Spatial-temporal Analysis of Low Density Development in the Northwestern U.S.

Authors: Rachel Ulrich*, Montana State University, Scott Powell, Montana State University
Topics: Land Use and Land Cover Change, Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: land cover, land use, land use change, spatial-temporal analysis, Northwestern U.S.
Session Type: Paper
Day: 4/12/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Poydras, Sheraton, 3rd Floor
Presentation File: No File Uploaded

Land use and land cover maps for the northwestern U.S. have typically failed to accurately depict low density exurban development.  As one of the fastest-growing types of land cover and land use change (LCLUC) in this region, inaccuracies have limited efforts to assess vulnerability of wildland ecosystems.  Under NASA’s LCLUC program, this study uses fine-scale aerial imagery to quantify exurban development in an effort to better understand context-dependent drivers.  A stratified sampling technique was developed using geospatial data layers to define strata for rural/urban counties, new west/traditional counties, climate gradients, natural amenities, and night light intensity, yielding a total of 96 strata.  We interpreted land use indicators in 3,090 plots at scales of three and sixteen hectares, and in 618 plots at a scale of 96 hectares, at 1990, 2000, and 2010 time intervals. Exploratory data analysis revealed that at the finest scale of interpretation, exurban development may be influenced by different drivers depending on associated land use indicators. When agricultural indicators were present with exurban development, transition stemmed primarily from natural resource extraction and undeveloped classes. If agricultural indicators were lacking, exurban development originated more from cultivated cropland. We are currently investigating the spatial distribution of land use and land use transitions across strata, and whether the results vary by scale of interpretation. These results will inform future analyses of hypothesized drivers of development, prediction modeling and greater wildland ecosystems vulnerability assessment.

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