The NLCD 2001 is created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. Mapping zone 32 encompasses whole or portions of several states, including the states of Texas, Oklahoma, and Kansas. Mapping zone 35 encompasses whole or portions of several states, including the state of Texas. Mapping zone 36 encompasses whole or portions of several states, including the states of Texas. Questions about NLCD mapping zones 32, 35, 36, and 37A can be directed to the NLCD 2001 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
Zone 35 Corner Coordinates (center of pixel, projection meters) Upper Left Corner: 514320.0 meters(X), 1182330.0 meters(Y) Lower Right Corner: -69060.0 meters(X), 651270.0 meters(Y) Spatial-specific information not available
Zone 36 Corner Coordinates (center of pixel, projection meters)Upper Left Corner: -471810.00 meters(X), 940260.00 meters(Y)Lower Right Corner: 132900.00 meters(X), 305670.00 meters(Y)Spatial-specific information not available
Zone 37A Corner Coordinates (center of pixel, projection meters) Upper Left Corner: -45060.0 meters(X), 1308930.0 meters(Y) Lower Right Corner: -420360.0 meters(X), 689850.0 meters(Y) Spatial-specific information not available
U.S. Geological Survey, 20061214 (zone 32); 20030901 (zone 35); 20030901 (zone 36); 20030901 (zone 37A), SDERaster.DBO.TX_NLCD_LCOVER.Online Links:
References:Homer, C., C. Huang, L. Yang, B. Wylie and M. Coan, 2004. Development of a 2001 national land cover database for the United States. Photogrammetric Engineering and Remote Sensing Vol.70,No.7,pp 829-840 or online at www.mrlc.gov/publications.The USGS acknowledges the support of Rocky Mountain Geographic Science Center (RMGSC), NLCD Land Cover Team in development of data in this zone.
References:Homer, C., C. Huang, L. Yang, B. Wylie and M. Coan, 2004. Development of a 2001 national land cover database for the United States. Photogrammetric Engineering and Remote Sensing Vol.70,No.7,pp 829-840 or online at www.mrlc.gov/publications.The USGS acknowledges the support of NOAA and Sanborn in development of data in this zone.
References:Homer, C., C. Huang, L. Yang, B. Wylie and M. Coan, 2004. Development of a 2001 national land cover database for the United States. Photogrammetric Engineering and Remote Sensing Vol.70,No.7,pp 829-840 or online at www.mrlc.gov/publications.The USGS acknowledges the support of USGS EROS NLCD Mapping Team in development of data in this zone.
This is a Raster data set. It contains the following vector data types (SDTS terminology):
Planar coordinates are encoded using row and column
Abscissae (x-coordinates) are specified to the nearest 30.000000
Ordinates (y-coordinates) are specified to the nearest 30.000000
Planar coordinates are specified in meters
The horizontal datum used is North American Datum of 1983.
The ellipsoid used is Geodetic Reference System 80.
The semi-major axis of the ellipsoid used is 6378137.000000.
The flattening of the ellipsoid used is 1/298.257222.
Sequential unique whole numbers that are automatically generated.
Coordinates defining the features.
Sequential unique whole numbers that are automatically generated.
Integer
| Value | Definition |
|---|---|
| 1 | No data value, Alaska zones only |
| 11 | Open Water - All areas of open water, generally with less than 25% cover or vegetation or soil |
| 12 | Perennial Ice/Snow - All areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover. |
| 21 | Developed, Open Space - Includes areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20 percent of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes |
| 22 | Developed, Low Intensity -Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20-49 percent of total cover. These areas most commonly include single-family housing units. |
| 23 | Developed, Medium Intensity - Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50-79 percent of the total cover. These areas most commonly include single-family housing units. |
| 24 | Developed, High Intensity - Includes highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80 to100 percent of the total cover. |
| 31 | Barren Land (Rock/Sand/Clay) - Barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover. |
| 32 | Unconsolidated Shore* - Unconsolidated material such as silt, sand, or gravel that is subject to inundation and redistribution due to the action of water. Characterized by substrates lacking vegetation except for pioneering plants that become established during brief periods when growing conditions are favorable. Erosion and deposition by waves and currents produce a number of landforms representing this class. |
| 41 | Deciduous Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species shed foliage simultaneously in response to seasonal change. |
| 42 | Evergreen Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species maintain their leaves all year. Canopy is never without green foliage. |
| 43 | Mixed Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75 percent of total tree cover. |
| 51 | Dwarf Scrub - Alaska only areas dominated by shrubs less than 20 centimeters tall with shrub canopy typically greater than 20% of total vegetation. This type is often co-associated with grasses, sedges, herbs, and non-vascular vegetation. |
| 52 | Shrub/Scrub - Areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions. |
| 71 | Grassland/Herbaceous - Areas dominated by grammanoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling, but can be utilized for grazing. |
| 72 | Sedge/Herbaceous - Alaska only areas dominated by sedges and forbs, generally greater than 80% of total vegetation. This type can occur with significant other grasses or other grass like plants, and includes sedge tundra, and sedge tussock tundra. |
| 73 | Lichens - Alaska only areas dominated by fruticose or foliose lichens generally greater than 80% of total vegetation. |
| 74 | Moss- Alaska only areas dominated by mosses, generally greater than 80% of total vegetation. |
| 81 | Pasture/Hay - Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20 percent of total vegetation. |
| 82 | Cultivated Crops - Areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20 percent of total vegetation. This class also includes all land being actively tilled. |
| 90 | Woody Wetlands - Areas where forest or shrub land vegetation accounts for greater than 20 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. |
| 91 | Palustrine Forested Wetland* -Includes all tidal and non-tidal wetlands dominated by woody vegetation greater than or equal to 5 meters in height and all such wetlands that occur in tidal areas in which salinity due to ocean-derived salts is below 0.5 percent. Total vegetation coverage is greater than 20 percent. |
| 92 | Palustrine Scrub/Shrub Wetland* - Includes all tidal and non-tidal wetlands dominated by woody vegetation less than 5 meters in height, and all such wetlands that occur in tidal areas in which salinity due to ocean-derived salts is below 0.5 percent. Total vegetation coverage is greater than 20 percent. The species present could be true shrubs, young trees and shrubs or trees that are small or stunted due to environmental conditions. |
| 93 | Estuarine Forested Wetland* - Includes all tidal wetlands dominated by woody vegetation greater than or equal to 5 meters in height, and all such wetlands that occur in tidal areas in which salinity due to ocean-derived salts is equal to or greater than 0.5 percent. Total vegetation coverage is greater than 20 percent. |
| 94 | Estuarine Scrub/Shrub Wetland* - Includes all tidal wetlands dominated by woody vegetation less than 5 meters in height, and all such wetlands that occur in tidal areas in which salinity due to ocean-derived salts is equal to or greater than 0.5 percent. Total vegetation coverage is greater than 20 percent. |
| 95 | Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for greater than 80 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. |
| 96 | Palustrine Emergent Wetland (Persistent)* - Includes all tidal and non-tidal wetlands dominated by persistent emergent vascular plants, emergent mosses or lichens, and all such wetlands that occur in tidal areas in which salinity due to ocean-derived salts is below 0.5 percent. Plants generally remain standing until the next growing season. |
| 97 | Estuarine Emergent Wetland* - Includes all tidal wetlands dominated by erect, rooted, herbaceous hydrophytes (excluding mosses and lichens) and all such wetlands that occur in tidal areas in which salinity due to ocean-derived salts is equal to or greater than 0.5 percent and that are present for most of the growing season in most years. Perennial plants usually dominate these wetlands. |
| 98 | Palustrine Aquatic Bed* - The Palustrine Aquatic Bed class includes tidal and nontidal wetlands and deepwater habitats in which salinity due to ocean-derived salts is below 0.5 percent and which are dominated by plants that grow and form a continuous cover principally on or at the surface of the water. These include algal mats, detached floating mats, and rooted vascular plant assemblages. |
| 99 | Estuarine Aquatic Bed* - Includes tidal wetlands and deepwater habitats in which salinity due to ocean-derived salts is equal to or greater than 0.5 percent and which are dominated by plants that grow and form a continuous cover principally on or at the surface of the water. These include algal mats, kelp beds, and rooted vascular plant assemblages. |
| Range of values | |
|---|---|
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| Maximum: | 100 |
| Units: | CSS Color Value Percentage |
| Resolution: | 0.1 |
| Range of values | |
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| Minimum: | 0 |
| Maximum: | 100 |
| Units: | CSS Color Value Percentage |
| Resolution: | 0.1 |
| Range of values | |
|---|---|
| Minimum: | 0 |
| Maximum: | 100 |
| Units: | CSS Color Value Percentage |
| Resolution: | 0.1 |
| Range of values | |
|---|---|
| Minimum: | 0 |
| Maximum: | 100 |
| Units: | Percentage |
| Resolution: | 0.1 |
U.S. Geological Survey
605-594-6151 (voice)
605-594-6589 (FAX)
custserv@usgs.gov
The goal of this project is to provide the Nation with complete, current and consistent public domain information on its land use and land cover.
The land cover classification was achieved by use of a classification and decision tree method (DT) using a combination of Landsat imagery and ancillary data. The specific DT program employed is called C5, which implements a gain ratio criterion in tree development and pruning (Quinlan, 1993). C5 also implemented several advanced features that can aid and improve land cover classification, including boosting and cross-validation. Boosting is a technique for improving classification accuracy, while cross-validation can provide certain level of estimation regarding the land cover classification quality. In addition, C5 can generate a confidence estimate for each classified pixel and record the associated classification logic in a text file that can be readily interpreted and incorporated into a metadata system.
To conduct the land cover classification using DT, a large quantity of training data is required. For mapping zone 32 training data were collected from several combined sources including ancillary land cover maps such as USGS Multi-resolution Land Cover (MRLC) 1992 maps for Texas, Oklahoma, and Kansas, GAP Regional Land Cover maps for Oklahoma and Kansas, numerous USGS Digital Orthophoto Quarter Quadrangles (DOQQs), and 2004 CIR DOQQs from the USDA National Agricultural Imagery Program (NAIP). Land cover classes from ancillary land cover map datasets were cross-walked to NLCD 2001 equivalent classification codes prior to use. A unique source image for random sampling of training points was created from multiple datasets (two or more) to take advantage of previous land cover mapping efforts. ERDAS Imagine models were designed to intersect map images to determine the spatial extent of areas where two or more existing maps agreed on the land cover classification. Classes were randomly sampled on an individual basis in a proportion roughly approximating the percentage of pixels of the sample class in the training image.
Note that the training data were used to map all land cover classes except for four classes in urban and sub-urban areas (developed open space, low intensity developed, medium intensity developed, high intensity developed). All urban and suburban land cover classes were mapped and quality assessed separately through a sub-pixel quantification of impervious surfaces using a regression tree modeling method.
Following the development of the best classification through decision tree modeling, additional steps were required to complete the final land cover product. The extra steps used in the final classification were a combination of models created in ERDAS Imagine and manual edits. The four classes in urban and suburban areas were determined from the percent impervious mapping product (described in the next section). The threshold for the four classes is: (1) developed open space (imperviousness < 20%), (2) low-intensity developed (imperviousness from 20 - 49%), (3) medium intensity developed (imperviousness from 50 -79%), and (4) high-intensity developed (imperviousness > 79%). Other classes of forest and non-forest were combined with the urban classes to complete the land cover product. Finally visual inspection of the classification was made with areas/pixels that were wrongly classified delineated first as an "area of interest" (AOI), subsequently then limited manual editing performed to eliminate the classification error within the AOI. The completed single pixel product was then generalized to a 1 acre (approximately 5 ETM+ 30 m pixel patch) minimum mapping unit product using a "smart eliminate" algorithm. This aggregation program subsumes pixels from the single pixel level to a 5-pixel patch using a queens algorithm at doubling intervals. The algorithm consults a weighting matrix to guide merging of cover types by similarity, resulting in a product that preserves land cover logic as much as possible.
Acquisition dates of Landsat ETM+ (TM) scenes used for land cover classification in zone 32 are as follows:
SPRING- Index 1 for Path 26/Row 35 on 02/23/02 = Scene_ID 7026035000205450 Index 1 for Path 26/Row 36 on 02/23/02 = Scene_ID 7026036000205450 Index 1 for Path 26/Row 37 on 02/23/02 = Scene_ID 7026037000205450 Index 1 for Path 26/Row 38 on 02/23/02 = Scene_ID 7026038000205450 Index 4 for Path 27/Row 34 on 04/24/01 = Scene_ID 5027034000111410 Index 3 for Path 27/Row 35 on 04/03/02 = Scene_ID 7027035000209350 Index 3 for Path 27/Row 36 on 04/03/02 = Scene_ID 7027036000209350 Index 3 for Path 27/Row 37 on 04/03/02 = Scene_ID 7027037000209350 Index 2 for Path 27/Row 38 on 01/13/02 = Scene_ID 7027038000201350 Index 5 for Path 28/Row 35 on 03/09/02 = Scene_ID 7028035000206850 Index 5 for Path 28/Row 36 on 03/09/02 = Scene_ID 7028036000206850 Index 6 for Path 28/Row 37 on 02/02/01 = Scene_ID 7028037000103350 Index 7 for Path 29/Row 35 on 04/01/02 = Scene_ID 7029035000209150 LEAF ON (Summer)- Index 3 for Path 26/Row 35 on 06/12/01 = Scene_ID 7026035000116350 Index 4 for Path 26/Row 36 on 06/15/02 = Scene_ID 7026036000216650 Index 4 for Path 26/Row 37 on 06/15/02 = Scene_ID 7026037000216650 Index 5 for Path 26/Row 38 on 07/19/00 = Scene_ID 5026038000020110 Index 1 for Path 27/Row 34 on 06/22/02 = Scene_ID 7027034000217350 Index 1 for Path 27/Row 35 on 06/22/02 = Scene_ID 7027035000217350 Index 1 for Path 27/Row 36 on 06/22/02 = Scene_ID 7027036000217350 Index 9 for Path 27/Row 36 on 07/21/01 = Scene_ID 7027036000120250 Index 2 for Path 27/Row 37 on 05/21/02 = Scene_ID 7027037000214150 Index 2 for Path 27/Row 38 on 05/21/02 = Scene_ID 7027038000214150 Index 6 for Path 28/Row 35 on 06/02/01 = Scene_ID 5028035000115310 Index 7 for Path 28/Row 36 on 06/10/01 = Scene_ID 7028036000116150 Index 7 for Path 28/Row 37 on 06/10/01 = Scene_ID 7028037000116150 Index 8 for Path 29/Row 35 on 05/19/02 = Scene_ID 7029035000213950
LEAF-OFF (Fall)- Index 6 for Path 26/Row 35 on 10/13/99 = Scene_ID 7026035009928650 Index 6 for Path 26/Row 36 on 10/13/99 = Scene_ID 7026036009928650 Index 6 for Path 26/Row 37 on 10/13/99 = Scene_ID 7026037009928650 Index 7 for Path 26/Row 38 on 09/29/00 = Scene_ID 7026038000027350 Index 1 for Path 27/Row 34 on 10/25/01 = Scene_ID 7027034000129850 Index 1 for Path 27/Row 35 on 10/25/01 = Scene_ID 7027035000129850 Index 2 for Path 27/Row 36 on 10/20/99 = Scene_ID 7027036009929350 Index 1 for Path 27/Row 37 on 10/25/01 = Scene_ID 7027037000129850 Index 1 for Path 27/Row 38 on 10/25/01 = Scene_ID 7027038000129850 Index 3 for Path 28/Row 35 on 10/16/01 = Scene_ID 7028035000128950 Index 3 for Path 28/Row 36 on 10/16/01 = Scene_ID 7028036000128950 Index 4 for Path 28/Row 37 on 09/30/01 = Scene_ID 7028037000127350 Index 5 for Path 29/Row 35 on 10/23/01 = Scene_ID 7029035000129650
Landsat data and ancillary data used for the land cover prediction - -Data Type of DEM composed of 1 band of Continuous Variable Type. -Data Type of Slope composed of 1 band of Continuous Variable Type. -Data Type of Aspect composed of 1 band of Categorical Variable Type. -Data type of Position Index composed of 1 band of Continuous Variable Type.
Person who carried out this activity:
605-594-6151 (voice)
605-594-6589 (FAX)
custserv@usgs.gov
Data sources produced in this process:
To conduct the land cover classification using DT, a large quantity of training data is required. For mapping zone 35, training data were collected from several combined sources including the GAP and NLCD '92 projects.
Note that the training data were used to map all land cover classes except for four classes in urban and sub-urban areas (developed open space, low intensity developed, medium intensity developed, high intensity developed). All urban and suburban land cover classes were mapped and quality assessed separately through a sub-pixel quantification of impervious surfaces using a regression tree modeling method.
Following the development of the best classification through decision tree modeling, additional steps were required to complete the final land cover product. The four classes in urban and suburban areas were determined from the percent imperviousness mapping product (described in the next section). The threshold for the four classes is: (1) developed open space (imperviousness < 20%), (2) low-intensity developed (imperviousness from 20 - 49%), (3) medium intensity developed (imperviousness from 50 -79%), and (4) high-intensity developed (imperviousness > 79%). Other classes of forest and non-forest were combined with the urban classes to complete the land cover product. Finally visual inspection of the classification was made with areas/pixels that were wrongly classified delineated first as an "area of interest" (AOI), subsequently then limited manual editing was done to eliminate the classification error within the AOI.
The completed single pixel product was then generalized to a 1 acre (approximately 5 ETM+ 30 m pixel patch) minimum mapping unit product using a "smart eliminate" algorithm. This aggregation program subsumes pixels from the single pixel level to a 5-pixel patch using a queens algorithm at doubling intervals. The algorithm consults a weighting matrix to guide merging of cover types by similarity, resulting in a product that preserves land cover logic as much as possible.Acquisition dates of Landsat ETM+ (TM) scenes used for land cover classification in zone 35 are as follows: SPRING- Index 5 for Path 27/Row 37 on 03/15/01 = Scene_ID 7027037000107450 Index 4 for Path 27/Row 38 on 01/13/02 = Scene_ID 7027038000201350
Index 4 for Path 27/Row 39 on 01/13/02 = Scene_ID 7027039000201350 Index 3 for Path 27/Row 40 on 02/03/01 = Scene_ID 5027040000103410 Index 2 for Path 28/Row 37 on 02/02/01 = Scene_ID 7028037000103350 Index 1 for Path 28/Row 38 on 03/09/02 = Scene_ID 7028038000206850 Index 1 for Path 28/Row 39 on 03/09/02 = Scene_ID 7028039000206850 Index 1 for Path 28/Row 40 on 03/09/02 = Scene_ID 7028040000206850 Index 6 for Path 29/Row 37 on 04/01/02 = Scene_ID 7029037000209150 Index 8 for Path 29/Row 38 on 02/12/02 = Scene_ID 7029038000204350 Index 7 for Path 29/Row 39 on 02/25/01 = Scene_ID 7029039000105650 Index 7 for Path 29/Row 40 on 02/25/01 = Scene_ID 7029040000105650 LEAF ON (Summer)- Index 3 for Path 27/Row 37 on 05/21/02 = Scene_ID 7027037000214150 Index 3 for Path 27/Row 38 on 05/21/02 = Scene_ID 7027038000214150 Index 4 for Path 27/Row 39 on 07/21/01 = Scene_ID 7027039000120250 Index 4 for Path 27/Row 40 on 07/21/01 = Scene_ID 7027040000120250 Index 1 for Path 28/Row 37 on 06/10/01 = Scene_ID 7028037000116150 Index 2 for Path 28/Row 38 on 06/13/02 = Scene_ID 7028038000216450 Index 2 for Path 28/Row 39 on 06/13/02 = Scene_ID 7028039000216450 Index 2 for Path 28/Row 40 on 06/13/02 = Scene_ID 7028040000216450 Index 5 for Path 29/Row 37 on 05/19/02 = Scene_ID 7029037000213950 Index 5 for Path 29/Row 38 on 05/19/02 = Scene_ID 7029038000213950 Index 5 for Path 29/Row 39 on 05/19/02 = Scene_ID 7029039000213950 Index 6 for Path 29/Row 40 on 05/03/02 = Scene_ID 7029040000212350 LEAF-OFF (Fall)- Index 1 for Path 27/Row 37 on 10/25/01 = Scene_ID 7027037000129850 Index 1 for Path 27/Row 38 on 10/25/01 = Scene_ID 7027038000129850 Index 1 for Path 27/Row 39 on 10/25/01 = Scene_ID 7027039000129850 Index 2 for Path 27/Row 40 on 10/17/01 = Scene_ID 5027040000129010 Index 3 for Path 28/Row 37 on 09/30/01 = Scene_ID 7028037000127350 Index 4 for Path 28/Row 38 on 10/16/01 = Scene_ID 7028038000128950 Index 4 for Path 28/Row 39 on 10/16/01 = Scene_ID 7028039000128950 Index 4 for Path 28/Row 40 on 10/16/01 = Scene_ID 7028040000128950 Index 5 for Path 29/Row 37 on 10/23/01 = Scene_ID 7029037000129650 Index 6 for Path 29/Row 38 on 10/07/01 = Scene_ID 7029038000128050 Index 7 for Path 29/Row 39 on 09/24/02 = Scene_ID 7029039000226750 Index 8 for Path 29/Row 40 on 10/04/00 = Scene_ID 7029040000027850
Landsat data and ancillary data used for the land cover prediction -
Data Type of DEM composed of 1 band of Continuous Variable Type.
Data Type of Slope composed of 1 band of Continuous Variable Type.
Data Type of Aspect composed of 1 band of Categorical Variable Type.
Data type of Position Index composed of 1 band of Continuous Variable Type.
Person who carried out this activity:
605-594-6151 (voice)
605-594-6589 (FAX)
custserv@usgs.gov
Data sources produced in this process:
Acquisition dates of Landsat ETM+ (TM) scenes used for land cover classification in zone 36 are as follows:SPRING- Index 1 for Path 25/Row 39 on 01/15/02 = Scene_ID 7025039000201550 Index 1 for Path 25/Row 40 on 01/15/02 = Scene_ID 7025040000201550 Index 2 for Path 26/Row 38 on 02/23/02 = Scene_ID 7026038000205450 Index 2 for Path 26/Row 39 on 02/23/02 = Scene_ID 7026039000205450 Index 2 for Path 26/Row 40 on 02/23/02 = Scene_ID 7026040000205450 Index 2 for Path 26/Row 41 on 02/23/02 = Scene_ID 7026041000205450 Index 3 for Path 26/Row 42 on 01/06/02 = Scene_ID 7026042000200650 Index 4 for Path 27/Row 39 on 01/13/02 = Scene_ID 7027039000201350 Index 5 for Path 27/Row 40 on 02/03/01 = Scene_ID 5027040000103410 Index 6 for Path 27/Row 41 on 03/15/01 = Scene_ID 7027041000107450 Index 7 for Path 27/Row 42 on 02/17/03 = Scene_ID 7027042000304850 Index 9 for Path 28/Row 40 on 03/09/02 = Scene_ID 7028040000206850 Index 8 for Path 28/Row 41 on 02/21/02 = Scene_ID 7028041000205250 Index10 for Path 29/Row 40 on 02/25/01 = Scene_ID 7029040000105650 LEAF ON (Summer)- Index 4 for Path 25/Row 39 on 08/03/02 = Scene_ID 5025039000221510 Index 4 for Path 25/Row 40 on 08/03/02 = Scene_ID 5025040000221510 Index 3 for Path 26/Row 38 on 05/14/02 = Scene_ID 7026038000213450 Index 1 for Path 26/Row 39 on 07/22/01 = Scene_ID 5026039000120310 Index 1 for Path 26/Row 40 on 07/22/01 = Scene_ID 5026040000120310 Index 1 for Path 26/Row 41 on 07/22/01 = Scene_ID 5026041000120310 Index 2 for Path 26/Row 42 on 06/15/02 = Scene_ID 7026042000216650 Index 5 for Path 27/Row 39 on 07/21/01 = Scene_ID 7027039000120250 Index 5 for Path 27/Row 40 on 07/21/01 = Scene_ID 7027040000120250 Index 5 for Path 27/Row 41 on 07/21/01 = Scene_ID 7027041000120250 Index 5 for Path 27/Row 42 on 07/21/01 = Scene_ID 7027042000120250 Index 6 for Path 28/Row 40 on 07/12/01 = Scene_ID 7028040000119350 Index 6 for Path 28/Row 41 on 07/12/01 = Scene_ID 7028041000119350 Index 7 for Path 29/Row 40 on 05/03/02 = Scene_ID 7029040000212350 LEAF-OFF (Fall)- Index 3 for Path 25/Row 39 on 10/06/99 = Scene_ID 7025039009927950 Index 3 for Path 25/Row 40 on 10/06/99 = Scene_ID 7025040009927950 Index 1 for Path 26/Row 38 on 09/29/00 = Scene_ID 7026038000027350 Index 1 for Path 26/Row 39 on 09/29/00 = Scene_ID 7026039000027350 Index 1 for Path 26/Row 40 on 09/29/00 = Scene_ID 7026040000027350 Index 1 for Path 26/Row 41 on 09/29/00 = Scene_ID 7026041000027350 Index 2 for Path 26/Row 42 on 09/27/02 = Scene_ID 5026042000227010 Index 4 for Path 27/Row 39 on 10/25/01 = Scene_ID 7027039000129850 Index 5 for Path 27/Row 40 on 09/04/00 = Scene_ID 7027040000024850 Index 5 for Path 27/Row 41 on 09/04/00 = Scene_ID 7027041000024850 Index 6 for Path 27/Row 42 on 09/07/01 = Scene_ID 7027042000125050 Index10 for Path 28/Row 40 on 11/20/02 = Scene_ID 7028040000232450 Index 9 for Path 28/Row 40 on 10/16/01 = Scene_ID 7028040000128950 Index 8 for Path 28/Row 41 on 11/01/01 = Scene_ID 7028041000130550 Index 7 for Path 29/Row 40 on 09/18/00 = Scene_ID 7029040000026250
Field-Collected Data-The goals of the field data collection were to sample thediversity of the landscape, within the classes, and amongimage dates. Classes that would be more difficult tocollect from air photos were targeted for field datacollection. To meet these goals, Sanborn stratified theimage into spectral clusters and located the field sitesthroughout the study area based on these. In addition tothese pre-arranged sites, Sanborn collected points whiledriving between locations. Due to limited time andaccessibility, not all polygons were assessed in the field.Those that we did not visit on the ground were labeled withdigital orthophotographs or Ikonos data if it wasavailable. Both training and validation points werecollected together. See the accuracy assessment section tosee how the points were split into training and validationpoints. Sanborn used laptop computers and GPS (GlobalPositioning System) to correctly locate field points onthe TM imagery. Software downloaded from the Minnesota'sDepartment of Natural Resources (DNR) was used to connectthe Garmin GPS to the laptop computer and ESRI's ArcViewsoftware.Sanborn's programmer developed an ArcMap application thatallowed entry of location and field notes with a click of the mouse. These data were stored in a shapefile.Items that were collected were:Land Cover characterization Special conditions and remarks Photograph NumberDate/time location The data and equipment used for the fieldwork are asfollows:Ancillary datasets:TIGER 2000NLCD - mosaicked into zonesState road map and Delorme state atlas www.delorme.comHardware: with ArcView/ArcGIS and dataGARMIN GPS modules and external antennae, redundant datacablesCameras devices (Floppy Drives, CD Burners, external HDD)Extra batteries (lap-top and GPS)Mobile phonesSystem backup CD's with data and softwareCompass notebooks with instructions and road maps withpre-determined routes Wetland and Vegetation Field GuidesImagery:Image data for each zoneInitial classificationsClassification:After the field points for training were collected, theywere combined with photo-interpreted points and used asthe dependent variable in a CART classification approach.Many layers tested as independent layers. They includedthree dates of spectral and tasseled cap imagery, DEM,slope, aspect, texture, band indices (NDVI, Moisture,NDVI-Green), shape indices fractal dimension, compactness,convexity, and form), Census data (housing and populationdensity). Statistical analyses and visual inspection of the output was used to eliminate data that was redundant or not useful in the classification. Additional training points were added to help reduce some of the confusion between classes. The rough classification was created at the end ofthis process using only the CART discrete decision-treesoftware. A provisional classification was produced byapplying spatial models using ancillary data to the rough classification. The final automated map was then editedusing hand editing techniques while using high resolutionimagery from as reference data. Independently, of thisprocess, NOAA produced percent impervious data layers for Zone 36. This layer was developed from Regression Tree andused impervious classifications from IKONOS imagery topredict pixel level percent impervious at the TM pixellevel. The continuous percent impervious data wasthresholded to produce the developed categories andimbedded into the final map.Attributes for this product are as follows: 0 Background 1 Unclassified (Cloud, Shadow, etc) 2 High Intensity Developed 3 Medium Intensity Developed 4 Low Intensity Developed 5 Open Spaces Developed 6 Cultivated Land 7 Pasture/Hay 8 Grassland 9 Deciduous Forest 10 Evergreen Forest 11 Mixed Forest 12 Scrub/Shrub 13 Palustrine Forested Wetland 14 Palustrine Scrub/Shrub Wetland 15 Palustrine Emergent Wetland 16 Estuarine Forested Wetland 17 Estuarine Scrub/Shrub Wetland 18 Estuarine Emergent Wetland 19 Unconsolidated Shore 20 Bare Land 21 Water 22 Palustrine Aquatic Bed 23 Estuarine Aquatic Bed 24 Tundra (N/A) 25 Snow/Ice (N/A) Ancillary Datasets:Non-TM image datasets used are DEM (Digital ElevationModel), slope, aspect, positional index, NWI, NLCD,TIGER2000, field-collected points, photo-interpretedpoints, zone 36 GAP reclassified by NOAA (Gap AnalysisProgram),Census data (housing and population density),Ecoregions.QA/QC Process:There were several QA/QC steps involved in the creation ofthis product. First, there was an internal QA/QC. This wasdone by viewing the classification frame-by-frame alongwith the TM imagery, the classification, and highresolution reference imagery. NOAA staff completed asimilar review and provided both general and point comments.
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The completed single pixel product was then generalized to a 1 acre (approximately5 ETM+ 30 m pixel patch) minimum mapping unit product using a "smart eliminate"algorithm. This aggregation program subsumes pixels from the single pixel level to a 5-pixel patch using a queens algorithm at doubling intervals. The algorithm consults aweighting matrix to guide merging of cover types by similarity, resulting in a product that preserves land cover logic as much as possible.
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Data sources produced in this process:
To conduct the land cover classification using DT, a large quantity of training data is required. For mapping zone 37A, training data were collected from several combined sources including the GAP and NLCD '92 projects.
Note that the training data were used to map all land cover classes except for four classes in urban and sub-urban areas (developed open space, low intensity developed, medium intensity developed, high intensity developed). All urban and suburban land cover classes were mapped and quality assessed separately through a sub-pixel quantification of impervious surfaces using a regression tree modeling method.
Following the development of the best classification through decision tree modeling, additional steps were required to complete the final land cover product. The four classes in urban and suburban areas were determined from the percent imperviousness mapping product (described in the next section). The threshold for the four classes is: (1) developed open space (imperviousness < 20%), (2) low-intensity developed (imperviousness from 20 - 49%), (3) medium intensity developed (imperviousness from 50 -79%), and (4) high-intensity developed (imperviousness > 79%). Other classes of forest and non-forest were combined with the urban classes to complete the land cover product. Finally visual inspection of the classification was made with areas/pixels that were wrongly classified delineated first as an "area of interest" (AOI), subsequently then limited manual editing was done to eliminate the classification error within the AOI.
The completed single pixel product was then generalized to a 1 acre (approximately 5 ETM+ 30 m pixel patch) minimum mapping unit product using a "smart eliminate" algorithm. This aggregation program subsumes pixels from the single pixel level to a 5-pixel patch using a queens algorithm at doubling intervals. The algorithm consults a weighting matrix to guide merging of cover types by similarity, resulting in a product that preserves land cover logic as much as possible.Acquisition dates of Landsat ETM+ (TM) scenes used for land cover classification in zone 37A are as follows: SPRING- Index 5 for Path 23/Row 38 on 02/21/00 = Scene_ID 5023038000005210 Index 3 for Path 24/Row 36 on 02/20/00 = Scene_ID 7024036000005150 Index 3 for Path 24/Row 37 on 02/20/00 = Scene_ID 7024037000005150 Index 3 for Path 24/Row 38 on 02/20/00 = Scene_ID 7024038000005150 Index 4 for Path 24/Row 39 on 01/08/02 = Scene_ID 7024039000200850 Index 2 for Path 24/Row 40 on 02/20/00 = Scene_ID 7024040000005150 Index 2 for Path 25/Row 37 on 01/10/00 = Scene_ID 7025037000001050 Index 2 for Path 25/Row 38 on 01/10/00 = Scene_ID 7025038000001050 Index 2 for Path 25/Row 40 on 01/10/00 = Scene_ID 7025040000001050 Index 1 for Path 26/Row 37 on 02/23/02 = Scene_ID 7026037000205450 Index 1 for Path 26/Row 38 on 02/23/02 = Scene_ID 7026038000205450 Index 1 for Path 26/Row 39 on 02/23/02 = Scene_ID 7026039000205450 LEAF ON (Summer)- Index 8 for Path 23/Row 38 on 05/14/01 = Scene_ID 5023038000113410 Index 7 for Path 24/Row 36 on 05/10/00 = Scene_ID 7024036000013150 Index 6 for Path 24/Row 37 on 04/08/00 = Scene_ID 7024037000009950 Index 6 for Path 24/Row 38 on 04/08/00 = Scene_ID 7024038000009950 Index 6 for Path 24/Row 39 on 04/08/00 = Scene_ID 7024039000009950 Index 4 for Path 25/Row 37 on 04/26/01 = Scene_ID 5025037000111610 Index 4 for Path 25/Row 38 on 04/26/01 = Scene_ID 5025038000111610 Index 4 for Path 25/Row 39 on 04/26/01 = Scene_ID 5025039000111610 Index 5 for Path 25/Row 40 on 05/07/99 = Scene_ID 5025040009912710 Index 1 for Path 26/Row 37 on 06/15/02 = Scene_ID 7026037000216650 Index 2 for Path 26/Row 38 on 05/14/02 = Scene_ID 7026038000213450 Index 3 for Path 26/Row 39 on 04/28/99 = Scene_ID 5026039009911810 LEAF-OFF (Fall)- Index 6 for Path 23/Row 38 on 10/24/99 = Scene_ID 7023038009929750 Index 5 for Path 24/Row 36 on 11/16/99 = Scene_ID 7024036009932050 Index 1 for Path 24/Row 37 on 10/15/99 = Scene_ID 7024037009928850 Index 5 for Path 24/Row 38 on 11/16/99 = Scene_ID 7024038009932050 Index 5 for Path 24/Row 39 on 11/16/99 = Scene_ID 7024039009932050 Index 2 for Path 25/Row 37 on 10/06/99 = Scene_ID 7025037009927950 Index 2 for Path 25/Row 38 on 10/06/99 = Scene_ID 7025038009927950 Index 2 for Path 25/Row 39 on 10/06/99 = Scene_ID 7025039009927950 Index 2 for Path 25/Row 40 on 10/06/99 = Scene_ID 7025040009927950 Index 4 for Path 26/Row 37 on 10/13/99 = Scene_ID 7026037009928650 Index 3 for Path 26/Row 38 on 09/29/00 = Scene_ID 7026038000027350 Index 3 for Path 26/Row 39 on 09/29/00 = Scene_ID 7026039000027350
Landsat data and ancillary data used for the land cover prediction -
Data Type of DEM composed of 1 band of Continuous Variable Type.
Data Type of Slope composed of 1 band of Continuous Variable Type.
Data Type of Aspect composed of 1 band of Categorical Variable Type.
Data type of Position Index composed of 1 band of Continuous Variable Type.
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Data sources produced in this process:
The information on data quality for mapping zones 32, 35, 36, and 37A was generated by the Decision Tree algorithm that conducts a cross-validation for assessing classification and prediction reliability. No formal independent accuracy assessment of mapping zones 32, 35, 36, and 37A land cover has been made. The regression tree algorithm employed in NLCD 2001 mapping offers a cross-validation option for assessing classification and prediction reliability. Cross-validation can provide relatively reliable estimates for land cover predictions if the reference data used for cross-validation are collected based on a statistically valid sampling design. For mapping zones 32, 35, 36, and 37A land cover modeling, a 5-fold cross-validation was conducted by dividing the entire training data set into 5 subsets of equal size. For each model run, an accuracy estimate was derived using one subset to evaluate the model prediction (with the model developed using the remaining training samples). This process was repeated 5 times. After all 5 runs, an average value of all accuracy estimates from the 5 runs were computed. Users should be cautioned that these cross-validation results provide users with only first-order estimates of data quality, and should not be considered a formal accuracy assessment. This landcover map and all documents pertaining to it are considered "provisional" until a formal accuracy assessment can be conducted.
According to accuracy assessment of zone 36 performed by Sanborn, the overall accuracy is 84.1% and 83.5% Kappa. The accuracyresults shown below are from US Gulf Coast zone 36 and zone 37A. A total of 1696 points are located in US Gulf Coastzone 36 and zone 37A. Each class accuracy is as follows:
(Errors of Omission/Commission) 0 Background (N/A) 1 Unclassified (Cloud, Shadow, etc)(N/A) 2 High Intensity Developed (48%/93%) 3 Medium Intensity Developed (69%/67%) 4 Low Intensity Developed (80%/48%) 5 Open Spaces Developed (36%/50%) 6 Cultivated Land (88%/85%) 7 Pasture/Hay (81%/86%) 8 Grassland (77%/75%) 9 Deciduous Forest (67%/69%) 10 Evergreen Forest (85%/89%) 11 Mixed Forest (70%/80%) 12 Scrub/Shrub (91%/86%) 13 Palustrine Forested Wetland (90%/84%) 14 Palustrine Scrub/Shrub Wetland (58%/66%) 15 Palustrine Emergent Wetland (76%/81%) 16 Estuarine Forested Wetland (N/A) 17 Estuarine Scrub/Shrub Wetland (0%/0%) 18 Estuarine Emergent Wetland (94%/85%) 19 Unconsolidated Shore (80%/85%) 20 Bare Land (66%/72%) 21 Water (94%/96%) 22 Palustrine Aquatic Bed (0%/0%) 23 Estuarine Aquatic Bed (0%/0%) 24 Tundra (N/A) 25 Snow/Ice (N/A) The validation points were both collected in the field and photo interpreted. The accuracy assessment selectionmethods were developed to minimize spatial autocorrelationbetween the training and accuracy assessment. The firstpool of accuracy assessment sites came from field data andphoto interpretation of black and white digital orthophotosand digital color infrared imagery (primarily Ikonos data).These sites were collected prior to initial mapping andwere collected at the same time as the training data. Thesites were selected to capture the physical and spectraldiversity of the land cover. After the first criteria wasmet, the accuracy assessment sites were buffered to see ifthey fell within 1000 meters of another accuracy assessment site of the same class or within 1000 meters of a trainingsite of the same class. Those that fell within the 1000meter buffer were eliminated. All sites were to be from a homogeneous 3x3 area.After an analysis of the point distribution, it becameclear that there were not enough samples for every class.The remaining points were selected from the initial draftfinal classification and had to be a homogeneous 3x3 area.Sampling was limited to areas where there was highresolution color infrared imagery. The imagery included theprevious Ikonos imagery, but also included an additional 20scenes of Ikonos imagery. The additional Ikonos imageryprovided sampling areas across the entire study area.Classes which had a proportionally larger representativeland area had larger numbers of samples . When possible, wetried to identify 50 samples of each of the classes.Exceptions were for the following classes (actual samplenumbers): Deciduous Forest (43)- Bare Land (31)- EstuarineScrub/Shrub Wetland (3)- Palustrine Aquatic Bed (10)-Estuarine Aquatic Bed (0). A total of 1696 accuracyassessment points were used excluding urban classes. 1833accuracy assessment points were used including urbanpoints. All classes have a minimum of 50 accuracyassessment points except for the classes mentioned above.These classes are limited in the study area and to some extent in the imagery that was available to sample from.
Post-Processing Steps: NoneKnown Problems: NoneSpatial Filters: None
NA
This NLCD product of mapping zone 32 Land Cover layer is the version dated December 14, 2006
This NLCD product of mapping zone 35 Land Cover layer is the version dated 120106.
This NLCD product of mapping zone 36 Land Cover layer is the version dated September 26,2006.
This NLCD product of mapping zone 37A Land Cover layer is the version dated 120106.
The NLCD 2001 database for mapping zones 32, 35, 36, and 37A consists of three main data products including: (1) per pixel classified land-cover data (2) sub-pixel percent imperviousness and (3) sub-pixel percent tree canopy density. The land-cover database also includes three additional metadata layers that provide users a spatial node map of the land cover classification. The three layers are: (a) a spatial node map of the land cover classification, (b) a spatial confidence map of the land cover classification, and, (c) a text file of logical statements related to the land cover classification.
Conceptually, the descriptive tree is a classification tree generated by using the final minimum-map- unit land cover product (1 acre) as training data, and Landsat and other ancillary data as predictors. The goal of the descriptive tree is to summarize the effects of boosted trees (10 sequential classification trees) into a single condensed decision tree that can be used as a diagnostic tool for the classification process. This descriptive tree can be used to assess the relative importance of each of the input data sets on each land cover class. Such information may also be useful to customize the minimum-mapping-unit classification to meet a user's specific needs through raster modeling. Descriptive trees usually capture 60 to 80% of the information from the original land cover data.
The leaf or terminal nodes of the descriptive tree are assigned to sequential numbers (called node numbers) and mapped across the entire mapping zone on a pixel-by-pixel basis. These node numbers can then be matched with the various conditional statements associated with each respective terminal node. This spatial layer appears similar to a cluster map, but is the result of a supervised classification - not an unsupervised clustering. This node map can potentially be used as input by users to customize NLCD land cover, by linking the spatial extent of an individual node with the rules of the conditional statement.
The Land Cover spatial classification confidence data layer is provided to users to help determine the per-pixel spatial confidence of the NLCD 2001 land cover prediction from the descriptive tree. The C5 algorithm produces an estimate (a value between 0% and 100%) that indicates the confidence of rule predictions at each node based on the training data. This spatial confidence map should be considered as only one indicator of relative reliability of the land cover classification, rather than a precise estimate. Users should be aware that this estimate is made based on only training data, and is derived from a generalized descriptive decision tree that reproduces the final land cover data. However, this layer provides valuable insight for a user to determine the risk or confidence they choose to place in each pixel of land cover.
A logic statement from a descriptive tree classification describes each classification rule for each classified pixel. An example of the logic statement follows:
IF tasseled-cap wetness > 140 and imperviousness = 0 and canopy density < 4, then classify as Water
This logic file can be used in combination with the spatial node map to identify classification logic and allow modifications of the classification based on user's knowledge and/or additional data sets.
Additional information may be found at <http://www.mrlc.gov/mrlc2k_nlcd.asp>.
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- Access_Constraints: None
- Use_Constraints: None
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Downloadable Data
Although these data have been processed successfully on a computer system at the USGS, no warranty expressed or implied is made by the USGS regarding the use of the data on any other system, nor does the act of distribution constitute any such warranty. Data may have been compiled from various outside sources. Spatial information may not meet National Map Accuracy Standards. This information may be updated without notification. The USGS shall not be liable for any activity involving these data, installation, fitness of the data for a particular purpose, its use, or analyses results.
| Data format: | Arc/Info Export Format and/or ArcView Shapefile (version ArcGIS 9.0) ASCII Size: zone 32--69.562, zone 35--0.001, zone 36--0.001, zone 37A--0.001 |
|---|---|
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<http://seamless.usgs.gov> |
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