Social and biophysical data were collected, integrated, and analyzed to examine scale-dependent relationships between selected population and environmental variables for a study site in northeast Thailand. Data sets were generated through the use of remote sensing to characterize land-use/land-cover and plant biomass variation across the Nang Rang district; GIS to derive elevation, slope angle, and soil moisture potential; social survey data at the village level to categorize demographic variables; and a population distribution model to transform demographic data collected at discrete village locations to spatially continuous surfaces stratified by agricultural land uses. Statistical analysis employed multiple regression to estimate population density in relation to social and biophysical variables, and canonical analysis to relate population variables to environmental variables across a range of spatial scales extending from 30 to 1050 m. Findings indicate the importance of spatial scale in the study of population and the environment. Regression models reflect the scale dependence of the selected variables through plots of slope coefficients and R2 values across nine scale steps. The variation in relationships among environment and population variables, evidenced through factor loadings associated with canonical correlation, suggest that relationships are not generalizeable across the sampled spatial scales.
|Original language||English (US)|
|Number of pages||9|
|Journal||Photogrammetric Engineering and Remote Sensing|
|State||Published - Jan 1999|
ASJC Scopus subject areas
- Computers in Earth Sciences