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 03 encompasses whole or portions of several states, including the states of California. Questions about the NLCD mapping zone 03 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.
Post-Processing Steps: None
Known Problems: None
Spatial Filters: None
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>.
Acquisition dates of Landsat ETM+ (TM) scenes used for land cover classification in zone 03 are as follows:
SPRING-
Index 1 for Path 44/Row 33 on 04/23/01 = Scene_ID 7044033000111350
Index 2 for Path 44/Row 34 on 04/20/00 = Scene_ID 7044034000011150
Index 3 for Path 45/Row 31 on 04/14/01 = Scene_ID 7045031000110450
Index 4 for Path 45/Row 32 on 03/26/00 = Scene_ID 7045032000008650
Index 4 for Path 45/Row 33 on 03/26/00 = Scene_ID 7045033000008650
Index 5 for Path 46/Row 30 on 05/07/01 = Scene_ID 7046030000112750
Index 5 for Path 46/Row 31 on 05/07/01 = Scene_ID 7046031000112750
Index 6 for Path 46/Row 32 on 04/02/00 = Scene_ID 7046032000009350
LEAF ON (Summer)-
Index 1 for Path 44/Row 33 on 07/28/01 = Scene_ID 7044033000120950
Index 2 for Path 44/Row 34 on 06/10/01 = Scene_ID 7044034000116150
Index 3 for Path 45/Row 31 on 06/17/01 = Scene_ID 7045031000116850
Index 3 for Path 45/Row 32 on 06/17/01 = Scene_ID 7045032000116850
Index 4 for Path 45/Row 33 on 08/17/00 = Scene_ID 7045033000023050
Index 5 for Path 46/Row 30 on 07/26/01 = Scene_ID 7046030000120750
Index 5 for Path 46/Row 31 on 07/26/01 = Scene_ID 7046031000120750
Index 5 for Path 46/Row 32 on 07/26/01 = Scene_ID 7046032000120750
Index 6 for Path 46/Row 32 on 07/23/00 = Scene_ID 7046032000020550
LEAF-OFF (Fall)-
Index 1 for Path 44/Row 33 on 10/13/00 = Scene_ID 7044033000028750
Index 2 for Path 44/Row 34 on 09/30/01 = Scene_ID 7044034000127350
Index 3 for Path 45/Row 31 on 11/08/01 = Scene_ID 7045031000131250
Index 3 for Path 45/Row 32 on 11/08/01 = Scene_ID 7045032000131250
Index 3 for Path 45/Row 33 on 11/08/01 = Scene_ID 7045033000131250
Index 4 for Path 46/Row 30 on 08/27/01 = Scene_ID 7046030000123950
Index 4 for Path 46/Row 31 on 08/27/01 = Scene_ID 7046031000123950
Index 5 for Path 46/Row 32 on 09/28/01 = Scene_ID 7046032000127150
Field-Collected Data- EarthSat's primary method of field point collection uses the locations generated by the statistical sample selection to guide both training and validation point selection. Training and validation points were collected continuously on routes that pass through all or most of these sample areas. Using available GPS (Global Positioning System)/laptop computers, C-CAP field teams can reach up to 1000 sites per day. The technology includes: Laptop computers, Real-time GPS Receiver and interface software with database applications, Computer based real-time fieldwork database entry and manipulation, Geo-referenced digital satellite imagery and classified land cover analysis imagery, GIS ancillary data, such as roads, other land cover analyses, paper maps, and digital elevation models. The first step to field data collection is to determine where to take points. This can be decided by using EarthSat's GeoTools software, a spatial statistics package that will be a standard part of the next installment of ERDAS IMAGINE. First, TIGER (Topologically Integrated Geographic Encoding and Referencing System) roads are acquired, registered to the digital imagery, and mosaicked. A 300-meter buffer of the land cover imagery is generated based upon the TIGER roads. This process of sampling is done to ensure that many factors that account for a spectral value will be considered during the field collection. A data layer is created that is made of hundreds of stratifications based on all of the different types of input data. The three seasons of tasseled cap data are layer-stacked to create a nine-band file. Then the data are masked based on the ten pixel buffered TIGER road file so that only the accessible portions of the imagery remain. These data are stacked in order to incorporate the seasonality into the sample selection. This creates a nine-band file which is clustered using ISODATA to 250 classes. Then data layers representing the dates of the images used in the zone mosaics for each season are incorporated by matrixing the dates together. Also matrixed to the dataset is the NLCD (National Land Cover Dataset) recoded to match the C-CAP classification scheme and masked by the buffered TIGER roads file. This result is matrixed to the 250-cluster file. This file incorporates information from all of these datasets to form stratifications for the random sampling process. The matrixed one-band layer is then input into GeoTools. A 10,000-meter grid is produced in GeoTools. Fifty stratified grid samples per mosaic are selected based on the stratifications of the imagery. These grids are used to determine where the field route will occur. The route passed through nearly all of the grids. This guarantees the best mix of the field points based on the factors mentioned. CAP classification scheme and masked by the buffered TIGER roads file. This result is matrixed to the 250-cluster file. This file incorporates information from all of these datasets to form stratifications for the random sampling process. The matrixed one-band layer is then input into GeoTools. A 10,000-meter grid is produced in GeoTools. Fifty stratified grid samples per mosaic are selected based on the stratifications of the imagery. These grids are used to determine where the field route will occur. The route passed through nearly all of the grids. This guarantees the best mix of the field points based on the factors mentioned. A version of this layer was made that is not limited by the TIGER roads buffer. This version was used for Digital Ortho-photograph Quadrangle (DOQ) selection. A list of stratified random DOQ's were submitted to USGS/EROS. These DOQ's were used as ground truth for impervious features in the classification of the developed categories. Fifteen samples were selected for each zone. The field points were collected by GPS (Global Positioning System). The GPS is connected to a lap-top computer that is used as a data logger. IMAGINE software (GPS Tool) allows the GPS location to be tracked over the imagery displayed in the viewer. Another module (RGMID), which was designed by EarthSat and programmed by ERDAS, allows the selection of a pixel from the viewer and the association of various characteristics gleaned from the field to be recorded in a table. The items that are typically noted in the field include: Canopy cover Vegetation types by species (where applicable) Land Cover characterization Soils (if relevant) Special conditions and remarks Photography/video Date/time X,Y location (Z if relevant) The data and equipment used for the fieldwork are as follows: Ancillary datasets: TIGER 2000 NLCD NWI - mosaicked into zones State road map and Delorme state atlas www.delorme.com Hardware: Lap-tops with IMAGINE and data GARMIN GPS modules and external antennae, redundant data cables Digital Cameras Backup devices (CD writers) Extra batteries (lap-top and GPS) DC to AC adapters, and splitters Car fuses, flashlights, basic tools Mobile phones (if available) Calculator System backup CD's with operating system and software Compass Field notebooks with instructions and road maps with pre-determined routes Imagery: Multi-spectral data for each zone Initial classifications EarthSat utilized the RGMID software from ERDAS IMAGINE to facilitate field efforts. This software allows GPS tracking over imagery in its native IMAGINE file format (.img). Classification: After the field points for training were collected, they were used as the dependent variable in a CART classification approach. Many layers used as the independent variables such as tasseled cap imagery, DEM's (Digital Elevation Model), slope and aspect, NWI, other classifications, and an image date file that corresponds to the mosaics. The rough classification was created using only the CART discrete decision-tree approach. Then the provisional classification was produced by doing some regression tree analysis on certain classes such as urban and to distinguish certain feature types from each other such as grass and scrub, or scrub and trees, etc. The final-no-edits version was created using the latest file applied to many models that incorporated some ancillary data and spatial analysis on the data. Then this data was hand edited using screen digitizing techniques while training on the terraserver ortho-photos to produce the final-with-edits classification. Attributes for this product are as follows: 0 Background 1 Unclassified (Cloud, Shadow, etc) 2 High Intensity Developed 3 Low Intensity Developed 4 Cultivated Land 5 Grassland 6 Deciduous Forest 7 Evergreen Forest 8 Mixed Forest 9 Scrub/Shrub 10 Palustrine Forested Wetland 11 Palustrine Scrub/Shrub Wetland 12 Palustrine Emergent Wetland 13 Estuarine Forested Wetland 14 Estuarine Scrub/Shrub Wetland 15 Estuarine Emergent Wetland 16 Unconsolidated Shore 17 Bare Land 18 Water 19 Palustrine Aquatic Bed 20 Estuarine Aquatic Bed 21 Tundra 22 Snow/Ice Ancillary Datasets: Non-TM image datasets used are DEM (Digital Elevation Model), NWI, TIGER2000, field-collected points, California GAP (Gap Analysis Program) , FRAP (Fire Risk Assessment Program), and CERES (California Environmental Resources Evaluation System). Non-TM image datasets used specifically for this classification are DEM, Slope, Topographic Position Index, and National Wetlands Inventory. There were several QA/QC steps involved in the creation of this product. First, there was an internal QA/QC. This was done by viewing the classification frame- by-frame along with the TM imagery and the Terraserver ortho-photos, then recording a point everywhere there was a classification error along with comments. NOAA staff did the same to our product as our internal review. A third review occurred when a Boeing/Autometric representative reviewed the data mainly for issues that may occur with the format, attributes, slivers, grid, etc. Finally, a plant identification specialist was hired to field verify the late-date classification. Post-Processing Steps: The three zones were analyzed separately and so were mosaicked as a final post- processing step.
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.