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Land Cover Data and Modeling

The USGS GAP Land Cover Data Set includes detailed vegetation and land use patterns for the continental United States. The data set incorporates the Ecological System classification system developed by NatureServe to represent natural and semi-natural land cover. The 590 land use classes in the data set can be displayed at three levels of detail, from general (8 classes) to most detailed. The Land Cover Data Set can be used to identify those places in the country with sufficient good quality habitat to support wildlife, a key step in developing sound conservation plans.

The GAP Land Cover data set is mainly focused on habitat identification.  The USGS National Land Cover Database (NLCD) is also available and has more detail in developed areas.  Learn more about NLCD 2006 >>

Why are these data important?

National GAP Land Cover Data provide information on the distribution of native vegetation types, modified and introduced vegetation, developed areas, and agricultural areas of the United States. For all areas of the county except Hawaii, native vegetation areas are classified to the Ecological System types developed by NatureServe. Ecological Systems provide detailed information on the vegetative communities of an area that is not available in most other regional or national land cover products. This level of thematic detail makes possible the construction of wildlife habitat distribution models, and the construction of complicated hydrology and fire dynamics models, and many other applications. Information about land cover is a key component of effective conservation planning and the management of biological diversity as it is used to build predictive models of wildlife distribution and biodiversity across large geographic areas. When used in conjunction with protected areas data (PAD-US), land cover data can be used to identify habitat types that may be under-protected so management activities can be adjusted. These maps and data can be used to identify those places in the country with sufficient good quality habitat to support wildlife, a key step in developing sound conservation plans.

How National GAP Land Cover Data are being used

Information about land cover is used to develop models showing where there is appropriate habitat for the nation’s vertebrate species. This information is important for decision makers, planners, researchers, private interests and others:

  • Biodiversity: National GAP Land Cover data is key to helping land conservation decision makers better match biodiversity goals to land protection programs and activities.
  • Habitat Loss: Human population in the U.S. is predicted to grow by 25% in the next 50 years. This population increase, coupled with our land consumption patterns, means that there will be significant decreases in habitat for other species. Knowing where large tracts of intact habitat occur is key to targeting the most effective lands for biodiversity conservation which, in turn, can offset some of the effects of habitat loss.
  • Climate: Accelerating climate change is elevating the importance of effectively targeted species protection efforts. For many species, warming climates could push them to the brink of extinction unless habitat migration corridors can be set aside. Information about land cover is critical to understanding where to focus such corridor planning.
  • Management: Agencies and non-profits that manage protected areas often lack complete information about the land cover types that might be present or could be restored on their lands. Land cover maps and data are crucial to the development of improved land management practices that support continued biodiversity.

Description of the data

Citation:  US Geological Survey, Gap Analysis Program (GAP). May 2011. National Land Cover, Version 2

Classification System

Previous land cover mapping projects made clear the need for a nationally consistent classification scheme mappable at a meso-scale. In response to this need, NatureServe developed the Terrestrial Ecological Systems Classification framework (Comer et al. 2003). Ecological systems are defined as “groups of plant community types that tend to co-occur within landscapes with similar ecological processes, substrates and/or environmental gradients” (Comer et al. 2003). Although distinct from the US-NVC, the vegetation component of an ecological system is described by one or more NVC alliances or associations. While the ecological system concept emphasizes existing dominant vegetation types, it also incorporates physical components such as landform position, substrates, hydrology, and climate. More detailed information about the Terrestrial Ecological Systems Classification for the United States is available at NatureServe.

National GAP Land Cover combines ecological system data from previous GAP projects in the Southwest , Southeast, and Northwest United States. with recently updated California data. For Alaska and areas of the continental United States where ecological system-level GAP data has not yet been developed, data from the LANDFIRE project were used. This approach allowed GAP mappers to construct a seamless representation of ecological system distributions across the continental United States and Alaska. In Hawaii, data created by the Hawaii GAP project were used. This data set uses a classification system developed by the project specifically for Hawaii and not the ecological system.

The Alaska and Continental U.S. portion of the data set contains 680 Ecological systems and 28 land use, introduced vegetation or disturbed classes. The Hawaii data set contains 28 natural vegetation classes and nine land use, introduced vegetation or disturbed classes. Frequently, this high number of classes provides a level of detail that exceeds a user’s needs. To accommodate these users, we have crosswalked the ecological system level data to the five highest levels of the National Vegetation Classification System (NVC). The vegetation features used to distinguish these classes range from growth form, and climate regimes at the Class level to regional differences in substrate and hydrology at the Macrogroup level (Table 1; The NVC levels provide the user with a variety of options allowing the choice of making a map of the Continental U.S. with eleven classes at the NVC Class level to 583 classes at the Ecological system level.

Table 1. Features used to delineate National Vegetation Classification (NVC) levels
Class dominant general growth forms adapted to basic moisture, temperature, and/or substrate or aquatic
Subclass global macroclimatic factors driven primarily by latitude and continental position, or reflect overriding substrate or aquatic conditions
Formation global macroclimatic conditions as modified by altitude, seasonality of precipitation, substrates, hydrological conditions
Division continental differences in mesoclimate, geology, substrates, hydrology, disturbance regimes
Macrogroup sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, disturbance regimes
Table 2. Distribution of GAP land cover data classes within mapping areas, the five highest levels of the NVC and ecological systems.
Hierarchy Level Alaska Continental United States Hawaii
Natural Land use or Disturbed Natural Land use or Disturbed Natural Land use or Disturbed
Class 6 3 7 4 5 3
Subclass 11 3 17 6 not available
Formation 19 3 30 6 not available
Division 21 3 52 6 not available
Macrogroup 27 3 117 6 not available
Ecological System 136 7 556 27 28 9


The National GAP Land Cover was derived using Landsat TM satellite imagery from 1999-2001 as its primary base. This imagery was acquired from the USGS National Center for Earth Resources Observation and Science (EROS) through the Multi-Resolution Land Characteristics Consortium (MRLC). EROS did the preprocessing and atmospheric correction of the images and developed three clear, low cloud images for all areas of the United States. These mosaics represented the spring, summer, and fall seasons. Having images of vegetation during these three different times of the year is very helpful for correctly classifying vegetation types that look the same during one season but different in another.

A variety of other dataset were used in all or some of the regions to help with the land cover classification process. Normalized Difference Vegetation Index (NDVI), and brightness, greenness and wetness indices were created using Landsat ETM+ coefficients from Huang et al. (2002). Digital elevation model-derived data sets were also used and included elevation, slope, aspect and landform. Other ancillary data sets used depend on the region but include digital data on soils, geology, stream and wetland location, point locations for rare plant communities and fire and tree harvest information. Locations of agricultural areas and developed lands were pulled into the GAP land cover maps directly from the National Land Cover Database 2001 (NLCD 2001).

Training Data

The models predicting the land cover distribution for the regional projects used field data collected by gap personnel specifically for the project and vegetation data collected for other projects to instruct the model. GAP field samples were collected by traversing navigable roads in a mapping zone and opportunistically selecting plots that met criteria of appropriate size (1-hectare minimum) and composition (stand homogeneity). Plot data were collected using ocular estimates of biotic and abiotic land cover elements, including percent cover of dominant species by life form (i.e. trees, shrubs, grasses, and forbs) and physical data such as elevation, slope, aspect and landform. Visual interpretation of aerial photography, digital orthophoto quads, or other remotely sensed imagery was also used to generate training data when sufficient information could be obtained to make and Ecological system determination. This technique was most often used for rare systems with insufficient training data and where additional ancillary data was available to identify image features.

Additional vegetation data was collected from Federal, State, and Non-Governmental agencies and Ecological Systems labels were assigned to their existing plots. The LANDFIRE project conducted a similar effort to collect existing vegetation data, and assigning Ecological System classifications. They shared their database with GAP for use in the Northwest Regional Gap Analysis project.

Modeling Techniques

Classification and regression trees (CART) are a valuable tool for discriminating complex relationships among environmental variables. Decision trees use a binary partitioning algorithm to successively split a multidimensional “cloud” of explanatory data into increasingly homogenous subsets. Each binary split is considered a single rule in a chain of rules defining the characteristics of the response variable. For land cover mapping, explanatory variables are the spectral and ancillary data sets and the response variable is the land cover classes (see Lowry et al. 2005 for more information on CART modeling techniques).

Decision tree classifiers are well suited for land cover mapping as they do not require normally distributed training data, they can accommodate a wide variety of predictor variables, and they have demonstrated improved accuracies over the use of traditional classifiers (Hansen et al. 1996, Pal and Mather 2003). Decision tree software is readily available, computationally efficient, and by using a hierarchical approach to define decision rules, is intuitive to a variety of users.

CART modeling techniques were used to map the majority of the Ecological systems in the Southwest and Northwest regional projects. CART tended to work best for widespread matrix systems with sufficient training data. For rarer, patch systems and systems in the Southeast regional project where topographic relief was not sufficient to explain vegetative community distribution more manual modeling techniques were used.

Data Limitations

The National GAP Land Cover data attempts to map as accurately as possible the fine vegetative details of the nation’s vegetation. However, there are some limitations to the data that users should keep in mind. The data set uses a 30 meter pixel cell and in most areas a minimum mapping unit of 0.4 ha (1 acre) this means that small patches of vegetation can be missed in the modeling process. By nature of their patchy distributions and frequently small extents wetlands, riparian habitats and rare habitat types can be the most frequently missed types. Some Ecological Systems have spectral signatures, and occur at similar elevations, aspects etc. to other Ecological Systems. Because of these similarities, the modeling process used to create the land cover data has difficulty differentiating between these types of systems and a significant amount of confusion may occur. Detailed accuracy assessments have been conducted for the Ecological Systems maps generated by the Southwest Regional Projects and portions of the Northwest Regional Project. Evaluating these accuracy assessments may help users evaluate the suitability of the National GAP Land Cover data for their projects.

National GAP Land Cover data uses Landsat 1999-2001 imagery as the base for its models. In areas where there have been significant changes to the vegetation since this time period the data may need modification before use or may be inappropriate for use.


Comer, P., D. Faber-Langendoen, R. Evans, S. Gawler, C. Josse, G. Kittel, S. Menard, S. Pyne, M. Reid, K. Schulz, K. Snowand, J. Teague, 2003. Ecological systems of the United States: A working classification of U.S. terrestrial systems. NatureServe, Arlington, Virginia.

Hansen, M., R. Dubayah, and R. DeFries, 1996. Classification trees: An alternative to traditional land cover classifiers. International Journal of Remote Sensing 17(5): 1075-1081.

Lowry, J. H, Jr., R. D. Ramsey, K. Boykin, D. Bradford, P. Comer, S. Falzarano, W. Kepner, J. Kirby, L. Langs, J. Prior-Magee, G. Manis, L. O’Brien, T. Sajwaj, K. A. Thomas, W. Rieth, S. Schrader, D. Schrupp, K. Schulz, B. Thompson, C. Velasquez, C. Wallace, E. Waller and B. Wolk. 2005. Southwest Regional Gap Analysis Project: Final Report on Land Cover Mapping Methods, RS/GIS Laboratory, Utah State University, Logan, Utah.

Pal, M. and P. M. Mather, 2003. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment 86, 554-565


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