Important Caution Advisory
With this in mind, users are cautioned to carefully scrutinize the data to see if they are of sufficient reliability before attempting to use the dataset for larger-scale or local analyses. This evaluation must be made remembering that the NLCD represents conditions in the early 1990s.
The New Hampshire portion of the NLCD was created as part of land cover mapping activities for Federal Region I that includes the States of Connecticut, Maine, Vermont, Rhode Island, New Hampshire, and Massachusetts. The NLCD classification contains 21 different land cover categories with a spatial resolution of 30 meters. The NLCD was produced as a cooperative effort between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA) to produce a consistent, land cover data layer for the conterminous U.S. using early 1990s Landsat thematic mapper (TM) data purchased by the Multi-resolution Land Characterization (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies that produce or use land cover data. Partners include the USGS (National Mapping, Biological Resources, and Water Resources Divisions), USEPA, the U.S. Forest Service, and the National Oceanic and Atmospheric Administration.
The original NLCD grid was projected into UTM Zone 19 and was clipped to a box surrounding the USDA Forest Service, Hubbard Brook Experimental Forest.
Important Caution Advisory
With this in mind, users are cautioned to carefully scrutinize the data to see if they are of sufficient reliability before attempting to use the dataset for larger-scale or local analyses. This evaluation must be made remembering that the NLCD represents conditions in the early 1990s.
The New Hampshire portion of the NLCD was created as part of land cover mapping activities for Federal Region I that includes the States of Connecticut, Maine, Vermont, Rhode Island, New Hampshire, and Massachusetts. The NLCD classification contains 21 different land cover categories with a spatial resolution of 30 meters. The NLCD was produced as a cooperative effort between the U.S. Geological Survey (USGS) and the U.S. Environmental Protection Agency (USEPA) to produce a consistent, land cover data layer for the conterminous U.S. using early 1990s Landsat thematic mapper (TM) data purchased by the Multi-resolution Land Characterization (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies that produce or use land cover data. Partners include the USGS (National Mapping, Biological Resources, and Water Resources Divisions), USEPA, the U.S. Forest Service, and the National Oceanic and Atmospheric Administration.
An accuracy assessment is done on all NLCD on a Federal Region basis following a revision cycle that incorporates feedback from MRLC Consortium partners and affiliated users. The accuracy assessments are conducted by private sector vendors under contract to the USEPA. A protocol has been established by the USGS and US EPA that incorporates a two-stage, geographically stratified cluster sampling plan (Zhu et al., 1999) utilizing National Aerial Photography Program (NAPP) photographs as the sampling frame and the basic sampling unit. In this design a NAPP photograph is defined as a 1st stage or primary sampling unit (PSU), and a sampled pixel within each PSU is treated as a 2nd stage or secondary sampling unit (SSU).
PSU's are selected from a sampling grid based on NAPP flight-lines and photo centers, each grid cell measures 15' X 15' (minutes of latitude/longitude) and consists of 32 NHAP photographs. A geographically stratified random sampling is performed with 1 NAPP photo being randomly selected from each cell (geographic strata), if a sampled photo falls outside of the regional boundary it is not used. Second stage sampling is accomplished by selecting SSU's (pixels) within each PSU (NAPP photo) to provide the actual locations for the reference land cover classification.
The SSU's are manually interpreted and misclassification errors are estimated and described using a traditional error matrix as well as a number of other important measures including the overall proportion of pixels correctly classified, user's and producer's accuracy's, and omission and commission error probabilities.
At the time of CD release (Summer 2000), the accuracy assessment was not complete. For the Region I accuracy assessment, please check the NLCD Website: <http://edcwww.usgs.gov/programs/lccp/nationallandcover.html>. The accuracy assessment numbers will be posted there around September, 2000.
While we believe that the approach taken has yielded a very good general land cover classification product for Region I, it is important to indicate to the user where there might be some potential problems. The biggest concerns for Region I are listed below: 1) Accurate definition of the transitional barren class was extremely difficult. The majority of pixels in this class correspond to clear-cut forests in various stages of regrowth. Spectrally, fresh clear-cuts are very similar to row-crops in the leaves-off data. Manual correction of coding errors was performed to improve differentiation between row-crops and clear-cuts, but some errors may still be found. As regrowth occurs in a clear-cut region, the definition of transitional barren versus a forested class becomes problematic. An attempt was made to classify only fresh clear-cuts or those in the earliest stages of regrowth, but there are likely forested regions classed as transitional barren and vice versa.
Two seasonally distinct TM mosaics are produced, a leaves-on version (summer) and a leaves-off (spring/fall) version. TM bands 3, 4, 5, and 7 are mosaicked for both the leaves-on and leaves-off versions. For mosaick purposes, a base scene is selected for each mosaic and the other scenes are adjusted to mimic spectral properties of the base scene using histogram matching in regions of spatial overlap. Following mosaicking, either the leaves-off version or leaves-on version is selected to be the "base" for the land cover mapping process. The 4 TM bands of the "base" mosaic are clustered to produce a single 100- class image using an unsupervised clustering algorithm. Each of the spectrally distinct clusters/classes is then assigned to one or more Anderson level 1 and 2 land cover classes using National High Altitude Photography program (NHAP) and National Aerial Photography program (NAPP) aerial photographs as a reference. Almost invariably, individual spectral clusters/classes are confused between two or more land cover classes.
Separation of the confused spectral clusters/classes into appropriate NLCD class is accomplished using ancillary data layers. Standard ancillary data layers include: the "non-base" mosaic TM bands and 100- class cluster image; derived TM normalized vegetation index (NDVI), various TM band ratios, TM date bands; 3-arc second Digital Terrain Elevation Data (DTED) and derived slope, aspect and shaded relief; population and housing density data; USGS land use and land cover (LUDA); and National Wetlands Inventory(NWI) data if available. Other ancillary data sources may include soils data, unique state or regional land cover data sets, or data from other federal programs such as the National Gap Analysis Program (GAP) of the USGS Biological Resources Division (BRD). For a given confused spectral cluster/class, digital values of the various ancillary data layers are compared to determine: (1) which data layers are the most effective for splitting the confused cluster/class into the appropriate NLCD class, and (2) the appropriate layer thresholds for making the split(s). Models are then developed using one to several ancillary data layers to split the confused cluster/class into the NLCD class. For example, a population density threshold is used to separate high-intensity residential areas from commercial/industrial/transportation. Or a cluster/class might be confused between row crop and grasslands. To split this particular cluster/class, a TM NDVI threshold might be identified and used with an elevation threshold in a class-splitting model to make the appropriate NLCD class assignments. A purely spectral example is using the temporally opposite TM layers to discriminate confused cluster/classes such as hay pasture vs. row crops and deciduous forests vs. evergreen forests; simple thresholds that contrast the seasonal differences in vegetation between leaves-on vs. leaves-off.
Not all cluster/class confusion can be successfully modeled out. Certain classes such as urban/recreational grasses or quarries/strip mines/gravel pits that are not spectrally unique require manual editing. These class features are typically visually identified and then reclassified using on-screen digitizing and recoding. Other classes such as wetlands require the use of specific data sets such as NWI to provide the most accurate classification. Areas lacking NWI data are typically subset out and modeling is used to estimate wetlands in these localized areas. The final NLCD product results from the classification (interpretation and labeling) of the 100-class "base" cluster mosaic using both automated and manual processes, incorporating both spectral and conditional data layers. For a more detailed explanation please see Vogelmann et al. 1998 and Vogelmann et al. 1998.
Discussion:
While we believe that the approach taken has yielded a very good general land cover classification product for the nation, it is important to indicate to the user where there might be some potential problems. The biggest concerns are listed below:
1) Some of the TM data sets are not temporally ideal. Leaves-off data Sets are heavily relied upon for discriminating between hay/pasture and row crop, and also for discriminating between forest classes. The success of discriminating between these classes using leaves-off data sets hinges on the time of data acquisition. When hay/pasture areas are non-green, they are not easily distinguishable from other agricultural areas using remotely sensed data. However, there is a temporal window during which hay and pasture areas green up before most other vegetation (excluding evergreens, which have different spectral properties); during this window these areas are easily distinguishable from other crop areas. The discrimination between hay/pasture and deciduous forest is likewise optimized by selecting data in a temporal window where deciduous vegetation has yet to leaf out. It is difficult to acquire a single-date of imagery (leaves-on or leaves-off) that adequately differentiates between both deciduous/hay and pasture and hay pasture /row crop.
2) The data sets used cover a range of years (see data sources), and Changes that have taken place across the landscape over the time period may not have been captured. While this is not viewed as a major problem for most classes, it is possible that some land cover features change more rapidly than might be expected (e.g. hay one year, row crop the next).
3) Wetlands classes are extremely difficult to extract from Landsat TM spectral information alone. The use of ancillary information such as National Wetlands Inventory (NWI) data is highly desirable. We relied On GAP, LUDA, or proximity to streams and rivers as well as spectral data to delineate wetlands in areas without NWI data.
4) Separation of natural grass and shrub is problematic. Areas observed on the ground to be shrub or grass are not always distinguishable spectrally. Likewise, there was often disagreement between LUDA and GAP on these classes.
Acknowledgments
This work was performed under contract the U.S. Geological Survey(Contract 1434-CR-97-CN-40274).
References
More detailed information on the methodologies and techniques employed In this work can be found in the following:
Kelly, P.M., and White, J.M., 1993. Preprocessing remotely sensed data for efficient analysis and classification, Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, Proceeding of SPIE, 1993, 24-30.
Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe, 1979. Classification of Wetlands and Deepwater Habitats of the United States, Fish and Wildlife Service, U.S. Department of the Interior, Washington, D.C.
Vogelmann, J.E., Sohl, T., and Howard, S.M., 1998. "Regional Characterization of Land Cover Using Multiple Sources of Data." Photogrammetric Engineering & Remote Sensing, Vol. 64, No. 1, pp. 45-47.
Vogelmann, J.E., Sohl, T., Campbell, P.V., and Shaw, D.M., 1998. "Regional Land Cover Characterization Using Landsat Thematic Mapper Data and Ancillary Data Sources." Environmental Monitoring and Assessment, Vol. 51, pp. 415-428.
Zhu, Z., Yang, L., Stehman, S., and Czaplewski, R., 1999. "Designing an Accuracy Assessment for USGS Regional Land Cover Mapping Program." (In review) Photogrametric Engineering & Remote Sensing.
Much of the USDA Forest Service data are compiled, processed, and maintained with ARC/INFO software developed by the Environmental Systems Research Institute (ESRI). Much of the information presented uses conventions and terms popularized by ARC/INFO and its user community. The mention of commercial firms or products is for clarity and identification of procedures and methods only, and no endorsements are implied by the USDA Forest Service.