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Definition - “the vegetational and artificial constructions covering
the land surface" (Burley 1961).
Advent of remote sensing and needs of resource managers, etc.,
has led to increased development and use of land cover data.
Persistent myth that land cover data is accurate and up-to-date.
All the different land cover classifications have been likened to the
mythical Tower of Babel where everyone is working hard but all
speaking different languages – “progress that has been made so far
is despite the large number of schemes, not because of them”
(Adams 1999).
Land cover data typically forms the basis for conservation planning
such as habitat suitability for focal species.
Typically use “available” data rather than data that is really needed.
A working knowledge of remote sensing is needed since it forms
the basis for most land cover data
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Remote sensing is “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” (Lillesand and
Kiefer 1994).
Brief history, beginning with the use of balloons and
pigeons to carry cameras to current use of satellites
carrying active and passive sensors.
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Spatial Resolution
Figure 1 indicates what sensors with different spatial
resolutions “see” on the ground within a 1km extent.
A
B
C
D
a) 1 km AVHRR imagery
b) 30m Landsat
c) 4m IKONOS
d) 1m orthophoto
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Spectral Resolution
All surfaces reflect, absorb, or transmit energy. Figure 2 provides reflectance
curves for bare soil, green vegetation, and water. Measuring reflectance
within discrete intervals corresponding to these peaks and valleys forms the
basis for classification with optical-based remote sensing data. Figure from
Lillesand and Kiefer (1994).
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Temporal Resolution - the amount of time between
repeat coverage for a sensor. Important to collect data
over a large area, to provide multiple dates for better
classification, and for intra-annual change detection.
Scale - 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. 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).
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Classification Accuracy and Precision - accuracy defines
“correctness”, the agreement between an assumed standard and
the predicted class of data, precision defines “detail” of land cover
classification. Precision determines the usefulness for a specific
application. Figure 3 indicates the general relationship between
accuracy and precision of land cover data.
Producer’s accuracy - percent of features
classified correctly amongst those that are 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%.
Consumer’s accuracy - 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%.
This section describes the difficulties in matching what we see on the
ground with remote sensing data and the classification process
 There is a disconnect between the natural science community for
defining vegetation in the field and the remote sensing community
for providing useful classifications of land cover data.
e.g. common taxonomic classification such as Idaho
fescue/bluebunch wheatgrass and Idaho fescue/tufted hairgrass
cannot be differentiated with remote sensing data.
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The interaction of grain and extent with heterogeneity of the
landscape from an ecological perspective is analogous to the
influences of pixel size and extent from a remote sensing perspective.
“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.”
Figure and quotation from Wiens 1989
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Pixel Size (grain) in Relation to Land Cover Classification:
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. Smaller pixel size can therefore increase the precision of land
cover classification
Other factors influencing the precision are
spectral resolution, issues associated with
image registration, topographic
shadowing, vegetative structure, etc.
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Extent in Relation to Land Cover Classification
and Conservation Planning
Similar to ecological aspects, heterogeneity of the landscape increases
at larger spatial extents which decreases the precision of classification.
Additionally, the use of high resolution data is currently limited to small
areas due to cost and size of data sets. These factors generally
contribute to a decline in precision as spatial extents increase.
It is unlikely classification using high
resolution data is available for large areas
commonly used in conservation planning
or encompassing entire areas of interest.
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This section describes the requirements of landcover data for
developing accurate habitat models
Vegetative requirements of wildlife can be floristic, structural, or a
combination of the 2.
E.g. fisher generally avoid open areas and utilize a range of plant communities
containing high amounts of vegetative structure, while sage grouse are strongly
associated with sagebrush habitat but require specific structural components of
sagebrush and amounts of herbaceous cover within sagebrush habitat.
Habitat
Specialists
Habitat
Generalists
Precision of landcover must match the
requirements of species modeled.
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Every species observes the environment through its own
unique suite of scales for space and time (Levin 1992).
E.g. hierarchical orders of selection by herbivores (Senft et al. 1987,
Bailey et al. 1996).
e.g. third order for grizzly
bears but second order for
sage grouse
Requirements for precision of land cover
data varies depending on the level of
selection within species as well as
amongst species.
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2)
3)
4)
Subsequent Problems
Existing land cover classifications do not meet
requirements for all species or different selection
levels within species.
Project-specific classifications are often developed, but
they seldom cover desired areas of interest or all
requirements of the suite of focal species selected.
Important parameters such as “early seral lodgepole
forest” may need to be extrapolated onto broader
classes of “lodgepole forest” or “coniferous forest”
which will in itself overestimate the amount of suitable
habitat.
Classification errors can potentially compound the lack
of precision in many applications.
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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).
Due to the advancement in available sensors and techniques, this
statement is probably truer today than when it was first made.
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In addition to issues concerning precision and accuracy, it can
also be difficult to find consistent land cover data across
jurisdictional boundaries that are common in conservation
planning.
The following are some sources of commonly available land cover data
and examples of other options for classification. Some of the benefits and
associated problems of each are discusses.
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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.
Advantages are availability and consistency worldwide but coarse resolution and
very general categories.
Table from Strand et al. 2007
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National Landcover Database (NLCD)
Advantages:
Available for the conterminous United States, Alaska, and Puerto Rico.
Use of 30m Landsat data provides good spatial heterogeneity of cover types.
Estimates of canopy coverage for the 2001 version.
Disadvantages:
Broad classes not well suited for delineating wildlife habitat
Poor accuracy of some classes and locations
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NLCD Categories
Comparison of 1992 and 2001National Land Cover Data classes (NLCD)
1992 NLCD Classes
2001 NLCD Classes
Open Water
Open Water
Perennial Ice/Snow
Perennial Ice/Snow
Low Intensity Residential
Developed, Open Space
High Intensity Residential
Developed, Low Intensity
Commercial/Industrial/Transportation
Developed, Medium Intensity
Bare Rock/Sand/Clay
Developed, High Intensity
Quarries/Strip Mines/Gravel Pits
Barren Land
Transitional Barren
Deciduous Forest
Deciduous Forest
Evergreen Forest
Evergreen Forest
Mixed Forest
Mixed Forest
Dwarf Shrub *
Shrubland
Shrub/Scrub
Orchards/Vineyards/Other
Grassland/Herbaceous
Grassland/Herbaceous
Sedge/Herbaceous *
Pasture/Hay
Moss *
Row Crops
Pasture Hay
Small Grain
Cultivated Crops
Fallow
Woody Wetlands
Urban/Recreational Grasses
Emergent Herbaceous Wetlands
Woody Wetlands
Emergent Herbaceous Wetlands
* = Alaska Only
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NLCD Region 8 (MT, WY, ND, SD, UT, CO) Accuracy
GAP Land Cover
Advantages:
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Available for the U S and Puerto Rico.
Generally higher precision than NLCD due to smaller extents.
Disadvantages:
Although the intent of GAP was to maintain animal and plant
species, the broad classes that are typically used often are not of
sufficient precision to define habitat for many species.
Inconsistency in classification amongst states.
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Montana GAP Categories
Better suited for habitat delineation than NLCD classes
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Comparison of Montana vs. Wyoming GAP Classification
8,028 acres where Montana
and Wyoming GAP data overlap
in Yellowstone National Park
Wyoming: 80% lodgepole pine, 20%
subalpine meadow
Montana: 6 distinct types, spatially
arranged within the polygon
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Crosswalking vegetation types is often used when the
area of interest crosses jurisdictional boundaries.
However, detailed information on floristic composition and canopy cover are
required to correctly crosswalk between vegetation types (Brohman and
Bryant 2005, FGDC 1997, FGDC 2008, Jennings et al. 2004).
Example of crosswalked vegetation types
for a portion of the Inland Temperate
Rainforest encompassing parts of MT, ID,
WA, and British Columbia.
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
state projects produced.
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Classification Using High Resolution Data such as
IKONOS or QuickBird.
Potential for greater precision: 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).
However, there currently are not any widespread classifications 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
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Vegetation Resource Inventory of British Columbia
A hierarchical classification based on physiognomy of vegetation
Many species are more concerned with
vegetation structure than plant species.
Land cover data providing taxonomic
classes often require assumptions about
structural or density classes for habitat
models (e.g. denser conifers on north
slopes).
This classification provides the life form
and density. It is easier to use this
classification and a DEM to make
assumptions about plant species (when
required) than vice versa.
Major drawbacks to this classification
system is that it is based on air photo
interpretation.
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Classification Using Digital Orthophotos
A combination of air photo interpretation and machine classification can be
used to classify land cover.
Similar to satellite imagery, digital color air photos provide distinct
values of red, green, and blue for each pixel that allows classification
with remote sensing software.
A
B
C
Advantages:
Better able to determine landscape heterogeneity.
Should provide greater accuracy associated with air photo
interpretation but with the greater speed of machine classification.
Disadvantages:
Digital values do not correspond to peaks and valleys of objects.
Limited to small areas.
Potential need for classification of individual photos depending on
required precision.
Example of method: A = color orthophoto, B = corresponding GAP
classification, C = classification using digital orthophoto.
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Conservation planning often uses “available” land cover data and seldom
acknowledges different seasonal requirements or the orders of selection
that habitat delineation encompasses. Similar to the rigorous selection
process for focal species, methods could be developed to define what
species and selection orders can be defined using available data as well
as land cover requirements for desired species and selection orders.
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LiDAR, hyperspectral, and high resolution multispectral such as IKONOS
and QuickBird will become more common and improve classification
abilities.
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As sensor technology increases, so will the ability to fuse active and
passive sensors to obtain the benefits of each.
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But we cannot wait for these technologies to advance and must strive to
make improvements with the tools we have. Therefore, I have proposed
the following land cover classification using readily available satellite
imagery to better meet the needs of conservation planning.
Proposed Classification
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Based on the use of readily available Landsat imagery or other
optical-based data and other available GIS data.
Hierarchical classification – nested levels of classification to
improve accuracy, better adapt to combining different types of
remote sensing data, provides the ability to define habitat for
specific species or different levels of selection within subsets of
each level.
A combination of traditional remote sensing techniques and GIS
modeling applications. Different methods and hierarchical levels
are intended to match the desired output at each level.
Separate classification by Ecological Sections (McNab et al. 2007)
rather than just a desired area of interest to decrease
consequences of errors.
Proposed Classification
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Provides both structural and taxonomic information. The
interaction between these variables should improve the accuracy
of both.
Applicable over large areas, increased precision for smaller areas.
Provides compatibility when different locations are classified at
different times. Different sub-models can be incorporated at
different times to account for different species, orders of
selection, or broad-scale changes in underlying factors (e.g. fire,
influences of climate change).
Although it has not actually been used, data are available and
methods (GIS models, etc.) have been developed to implement
the classification process in a pilot study or actual project.
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Land cover data is often the limiting factor in what we can and
cannot do in conservation planning. It should therefore be a major
consideration early on when defining the goals and objectives of
any project.
Limitations of results due to the accuracy and precision of land
cover data should be detailed in results. Accuracy of land cover data
can be variable across an AOI. Accuracy assessments should be
conducted for the AOI using air photo interpretation or field data. A
brief discussion of confusion between classes and limitations of the
data may explain reasons for discrepancies in results.
FGDC standards for collection of field data should be followed for
accuracy assessments and use in other classifications.