This dataset protrays forest/non-forest for Alaska. Models built by See5, a nonparametric classifier, were developed using USDA Forest Service Forest Inventory and Analysis Program data, MODIS composite images from the 2002 growing season, and nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions.
The dataset was developed as a collaborative effort between the USFS Forest Inventory and Analysis Program and the USFS Geospatial Technology and Applications Center.
Purpose:
The purpose of this dataset is to portray broad distribution patterns of forest/non-forest cover in Alaska and provide input to national scale modeling projects.
Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2004
Currentness_Reference: ground condition
Status:
Progress: Complete
Maintenance_and_Update_Frequency: Irregular
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: 162.686517
East_Bounding_Coordinate: -132.241669
North_Bounding_Coordinate: 66.63221
South_Bounding_Coordinate: 51.51789
Keywords:
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: Forest Mask
Theme_Keyword: Forest Cover
Theme_Keyword: Forest Inventory and Analysis
Theme_Keyword: FIA
Theme_Keyword: CART Modeling
Place:
Place_Keyword: AK
Place_Keyword: Alaska
Access_Constraints: None
Use_Constraints:
None. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. Using the data for other than their intended purpose may yield inaccurate or misleading results.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service Forest Inventory and Analysis
Acknowledgement of the USDA Forest Service Forest Inventory and Analysis Program and Geospatial Technology and Applications Center would be appreciated in products derived from these data.
Native_Data_Set_Environment:
Microsoft Windows 2000 Version 5.0 (Build 2195) Service Pack 4; ESRI ArcCatalog 9.1.0.722
Attribute_Accuracy_Report: Overall accuracy was 94% with a Kappa of .88.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service Geospatial Technology and Applications Center
Publication_Date: 2002
Title: Dominate Aspect
Geospatial_Data_Presentation_Form: raster digital data
Other_Citation_Details:
Created using USGS National Elevation Dataset (<https://www.usgs.gov>) Processing Steps 1. Imported BILmeters format into ESRI GRID format. 2. Reprojected into Albers Conical Equal Area NAD 27 with a 60m resolution 3. Mosaicked tiles into a contiguous dataset 4. Resampled to 30m resolution to maintan continuity with CONUS dataset 5. Used a 3x3 focal mean function to output a 90m DEM dataset 6. Created an Aspect Dataset from the 90m DEM 7. Reclassified the Aspect dataset into 4 categories Category 1: 0° - 90° Category 2: 90° - 180° Category 3: 180° - 270° Category 4: 270° - 360° 8. Performed a 3x3 Focal Majority output to 270m resolution 9. Reprojected/Resampled to a 250m NAD83 dataset.
Source_Scale_Denominator: 30-meter
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2002
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Dominant Aspect
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service Geospatial Technology and Applications Center
Publication_Date: 2002
Title: Mean Elevation
Other_Citation_Details:
Created using USGS National Elevation Dataset (<https://www.usgs.gov>) Processing Steps 1. Imported BILmeters format into ESRI GRID format. 2. Reprojected into Albers Conical Equal Area NAD 27 with a 60m resolution 3. Mosaicked tiles into a contiguous dataset 4. Resampled to 30m resolution to maintan continuity with CONUS dataset 5. Used a 3x3 focal mean function to output a 90m DEM dataset 6. Reprojected / Resampled to NAD83 with 250m cell size using Bilear Interpolation.
Source_Scale_Denominator: 30-meter
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2002
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Mean Elevation
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service Geospatial Technology and Applications Center
Publication_Date: 2002
Title: Percent Slope
Other_Citation_Details:
Created using USGS National Elevation Dataset (<https://www.usgs.gov>) Processing Steps 1. Imported BILmeters format into ESRI GRID format. 2. Reprojected into Albers Conical Equal Area NAD 27 with a 60m resolution 3. Mosaicked tiles into a contiguous dataset 4. Resampled to 30m resolution to maintan continuity with CONUS dataset 5. Used a 3x3 focal mean function to output a 90m DEM dataset 6. Reprojected / Resampled to NAD83 with 250m cell size using Bilear Interpolation.
Source_Scale_Denominator: 30-meter
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 2002
Source_Currentness_Reference: ground condition
Source_Citation_Abbreviation: Percent Slope
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service Geospatial Technology and Applications Center
Publication_Date: 2002
Title: Variety Dominate Aspect
Other_Citation_Details:
Created using USGS National Elevation Dataset (<https://www.usgs.gov>) Processing Steps 1. Imported BILmeters format into ESRI GRID format. 2. Reprojected into Albers Conical Equal Area NAD 27 with a 60m resolution. 3. Mosaicked tiles into a contiguous dataset. 4. Resampled to 30m resolution to maintan continuity with CONUS dataset. 5. Used a 3x3 focal mean function to output a 90m DEM dataset. 6. Created an Aspect Dataset from the 90m DEM. 7. Reclassified the Aspect dataset into 4 categories. Category 1: 0° - 90° Category 2: 90° - 180° Category 3: 180° - 270° Category 4: 270° - 360° 8. Performed 3x3 Focal Variety function output to 270m. 9. Reprojeced / Resampled to NAD83 at 250m resolution.
Bailey's Ecoregions and Subregions of the United States, Puerto Rico, and the U.S. Virgin Islands
Geospatial_Data_Presentation_Form: vector digital data
Other_Citation_Details:
This map layer is commonly called Bailey's ecoregions and shows ecosystems of regional extent in the United States, Puerto Rico, and the U.S. Virgin Islands.
Processing Steps: 1. Downloaded file from <https://www.fs.fed.us/institute/ecoregions/eco_download.html>. 2. Imported ArcInterchange file into ArcCoverage format (Albers Conical Equal Area Clark1866) 3. Imported ArcCoverage file into raster format with 250m cell resolution. 4. Reprojected / Resampled to common Albers Conical Equal Area NAD83 projection.
Geospatial_Data_Presentation_Form: vector digital data
Other_Citation_Details:
This ecoregion map combines the Bailey and Omernik approach to ecoregion mapping in Alaska. The ecoregions were developed cooperatively by the U.S. Forest Service, National Park Service, U.S. Geological Survey, The Nature Conservancy, and personnel from many other agencies and private organizations.
Processing Steps: 1. Downloaded file from <https://www.nps.gov/akso/gis/Alaska/alaskaBiol.htm> 2. Imported ArcInterchange file into ArcCoverage format (Albers Conical Equal Area NAD27) 3. Imported ArcCoverage file into raster format with 250m cell resolution. 4. Reprojected / Resampled to common Albers Conical Equal Area NAD83 projection.
Created using MODIS data from the Land Processes Distribution Active Archive Center (<https://edcdaac.usgs.gov/main.html>) LP DAAC Data Set - MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 500m ISIN v003 MODIS Product - MOD44B Processing Steps: 1. Imported MODIS EOD HDF format file into ERDAS Imagine (*,img) format. 2. Reprojected into Lambert Conformal Conic NAD27 from Integerized Sinusoidal using ERDAS Imagine 8.5 with the Nearest Neighbor and Rigerous Transformation options selected. 3. Subset area of interest from entire image 4. Resampled / reprojected to a common coordinate system & resolution (250m)
Created using MODIS data from the Land Processes Distribution Active Archive Center (<https://edcdaac.usgs.gov/main.html>) LP DAAC Data Set - MODIS/Terra Vegetation Indices 16-Day L3 Global 250 ISIN GRID v003 MODIS Product - MOD13Q1 Processing Steps: 1. Imported MODIS EOD HDF format file into ERDAS Imagine (*,img) format. 2. Reprojected into Albers Conical Equal Area NAD27 from Integerized Sinusoidal using ERDAS Imagine 8.5 with the Nearest Neighbor and Rigerous Transformation options selected. 3. Mosaicked Tiled data into a contiguous dataset. 4. Subset area of interest from entire image 5. Resampled / reprojected to a common coordinate system & resolution (250m) in an Albers Conical Equal Area NAD83 projection.
Created using MODIS data from the Land Processes Distribution Active Archive Center (<https://edcdaac.usgs.gov/main.html>) LP DAAC Data Set - MODIS/Terra Vegetation Indices 16-Day L3 Global 250 ISIN GRID v003 MODIS Product - MOD13Q1 Processing Steps: 1. Imported MODIS EOD HDF format file into ERDAS Imagine (*,img) format. 2. Reprojected into Albers Conical Equal Area NAD27 from Integerized Sinusoidal using ERDAS Imagine 8.5 with the Nearest Neighbor and Rigerous Transformation options selected. 3. Mosaicked Tiled data into a contiguous dataset. 4. Subset area of interest from entire image 5. Resampled / reprojected to a common coordinate system & resolution (250m) in an Albers Conical Equal Area NAD83 projection.
The methodology used to produce the database combined ground-truth (from FIA plot data) with multi-date imagery and variety of other spatially continuous geospatial data. The predictor data themes include,
- Elevation, slope, and aspect
- Unified Ecoregions
- MODIS Vegetation Indices such as EVI, NDVI.
- MODIS Vegetation Continuous Fields
- MODIS fire points for developed from the MODIS Active Fire Maps
- MODIS 8-day composites
Statistical models developed in Rulequest's See5 data mining software link the FIA plot variables with the imagery and geospatial data. See5 creates classification trees, which have the advantage of not assuming parametric properties within the predictor data and are thus are more appropriate for the multi-scale, multi-source data, which are being used.
Contact_Organization: USDA Forest Service Geospatial Technology and Applications Center
Contact_Address:
Address: 125 South State Street, Suite 7105
City: Salt Lake City
State_or_Province: Utah
Postal_Code: 84138
Country: USA
Contact_Voice_Telephone: 801-975-3750
Contact_Facsimile_Telephone: 801-975-3478
Resource_Description: Downloadable Data
Distribution_Liability:
Although these data have been used by the USDA Forest Service, the USDA Forest Service shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data are not legal documents and are not intended to be used as such.
It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. Using the data for other than their intended purpose may yield inaccurate or misleading results. The USDA Forest Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from the USDA Forest Service server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on a computer system at the USDA Forest Service, no warranty expressed or implied is made regarding the utility of the data on another 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.
The USDA Forest Service reserves the right to correct, update or modify this data and related materials without notification.
