SDE Raster Dataset
Tags
Uplands, geoscientificInformation, North Dakota, 1992-1996, environment, SD, ND, South Dakota, imageryBaseMapsEarthCover, May Imagery, 16 classes, detailed landcover, Prairie Pothole Joint Venture, farming, wetland, September Imagery, Prairie Pothole Region
In 1996, the US Fish and Wildlife Service (FWS), Region 6 Habitat and Population Evaluation Team, initiated a pilot project to evaluate the potential to use Thematic Mapper satellite imagery to map upland waterfowl nesting cover in the North and South Dakota and northeast Montana portion of the Prairie Pothole Region. The results of the pilot project suggestted that we could expect more than 80 percent overall classification accuracy was likely and the project was expanded to the area of interest. Ducks Unlimited's Great Plains Regional Office joined the project in late 1996 and co-funded the remainder of the project. Imagery dates ranged from 1992-96 and the process resulted in the landcover classification of nearly 78 million acres into 16 wetland and upland classes.
The development of a FWS Region 6, Prairie Pothole Region landcover image was initiated to support the implementation, evaluation, and monitoring of the North American Waterfowl Management Plan Prairie Pothole Joint Venture. Wetland digital data was available via National Wetlands Inventory, and a complementory landcover layer was needed as a base information layer for modeling landscape variables that relate physical attributes of the landscape with the spatial, temporal, and habitat needs of wetland and grassland dependent migratory birds.
US Fish and Wildlife Service, Ducks Unlimited Great Plains Regional Office
Use at your own risk
Research of Region 6 Realty files
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USFWS Although these data have been processed successfully on a computer system at the U.S. Fish and Wildlife Service, no warranty expressed or implied is made regarding the accuracy or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data. It is strongly recomended that these data are directly acquired from the US Fish and Wildlife Service, and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data. The U.S. Fish and Wildlife Service shall not be held liable for improper use of the data described and or contained herein.
Use at your own risk
Logical Consistency is implicit in the raster image data structure.
Data completeness digital files reflects the extent to which geographic features may be extracted from the source image data. Features may have been eliminated or generalized due to scaling, resampling, majority filtering, and pixel clump elimination generalization constraints. The content will include only those features that are discernable through the multispectral image processing techniques applied to the source image data.
Source TM image wsa geocoded using 7.5' 1:24000 scale topgraphic sheets. The minimum mapping unit is 10 28.5 meter cells, or five acres.
Landcover database derived from 2 co-registered multispectral landsat TM 5 scenes (P34R26; P33R26-27; P32R26-28; P31R26-29; P30R27-30; and P29R29-30). Geocoding and image-to-image referencing was conducted on each of the 16 pairs of images, which were georeferenced to USGS 1:24000 scale topographic map sheets. Six bands (bands 1-5 and 7) from each co-registered scene were stacked to produce a single 12-band imagine raster file. The Imagine ISODATA algorithm was applied to the 12-band image to generate 240 spectral class signatures The Imagine maximum-likelihood classifier was then applied using the resulting signatures to assign all image pixels from the 1-2-band image to 240 classes. The resulting classes were compared to ground truth information collected during summers 1996 and 1997 for the purpose of interpretation and grouping. The 240 classes were interpreted and grouped into eight landcover classes (grassland, undisturbed grass, alfalfa hayland, cropland, forest, barren, water, and riparian). A 3x3 cell majority filter was passed once over the resulting landcover image to eliminate noise. he image was then tiled into 9 separate subwindows which were each processed with the Imagine clump process to identify contiguous areas of landcover. The imagine eliminate process was then conducted on each of the nine clump subwindows to eliminate clumps smaller than 10 pixels, or five acres in the area. The generalized images were then stitched together using the Imagine mosaic process. Wetland basin information provided by the US Fish and Wildlife Service were overlaid with the resulting 8-class image to add wetland classes (temporary, seasonal, semipermanent, lake, river). Manual interpretation of the image to identify urban built up landcover was conducted by EarthSatellite Corp. These urban built-up areas were overlaid with the landcover image and combined with the barren landcover class. An accuracy assessment was conducted upon the completion of the classification process for each scene. Ground data collected on 954,865 acres was partitioned into 2 data sets, one was used as training data and the remaining 50 percent was used for assessment of classification accuracy. An assessment of accuracy was conducted both on individual scenes and on a final mosaiked image. Imagine software was used to stitch all 16 scenes together into one image using the mosaic command. Users accuracy for individual scenes for both the grassland and undisturbed grassland was used to determine the order of overlay in the mosaiked image. Classification accuracy for individual scenes ranged from 80-92% and an assessment of the mosaiked image indicated an accuracy more than 80 percent.
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spatial and attribute data
publication date
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email contact
unix
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hardcopy
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code representing 1 of 16 classified landcovers
these codes are specific to resource issues in the prairies concerning nesting waterfowl. A crosswalk to NVCS has been completed and is available from the HAPET Office.
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
Landcover entities are identified through attributes for image cell values. A single landcover type is assigned as an attribute to each image cell. The SDTS model of spatial phenomena describes the real world as consisting of entities which are characterized by attributes which have attribute values. Imagine raster images do not explicitly use this entity-attribute-attribute value model.
Items, Codes, and Description of each code
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None ------------------------------------------------------------------------ Generated by mp <http: geology.usgs.gov tools metadata tools doc mp.html> version 2.8.21 on Wed Mar 23 11:07:41 2005