool to hold over for next year 0.100000 None Waring and Running 1998 MAXLEAFC Peak growing season leaf carbon 0.050000 kg C/m2 Waring and Running 1998 BEGNPP Starting proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998 ENDNPP Ending proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998 LEAFC Leaf carbon 3.015195 kg C/m2 Calculated from field data FROOTC Fine root carbon 1.807775 kg C/m2 Calculated from field data SAPC Sapwood carbon 0.252453 kg C/m2 Calculated from field data STEMC Stem carbon 0.252453 kg C/m2 Calculated from field data CROOTLC Live coarse root carbon 5.770392 kg C/m2 Calculated from field data CROOTDC Dead coarse root carbon 5.770392 kg C/m2 Calculated from field data WOODC Woody debris carbon 2.846410 kg C/m2 Calculated from field data LITLC Labile litter carbon 0.081671 kg C/m2 Calculated from field data LITUC Unshielded litter carbon, cellulose 0.163342 kg C/m2 Calculated from field data LITSC Shielded litter carbon, cellulose 0.245013 kg C/m2 Calculated from field data LITGC Litter carbon, lignin 0.326684 kg C/m2 Calculated from field data SOILFC Soil carbon, fast microbial recycling pool 0.180777 kg C/m2 Calculated from field data SOILMC Soil carbon, medium microbial recycling pool 0.180777 kg C/m2 Calculated from field data SOILSC Soil carbon, slow microbial recycling pool 0.180777 kg C/m2 Calculated from field data SOILRC Soil carbon, recalcitrant (slowest) 1.265442 kg C/m2 Calculated from field data LITN Litter nitrogen, labile pool 0.020418 kg N/m2 Calculated from field data SOILN Soil nitrogen, mineral pool 0.018078 kg N/m2 Calculated from field data PSME (Douglas fir) 7 Parameter name Description Value Units Source STATSGO, DEM, SDEPTH Soil depth Varies m Hydrologic model SAND Percent sand Varies Percent STATSGO SILT Percent silt Varies Percent STATSGO CLAY Percent clay Varies Percent STATSGO ELEV Elevation Varies Meters DEM LATITUDE Latitude Varies Decimal degrees GPS

ALBEDO Fraction of incident radiation reflected by surface 0.800000 None Keane and others 1996b
USDA Forest Service RMRS-GTR-92 57

ATMN Atmospheric nitrogen deposition 0.000400 kg N m-2 yr-1 Waring and Running 1998

SNOWWATER Default for amount water in snowpack 3.000000 kg/m2 Keane and others 1996a

SOILWATER Initial soil saturation 0.300000 m3 H O m 2 -3Soil Keane and others 1996a

SLA Specific Leaf Area 20.000000 None Measured, satellite imagery

SUNSHADE Ratio of shaded to sunlit leaves 3.000000 None Keane and others 1996a

PLAR All-sided to projected leaf area ratio 2.200000 None Waring and Running 1998

CIC Canopy water interception coefficient 0.100000 kg m-2 LAI-1 Diameter-1 Keane and others 1996a

K Light extinction coefficient 0.500000 None Waring and Running 1998

LEAFNRUB Fraction of leaf N in rubisco 0.080000 Ratio Waring and Running 1998

TOPT Optimum temperature for stomatal conductance 25.000000 ∞C Waring and Running 1998

TMAX Maximum temperature for stomatal conductance 40.000000 ∞C Waring and Running 1998

LWP1 Leaf water potential: start of conductance reduction –0.400000 Mpa Waring and Running 1998

LWP2 Leaf water potential: complete conductance reduction –1.600000 Mpa Waring and Running 1998

VPD1 Vapor pressure deficit: start of conductance reduction 500.000000 Pa Waring and Running 1998

VPD2 Vapor pressure deficit: complete conductance reduction 2000.00000 Pa Waring and Running 1998

GMAX Maximum leaf-scale stomatal conductance 0.006000 m s-1 Waring and Running 1998

GCUT Leaf-scale cuticular conductance 0.000050 m s-1 Waring and Running 1998

GBOUND Leaf-scale boundary layer conductance 0.080000 m s-1 Waring and Running 1998

LEAFON Yearday leaves on 0.000000 Julian day Keane and others 1996a

LEAFOFF Yearday leaves off 365.000000 Julian day Keane and others 1996a

LEAFEXP Days it takes for 50 percent expansion 21.000000 # Days Keane and others 1996a

LEAFDROP Days it takes for 50 percent drop 21.000000 # Days Keane and others 1996a

LEAFTURN Annual leaf turnover fraction 0.125000 None Keane and others 1996a

MORT Annual whole plant mortality fraction 0.001000 None Keane and others 1996a

LEAFCN C:N ratio of leaves 35.000000 None Waring and Running 1998

LITCN C:N ratio of litter 80.000000 None Waring and Running 1998

FROOTCN C:N ratio of fine roots 35.000000 None Waring and Running 1998

STEMCN C:N ratio of stem 50.000000 None Waring and Running 1998

LITLAB Leaf litter labile proportion 0.300000 None Waring and Running 1998

LITCELL Leaf litter cellulose proportion 0.500000 None Waring and Running 1998

LITLIG Leaf litter lignin proportion 0.200000 None Waring and Running 1998

FROOTLAB Fine root labile proportion 0.250000 None Waring and Running 1998

FROOTCELL Fine root cellulose proportion 0.500000 None Waring and Running 1998

FROOTLIG Fine root lignin proportion 0.250000 None Waring and Running 1998

WOODCELL Dead wood cellulose proportion 0.500000 None Waring and Running 1998

WOODLIG Dead wood lignin proportion 0.500000 None Waring and Running 1998

RLC Proportion C allocated to fine roots vs. Leaf carbon 0.600000 None Waring and Running 1998

CSC Proportion C allocated to coarse roots vs. stems 0.300000 None Waring and Running 1998

STOREC Fraction of C pool to hold over for next year 0.100000 None Waring and Running 1998

STOREN Fraction of N pool to hold over for next year 0.100000 None Waring and Running 1998

MAXLEAFC Peak growing season leaf carbon 0.050000 kg C/m2 Waring and Running 1998

BEGNPP Starting proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998
58 USDA Forest Service RMRS-GTR-92 ENDNPP Ending proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998 LEAFC Leaf carbon 37.94982 kg C/m2 Calculated from field data FROOTC Fine root carbon 24.78121 kg C/m2 Calculated from field data SAPC Sapwood carbon 0.236728 kg C/m2 Calculated from field data STEMC Stem carbon 0.236728 kg C/m2 Calculated from field data CROOTLC Live coarse root carbon 9.445276 kg C/m2 Calculated from field data CROOTDC Dead coarse root carbon 9.445276 kg C/m2 Calculated from field data WOODC Woody debris carbon 1.941870 kg C/m2 Calculated from field data LITLC Labile litter carbon 0.038222 kg C/m2 Calculated from field data LITUC Unshielded litter carbon, cellulose 0.076444 kg C/m2 Calculated from field data LITSC Shielded litter carbon, cellulose 0.114666 kg C/m2 Calculated from field data LITGC Litter carbon, lignin 0.152888 kg C/m2 Calculated from field data SOILFC Soil carbon, fast microbial recycling pool 0.247812 kg C/m2 Calculated from field data SOILMC Soil carbon, medium microbial recycling pool 0.247812 kg C/m2 Calculated from field data SOILSC Soil carbon, slow microbial recycling pool 0.247812 kg C/m2 Calculated from field data SOILRC Soil carbon, recalcitrant (slowest) 1.734685 kg C/m2 Calculated from field data LITN Litter nitrogen, labile pool 0.009555 kg N/m2 Calculated from field data SOILN Soil nitrogen, mineral pool 0.024781 kg N/m2 Calculated from field data SHRUB (Shrubby vegetation) 8 Parameter name Description Value Units Source STATSGO, DEM, SDEPTH Soil depth Varies m Hydrologic model SAND Percent sand Varies Percent STATSGO SILT Percent silt Varies Percent STATSGO CLAY Percent clay Varies Percent STATSGO ELEV Elevation Varies Meters DEM LATITUDE Latitude Varies Decimal degrees GPS ALBEDO Fraction of incident radiation reflected by surface 0.800000 None Keane and others 1996b ATMN Atmospheric nitrogen deposition 0.000400 kg N m-2 yr-1 Waring and Running 1998 SNOWWATER Default for amount water in snowpack 3.000000 kg/m2 Keane and others 1996a SOILWATER Initial soil saturation 0.300000 m3 H O m 2 -3Soil Keane and others 1996a SLA Specific leaf area 20.00000 None Measured, satellite imagery SUNSHADE Ratio of shaded to sunlit leaves 3.000000 None Keane and others 1996a PLAR All-sided to projected leaf area ratio 2.200000 None Waring and Running 1998 CIC Canopy water interception coefficient 0.100000 kg m-2 LAI-1 Diameter-1 Keane and others 1996a

K Light extinction coefficient 0.500000 None Waring and Running 1998

LEAFNRUB Fraction of leaf N in rubisco 0.080000 Ratio Waring and Running 1998

TOPT Optimum temperature for stomatal conductance 25.000000 ∞C Waring and Running 1998

TMAX Maximum temperature for stomatal conductance 40.000000 ∞C Waring and Running 1998

LWP1 Leaf water potential: start of conductance reduction –0.400000 Mpa Waring and Running 1998
USDA Forest Service RMRS-GTR-92 59

LWP2 Leaf water potential: complete conductance reduction –1.300000 Mpa Waring and Running 1998

VPD1 Vapor pressure deficit: start of conductance reduction 500.000000 Pa Waring and Running 1998

VPD2 Vapor pressure deficit: complete conductance reduction 2000.000000 Pa Waring and Running 1998

GMAX Maximum leaf-scale stomatal conductance 0.006000 m s-1 Waring and Running 1998

GCUT Leaf-scale cuticular conductance 0.000050 m s-1 Waring and Running 1998

GBOUND Leaf-scale boundary layer conductance 0.080000 m s-1 Waring and Running 1998

LEAFON Yearday leaves on 0.000000 Julian day Keane and others 1996a

LEAFOFF Yearday leaves off 365.000000 Julian day Keane and others 1996a

LEAFEXP Days it takes for 50 percent expansion 21.000000 # Days Keane and others 1996a

LEAFDROP Days it takes for 50 percent drop 21.000000 # Days Keane and others 1996a

LEAFTURN Annual leaf turnover fraction 0.333333 None Keane and others 1996a

MORT Annual whole plant mortality fraction 0.001000 None Keane and others 1996a

LEAFCN C:N ratio of leaves 35.000000 None Waring and Running 1998

LITCN C:N ratio of litter 90.000000 None Waring and Running 1998

FROOTCN C:N ratio of fine roots 35.000000 None Waring and Running 1998

STEMCN C:N ratio of Stem 50.000000 None Waring and Running 1998

LITLAB Leaf litter labile proportion 0.300000 None Waring and Running 1998

LITCELL Leaf litter cellulose proportion 0.500000 None Waring and Running 1998

LITLIG Leaf litter lignin proportion 0.200000 None Waring and Running 1998

FROOTLAB Fine root labile proportion 0.250000 None Waring and Running 1998

FROOTCELL Fine root cellulose proportion 0.500000 None Waring and Running 1998

FROOTLIG Fine root lignin proportion 0.250000 None Waring and Running 1998

WOODCELL Dead wood cellulose proportion 0.500000 None Waring and Running 1998

WOODLIG Dead wood lignin proportion 0.500000 None Waring and Running 1998

RLC Proportion C allocated to fine roots vs. Leaf carbon 0.600000 None Waring and Running 1998

CSC Proportion C allocated to coarse roots vs. stems 0.300000 None Waring and Running 1998

STOREC Fraction of C pool to hold over for next year 0.100000 None Waring and Running 1998

STOREN Fraction of N pool to hold over for next year 0.100000 None Waring and Running 1998

MAXLEAFC Peak growing season leaf carbon 0.050000 kg C/m2 Waring and Running 1998

BEGNPP Starting proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998

ENDNPP Ending proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998

LEAFC Leaf carbon 0.684305 kg C/m2 Calculated from field dataFROOTC Fine root carbon 0.244300 kg C/m2 Calculated from field data

SAPC Sapwood carbon 0.048629 kg C/m2 Calculated from field data

STEMC Stem carbon 0.048629 kg C/m2 Calculated from field data

CROOTLC Live coarse root carbon 0.615581 kg C/m2 Calculated from field data

CROOTDC Dead coarse root carbon 0.615581 kg C/m2 Calculated from field data

WOODC Woody debris carbon 0.850640 kg C/m2 Calculated from field data

LITLC Labile litter carbon 0.048946 kg C/m2 Calculated from field data

LITUC Unshielded litter carbon, cellulose 0.097891 kg C/m2 Calculated from field data

LITSC Shielded litter carbon, cellulose 0.146837 kg C/m2 Calculated from field data

LITGC Litter carbon, lignin 0.195782 kg C/m2 Calculated from field data
60 USDA Forest Service RMRS-GTR-92 SOILFC Soil carbon, fast microbial recycling pool 0.024430 kg C/m2 Calculated from field data SOILMC Soil carbon, medium microbial recycling pool 0.024430 kg C/m2 Calculated from field data SOILSC Soil carbon, slow microbial recycling pool 0.024430 kg C/m2 Calculated from field data SOILRC Soil carbon, recalcitrant (slowest) 0.171010 kg C/m2 Calculated from field data LITN Litter nitrogen, labile pool 0.012236 kg N/m2 Calculated from field data SOILN Soil nitrogen, mineral pool 0.002443 kg N/m2 Calculated from field data THPL (Western redcedar) 7 Parameter name Description Value Units Source STATSGO, DEM, SDEPTH Soil depth Varies m Hydrologic model SAND Percent sand Varies Percent STATSGO SILT Percent silt Varies Percent STATSGO CLAY Percent clay Varies Percent STATSGO ELEV Elevation Varies Meters DEM LATITUDE Latitude Varies Decimal degrees GPS ALBEDO Fraction of incident radiation reflected by surface 0.792000 None Keane and others 1996b ATMN Atmospheric nitrogen deposition 30.000000 kg N m-2 yr-1 Waring and Running 1998 SNOWWATER Default for amount water in snowpack 30.000000 kg/m2 Keane and others 1996a SOILWATER Initial soil saturation 40.000000 m3 H O m 2 -3Soil Keane and others 1996a SLA Specific leaf area 1341.000000 None Measured, satellite imagery SUNSHADE Ratio of shaded to sunlit leaves 46.500000 None Keane and others 1996a PLAR All-sided to projected leaf area ratio 0.800000 None Waring and Running 1998 CIC Canopy water interception coefficient 0.000400 kg m-2 LAI-1 Diameter-1 Keane and others 1996a K Light extinction coefficient 16.000000 None Waring and Running 1998 LEAFNRUB Fraction of leaf N in rubisco 0.500000 Ratio Waring and Running 1998 TOPT Optimum temperature for stomatal conductance 30.000000 ∞C Waring and Running 1998 TMAX Maximum temperature for stomatal conductance 3.000000 ∞C Waring and Running 1998 LWP1 Leaf water potential: start of conductance reduction 2.200000 Mpa Waring and Running 1998 LWP2 Leaf water potential: complete conductance reduction 0.100000 Mpa Waring and Running 1998 VPD1 Vapor pressure deficit: start of conductance reduction 0.500000 Pa Waring and Running 1998 VPD2 Vapor pressure deficit: complete conductance reduction 0.120000 Pa Waring and Running 1998 GMAX Maximum leaf-scale stomatal conductance 25.000000 m s-1 Waring and Running 1998 GCUT Leaf-scale cuticular conductance 40.000000 m s-1 Waring and Running 1998 GBOUND Leaf-scale boundary layer conductance -0.200000 m s-1 Waring and Running 1998 LEAFON Yearday leaves on -1.300000 Julian day Keane and others 1996a LEAFOFF Yearday leaves off 500.000000 Julian day Keane and others 1996a LEAFEXP Days it takes for 50 percent expansion 2000.000000 # Days Keane and others 1996a LEAFDROP Days it takes for 50 percent drop 0.008000 # Days Keane and others 1996a LEAFTURN Annual leaf turnover fraction 0.000050 None Keane and others 1996a

MORT Annual whole plant mortality fraction 0.080000 None Keane and others 1996a
USDA Forest Service RMRS-GTR-92 61

LEAFCN C:N ratio of leaves 0.000000 None Waring and Running 1998

LITCN C:N ratio of litter 0.000000 None Waring and Running 1998

FROOTCN C:N ratio of fine roots 21.000000 None Waring and Running 1998

STEMCN C:N ratio of Stem 21.000000 None Waring and Running 1998

LITLAB Leaf litter labile proportion 0.100000 None Waring and Running 1998

LITCELL Leaf litter cellulose proportion 0.001000 None Waring and Running 1998

LITLIG Leaf litter lignin proportion 35.000000 None Waring and Running 1998

FROOTLAB Fine root labile proportion 90.000000 None Waring and Running 1998

FROOTCELL Fine root cellulose proportion 35.000000 None Waring and Running 1998

FROOTLIG Fine root lignin proportion 50.000000 None Waring and Running 1998

WOODCELL Dead wood cellulose proportion 0.300000 None Waring and Running 1998

WOODLIG Dead wood lignin proportion 0.500000 None Waring and Running 1998

RLC Proportion C allocated to fine roots vs. Leaf carbon 0.200000 None Waring and Running 1998

CSC Proportion C allocated to coarse roots vs. stems 0.250000 None Waring and Running 1998

STOREC Fraction of C pool to hold over for next year 0.500000 None Waring and Running 1998

STOREN Fraction of N pool to hold over for next year 0.250000 None Waring and Running 1998

MAXLEAFC Peak growing season leaf carbon 0.500000 kg C/m2 Waring and Running 1998

BEGNPP Starting proportion of daily NPP available for new growth 0.500000 None Waring and Running 1998

ENDNPP Ending proportion of daily NPP available for new growth 0.600000 None Waring and Running 1998

LEAFC Leaf carbon 0.300000 kg C/m2 Calculated from field data

FROOTC Fine root carbon 0.100000 kg C/m2 Calculated from field data

SAPC Sapwood carbon 0.100000 kg C/m2 Calculated from field data

STEMC Stem carbon 0.050000 kg C/m2 Calculated from field data

CROOTLC Live coarse root carbon 0.500000 kg C/m2 Calculated from field data

CROOTDC Dead coarse root carbon 0.500000 kg C/m2 Calculated from field data

WOODC Woody debris carbon 9.082987 kg C/m2 Calculated from field data

LITLC Labile litter carbon 3.624746 kg C/m2 Calculated from field data

LITUC Unshielded litter carbon, cellulose 0.555778 kg C/m2 Calculated from field data

LITSC Shielded litter carbon, cellulose 0.555778 kg C/m2 Calculated from field data

LITGC Litter carbon, lignin 13.840124 kg C/m2 Calculated from field data

SOILFC Soil carbon, fast microbial recycling pool 13.840124 kg C/m2 Calculated from field data

SOILMC Soil carbon, medium microbial recycling pool 2.934610 kg C/m2 Calculated from field data

SOILSC Soil carbon, slow microbial recycling pool 0.160047 kg C/m2 Calculated from field data

SOILRC Soil carbon, recalcitrant (slowest) 0.320095 kg C/m2 Calculated from field data

LITN Litter nitrogen, labile pool 0.480142 kg N/m2 Calculated from field data

SOILN Soil nitrogen, mineral pool 0.640189 kg N/m2 Calculated from field data
The Rocky Mountain Research Station develops scientific

information and technology to improve management, protection, and use of the forests and rangelands. Research is designed to meet the needs of National Forest managers, Federal

and State agencies, public and private organizations, academic

institutions, industry, and individuals.

Studies accelerate solutions to problems involving ecosystems, range, forests, water, recreation, fire, resource inventory,

land reclamation, community sustainability, forest engineering

technology, multiple use economics, wildlife and fish habitat,

and forest insects and diseases. Studies are conducted cooperatively, and applications may be found worldwide.

Research Locations

Flagstaff, Arizona Reno, Nevada

Fort Collins, Colorado* Albuquerque, New Mexico

Boise, Idaho Rapid City, South Dakota

Moscow, Idaho Logan, Utah

Bozeman, Montana Ogden, Utah

Missoula, Montana Provo, Utah

Lincoln, Nebraska Laramie, Wyoming

*Station Headquarters, Natural Resources Research Center, 2150 Centre Avenue, Building A, Fort Collins, CO 80526

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in all its programs and activities on the basis of race, color, national

origin, sex, religion, age, disability, political beliefs, sexual orientation,

or marital or family status. (Not all prohibited bases apply to all programs.) Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.)

should contact USDA's TARGET Center at (202) 720-2600 (voice and

TDD).

To file a complaint of discrimination, write USDA, Director, Office of

Civil Rights, Room 326-W, Whitten Building, 1400 Independence Avenue, SW, Washington, DC 20250-9410 or call (202) 720-5964 (voice

or TDD). USDA is an equal opportunity provider and employer.

ROCKY MOUNTAIN RESEARCH STATION

RMRS

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