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 37B encompasses whole or portions of several states, including the states of Texas, Louisiana, and Mississippi. Questions about the NLCD mapping zone 37B 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.
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 37B are as follows:
SPRING-
Index 1 for Path 23/Row 38 on 02/21/00 = Scene_ID 5023038000005210
Index 2 for Path 23/Row 39 on 02/05/00 = Scene_ID 5023039000003610
Index 3 for Path 23/Row 40 on 01/04/03 = Scene_ID 7023040000300450
Index 4 for Path 22/Row 39 on 01/21/00 = Scene_ID 7022039000002150
Index 4 for Path 22/Row 40 on 01/21/00 = Scene_ID 7022040000002150
Index 5 for Path 21/Row 39 on 01/03/02 = Scene_ID 7021039000200350
Index 6 for Path 21/Row 40 on 01/14/00 = Scene_ID 7021040000001450
Index 7 for Path 24/Row 38 on 02/20/00 = Scene_ID 7024038000005150
LEAF ON (Summer)-
Index 1 for Path 23/Row 38 on 05/14/01 = Scene_ID 5023038000113410
Index 2 for Path 22/Row 39 on 07/15/00 = Scene_ID 7022039000019750
Index 2 for Path 22/Row 40 on 07/15/00 = Scene_ID 7022040000019750
Index 3 for Path 21/Row 39 on 06/17/01 = Scene_ID 5021039000116810
Index 4 for Path 21/Row 40 on 05/24/01 = Scene_ID 7021040000114450
Index 5 for Path 23/Row 40 on 04/26/03 = Scene_ID 7023040000311650
Index 6 for Path 23/Row 39 on 06/23/01 = Scene_ID 7023039000117450
Index 7 for Path 24/Row 38 on 04/08/00 = Scene_ID 7024038000009950
LEAF-OFF (Fall)-
Index 1 for Path 23/Row 38 on 10/24/99 = Scene_ID 7023038009929750
Index 1 for Path 23/Row 39 on 10/24/99 = Scene_ID 7023039009929750
Index 1 for Path 23/Row 40 on 10/24/99 = Scene_ID 7023040009929750
Index 2 for Path 24/Row 38 on 04/08/00 = Scene_ID 7024038000009950
Index 3 for Path 22/Row 39 on 11/07/01 = Scene_ID 7022039000131150
Index 3 for Path 22/Row 40 on 11/07/01 = Scene_ID 7022040000131150
Index 4 for Path 21/Row 39 on 10/15/01 = Scene_ID 7021039000128850
Index 4 for Path 21/Row 40 on 10/15/01 = Scene_ID 7021040000128850
Field-Collected Data-
The goals of the field data collection were to sample the diversity of the landscape, within the classes, and among image dates. Classes that would be more difficult to collect from air photos were targeted for field data collection. To meet these goals, Sanborn stratified the image into spectral clusters and located the field sites throughout the study area based on these. In addition to these pre-arranged sites, Sanborn collected points while driving between locations. Due to limited time and accessibility, not all polygons were assessed in the field. Those that we did not visit on the ground were labeled with digital orthophotographs or Ikonos data if it was available. Both training and validation points were collected together. See the accuracy assessment section to see how the points were split into training and validation points. Sanborn used laptop computers and GPS (Global Positioning System) to correctly locate field points on the TM imagery. Software downloaded from the Minnesota's Department of Natural Resources (DNR) was used to connect the Garmin GPS to the laptop computer and ESRI's ArcView software. Sanborn's programmer developed an ArcMap application that allowed 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 Number
Date/time location
The data and equipment used for the fieldwork are as follows:
Ancillary datasets:
TIGER 2000
NLCD - mosaicked into zones
State road map and Delorme state atlas www.delorme.com
Hardware: with ArcView/ArcGIS and data
GARMIN GPS modules and external antennae, redundant data
cables
Cameras devices (Floppy Drives, CD Burners, external HDD)
Extra batteries (lap-top and GPS)
Mobile phones
System backup CD's with data and software
Compass notebooks with instructions and road maps with
pre-determined routes Wetland and Vegetation Field Guides
Imagery:
Image data for each zone
Initial classifications
Classification:
After the field points for training were collected, they were combined with photo-interpreted points and used as the dependent variable in a CART classification approach. Many layers tested as independent layers. They included three 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 population density). 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 of this process using only the CART discrete decision-tree software. A provisional classification was produced by applying spatial models using ancillary data to the rough classification. The final automated map was then edited using hand editing techniques while using high resolution imagery from as reference data. Independently, of this process, NOAA produced percent impervious data layers for Zone 37. This layer was developed from Regression Tree and used impervious classifications from IKONOS imagery to predict pixel level percent impervious at the TM pixel level. The continuous percent impervious data was thresholded to produce the developed categories and imbedded 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
25 Snow/Ice
Ancillary Datasets:
Non-TM image datasets used are DEM (Digital Elevation Model), slope, aspect, positional index, NWI, NLCD, TIGER2000, field-collected points, photo-interpreted points, zone 37 GAP reclassified by NOAA (Gap Analysis Program),Census data (housing and population density), Ecoregions. QA/QC Process: 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, the classification, and high resolution reference imagery. NOAA staff completed a similar review and provided both general and point comments.
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.