Land Cover Data: The Foundation for Conservation Planning
Vegetation is an integral component of our environment. It is a strong, although complex,
indicator of the ecological function of natural systems (Grossman et al. 1998). Nearly all animal
life is in some way dependent on vegetation for food and shelter and the influence it has on water
cycles and albedo ultimately affect our global climate. Understanding the world we live in,
assessing environmental issues, and conserving biodiversity are therefore dependent on our
ability to accurately characterize, map, and monitor the vegetation around us.
The need and desire to characterize vegetative land cover has long been recognized. The ancient
Greek scholar Theophrastus observed that relationships existed between plants and their
environments, but it wasn’t until ~1900 that the first known quantitative measure of plant species
was proposed by Raunkiaer (Bonham 1989). A rapid increase in our understanding of the
ecological world and the need to map the landscape around us occurred in the first half of the
20th century. Chase (1949) pointed out the need for accurately mapped vegetation types for
resource management as far back as 1949. The term “land cover” that we commonly use today
to describe the physical aspects of the landscape was defined in 1961 as “the vegetational and
artificial constructions covering the land surface" (Burley 1961). However, land cover data did
not become commonly available until the first remote sensing satellite dedicated to providing
information about the earth’s surface was launched in 1972 followed by development of Global
Positioning Systems (GPS) and the availability of advanced computers and software in the
1990’s. The advent of these sophisticated tools coupled with the needs of resource managers,
environmental modelers, and policy makers has led to increased development and use of land
cover data. We have now progressed to the point where digital land cover data are among the
most popular data used for resource applications (Thogmartin et al. 2004).
The increased activity in land cover mapping has led to a divergence of approaches resulting
from various needs, types of remote sensing equipment, and methods for processing and
interpreting remote sensing data. The end result is an excessively large number of land cover
mapping schemes that tend to be distinct, incompatible with each other, and often only
applicable for the application or area of interest for which they were designed. Adams (1999)
likened all the different land cover classifications to the mythical Tower of Babel where
everyone is working hard but all speaking different languages and went on to state that “The
progress that has been made so far is despite the large number of schemes, not because of them.”
The myth persists that land cover data is accurate and up-to-date (Estes and Mooneyhan 1994)
and in our haste we often help perpetuate this myth by using whatever land cover data are
“available” for a project without fully considering accuracy of the data and what effects it will
have on our results.
Land cover data commonly forms the foundation for conservation planning. Used alone, it offers
the potential for identifying unique or rare plant communities to conserve but is more often used
as a variable in defining broader habitats of importance across the landscape. Conservation
biology has long relied on the idea that protecting habitat for surrogate wildlife species will
protect habitat for other species with similar requirements (e.g. focal species (Lambeck 1997),
umbrella species (Andleman and Fagan 2000), flagship species (Caro and O’Doherty 1999), and
indicator species (Landres et al. 1988). Similar to the use for defining habitat suitability for
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wildlife, land cover data is often a component for defining connectivity habitat. Identification of
these important habitats may subsequently be used in the planning and policy making process It
is therefore critical that accurate land cover data be used not only to identify the most important
habitats, but to also reduce the cumulative effect of errors in the subsequent decision making
process.
Remote sensing specialists and resource managers have often had difficulty communicating the
needs of resource management with the limitations and capabilities of remote sensing data
(Hoffer 1994). Additionally, there is somewhat of a disconnect between classification of
vegetation in the field by ecologists and classification by remote sensing specialists. The intent
of this chapter is to help bridge the information and communication gap between these
disciplines. It is geared towards the conservation planner who desires to use digital land cover
data for the delineation of wildlife habitat. However, it may also help remote sensing specialists
better understand the needs and requirements of conservation planners and other resource
managers. Since there is not one ideal classification of land use and land cover to meet all
applications and it is unlikely that one could ever be developed (Anderson et al. 1976, Franklin
and Wulder 2002), the focus is on the factors that influence the accuracy of land cover data for
the intended purpose and how remote sensing data is tied to the vegetation we see on the ground.
By better understanding these factors, conservation planners can make improved decisions about
the selection, use, or development of new land cover data.
The Basics of Remote Sensing
Lillesand and Kiefer (1994) define remote sensing as “the science and art of obtaining
information about an object, area, or phenomenon through the analysis of data acquired by a
device that is not in contact with the object, area, or phenomenon under investigation”. The
earliest and still pertinent application of remote sensing data meeting this definition is from aerial
photography. By the 1850s balloons and pigeons were being used to carry cameras for use in
land surveys and a passenger of Wilbur Wright provided the first known photograph from an
airplane in 1908 (Belward and Eva 2004). In the mid-1940's, Francis J. Marschner began
mapping major land use associations for the entire United States, using aerial photographs taken
during the late 1930's and the early 1940's (Anderson 1976).
Today, the most common and widely used types of sensors for land cover classification are
multispectral scanners. All objects on the earth’s surface emit, reflect, or absorb energy and
multispectral scanners are termed “passive sensors” because they measure the amount of energy
that is emitted or reflected from the sun back to the sensor. These sensors typically provide
measurements in the blue, green, red, and infrared bands of the electromagnetic spectrum but the
number of bands and specific wavelengths that they measure are dependent on the specific
sensor. Since they are so dependent on illumination from the sun, clouds prevent the acquisition
of data and even water vapor, particulate matter, and variation in the sun’s angle due to time of
day, seasonality, and topographic influence affect reflectance measurements. Vegetation indices
such as the Normalized Difference Vegetation Index (NDVI, Rouse et al. 1973) were developed
in part to reduce these effects and numerous other methodologies have been developed to
decrease variability of sensor data resulting from these factors. These are topics that everyone
working with remote sensing data should be familiar with and have a working knowledge about.
Further descriptions and methods for correcting these effects can be found in many remote
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sensing textbooks such as Campbell (1996), Lillesand and Kiefer (1994), Jensen (1996) as well
as numerous journal articles and manuals for remote sensing software. Appendix A contains a
brief bibliography of the many sources of remote sensing data that are available. This will be
updated online on the Conservation Planning website that accompanies this book (URL).
Advances are continually being made in the types of available remote sensing equipment,
including active sensors that emit their own energy source and measure the return. Sensors such
as Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are active sensors
that do not rely on energy from the sun and are therefore not prone to many of the issues of
passive sensors. These sensors provide the ability to directly measure physical parameters of
vegetation such as height that must be inferred from passive sensors. Hyperspectral imagery is a
very specialized type of passive sensor that slices the electromagnetic spectrum into many more
discrete bands than multispectral sensors (often hundreds) that may allow the detection of
specific plant species. These next generation sensors will undoubtedly improve the abilities for
classifying land cover when they become fully operational. However, many are still in the
research phase and beyond the capabilities of most researchers (Turner et al. 2003) or too
expensive to be practical for large areas (Donoghue et al. 2004). Therefore, this chapter will
focus on multispectral data since they have been used for virtually all existing land cover
classifications and are the most cost effective for new classification work over large areas that
are typical of conservation planning.
Differences amongst multispectral sensors and the data they provide are generally due to the
spatial, spectral, and temporal resolutions of the sensors, as defined below. I have also included
brief discussions of several other terms that are pertinent to the following discussion of land
cover classifications and methods. NOTE: can add other definitions as desired.
Spatial Resolution – spatial resolution in remote sensing refers to the size of the area on the
ground that a single pixel of imagery provides information about. Spatial resolution of common
imagery are 1 km for the Advanced Very High Resolution Radiometer (AVHRR) and ~30 m for
most bands of Landsat data from 1982 onward. The multispectral bands of IKONOS imagery
have a spatial resolution of 3.2 m when looking straight down (at nadir). However, IKONOS
and some other sensors often acquire data at oblique angles (off-nadir) to increase the spatial
coverage and temporal frequency which can significantly increase the spatial resolution
depending on the angle. Figure 1 provides a comparison what satellite imagery of different
spatial resolutions actually “sees” on the ground. (Note: In actuality, sensors sample a circular
region rather than the square pixels that are portrayed to facilitate use of data. Sensors are
therefore only sampling the center 78.54% of each pixel.)
Spectral Resolution – Figure 2, taken from Lillesand and Kiefer (1994), provides typical
reflectance curves for soil, green vegetation, and water. These curves form the basis for the
ability of optical remote sensing to identify differences in land cover. Landsat TM samples the
electromagnetic spectrum in 7 discrete bands, 0.45-0.52um, 0.52-0.60um, 0.63-0.69um, 0.760.90um, 1.55-1.75um, 10.4-12.5um, and 2.08-2.35um which generally correspond to peaks and
valleys in the reflectance curves. IKONOS provides 4 multispectral bands similar to the first 4
bands of Landsat TM. AVHRR also provides 4 multispectral bands, but since it was designed
for meteorological purposes, only 2 of the bands (similar to bands 3 and 4 of Landsat) are
typically used for land cover classification.
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Temporal Resolution – temporal resolution refers to the amount of time between repeat
coverage for a sensor. The AVHRR sensor samples everywhere on the earth daily, while
Landsat takes 16 days between repeat coverage.
Selection of remote sensing data is often a tradeoff between spatial, temporal, and spectral
resolution in addition to cost, data volume, and image footprint. The spatial resolution of
IKONOS and QuickBird may be beneficial for defining fine-scale patterns in land cover over
small areas, but the cost, amount of data and difficulty in obtaining timely data over a large area
due to their small footprint make them prohibitive in many situations. Similarly, AVHRR is
ideal if the question of interest is to track vegetation phenology due to its frequency of re-visits,
but is of course spectral resolution and limited spectral resolution. Selection of a single data
source or the proper combination of data sources must match the desired classification scheme.
Scale – scale is an often misused term which states that one unit of distance on a map, aerial
photograph, etc. represents a specific unit of distance on the ground. It is often presented as a
fraction (1/25,000) or ratio (1:25,000) where 1 inch on the map represents 25,000 feet on the
ground. Subsequently, 1:25,000 defines a smaller scale than 1:10,000 and provides less detail
than 1:10,000 (larger scale).
Classification Accuracy and Precision – accuracy defines “correctness”, the agreement
between an assumed standard and the predicted class of data, while precision defines “detail” of
land cover classification (Campbell 1996). The distinction and interaction between these 2 terms
is important. As the precision increases along the gradient “forest, coniferous forest, lodgepole
pine”, so does the potential for errors which generally results in decreased accuracy (Figure 3).
Accuracy of land cover data is important to prevent confusion between classes, but the precision
of data is what determines the usefulness for a specific application.
There are 2 types of errors associated with each land cover class, errors of omission and errors of
commission. Errors of omission for a “grassland” class are those that assign actual grasslands on
the ground to another class. The known patch of grassland has been omitted from the resulting
classification. Errors of commission for the grassland class refer to locations incorrectly
classified as grassland. The classification has committed an error by classifying “forest” or
other types as grassland. The distinction between errors is important because a classification
could achieve 100% accuracy relative to the “grassland” class by delineating the entire area as
grassland.
Classification accuracy is often reported from the standpoint of “producer’s accuracy” and
“consumer’s accuracy” for each class and overall. Producer’s accuracy refers to the percent of
features classified correctly amongst those that are actually of that type on the ground;
classification of 400 features as forest from 500 features that are known to contain forest results
in a producers accuracy of 80.0%. In comparison, consumer’s accuracy describes the reliability
of the classification as a predictive device. In this situation, the correct classification of the 400
features as forest amongst 800 features that were classified as forest in the predictive map results
in a consumer’s accuracy of 50%.
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Linking Remote Sensing with Vegetation on the Ground - What exactly are we Mapping?
In the classic work on vegetation mapping, Kuchler (1967) defined vegetation as “the mosaic of
plant communities in the landscape” and further went on to state that “this definition implies that
vegetation consists of more or less distinct mappable units”. It therefore seems logical that we
should be able to map land cover with remote sensing data.
When using remote sensing data, it is of tremendous importance to get accurate information to
validate what the remote-sensing data products appear to be telling the user; remote sensing
products should not be taken at face value (Turner et al. 2003). Unfortunately, there is a lack of
coordination and standardization within the natural science community for defining plant
communities on the ground, the very descriptors for what remote sensing is sampling. Botanists
and field biologists often develop systems for classifying vegetation that are dissimilar, cannot be
extrapolated across large areas using remote sensing techniques, and may not be applicable for
certain uses within the wildlife and conservation fields. Naturally occurring vegetation is
dynamic and varies according to site-specific and environmental parameters. Plant species
composition at any given point will vary throughout the growing season according to the growth
cycle of existing plants. While any 2 locations may be close in space, plant species composition
and quantities may be quite different. Classifying vegetation under these conditions into a clear,
concise framework can be difficult. Botanists and field biologists typically classify vegetation as
either habitat types (Daubenmire 1952) delineating potential vegetative at climax conditions or
cover types defining existing vegetative conditions. They are commonly named using one
indicator or dominant species from the overstory (if present) and one from the understory.
Examples are the big sagebrush/Idaho fescue and Idaho fescue/bluebunch wheatgrass types
described by Mueggler and Stewart (1980). Depending on the seral stage, habitat types often do
not indicate the actual vegetation on the ground and 2 areas of very different plant species
composition can be classified as the same habitat type. Classification to habitat types using
remote sensing data is generally not possible and the use of field data that utilizes habitat type
descriptors will be problematic.
Recent trends have centered on using cover types (existing vegetation) rather than habitat types.
Several standards for classifying existing vegetation have been proposed but have not been fully
adopted. The Federal Geographic Data Committee (FGDC) National Vegetation Classification
Standards established an initial hierarchical classification with 9 levels (FGDC 1997). The 7
upper levels of the FGDC standards are based primarily on physiognomy and the 2 proposed
lower levels, although not finalized, are based on floristic attributes. Recent floristic standards
were drafted by the Ecological Society of America (ESA) Panel on Vegetation Classification
(Jennings et al. 2004). Final adoption of classification standards will promote the consistent
classification of existing vegetation by biologists in the field and facilitate communication
describing land cover. However, it is unlikely that multispectral data will be able to accurately
classify vegetation at the floristic level of the proposed standards. These sensors cannot “see”
through dense tree canopies to classify vegetation of the understory. They also have difficulty
differentiating amongst homogenous grass types much less between mixed types such as
between Idaho fescue/bluebunch wheatgrass and Idaho fescue/tufted hairgrass.
Defining land cover is essentially an exercise in detecting patterns across the landscape.
Accurate classification requires that the scale of remote sensing data matches the scale of field
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data used for classification purposes and the desired classes within the land cover classification.
In both the ecological and remote sensing fields, detecting landscape patterns is a function of the
size of individual sample units and the size of the area under investigation. Ecological studies
refer to the size of sample units as “grain” which is analogous to spatial resolution or pixel size
in remote-sensing. Both disciplines use the term “extent” or “area of interest” to refer to the area
under investigation.
Wiens (1989) describes the influence of extent, grain, and their interaction in ecology. Figure 4
indicates the relationship between heterogeneity of vegetation, extent, and grain and the
following text describes the relationship of grain and extent in a patchy landscape (from Wiens
1989).
“As the extent of the study is increased (large squares), landscape elements that were
not present in the original study area are encountered. As the grain of samples is
correspondingly increased (small squares), small patches that initially could be
differentiated are now included within samples and the differences among them are
averaged out.”
Wiens (1989) describes how spatial variance changes depending on grain and extent and
discusses the relationship between spatial and temporal scaling. Two very important points
should be realized about the influence of extent and grain on field classification of vegetation:
1) as grain increases, the number of classes and distinction between classes decreases because
more variability is encountered within each sample 2) as the extent increases, the number of
vegetation classes increases because more classes are encountered.
The 2 points enumerated above for field classification are the same as for classifying land cover
with remote sensing. Band values for each pixel (grain) of remote sensing data are a single
cumulative value from every tree, bush, blade of grass, rock, etc. that our eyes see within the
pixel. Vegetation classes contain a range of types and amounts of plant species and field
biologists use their ability to discern these differences for defining group membership. In
contrast, remote sensing “sees” only the cumulative values of each pixel to define group
membership. All other things being equal, a smaller pixel size will encounter less mixing of
vegetation types within each pixel and be able to detect smaller patches on the ground, similar to
that portrayed in Figure 4. Smaller pixel size can therefore increase the precision of the
classification, as indicated in Figure 5a. Spatial resolution corresponds scale in the remote
sensing context (Woodcock and Strahler 1987) and the x-axis of Figure 5a is also labeled as
“Scale of Data”.
Similarly, precision of any remote sensing classification is also expected to decline as the extent
increases (Figure 5b). More classes are encountered, as noted in point 2 above, which increases
the potential for confusion between classes. Classes must often be combined to maintain
accuracy which corresponds to a reduction in precision. Additionally, high costs and the large
amount of data typically limit classification over large extents to remote sensing data of coarser
spatial resolution, which also reduces precision.
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Although results of many land cover classifications are quite impressive, the inherent process
introduces the potential for errors. Field data used to “train” remote sensing data cannot provide
every possible combination of plants, rocks, etc. within each vegetation class or provide the full
range of values that remote sensing data samples. Landcover classes with distinct combinations
of reflectance values will always be more accurate as even the best remote sensing algorithms
will confuse pixels with similar values. Locations of data collected on the ground require an
accurate match with corresponding pixels of remote sensing data. Current Global Positioning
Systems (GPS) produce very accurate locations, often within 1m depending on the type of
receiver and satellite configuration at the time of each location. However, modern techniques are
still not able to make remote sensing data conform to actual locations on the earth. Loveland et
al. (2000) recommended image registration as an important area of future research. The best
technique is to match known points on the imagery (referred to as Ground Control Points
(GCP’s)) with GPS locations taken on the ground and “warp” the imagery to conform. Even
with this process, a general rule of thumb to account for inaccuracies in geographical locations is
to sample an area equivalent to a 3x3 pixel area (~90m x 90m) and apply field data to the center
pixel of the corresponding imagery. This method results in the grain of field samples to be quite
large in many occasions and limits the precision of vegetation classification.
Although there are limitations to producing land cover classifications with the desired precision,
we are coming closer to bridging this gap. Land cover mapping at any scale yields imperfect
results (Loveland et al. 2000). However, the recent availability of satellites with increased
spatial resolution is analogous to a smaller grain and sensors with greater spectral resolution
increase the ability to differentiate between vegetation types. Use of remote sensing for land
cover classification is the only practical method for covering large areas and both our use and
abilities in this field are steadily improving.
Matching the Needs of Wildlife with Land Cover Data
Wildlife typically have specific habitat requirements, an idea that David Lack (1933) may have
been the first to propose. It is therefore imperative that land cover mapping classes match the
type and scale of habitat selection for the wildlife species of interest. Habitat selection can be
considered either floristic in nature, determined more by structural components, or a combination
of the two. Numerous grazing studies have documented the section of specific plant species by
many wildlife. Fisher generally avoid open areas and utilize a range of plant communities
containing a high amount of vegetative structure (Jones and Garton 1994). Sage grouse are
strongly associated with sagebrush habitat, but they require specific structural components of
sagebrush and amounts of herbaceous cover within sagebrush habitat depending on season
(Connelly et al. 2000). The problem of relating phenomena across scales is the central problem
in biology and all of science (Levin 1992) and selection of these habitat components is scale
dependent. Owen (1972) stated that “selection can be exercised at different scales”. Johnson
(1980) suggested a natural ordering of selection processes from first-order selection defining the
physical or geographical range of a species, second-order selection determining home range,
third-order selection pertaining to usage of habitat within the home range, and fourth-order
selection the procurement of food items at a site. In the case of large herbivores, Senft et al.
(1987) proposed regional, landscape, and plant community scales of habitat selection where the
plant community level is essentially the same as the fourth order described by Johnson (1980).
As the order of selection increases from a regional or geographical order to a feeding site level
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for any animal, the extent of the search area decreases (scale becomes larger) and the specificity
of vegetation parameters increases. The exact level and rate these variables change is obviously
dependent on the species in question. Grizzly bears are a wide ranging species that vary their
habitat use amongst seasons and across their range. Their home ranges are much larger and are
of less specific habitat requirements than many other species (e.g. boreal toads) and the rate of
change from initial selection orders to the procurement of food items is overall much greater.
The level of precision required from land cover data to address the differences in specificity and
scale of habitat selection for wildlife can be generalized in Figures 6a and 6b. Amongst species,
the required precision increases as the specificity of habitat requirements increases from habitat
generalists to habitat obligate species (Fig. 6a). Similarly, the required precision also increases
as the order of selection increases within individual species (Fig. 6b). However, the relationship
between land cover precision and intraspecific order of use can be considered analogous to a
generalization of interspecific scale of use. Third-order selection for grizzly bears will equate to
a similar size area and specificity of habitat components as second-order selection for narrower
ranging species that tend to be more habitat obligates. Therefore, interspecific scale of habitat
selection is also indicated on the x-axis.
Many wildlife studies assume available land cover data are not accurate (Cunningham 2006) or
are of insufficient precision for the species or question of interest and develop their own
classification schemes for both field studies and developing digital land cover data. As an
example, there are 3 (at least) methods for field classification of vegetation in the central plateau
of Yellowstone National Park; a classification of grassland and shrublands (Mueggler and
Stewart 1980), a vegetation classification specific to Yellowstone National Park (Despain 1990),
and one specific for mapping grizzly bear habitat in the Yellowstone ecosystem (Mattson and
Despain 1985). However, none of these were sufficient for classifying vegetation in relation to a
grazing study for bison and an additional classification was developed (Olenicki and Irby, 2002).
All are valid classifications with precision intended for their specific use.
Information from research using various classification schemes similar to those noted above is
typically used for constructing habitat suitability models or identifying specific habitats for
conservation applications. Even if digital land cover is developed for a wildlife project, the
extent frequently does not cover conservation areas of interest and the best available data must
often be used. Specific parameters such as “early seral lodgepole forest” may need to be
extrapolated into the broader classes of “lodgepole forest” or “coniferous forest” which will in
itself overestimate the amount of suitable habitat. Additionally, the amount and location of
specific patches will depend on the accuracy of the land cover classification. Errors of omission
will assign the “coniferous forest” class in the above example to a different class while errors of
“commission” will erroneously assign “coniferous” forest to a class that is not coniferous forest.
Both types of errors will incorrectly identify patches of habitat.
In some situations, a conservation area of interest may cross jurisdictional or other boundaries
that result in the need to combine sets of land cover data. The difficulty in extrapolating habitat
requirements to a dissimilar classification is further compounded when 2 different classification
are used. Examples of many of the issues discussed in this section will be noted in the following
section comparing several examples of land cover data.
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Selection of Land Cover Data
There is not one ideal classification of land cover to meet all applications and it is unlikely that
one could ever be developed (Anderson et al. 1976). This is probably truer today than when
Anderson first made this statement. Advanced sensors, modern techniques to tease out
characteristics within pixels, and readily available remote sensing data have drastically increased
our abilities to develop land cover classifications for a variety of applications. The numerous
classifications that have been conducted often give the impression that land cover data is
available for any application and location, but this is not the case. While land cover data exists
for virtually the entire earth, much of it does not meet the needs for delineating habitat suitability
and other conservation uses in many situations.
Although the relationships presented in Figures 5 and 6 are not fully understood, the concepts
they represent can be used to help match land cover data with the needs for delineating habitat
suitability. Figures 6a and b can be used to help identify the required precision for the intended
task. Habitat suitability models for species that tend to be habitat obligates, utilize relatively
small areas, or for models of upper selection orders requires data of high precision. High
precision translates to the need for land cover data at a smaller scale (Fig. 5a) and subsequently
the ability to develop models over smaller areas (Fig. 5b). Conversely, developing a
conservation plan over a large area of interest (from Fig. 5b) generally translates into land cover
data of lower precision (and usually at a smaller scale as previously discussed). Habitat
suitability models can only be developed for situations appropriate for less precise data; habitat
generalists, wide-ranging species, or for lower selection orders within desired species. In
addition to habitat suitability, these ideas generally apply to the requirements of land cover data
for other ecological and conservation questions; more precise data is needed as more specific
questions are asked.
The following examples of digital land cover data are a few of the many available. They were
chosen to represent differences in spatial resolution (scale), extent, and precision of data. Their
advantages and disadvantages will be discussed as well as my own experience with their use for
modeling habitat suitability in the conservation field.
Global Land Cover
Global land cover data treating all parts of the world equally and representing actual land cover
of our planet have been available since the 1990’s (Belward and Eva 2004). Table 2 from Strand
et al. (2007) provides 5 sources of readily available global land cover data. The major
advantages of these data are their ability to cover any location on the earth with a consistent
classification scheme. One of these classifications, the AVHRR Global Land Cover (Hansen et
al. 2000) is available with a spatial resolution of 1 km, 8 km, and 1 degree and there are either 13
or 14 land cover classes depending on the spatial resolution. The 14 classes defined by the 1 km
and 8km data (Table 1) are very broad and limited to defining habitat suitability at a regional or
geographical scale. However, periodic updates (e.g. MODIS Land Cover) allows for change
detection and they all provide insight into areas for closer examination using other data sources.
National Landcover Database
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The National Landcover Database (NLCD, available at) is the product of a long-term, multipartner project dedicated to land cover products. Originally released in 1992, a newer version
using revised methodology was released in 2001 that includes percent tree canopy and percent
urban imperviousness. Slight modifications were made in classes (Table 2). Data are based on
Landsat imagery and are available for the conterminous United States, Alaska, and Puerto Rico
(http://www.mrlc.gov/index.php).
For delineating habitat, there are 2 main advantages of the NLCD data compared to the global
land cover previously discussed; 1) the increase in spatial heterogeneity of cover classes that the
30m pixel size of Landsat sensor provides compared to 1 km for AVHRR, and 2) estimates of
canopy coverage for the 2001 version. Actual classes of NLCD data are still fairly broad and
best suited for delineating lower orders of habitat selection, but estimates of canopy structure
increase habitat modeling capabilities for species such as fisher and lynx that rely on forest
structure.
Classification accuracy was assessed by mapping region for the 1992 NLCD data but has not yet
been conducted for the 2001 version. For region 8 (MT, WY, ND, SD, UT, CO), overall
consumer’s accuracy for single pixels is listed as 60% (Table 3). A closer look at accuracy for
individual classes indicates that 8 of the 20 classes have an estimated accuracy of 15% or less.
The specific methods utilizing these data will influence results, but the variability in class
accuracy would be expected to produce variable results depending on the species and their
dependence on classes with high or low classification accuracy. Cunningham (2006) and
Thogmartin et al. (2004) provide good discussions on the use of NLCD data for habitat studies
and some of the reasons for classification errors.
GAP Analysis Landcover
The intent of the Gap Analysis Program (GAP) is to identify and maintain non-threatened animal
species and plant communities that are not covered by other legislation and may not occur on
existing conservation lands. Mapping land cover and predicting species distribution across the
United States are 2 goals of this project. GAP is probably the best available source of
information pertaining to land cover data and species distribution modeling. Many research
projects, publications, and land cover classifications have been produced under this program.
GAP land cover is based on the same Landsat imagery as NLCD but is generally of higher
precision due to classification over smaller extents. Classifications have generally been
conducted individually for each state and are available at:
http://gapanalysis.nbii.gov/portal/community/GAP_Analysis_Program/Communities/GAP_Hom
e/ . However, there has been little standardization of methods amongst states that cause
problems when conducting conservation work across state boundaries. Figure 7 indicates a
location along the MT and WY border where GAP land cover data from MT and WY overlap.
The location is within the Greater Yellowstone Ecosystem (Noss et al. 2001) and Yellowstone
National Park. The red polygon indicated in the figure comprises ~8028 acres and is classified
under WY GAP as 80% lodgepole pine and 20% subalpine meadow. Figure 8 indicates this
same polygon as classified by MT GAP where each color represents a different land cover type.
The WY data provide useful information, but MT data are obviously more precise. Crosswalking of vegetation types has been used in these situations to produce consistent land cover
10
classification, but it is often a best guess exercise when actual plot data are not used. Detailed
information on floristic composition and canopy cover are required to correctly crosswalk
between vegetation types (Brohman and Bryant 2005, FGDC 1997, Jennings et al. 2004). The
new regional classifications being produced by the GAP program will resolve differences across
state boundaries but at the cost of more generalized classes than many of the individual state
projects produced.
I ran into a similar situation of differences across jurisdictional boundaries when developing
habitat models for the Inland Temperate Rainforest that encompasses parts of MT, ID, WA, and
British Columbia. Variation across boundaries did not allow even a close approximation for
crosswalking. Replacing vegetation types with a rating system also did not solve the problem
due to differences in precision and scale amongst the classifications. A more generalized
classification system covering the entire area was used. It provided consistent results but at a
much broader level of habitat selection than data from some of the individual jurisdictions could
provide.
Classifications Using IKONOS or QuickBird Imagery
Launches of the IKONOS and QuickBird sensors in recent years have greatly increased the
spatial resolution of available imagery. IKONOS imagery has shown the ability to identify tree
canopy (Snyder et al. 2005), individual trees (Read et al. 2003), and to differentiate between
vegetation types in dry shrub/grassland (Depew 2004). At this point, there are not any
widespread classifications available using these types of imagery. The extent of each scene is
generally small, data are fairly costly, and the spatial resolution provided by these sensors results
in very large data sets. The same could once be said for Landsat data and this will obviously be
less of an issue in the future. Although use of these types of data offer increased capabilities,
their current use for habitat modeling are limited to species occurring over small areas as an
intermediate sampling tool between ground surveys and coarser-grained imagery.
Vegetation Resource Inventory of British Columbia
The Vegetation Resource Inventory (VRI) of British Columbia (MSRM 2002) is a hierarchical
classification based on the physiognomy of vegetation. Figure 9 shows the various levels of
classification for a polygon initially classified as vegetated. In my opinion, this is one of the best
classification schemes for delineating habitat. The hierarchical structure of VRI reduces the
potential and severity of classification errors. The confusion amongst all classes (errors of
omission and commission) that is present in most classifications is reduced in the VRI scheme by
successively classifying polygons into each class. The resulting confusion between “dense
coniferous” and “dense broadleaf” habitat in the upland position of the VRI scheme does not
affect results as much as confusion between a “coniferous forest” and “low density residential”
that is more likely to be present in other schemes.
Most habitat delineation relies on ancillary data, such as a digital elevation model (DEM), in
addition to land cover data. Most land cover data provides the base vegetation such as
coniferous forest or the actual species of conifers as in the GAP data for MT, while the DEM
provides topographic variables (e.g. slope, aspect, elevation, roughness). Assumptions are often
made as to the structural or productivity characteristics using the topographic variables; north
slopes generally contain denser forests and more productive grasslands than south-facing slopes,
11
but the actual amounts and variability are unknown. In contrast, it is easier to make assumptions
about the composition of plant species and specific habitat using information from the VRI
scheme. As an example, the VRI descriptors “open, coniferous, uplands” on west-facing
intermediate slopes in southwest MT would be expected to contain mature Douglas fir mixed
with grasslands and provide a pretty specific habitat description. The same slope using a
“coniferous forest” descriptor could contain a range of structural classes for Douglas fir and
lodgepole pine and even the “Douglas fir” type could contain a range of classes from seedlings to
mature trees.
I used VRI as the main component for developing habitat models for 7 focal species across the
16.2 million hectare Muskwa-Kechika management area in northern British Columbia
(Heinemeyer et al. 2004). Models generally conformed to BC standards (RIC 1999), consisting
of separate feeding and living components for both winter and summer for each species. Results
proved reliable when validated with telemetry and aerial survey data. One disadvantage of this
classification is the size of the data set and the amount of computer time it took to run the
models. Additionally, classification methods rely on air photo interpretation which is variable
amongst interpreters, is quite time consuming to conduct over large areas, and difficult to update
because it is so time consuming. VRI is a very useful classification scheme and the use of newer
high resolution imagery or other methods streamline the classification process should help to
expand its use.
Classification Using Digital Orthophotos
Interpretation of air photos has long been an accurate method for land cover classification, but
even the availability of digital orthophotos that allow on-screen classification is time consuming
and impractical over large area. The ability for computers to discern the same objects photo
interpreters see would increase the use of this readily available data. Miller et al. (2004) were
able to use image processing software and digital air photos to identify tree canopies. Akbari et
al. (2003) had limited success with machine processing in an urban area.
Color air photos are similar to satellite imagery in the fact that each pixel is composed of distinct
values for red, green, and blue that produces the displayed color. The key difference is that color
air photos cover the full range of these colors rather than specific regions that imagery targets.
Nonetheless, color air photos can still be decomposed into their individual bands and treated like
satellite imagery and I used this potential in a hybrid cross between remote sensing classification
and air photo interpretation. The conservation project I used it for called for habitat models for 2
focal species (grizzly bears and elk) over a relatively small area. I initially used MT GAP land
cover data and general habitat models I developed for that data, but results were coarser than
desired for the size area. In some situations, the more detailed the feature becomes, the greater
the variation detected within a class rather than between classes (Grenzdorffer and Bill 1994).
This was the situation encountered for coniferous forests, where the shadowing between tree
canopies created more variation within rather than between classes of trees. Therefore I
aggregated each of the 3 bands from 1m to a 5m pixel size which smoothed values within
classes. I then ran an unsupervised classification and used a combination of the MT GAP land
cover data and photo interpretation of the original air photo to assign MT GAP descriptors to
output classes from the unsupervised classification. The end result was land cover data that were
more accurate and at a larger scale (Figure 10), but maintained the MT GAP classes so I could
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run the original habitat models. The method I used may work only within the extent of each
photo used to create digital air photo mosaics. Air photos are often taken at different times of the
day or even different months or years for large areas. The differences in actual color values due
to changes in sun angle and plant phenology may require a separate classification for each frame.
Stand alone software and modules or extensions for many remote sensing packages (e.g. ENVI
Feature Extraction Module) are now available to identify shapes within air photos and other high
resolution data. These products base their classification on the combination of shape and
reflectance of spectrally similar units rather than just spectral differences. Walker and Blaschke
(2008) used this process to classify vegetation types, buildings, and impervious surfaces within
an urban area. These products offer the potential to classify life forms of vegetation over large
areas using air photos alone or used in conjunction with satellite imagery and more common
techniques.
Recommendations for Future Research
New technologies are advancing land cover classification to a point where data matches our
needs and desires. The emerging LiDAR technology offers the ability to estimate structural
components of vegetation (Lefsky et al. 2002), the full potential from high resolution IKONOS
and QuickBird imagery are only beginning to be realized, and advances are being made in
hyperspectral sensors. By combining active sensors with existing multispectral sensors, we can
add a third dimension to the existing 2-dimension data we’ve been using. Mundt et al. (2006)
fused hyperspectral and LiDAR data to improve classification and provide structural components
of sagebrush in semi-arid rangeland. But we cannot wait for these technologies to advance and
must strive to make improvements with the tools we have. The destruction and fragmentation of
natural habitats is often considered to be a leading cause in the decline and loss of native species
(Newmark 1985, Sinclair et al. 1995, Turner et al. 2003). In the struggle to maintain biodiversity
and conserve habitat, remote sensing tools are there, let’s hope the users soon follow (Turner et
al. 2003).
There are many topics for further research, but 2 initial ones come to mind. The ability to better
match land cover data with the species, selection order, and area of interest would increase
accuracy of individual projects and consistency amongst projects. Although determining the
actual locations along the lines in Figures 5 and 6 is complex, a rating system could be developed
for the use of these figures. Even the relative locations for a range of species and selection
orders in Figure 6 (e.g. brood rearing habitat for sage grouse and home range for grizzly bears)
and their corresponding locations in Figure 5 could serve as a guideline for matching land cover
data with a specific application.
The second topic is the development of a land cover classification system using remote sensing
that is specific to the needs of delineating habitat. Although this seemingly adds to the confusion
described by Adams (1996, 1999) and deviates from the call for standardization, we may not be
at the point where standardization of methods is possible. Until technology provides the ability
to determine all desired land cover variables, specialized land cover classification will continue
to occur. Striving toward standardization is desirable, but should not limit conservation work
and the use of remote sensing data.
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The following conceptual classification is intended to provide structural information about
vegetation and reduce the consequences of confusion amongst classes, two important aspects of
land cover data for defining habitat. It is a hierarchical classification where the ecological
variable at each level determines the methodology. It is similar to the multi-level approach for
land cover classification described by Anderson et al. (1976) and considers all 10 criteria they
identify with particular attention to equal accuracy amongst classes, applicability over large
areas, allowing aggregation of categories, and comparability with future data.
The classification is geared toward the use of Landsat imagery, but should also be applicable for
higher resolution imagery such as QuickBird or IKONOS in specific applications; Landsat
imagery providing classification across large extents, with higher resolution imagery providing
increased spatial precision (e.g. Figure 10) over small areas for use at higher selection orders and
for animal species considered habitat specialists. Using one or several scenes of higher
resolution imagery in conjunction with a Landsat scene for the following classification will allow
a comparison of the utility of each type of imagery for this purpose. The inclusion of higher
resolution imagery will also help the scaling issues between on-the-ground data and Landsat
imagery. The combination of field data with higher resolution imagery and digital air photos can
be used to create large training sites for Landsat imagery and aid in accuracy assessment of the
resulting classification.
As previously stated, the following is a conceptual classification scheme. Many details need to
be worked out should it be conducted and changes will undoubtedly occur. Nevertheless, it
provides a starting point for a land cover classification specific to the needs of habitat delineation
for wildlife, provides options for other conservation needs, and offers points of discussion
between resource managers and remote sensing specialists for linking vegetation we experience
on the ground with digital land cover data. It also attempts to identify a few of the many
ecological variables and processes occurring across the landscape and use them as part of the
classification. These processes may or may not be useful for differentiating land cover classes,
but should always be kept in mind when conducting a new classification or using existing land
cover data.
Step 1: Inputs
There are a number of correction, enhancement and pre-processing techniques for satellite
imagery that are often project-specific and a matter of personal preference. These include
geometric, atmospheric, and topographic correction as well as calculating vegetation indices and
compressing the information from all available bands into fewer bands through the use of
principal components analysis (PCA). A review of their benefits and applications can be found
in most remote sensing books. Landsat imagery offers more options for pre-processing and
enhancement than QuickBird or IKONOS due to the longer duration it has been available and the
greater number of bands it posses. Additionally, the increased spatial resolution and off-nadir
angle at which QuickBird and IKONOS imagery are often acquired can complicate some preprocessing techniques. Wu et al. (2008) provides a discussion and methods for topographic
correction of QuickBird data. However, their methods may not be practical in most conservation
applications due to the need for detailed digital elevation models and the view angle of scenes
should be considered whenever using imagery from a taskable satellite, especially when multiple
scenes are used together.
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Figure 11 provides a generalized flow chart of the proposed classification process. The black
box on the left identifies the inputs from Landsat or higher resolution imagery into the process
and subsequent boxes indicate the resulting classification to each level. Table 4 provides brief
class descriptions at each level. Methods used for each level are indicated along the arrows
connecting the boxes and red ovals indicate the use of ancillary data at each step.
Anticipated inputs to the process are the individual bands of satellite imagery, principal
components images, and a vegetation index such as NDVI. Images from 2 different dates, early
and late in the growing season, are desirable to identify phenological changes.
Within the conterminous U S, separate classifications will occur for each ecological section
(McNab et al. 2007) when the area of interest crosses boundaries between sections (similar
delineation of ecological sections has occurred for many areas outside the U S). Sections are
defined as large areas of relatively homogeneous physical and biological components that
interact to form environments of similar productive capabilities where each map unit defines a
region of unique ecological characteristics that differs from its neighboring units (McNab et al.
2007). Limiting classification within sections provides the ability to match imagery with
phenology of the vegetative; optimal dates for imagery can be identified based on anticipated
phenological conditions. Each section should use imagery of the same approximate date to
prevent phenological differences. Additionally, errors at the floristic level will be limited to
confusion amongst classes that occur within the section rather than over a much larger extent
such as exist for many state GAP classifications.
Step 2: Classification to Level 1
A supervised classification using one of the hard classifier algorithms is proposed to assign
membership into the 6 broad classes of level 1 (Table 4). Confusion amongst classes at this level
may have the greatest consequences on accurate habitat delineation and these classes were
selected to represent biologically distinct and relatively easy to separate divisions in land cover.
In some ways this can be considered a dichotomous selection that indicates potential habitat for
most terrestrial species (vegetated) and non-habitat (other classes) and a hard classifier was
selected to make this distinction. User’s accuracy greater than 90% for each class is desired to
reduce the perpetuation of errors in subsequent levels. An accuracy assessment should be made
and methods adjusted if necessary to increase accuracy of all classes. In particular, errors of
commission for the vegetated class that incorrectly classify locations as vegetated should be
reduced. Since this is a conceptual process at this point, all classes at each level are subject to
change and some aspects of the classification process are glossed over. Similar to pre-processing
techniques, the effects of cloud shadows is a topic that needs to be addressed and the “urban”
class needs to be defined if this classification is implemented. The “burned forest” class was
included to identify this habitat for those species dependent on it, for the potential uniqueness in
reflectance characteristics, and for the ability to re-classify it as early successional forest in
subsequent years. Burned grasslands and shrub/grassland classes may also be incorporated for
areas burned a month or so prior to imagery acquisition. Areas classified as barren, water, urban,
burned, and clouds have reached their endpoint in the classification. Only those assigned the
vegetated class continue in the classification process.
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Step 3: Classification to Lifeform
Step 3 is a physiognomic classification that classifies vegetated pixels as conifers, broadleaf
trees, shrubs, herbaceous, or mixtures of these life forms. The use of imagery from different
dates and calculated vegetation indices may be especially helpful in this step due to differences
in phenology amongst classes. Fuzzy classification, which provides a value for membership in
each class rather than distinctly assigning it to one class, was chosen for several reasons. From a
remote sensing perspective, percent composition values other than those listed in Table 4 may
provide a clearer division of categories and examination of results from a fuzzy classification
will indicate and allow adjustments of breakpoints. From a biological perspective, pixels that
contain a mix of these types may be vegetatively more diverse and indicate better habitat for
certain animal species. The “fuzzy” assignment amongst multiple classes may therefore provide
useful information.
Step 4: Calculation of Topographical Position Index
In addition to variability in ecological sections discussed in step 1, vegetation at any site often
depends on the topographic position. For example, ridge tops are windier, drier, and soil depths
are often shallower than a gully at the bottom of a hill and vegetation therefore differs between
them. Step 4 is a modeling exercise to calculate the 6 topographic positions indicated in Table 4
as ancillary data for subsequent classification. Classes and original methodology are from Weiss
(2001) and the ability to easily calculate Topographical Position Index (TPI) was incorporated
into an ArcView extension (Jenness 2006). Calculation of TPI is scale sensitive and the ability
for topographic classes to match vegetation changes is likely influenced by local topography of
the classification area. The same ground-truth data that is typically used for accuracy assessment
of classifications may possibly be used to adjust TPI calculations to match vegetation change in
the area of interest. Additionally, the use of TP singly or in combination with aspect calculations
may be beneficial in correcting some of the topographical effects on QuickBird imagery
discussed by Wu (2008).
Steps 5 & 6: Structural Classes and Dominant Floristics
The last steps of the process classify vegetation into structural classes and identify the dominant
plant species to the extent possible. Of the 2, structural classes are the most important because
good inferences can be made as to vegetative composition if the ecological section, life form,
topographic position, aspect, elevation, and finally vegetative structure are known. However,
classification to dominant species is often useful in some applications and may be helpful for
determining structural aspects. I have suggested the use of unsupervised classification as the
driving force in this process to allow differences in reflectance be the determining factor for the
extent that floristic composition can be determined. The influence of the number of specified
classes on accuracy will need to be examined as will the use of unsupervised classification on
selected subsets of Levels 2 and 3 or in conjunction with multiple inputs from these levels.
Dominant species amongst herbaceous types may be the most difficult to determine, but specific
NDVI values and differences in NDVI values between imagery acquisition dates should allow
classes to be assigned such as irrigated cropland and low, medium, and high productivity
grasslands. However, highly productivity grasslands will generally also fall into the “dense
herbaceous” structural class and the dashed line connecting the floristic and structural boxes of
Figure 11 is meant to symbolize interactions between these levels that may occur. Assignment
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of final classes may require a feedback loop between these 2 levels and is the reason they are
depicted as simultaneous steps.
Decision trees (classification and regression trees) have been used to classify vegetation at finer
spatial resolution than the imagery used (Joy et al. 2003), including canopy coverage (Herold et
al. 2003). Although decision trees require a large sample size (Pal and Mather 2003), they are
non-parametric with the advantages of using different types of response variables, provide
invariance to transformation of explanatory variables, and have the capacity for interactive
exploration, description, and prediction of explanatory variables that is useful in ecological
studies (De’anth and Fabricius 2000). They were therefore chosen for estimating vegetative
structure. Canopy coverage for each class (upper stratum of tree and shrub classes that also
contain an understory) is presented in Table 4. Two size classes for the treed categories are also
proposed with the idea that the floristic classification and group membership from the fuzzy
classification of step 2 may especially help differentiate them. Similar to the use of NDVI within
the herbaceous class, changes in NDVI between dates for forested areas may be helpful in
estimating canopy cover. NDVI decreases most notably in relation to senescence of herbaceous
vegetation. For coniferous areas of similar topographic position, aspect, and life form, smaller
decreases may occur in locations where the shading effects of greater canopy coverage and to a
certain respect more acidic soils will reduce the amount of herbaceous ground cover. The same
may hold true for many broadleaf and shrub communities. However, exceptions exist to these
and most other situation. Sagebrush actively grows and flowers in the fall, potentially resulting
in higher NDVI values during that time. Larches (genus Larix) are one of the few conifers that
have deciduous leaves. These 2 exceptions to the norm can be factored into a classification
scheme, but are also examples that ecological process must always be considered during the
classification process.
Aspect and elevation are obvious ancillary inputs for steps 5 and 6, but hydrological data may be
useful for defining specific classes or situations. Depending on the scale for calculation of
topographic position, “valleys” containing streams or rivers may define riparian corridors and the
specific riparian vegetation that occurs along them. Highly productive herbaceous areas in
valleys within close proximity to water are likely to contain sedges, shrubs will often be willows,
and broadleaf trees will be cottonwoods in certain ecological sections.
The use of feature extraction software such as the ENVI Feature Extraction Module or Feature
Analyst by VLS Software offers a new approach to the remote sensing field when used in
conjunction with high resolution imagery (e.g. Walker and Blaschke 2008, San Souci and Doyle
2006). These programs essentially attempt to provide the machine learned equivalent of hand
digitizing visible homogenous polygons. Tree and shrub canopies can be defined in high
resolution imagery for use alone or as data inputs to Landsat imagery for conventional
classification. It is included in Figure 11 for both purposes.
Final Comment on Classification Methods
The topic of scale has often come up throughout this chapter; scale of vegetation patterns, scale
of remote sensing data, and scale of selection by animals. It is obvious that many of the
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ecological processes, classification variables, and methods discussed in the above classification
are also scale dependent, yet the influence of using an arbitrary uniform sampling grid to classify
scale-dependent variables was not addressed. Marceau et al. (1994) came to the conclusion that
remote sensing data are not independent of the sampling grid used for their acquisition, that
neglecting the scale and aggregation level can produce haphazard results with little
correspondence to geographical entities of the scene, and that there is not a unique spatial
resolution appropriate for all situations. Marceau and Hay (1999) provide an excellent review of
the issue and is an article that should be read by anyone involved with remote sensing or remote
sensing derived land cover classification.
The advent and use of higher resolution imagery has provided the ability to better understand
these relationships and correct for them in many situations. Niijland et al. (2009) found they
could improve classification accuracy for the variable of interest within their study area by resampling the original 5m imagery to 7m. Ideally, the perfect land cover classification system
would have the ability to adjust the spatial resolution of data to match the process or variable of
interest on the landscape. Similar to my previous comments, conservation and biodiversity
cannot wait for advances in technology and we must strive to make improvements with the tools
we have.
Recommendations for Conservation Applications
Success in conservation planning relies on acceptance and implementation of any work that is
done. Since land cover data often forms the foundation of conservation planning, it is critical
that limitations in accuracy and precision of the data are understood prior to their use as well as
how these limitation impact results. The following recommendations are intended to increase the
credibility of any results utilizing digital land cover data.
1) Land cover data must match the question of interest; habitat modeling or any other work
should be done within the limitations of land cover data being used. Delineating habitat
for a grassland species requires high accuracy of grassland classes. Most land cover data
works best for wide ranging habitat generalists than habitat obligates and for lower
selection orders.
2) Even if an accuracy assessment is available for data used, accuracy can be variable across
the classification area and it is a good idea to conduct an accuracy assessment for the
specific project area using field data or photointerpretation. Even the best model will not
produce accurate results if there are errors in the input data.
3) No land cover classification is completely accurate and a brief discussion of the
confusion between classes (from an accuracy assessment) and of the limitations in the
data may explain reasons for discrepancies between habitat predictions and observations.
4) FGDC standards for collection of vegetation data in the field should be followed for
accuracy assessments or collection of data for new classifications.
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Figure 1. Comparison of 1km AVHRR imagery, 30m Landsat imagery, 4m IKONOS imagery,
and a color orthophotos for a 1km area in Yellowstone National Park indicating what each data
type “sees”. The AVHRR data is a single color as this is the extent of a single pixel for this type
data.
Figure 2. Typical reflectance curves for vegetation, soil, and water. (From Lillesand and Kiefer
1994).
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Figure 3. Generalized relationship between precision and accuracy of land cover data.
20
Figure 4. Relationship between grain and extent (from Wiens 1989)
21
Figure 5a. Relationship between precision and the pixel size or scale of data.
Figure 5b. Relationship between precision and area extent.
22
Figure 6a. Relationship between pecision and habitat specificity.
Figure 6b. Relationip between precision and inraspecific order of selection and interspecific
scale of selection.
23
Figure 7. Boundary between GAP land cover at the MT WY border in Yellowstone National
Park.
Figure 8. Closepup of red polygon in Figure 7.
24
Figure 9. Hierarchical classification steps for vegetated polygons using VRI classification.
Figure 10. Original orthoophoto (top), MT GAP classification of the area (middle), and hybrid
classification using the orthophotos with MT GAP classes (bottom).
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Figure 11. Flowchart of conceptual classification.
26
Table 1. Global land cover classifications
Table 2. Comparison between land cover classes for 1992 and 2001 NLCD data.
27
Table 3. Producer’s accuracy (user’s accuracy) of NLCD land cover data for region 8.
Table 4. Proposed land cover classes (see attached spreadsheet).
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