6.0 Vegetation Mapping

6.1 Theoretical Background

A vegetation map is a special application of a vegetation classification
(Kuchler 1988). Vegetation classification defines units based on the
similarity of structural, floristic, and ecological characteristics of
the vegetation. The classification units are used to label homogeneous
patches of vegetation to make a vegetation map. A vegetation
classification is usually developed first, then the spatial
relationships of the vegetation units are described in a map.
Modifications to the classification system often occur as the mapping
proceeds. These map units, or polygons, represent various levels of
organization of vegetation information. The map products will differ
with the classification system that is used to label the vegetation.

Vegetation mapping requires a combination of knowledge and experience in
several disciplines. The investigator(s) must have considerable
ecological knowledge of the area to be mapped including the ability to
identify individual plant species, vegetation types, and the
relationships of these types to other factors, such as topography, soil
types, and moisture gradients, within the mapping area. It also requires
that the investigator(s) have experience with general cartographic and
aerial photo-interpretation techniques. This is particularly important
for the ecological interpretation of remote sensing data and digital
image processing and map preparation. Most importantly, the
investigator(s) must clearly understand the relationships between these
disciplines during the mapping process.

**6.1.1 Vegetation Mapping Standards**

Map scale is the extent of reduction required to display a portion of
the earth\'s surface on a map and is defined as a ratio of distances
between corresponding points on the map and on the ground (Robinson *et
al*. 1978). Scale indirectly determines the information content and size
of the area being represented. The mapping scale is determined by the
project objectives and the characteristics of the data obtained for the
project area.

Vegetation maps display every vegetation class that occurs in the
mapping area if the largest map unit equals or exceeds the predetermined
minimum mapping unit (MMU). Every polygon is usually labeled using one
vegetation class of the classification system any other attributes of
interest (e.g., height class, degree of disturbance). Additional mapping
conventions can be developed to display particular classes that are
smaller than the MMU and to map polygons that depict complexes of
vegetation types.

**6.1.2 Imagery Analysis and Vegetation Mapping**

The actual process of vegetation mapping requires the identification and
delineation of homogeneous vegetation types on aerial photographs or
satellite images, and portraying this information on a map using
standard cartographic methods. Several decisions must be made prior to
mapping, such as the level of hierarchy of a given classification system
that will be mapped, the level of accuracy, and minimum area and width
standards. Once identified, the polygons are labeled with the vegetation
units identified in the classification. If a map polygon does not fit
the listed vegetation classes, the classification must be modified, the
additional information included as a data attribute, or the map redrawn
to reflect the new information. Through this process, accurate
vegetation maps can be generated while the classification system is
tested and refined.

***6.1.2.1 Diagnostic Characteristics of the Signatures***

Characteristics of different vegetation types (e.g., physical
characteristics of individual species, the abundance and distribution of
species) can create visual differences on aerial photos. The major
diagnostic features the interpreter uses to recognize these
characteristics of particular vegetation types are photographic texture
(smoothness or coarseness of images), tonal contrast or color, pattern,
association, relative sizes of crown images, and topographic location or
site (Avery 1977, Lillesand and Kiefer 1987). When observed singly, most
of these features of the photo may not have strong diagnostic value.
Taken together, they make up a diagnostic \"signature\" which is an
effective tool in identifying vegetation patterns from the photos and
allows vegetation to be mapped without having to visit every vegetation
polygon on the ground. When delineating boundaries around polygons with
apparently different signatures, the photo interpreter looks for
repetitions of signature types, signatures that are commonly found
together, and associations of signatures with other features on the
photo such as a river\'s edge or a mountain slope.

The photo-interpretation process is facilitated if the interpreter has a
thorough understanding of the vegetation of the area to be mapped. With
knowledge of the classification for the area, the interpreter can begin
to create keys that link the signatures identified on the photographs to
the actual vegetation types on the ground and those listed in the
classification. For example, on color infrared photos, pocosins (a
deciduous saturated shrubland community element found in North Carolina
and possibly South Carolina --- scientific name: Zenobia
pulverulenta-Chamaedaphne calyculata Shrubland) have signatures that
appear as fine, even -textured, dark-colored ovals with relatively
distinct light-colored boundaries. The signatures also include regularly
scattered \"pock marks.\" In this case, the fine, even texture indicates
that the vegetation is shrub dominated. The oval shape and distinct
light-colored boundary indicates that the vegetation occurs in Carolina
Bays (a geomorphic feature) which have sandy rims. The scattered pock
marks indicate the emergent pond pines (Pinus serotina), which is one of
the diagnostic species for this community element. The combined clues
from signature and knowledge of the biological composition of the
community help the interpreter make the correct attribution of the
community on the photo.

***6.1.2.2 Challenges of Using Imagery Analysis for Vegetation
Mapping***

The concepts related to the \"continuum vs. community unit\" debate are
magnified when applying a vegetation classification to a map.
Delineation of vegetation boundaries on maps or photos requires drawing
sharp boundaries between different vegetation types. In nature, such
sharp boundaries are the exception rather than the rule. On the ground,
vegetation types tend to blend gradually into one another, often in
response to the environmental gradients. Steep environmental gradients
tend to produce distinct vegetation boundaries where gradual
environmental gradients tend to produce wider transition zones between
vegetation types. Vegetation mappers must identify discrete boundaries
and assign vegetation classes to each even though vegetation units on
the ground may grade gradually one into another. As a result, the
photo-interpretation process imposes a certain amount of error
regardless of how the vegetation map is made.

Vegetation mapping is also limited by the imagery interpretation and
other tools available for identifying vegetation polygons on the
landscape. The degree to which vegetation types can be recognized may
depend on the quality, scale, and season of photography, as well as the
type of film used. As a result, the relationship between the units
identified in the vegetation classification and the polygons identified
on the map is not always one-to-one. Sometimes the vegetation characters
that define a particular unit in the vegetation classification cannot be
identified on the imagery. Imagery only shows what can be seen from
above the vegetation canopy, so it can be difficult to discern the
understory species that may be the diagnostic species for a particular
community element. This is especially true in delineating forest types
with a closed canopy. For example, a photo interpreter may be able to
identify several white pine-dominated forests on imagery, but may not be
able to discern that the stands have very different understory species
compositions. In other words, they can identify an alliance clearly on
the imagery, but cannot confidently assign it a community element name.

This classification problem can be rectified by (1) visiting the polygon
on the ground and collecting the necessary information to assign the
correct community element name to polygon, or (2) predicting the
community element based on the correlation between the understory
composition and key geographic or environmental variables (if known). In
addition, some communities on the ground may be smaller than can be
mapped at a given scale causing the photo interpreter to make a decision
to label the polygon either (1) as a complex of more than one community
in the classification or (2) according to the class that covers the most
area in the polygon.

Vegetation mapping on aerial photographs requires a certain amount of
subjective judgment. Therefore, experience of the photo interpreter in
the general vegetation is an important factor in producing an accurate
map. In addition, it is impossible to field check every square foot on
the ground, necessitating the use of some type of sampling system which
will always have a certain (measurable) amount of inherent error. Most
of the difficulties of using imagery analysis to map vegetation are not
insurmountable. Though these limitations do introduce error into the
mapping process, consistent decision rules can be developed so the
errors are minimized and explicit.

6.2 Mapping the National Vegetation Classification System

The national vegetation classification system will be used to attribute
the vegetation polygons on all of the maps produced for the NPS/NBS
mapping project. Based on the objectives of this project, the map scale
of 1:24,000 was selected to portray the appropriate level of
classification and mapping required for the inventory and monitoring
objectives. The smallest vegetation polygons, or minimum mapping unit,
on the final maps will be 0.5 hectares. All existing vegetation types
within the mapping area will be mapped. The vegetation maps will
represent every vegetation class that occurs throughout the mapping area
if individual polygons are greater than minimum mapping unit. As a rule,
every polygon will be attributed using one vegetation class of the
classification system (see Section 6.2.2.2 for a discussion of mapping
complexes of communities). The per-class accuracy of the maps must
exceed 80 percent.

**6.2.1 Decision to Map the Alliance versus the Community Element**

Ideally, all polygons of the vegetation maps will be labeled at the
community element level and will meet the 80 percent class accuracy
requirement. However, due to the complexity of field conditions and
inherent limitations of aerial photography, it may be technically
infeasible and economically inappropriate to map vegetation polygons at
the community element level. Since the Alliance level is generally
determined by the overstory dominant and diagnostic species, this level
lends itself quite well to being identified on aerial photographs. As
stated above, it is often difficult to see the diagnostic species that
are required to classify to the community element on imagery. There are,
however, several ways to map to the community elements if the Alliance
is known.

It is estimated that more than half of all the community elements within
a given Alliance in the national vegetation classification are well
separated geographically. Therefore, if the Alliance is known as well as
geographic location, the community element can be predicted with
certainty. For example, if you are standing in a Pitch Pine--scrub oak
barren Alliance (scientific name: Pinus rigida/Quercus ilicifolia
Woodland Alliance) in Pennsylvania, it will most likely be the Pitch
Pine/Scrub Oak/Black Chokeberry community element (scientific name:
Pinus rigida/Quercus ilicifolia/Aronia melancarpa Woodland). But if you
are on eastern Long Island, it will definitely be the Pitch Pine/Scrub
Oak/Bayberry Woodland community element (scientific name: Pinus
rigida/Quercus ilicifolia/Myrica pennsylvanica).

Some community elements cannot be confidently predicted on the basis of
the Alliance and location alone. This is a more common occurrence in the
northwestern and southeastern forest communities. In these cases, a
single alliance may have continuous cover on a site but the understory
composition shifts so that more than one community element can occur. In
other words, what appears as a homogeneous vegetation unit on the aerial
photograph can be classified as one alliance but may actually represent
more than one community element. When this occurs, the community
elements can often be predicted based on their correlation to major
environmental gradients. For example, within the Douglas Fir Forest
Alliance (scientific name: Pseudotsuga menziesii Forest Alliance) the
Douglas Fir/Sword Fern Forest community element (scientific name:
Pseudotsuga menziesii/Polystichum munitum Forest) is generally found on
low moist sites, whereas the Douglas Fir/Salal Forest community element
(scientific name: Pseudotsuga menziesii/Gaultheria shalon Forest) is
generally found on dry sites.

For a relatively small number of communities, it may not be possible to
predict the community element based on knowledge of the alliance,
geographic location, or key environmental factors. The only way these
community elements of the classification can confidently be assigned to
the map units is by visiting them on the ground and collecting enough
field information to assign the correct community element name.

Most of these conditions will likely be encountered when mapping the
vegetation of a particular park. It will usually be possible to map the
community element directly from photography or to accurately predict the
community element from environmental and geographic information. If the
community element cannot be identified or predicted, there are three
choices that can be made: (1) The type can be mapped to community
element level accepting a lower degree of accuracy, (2) The type can be
mapped to the community element level and the necessary field data will
be collected to meet the minimum class accuracy requirements, (3) The
type can be mapped to the Alliance level. These decisions will be made
on a park-by-park basis and will largely be determined by the ecological
importance of the communities and the level of available funding.

When it is necessary to map a type at the Alliance level, it does not
infer that all of the vegetation on that park should similarly be mapped
at that level. The vegetation should be mapped at the finest level
possible, and accuracy would then be assessed at the level that the
polygon is attributed.

**6.2.2 Extension of the Proposed National Vegetation Classification
System for Application to Vegetation Maps**

***6.2.2.1 Mapping Different Expressions of the Floristic Units***

The vegetation maps must delineate vegetation units that will help the
park managers meet their resource planning, management, inventory, and
monitoring objectives. At the same time, the vegetation classification
must support the capability to assess regional and national issues.

To support regional and national assessments of vegetation resources, it
is essential that the polygons on all of the vegetation maps be
attributed to the Alliance or community element level classification
(see Section 6.2.1 for a discussion of this issue). However, the same
community element (or Alliance) may often have multiple physical
\"expressions\" on the ground based on past disturbance history, pest
infestations, old growth characteristics, etcetera, and these
expressions are often of great importance to park managers. For example,
Dry Rich Forests (scientific name: Carya sp.- Fraxinus americana-Quercus
sp. Forest) in the northeastern United States are becoming increasingly
infested with gypsy moths. In a given area, some occurrences of these
vegetation types are more severe than others. Because gypsy moths
typically strip the leaves from the deciduous trees, variation in the
level of infestation is often clearly discernable on the ground and on
aerial photos. These different expressions of infestation do not change
the classification of the community element, they are simply more
detailed characteristics of the occurrences of the Dry Rich Forest
types.

In addition to being attributed with the Alliance or community element
name, polygons on the maps can be attributed with these different
expressions. As with the floristic units, these additional expressions
of the vegetation should be discernable on imagery or easily predicted
based on correlations to key environmental variables. Each polygon will
be labeled with the name of the community element (or alliance) as well
as with a measure of the expression.

There are some expressions such as height classes and measures of vigor
(e.g., disease and pest infestations, amount of standing dead wood) that
will be of interest to a large number of park managers. A list of these
additional attributes of the floristic units is being developed so that
these attributes can be applied in a standardized fashion for this
mapping project. During the pilot phase of this project, the specific
values of each of these expressions will be determined. For example, if
insect infestation is chosen as a standard attribute to be mapped, then
the values (or classes) might include uninfested, low infestation,
moderate infestation, high infestation, and/or decimated. Guidelines for
assigning polygons to these classes will be produced.

Other vegetation expressions are only of interest at the level of the
individual park. These will be identified and mapped on a park-by-park
basis depending on the interest of the park manager and available
funding.

***6.2.2.2 Collecting and Tracking Additional Attribute Data on a
Park-by-Park basis***

To meet the objectives of different parks, additional data on attributes
other than those identified above will frequently be needed to
characterize the vegetation and their polygons across the landscape.
Many of these attributes may not need to be identified as formal
expressions of the type, but the information may need to be tracked for
resource management purposes. For example, it may be of critical
resource management importance to note the percent dead and down wood in
old growth stands, though there may be no need to recognize different
classes of old growth stands based on the amount of dead and down wood.
As with the expressions identified above, the classes of down/dead wood
will not change the classification unit. However, they provide critical
information in the characterization of the vegetation type and the
analysis of the data to build wildlife habitat and fire loading models.

During the planning phase of the project for each park, these important
additional attributes will be identified. Additional field data on these
attributes can be collected and the polygons can be attributed with
these data in the appropriate records of the relational database
management systems.

**6.2.3 Nonhomogeneous Mapping Units**

***6.2.3.1 Landscapes with Communities Less Than the Minimum Mapping
Unit***

Occurrences of vegetation types that are smaller than the minimum
mapping unit will generally be merged with neighboring occurrences and
the polygon will be named by the dominant class (by area). As a example,
in Everglades National Park, mahogany hammock communities less than 0.5
hectares can occur in a matrix of the sawgrass slough community. On the
vegetation maps, the polygons will be lumped and labeled as sawgrass
community elements. If these features that are less than the minimum
mapping unit are of significant ecological or management importance,
they will generally be mapped as separate points within the landscape
matrix and tracked separately in the spatial database. Otherwise, the
attributes of the larger polygon will document the relative coverage of
the different vegetation communities.

***6.2.3.2 Community Complexes***

Some plant associations occur with other plant associations in a
heterogeneous pattern and the components are uniquely tied together
ecologically. These occurrences are called community complexes. Though
these complexes have, as components, more than one plant association,
they are considered as a single element in the classification and mapped
as such. For example, wooded dune and swale communities have different
compositions but occur together in a complex pattern and are tracked a
single element in the classification and mapped as a single unit (Comer
and Albert 1993).

***6.2.3.3 Map Units Containing More Than One Community Element***

In some cases, more than one distinct community element can occur
together in repeating patches which are each smaller than the minimum
mapping unit. In these cases, the components are recognized as different
community elements, but since the patches of each component are less
than the minimum mapping unit, they are recognized as a single mapping
unit composed of both community elements.

***6.2.3.4 Transition Zones Greater Than the Minimum Mapping Unit***

In areas where the transition zone between two vegetation types is
greater than the minimum mapping unit and the vegetation does not meet
the requirements for being classified as a new community (i.e., it does
not have a significantly different biotic composition, is not associated
with different environmental conditions, or is not documented to recur
across the landscape), the zone will be mapped as a transition zone
between the neighboring types. It will be labeled with the names of both
communities and given a designation as transition zone.

6.3 Examples of Vegetation Mapping Projects

The Nature Conservancy has implemented multiple projects that have
applied the physiognomic--floristic vegetation classification system to
produce vegetation maps as a component of the conservation planning
methods. Though the general objectives have been consistent, the
applications have varied in terms of scale, resources, information base,
and desired end products to meet the specific objectives. Different
types of remote sensing data and supplementary thematic data are applied
to meet the different needs of these projects.

**6.3.1 John Crow and Blue Mountains of Jamaica**

In an effort to help develop conservation strategies for the country of
Jamaica, The Nature Conservancy performed a Rapid Ecological Assessment
(REA) of the Blue and John Crow Mountains National Park (Muchoney *et
al*. 1993). The REA process consists of a series of increasingly
detailed analyses, with each step identifying those sites of greatest
conservation interest and concentrating further analysis on
high-priority sites. REA has been developed in response to the need for
rapid information collection and analysis in areas that are either
biologically not well known or are exceptionally diverse at a habitat or
species level.

The goal of this REA was to complete a detailed, mapped inventory of the
important biological information needed to assist conservation planning
and management activities in and around the Blue and John Crow Mountains
National Park. This information included a land cover map that portrayed
a classification of natural and modified ecological communities, a list
of rare and endemic species, environmental data, and landscape and
topographic information.

The REA for the John Crow and Blue Mountains was completed through
aerial photo interpretation and computer-assisted analysis of
multispectral Landsat Thematic Mapper (TM) and SPOT panchromatic imagery
and digital environmental data. Computer classification of the TM data
was used to identify potential natural community classes as well as land
cover classes in and around the park. Aerial photography was acquired to
provide high-resolution current spatial information. Additional
environmental data including digital terrain, geology, hydrology,
transportation infrastructure, and soils were used to stratify for field
sampling, enhance the ecological classification, and meet the
information requirements for park design and management. Within the
park, survey sites were determined based on the analysis of the imagery,
soils, geology, and elevation data. Field surveys were conducted to
verify the classification and to acquire community data for
characterization of ecological communities and to provide detailed
biological data. The products of this effort included a refined
vegetation classification, a land cover map, maps of the other
environmental factors, and digital databases.

**6.3.2 Altamaha River Bioreserve, Georgia**

The Nature Conservancy conducted an ecological inventory of the Altamaha
River Bioreserve in Georgia to support conservation planning and
management of this ecosystem scale protection project (The Nature
Conservancy 1994). The inventory included the production of a land cover
map of the area which spanned 15 USGS quad maps (approximately 900
square miles). The land cover map was created using Landsat Thematic
Mapper satellite imagery, SPOT Panchromatic Quad maps, and USDA National
Aerial Photography Program (NAPP) photographs and extensive field
inventory, which included plot sampling. More than 12,000 polygons
representing ecological community boundaries were classified using 161
land cover classes. Land cover classes were based on The Nature
Conservancy\'s Southeastern Natural Community Classification (Allard
1990).