Geographic Resource Solutions

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This download contains an article excerpted from Fall/Winter 2013 Kaleidoscope Newsletter published by the NPS-Klamath Network, Ashland, Oregon that announces the completion of the Redwood National and State Parks Vegetation Classification and Mapping Project.  It describes some of the capabilities of the Discrete Classificaiton Map Data Sets that were produced by GRS and delivered to RNSP in June, 2013.

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GRS's Candidate Site Selection methodology excerpted from the WRST Report

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A Summary of GRS's Land Cover Feature Development Methodology based upon Discrete Classification

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A brief description of the Illumination Correction methodology used by GRS.

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Brown, G. K., Fox, L.; "Digital Classification of Landsat Thematic Imagery for Recognition of Wildlife Habitat Characteristics"

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Hill, T. 1996. ''Forest Biometrics from Space''

In 1996 Geographic Resource Solutions (GRS) completed mapping the Applegate River watershed in southern Oregon for existing vegetation. This project was a cooperative effort between the USDA Forest Service, and Bureau of Land Management. GRS used Landsat TM satellite imagery, Digital Elevation Models, measured field data, GIS, and GPS. The final database estimated polygon attributes using continuous variables including canopy closure, average tree size, species composition, trees per acre, and variance for tree size and canopy closure. This paper describes the various methodologies used in the project, that include: field data collection, image processing techniques for removing the effects of topography, hybrid supervised and unsupervised image classification techniques, ecological rule-based pixel aggregation, and quantitative accuracy assessment.

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Stumpf, K. A. "Forest Inventory Stratification based on the Classification of Landsat Thematic Mapper Imagery"

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Stumpf, K.A. 1993. ''From Pixels to Polygons: the Rule-based Aggregation of Satellite Image Classification Data Using Ecological Principles''

Raster pixel data developed using satellite image classification techniques are frequently difficult to convert to a polygon (vector) format due to the extreme heterogeneity of the pixel classification data. Many groups of pixels are too small to map as polygons without yielding an unusable database. The small areas that are less than a user defined minimum size mapping unit must be aggregated with neighboring groups (stands) prior to developing a usable vector database. Conventional spatial operators based on either grid or polygon analysis (neighborhood and/or sliver filters) often cause degradation of stand boundaries and descriptive attributes, and decrease the reliability of the final map. A rule-based pixel filtering methodology that is based on user-defined concepts of stand similarity is presented in this paper. This technique considers stand characteristics such as the major vegetation type, species composition, density of canopy closure, average tree size, and canopy structure during the evaluation of stand similarity. Aggregation rules representing ecological relationships, minimum size constraints, and the relative importance of the different vegetation characteristics are also used to guide the aggregation process. The rules are flexible and may be defined relative to project objectives and the desired use of the resulting database.

Presented at the GIS'93 Conference, Vancouver, BC

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Stumpf, K.A. ''The Aggregation of Pixel Data into Mapped Area Features.'' A slide presentation that contrasts inappropriate approaches of pixel aggregation, such as filtering and smoothing with more accurate and effective techniques like ecological rule-based pixel aggregation

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Stumpf, K. and Koltun, J. 1992. Rule Based Aggregation of Raster Image Classifications into Vector GIS Databases With Five- and Forty-Acre Minimum Size Mapping Units.

Pixel data developed using image classification techniques are frequently difficult to convert to a vector GIS format due to the heterogeneity of the pixel data. A rules based approach to aggregating pixels and resulting vegetation types based on the similarity of the landscape features (data) being mapped is presented. Aggregation was accomplished to both five-and forty-acre minimums for a 212,000 acre area. Preliminary comparative results and findings of this rules based pixel aggregation to five-and forty-acre minimum type sizes are presented and discussed.