The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcMap© software and are included in a comprehensive geodatabase. Draft maps created from the vegetation classification were field-tested and revised before independent ecologists conducted an assessment of the map’s accuracy during 2014.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcMap© software and are included in a comprehensive geodatabase. Draft maps created from the vegetation classification were field-tested and revised before independent ecologists conducted an assessment of the map’s accuracy during 2014.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GRBA’s spatial database and map layer was produced from high-resolution 2007 Digital Map, Inc. imagery provided to CTI by the NPS. By comparing the signatures on the imagery to field and ground data, 64 map units (48 vegetated, four barren geology and snow, and 12 land-use / land-cover) were developed and the vegetation map units were directly cross-walked or matched to their corresponding rUSNVC plant associations. The interpreted and remotely sensed data were converted to Geographic Information System (GIS) spatial geodatabases and maps were printed, field tested, reviewed, and revised.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GRBA’s spatial database and map layer was produced from high-resolution 2007 Digital Map, Inc. imagery provided to CTI by the NPS. By comparing the signatures on the imagery to field and ground data, 64 map units (48 vegetated, four barren geology and snow, and 12 land-use / land-cover) were developed and the vegetation map units were directly cross-walked or matched to their corresponding rUSNVC plant associations. The interpreted and remotely sensed data were converted to Geographic Information System (GIS) spatial geodatabases and maps were printed, field tested, reviewed, and revised.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The TOP 2015 imagery was mosaiced and manipulated using image processing and segmentation techniques (e.g. unsupervised image classification, normalized difference vegetation index, etc.) to highlight any subtle vegetation signature differences. All of the preliminary results were evaluated for usefulness and the best examples were first converted to digital lines and polygons, were next combined with other relevant AMIS GIS layers (such as the roads network), and the results were used as the base layer for the new AMIS vegetation mapping effort. Building off the base layer, all relevant lines and polygons were exported as shapefiles and converted to ArcGIS coverages. The resulting coverages were run through a series of smoothing routines provided in the ArcGIS software. Following the smoothing, all digital line-work was manipulated to remove extraneous lines, eliminate small polygons, and merged polygons that split obvious stands of homogeneous vegetation. The cleaning stage was considered complete when all resulting polygons matched homogenous stands of vegetation apparent on the TOP 2015 imagery. At this point, the mapping shifted to manual techniques and all vegetation lines and polygons were visually inspected and manually moved, edited and/or updated as needed.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The TOP 2015 imagery was mosaiced and manipulated using image processing and segmentation techniques (e.g. unsupervised image classification, normalized difference vegetation index, etc.) to highlight any subtle vegetation signature differences. All of the preliminary results were evaluated for usefulness and the best examples were first converted to digital lines and polygons, were next combined with other relevant AMIS GIS layers (such as the roads network), and the results were used as the base layer for the new AMIS vegetation mapping effort. Building off the base layer, all relevant lines and polygons were exported as shapefiles and converted to ArcGIS coverages. The resulting coverages were run through a series of smoothing routines provided in the ArcGIS software. Following the smoothing, all digital line-work was manipulated to remove extraneous lines, eliminate small polygons, and merged polygons that split obvious stands of homogeneous vegetation. The cleaning stage was considered complete when all resulting polygons matched homogenous stands of vegetation apparent on the TOP 2015 imagery. At this point, the mapping shifted to manual techniques and all vegetation lines and polygons were visually inspected and manually moved, edited and/or updated as needed.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. October 29, 1996: Aerial photography for Rock Creek Park is flown February 25, 1997: Initial meeting at Rock Creek Park headquarters - acquisition of aerial photography and ancillary data including existing vegetation maps March - April 1997: First cut delineations onto aerial photography of the photo signatures (see Figure 3 - Rock Creek Park Photo Index). May 12 - 16, 1997: Photointerpretation field reconnaissance trip, TNC training of park biologists in field sampling methodology May - June 1997: Develop initial list of photo signature types, Revise initial delineations based on field reconnaissance findings, Label existing polygons with photo signature types, Deliver copies of overlays to park biologists for plot selection and feedback June - September 1997: Park vegetation sampling effort February 19, 1998: Received draft TNC report of the vegetation classification for Rock Creek March 1998: Received final TNC report on the vegetation classification for Rock Creek, Received plot data and locations for vegetation sampling effort, Received TNC key for communities, Development of PI signature / TNC community lookup table, Polygons attributized with initial communities April 1998: Received DOQQ files (April 1989) May 11-12, 1998: Photointerpretation field verification trip May 1998: Revise photo signature / TNC community lookup table, Update and correct PI community calls and PI linework June - October 1998: Data rectification and conversion, Interim files and plots delivered to NPS-ROCR, Final documentation