SDE Raster Dataset
Tags
SD, ND, Montana, US, Base Maps, USGS, South Dakota, Earth Cover, Land Cover, digital spatial data, GIS, zone 30, United States, North Dakota, MT, U.S., imagery, U.S. Geological Survey
The National Land Cover Database 2001 land cover layer for mapping zone 30 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. This landcover map and all documents pertaining to it are considered "provisional" until a formal accuracy assessment can be conducted. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2004) and http://www.mrlc.gov/mrlc2k.asp. 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 30 encompasses whole or portions of several states, including the states of Montana, North Dakota, and South Dakota. Questions about the NLCD mapping zone 30 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.
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.
U.S. Geological Survey, Rocky Mountain Geographic Science Center (RMGSC)
There are no access and use limitations for this item.
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.
U.S. Geological Survey, Rocky Mountain Geographic Science Center (RMGSC)
ground condition
0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT)
The USGS point of contact is for questions relating to the data display and download from this web site. For questions regarding data content and quality, refer to: http://www.mrlc.gov/mrlc2k.asp or email: mrlc@usgs.gov
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.
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The NLCD 2001 database for mapping zone 30 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.
This NLCD product of mapping zone 30 Land Cover layer is the version dated 11/29/2006.
The information on data quality for mapping zone 30 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 zone 30 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 zone 30 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.
The above listed value is the overall accuracy obtained for the land cover data using a cross-validation estimate from the decision tree model. This document and the described landcover map are considered "provisional" until a formal accuracy assessment is completed. The U.S. Geological Survey can make no guarantee as to the accuracy or completeness of this information, and it is provided with the understanding that it is not guaranteed to be correct or complete. Conclusions drawn from this information are the responsibility of the user.
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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 30, training data were collected from several combined sources including ancillary land cover maps such as USGS Multi-resolution Land Cover (MRLC) 1992 datasets for Montana, North Dakota, and South Dakota, USGS GAP datasets for Montana, North Dakota, and South Dakota, USGS Digital Orthophoto Quarter Quadrangles (DOQQs), USDA ? National Agriculture Statistics Services (NASS) 1:100,000-scale 2000 - 2001 Cropland Data Layer, A Crop-Specific Digital Data Layer for North Dakota website: (http://www.nass.usda.gov/research/Cropland/SARS1a.htm), and USDA National Agriculture Imagery Program (NAIP) Compressed County Mosaic images website (http://www.fsa.usda.gov/FSA/apfoapp?area=home&subject=prog&topic=nai) Land cover classes from ancillary land cover map datasets were cross-walked to NLCD 2001 equivalent classification codes prior to use. 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. The resulting land cover image was split into separate images for each class. A convolve model was used on each single-class image to create separation between classes reducing the probability that training points over transitional pixels would be selected. Convolved single-class images were then recombined to create the final image for training point sampling. Training points were generated using the ERDAS Imagine Classifier module Accuracy Assessment tool. 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. The ERDAS NLCD Tool module and the utility Convert Pixels to ASCII were used interchangeably to generate independent variable values for use in the .data file. Once an initial classification was completed, a number of subsequent iterations were necessary to improve the classification result. A series of PERL scripts specifically written for this project were used to make adjustments to the C5 .data file as required to generate an acceptable map. 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 30 are as follows: SPRING- Index 1 for Path 32/Row 28 on 05/02/00 = Scene_ID 7032028000012350 Index 1 for Path 32/Row 29 on 05/02/00 = Scene_ID 7032029000012350 Index 2 for Path 33/Row 27 on 05/12/01 = Scene_ID 7033027000113250 Index 3 for Path 33/Row 28 on 05/01/00 = Scene_ID 5033028000012210 Index 3 for Path 33/Row 29 on 05/01/00 = Scene_ID 5033029000012210 Index 4 for Path 34/Row 26 on 04/30/00 = Scene_ID 7034026000012150 Index 4 for Path 34/Row 27 on 04/30/00 = Scene_ID 7034027000012150 Index 5 for Path 34/Row 28 on 04/25/01 = Scene_ID 5034028000111510 Index 5 for Path 34/Row 29 on 04/25/01 = Scene_ID 5034029000111510 Index 6 for Path 35/Row 26 on 05/13/02 = Scene_ID 7035026000213350 Index 6 for Path 35/Row 27 on 05/13/02 = Scene_ID 7035027000213350 Index 7 for Path 36/Row 26 on 04/20/00 = Scene_ID 5036026000011110 Index 8 for Path 36/Row 27 on 03/27/00 = Scene_ID 7036027000008750 Index 9 for Path 36/Row 28 on 05/17/01 = Scene_ID 7036028000113750 LEAF ON (Summer)- Index 1 for Path 32/Row 28 on 08/25/01 = Scene_ID 7032028000123750 Index 1 for Path 32/Row 29 on 08/25/01 = Scene_ID 7032029000123750 Index 2 for Path 33/Row 27 on 07/12/00 = Scene_ID 7033027000019450 Index 2 for Path 33/Row 28 on 07/12/00 = Scene_ID 7033028000019450 Index 2 for Path 33/Row 29 on 07/12/00 = Scene_ID 7033029000019450 Index 3 for Path 34/Row 27 on 07/06/01 = Scene_ID 7034027000118750 Index 4 for Path 34/Row 28 on 07/22/01 = Scene_ID 7034028000120350 Index 5 for Path 34/Row 29 on 07/09/02 = Scene_ID 7034029000219050 Index 6 for Path 35/Row 27 on 06/30/02 = Scene_ID 7035027000218150 Index 7 for Path 36/Row 27 on 08/18/00 = Scene_ID 7036027000023150 Index 8 for Path 36/Row 27 on 08/19/03 = Scene_ID 5036027000323110 Index 8 for Path 36/Row 28 on 08/19/03 = Scene_ID 5036028000323110 LEAF-OFF (Fall)- Index 1 for Path 32/Row 28 on 09/26/01 = Scene_ID 7032028000126950 Index 2 for Path 32/Row 29 on 09/05/02 = Scene_ID 5032029000224810 Index 3 for Path 33/Row 27 on 09/14/00 = Scene_ID 7033027000025850 Index 4 for Path 33/Row 28 on 09/25/01 = Scene_ID 5033028000126810 Index 3 for Path 33/Row 29 on 09/14/00 = Scene_ID 7033029000025850 Index 5 for Path 34/Row 26 on 10/23/00 = Scene_ID 7034026000029750 Index 6 for Path 34/Row 27 on 10/07/00 = Scene_ID 7034027000028150 Index 6 for Path 34/Row 28 on 10/07/00 = Scene_ID 7034028000028150 Index 7 for Path 34/Row 29 on 10/23/00 = Scene_ID 7034029000029750 Index 8 for Path 35/Row 26 on 11/02/01 = Scene_ID 7035026000130650 Index 9 for Path 35/Row 27 on 10/01/01 = Scene_ID 7035027000127450 Index10 for Path 36/Row 26 on 09/17/99 = Scene_ID 7036026009926050 Index11 for Path 36/Row 27 on 09/30/01 = Scene_ID 5036027000127310 Index12 for Path 36/Row 28 on 09/22/01 = Scene_ID 7036028000126550 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.
0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT)
0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT)
The USGS point of contact is for questions relating to the data display and download from this web site. Questions about the NLCD mapping zone 30 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.
Variable
Contact Customer Services
NLDC Land Cover Layer
National Land Cover Database 2001
Internal feature number
ESRI
Sequential unique whole numbers that are automatically generated.
A nominal integer value that designates the number of pixels that have each value in the file; histogram column in ERDAS Imagine raster attributes table
NLCD 2001
Integer
Land Cover Class Code Value. Class definitions marked with an asterisk (*) are Coastal NLCD Classes only.
NLCD 2001
Attributes defined by USGS and ESRI Value Class Name 0 Background 1 Open Water 2 Developed, Open Space 3 Developed, Low Intensity 4 Developed, Medium Intensity 5 Developed, High Intensity 6 Barren Land (Rock/Sand/Clay) 7 Deciduoud Forest 8 Evergreen Forest 9 Mixed Forest 10 Shrubland 11 Grassland/Herbaceous 12 Pasture/Hay 13 Cultivated Crops 14 Woody Wetlands 15 Emergent Herbaceous Wetlands
Attribute accuracy is described, where present, with each attribute defined in the Entity and Attribute Section. Note: To ensure all areas of mapping zone 30 are completely covered, a 3,000 meter (100 Landsat pixels) buffer was added to the boundary of mapping zone 30.
0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT)
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