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Transboundary Land Classification
Nathan Walker, Audubon Alaska, GIS Biologist
This dataset was created in order to facilitate transboundary conservation work and research projects, by integrating land cover maps into a single dataset from Cape Caution, BC, to Yakutat Bay, AK. It includes three levels of land classification, site index, elevation, hydric soils (yes/no), karst (yes/no), primary and secondary species, size class, and volume class. It also includes a number of other important attributes from individual datasets, which were not crosswalked between the different areas.
This dataset was created in order to facilitate transboundary conservation work and research projects, by integrating land cover maps into a single dataset from Cape Caution, BC, to Yakutat Bay, AK.
Time Period of Content
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Point of Contact
mailing and physical
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Regional overview of the dataset.
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Please cite Audubon Alaska and the data contributors listed in the Credits section in any products derived from these data. Data source constraints information provided below:VRI, BTM, and karst data:BC data is provided by the Province of British Columbia under the Open Government License for Government of BC Information v.BC1.0 (license information viewable at
land cover data:The Nature Conservancy shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of the Nature Conservancy. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of the Nature Conservancy. All parties receiving these data must be informed of these restrictions. The Nature Conservancy shall be acknowledged as data contributors to any reports or other products derived from these data.Alaska land cover, harvest, site index data: The Forest Service uses the most current and complete data available. GIS data and product accuracy may vary. They may be developed from sources of differing accuracy; accurate only at certain scales; based on modeling or interpretation; incomplete while being created or revised; etc. Using GIS products for purposes other than those for which they were created, may yield inaccurate or misleading results. The Forest Service reserves the right to correct, update, modify or replace GIS products without notification. Any hardcopy or electronic products utilizing these data sets shall clearly indicate their source. If the user has modified the data in any way, they are obligated to describe the types of modifications they have performed. User specifically agrees not to misrepresent these data sets, nor to imply that the US Forest Service, Tongass National Forest approved the changes.Canadian Digital Elevation Dataset:The Licensee shall identify GeoBase® as a data source where any of the Data are redistributed, or contained within Derivative Products, and use of the Data shall not be construed as an endorsement by GeoBase® of those Derivative Products. License information viewable at :
Elevation Data:None. Acknowledgement of the originating agencies would be appreciated in products derived from these data. Glacier Bay National Park land cover data:The Information Resource does not contain any sensitive information. No copyrights or other ownership issues. There are no distribution restrictions based solely on the proprietary evaluation.
Place Keyword Thesaurus:
Canada, Alaska, North America, Western British Columbia, United States, Southeast Alaska
Data Quality Information
Logical Consistency Report:
Land classification codes were assigned to each land classification class, and the codes for each level were appended into a single field; this field was then reviewed for contradictory information (e.g. LC1 = Water, LC 2 = Forest).
Of the initial planned extent, this excludes only land cover for the Yukon territory, and some minor no data holes and slivers (mostly along the border between Alaska and BC, in areas covered by neither dataset). The no data holes were filled with elevation data, but otherwise contain no attributes.
Varies depending on source data.
Step 1 Process Description:
D) Combined 1. Created template with relevant fields, merged in data from Alaska, Glacier Bay, BC, and BTM. 2. At this point, we discovered that some no-data holes, and slivers along the border remained in the dataset. These fell into two categories: places where no data were available in either dataset, and places where data from the VRI dataset had been left out (mostly in spots where the BTM dataset had been used to replace VRI data, but no BTM data had been available). For those areas where data were not available for any dataset, elevation was calculated. This was done using AK_BC_Blend: unlike previous elevation calculations, which were done entirely on the basis of a single dataset (AK_BC_50 prioritized Alaska, meaning that the BC data didn’t actually apply), these slivers included both BC and AK; for this reason, the AK and BC datasets were mosaicked a second time, this time using the blend operator. 3. We then joined these back to the sliver dataset, and appended the sliver dataset to previously created merge. 4. For the data gaps where VRI data had been available, first we converted the transboundary dataset to a raster and reclassed it such that all data had a value of 1, and no data had a value of 0. 5. Converted this raster to a polygon, and used a definition query to remove all the polygons with a value of 1. 6. Clipped the VRI polygon-to-raster-to-polygon file to the polygon file created in the previous step. 7. Used zonal stats to calculate elevation for these polygons using the AK_BC_Blend raster; joined these and the original VRI data back to these polygons. 8. Appended to the previously created-merge. (These were assigned a Dataset of “VRI” but a Project of ‘ “Slivers: “ & original Project entry,’ so that these records could be identified, and linked back to the original dataset if necessary, using the source ID field. 9. Ran attribute and spatial index on dataset. 10. Applied crosswalks (see crosswalk spreadsheet). 11. Created join table linking different land classification descriptions to land classification codes; joined this to dataset and transferred codes. 12. Created field appending the three LC codes, and exported a summary table of these values; reviewed table to identify, repair errors. 13. Created metadata.
Step 1 Process Date:
Step 2 Process Description:
A) BC 1. Merged together VRI datasets, to BC Albers Dataset. 2. Merged together TFLs 25, 39, and 41. 3. Selected and exported VRI features intersecting TFLs. 4. From this selection, selected VRI features with BCLCS1 = ‘U’ [unknown], and erased the TFLs from these areas. 5. Merged the remaining unknown VRI features with the TFLs. 6. From the step 3 subset, selected VRI features with BCLCS1 <> ‘U’, and merged these with the TFL/VRI_Unknown features. This gives a feature class representing the areas overlapping the TFLs, with the previous VRI replaced by the improved TFL data, when available (that is, where the old VRI dataset had no land classification information given). 7. Reversed the selection of the VRI features intersecting the TFLs (reverse of step 3 – all features not overlapping VRI features), and exported this. 8. Merged the updated TFL area polygons with the rest of the VRI polygons to create an updated VRI layer. 9. 30,000,000 acres, or over 50% of the entire study area, either have the attribute ‘BCLCS1 = U [Unknown]’ or ‘BCLCS1=N/BCLC2=L/BCLCS3=A/BCLCS4=RO/BCLCS5=BR’, which translates roughly as ‘alpine rock.’ According to Dave Leversee, a better translation would be ‘not productive forest, so not surveyed in detail.’ Because of this, we replaced these areas with the baseline thematic map (BTM) from BC, which provides coarse landsat-based landcover classification. Clipped BTM to VRI areas described above. Crosswalked PLU classes to BCLCS classes based on information from Dave Leversee (crosswalk spreadsheet). 10. Exported VRI not matching above characteristics, and merged to BTM dataset created in previous step. 11. Clipped and unioned the BC karst file to improved VRI file. 12. Exported polygon file to raster, snapped to tsys_dbase, at same resolution (25 m cell size, maximal area). 13. Exported raster file to polygon, without simplifying polygons (rasterized to be consistent with tsys_dbase, so both can be displayed as a raster or a polygon. 14. Used zonal statistics as table to calculate mean elevation (BC_50: created by mosaicking together various BC DEMs) for each polygon, joined this and original BC data back to polygons. 15. After finishing this step, I received an additional file from Dave Leversee representing TFL1 and Haida Gwai (gaps in previous dataset). I added these in by repeating the previous steps: clipped and unioned the karst file to the TFL1/Haida Gwai dataset, converted to rasters then back to polygons, used zonal stats to calculate the elevation of each polygon, then updated the existing polygons in these areas by selecting and exporting the intersecting polygons, erasing the new polygons from these, appending the new to the old, then merging the updated features with the VRI dataset.
Step 2 Process Date:
Step 3 Process Description:
B) Glacier Bay 1. Converted GLBA_LC_2007 to raster, with 25 m cell size, maximal area, snapped to and in coordinate system of tsys_dbase. 2. Converted GLBA raster to a polygon, not simplifying polygons. 3. Used zonal statistics as table to calculate mean elevation (AK_BC_50: created by mosaicking BC_50 DEM to ned_allseak [from Frances Biles, USFS], prioritizing AK, at 50m resolution and snapped to the BC DEM) for each polygon; joined this and original GLBA LC data back to polygons.
Step 3 Process Date:
Spatial Reference Information
Horizontal Datum Name:
D North American 1983
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Metadata Reference Information
Metadata Creation Date:
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Metadata Standard Name:
FGDC Content Standard for Digital Geospatial Metadata
Metadata Standard Version:
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