%0 Journal Article %J International Journal of Remote Sensing %D 1995 %T The effects of changes in forest biomass on radar backscatter from tree canopies %A Wang, Y. %A Davis, F. W. %A Melack, J. M. %A Kasischke, E. S. %A Christensen, N. L., Jr. %K (Aerospace and Underwater Biological Effects--General %K (Biophysics--Biocybernetics (1972- )) %K (Ecology %K (Mathematical Biology and Statistical Methods) %K (Plant Physiology, Biochemistry and Biophysics--Growth, Differentiation) %K Coniferopsida %K Environmental Biology--Plant) %K Gymnosperms %K Mathematical Model %K Methods) %K North Carolina %K Pine %K Plants %K Research Article %K Spermatophyta %K Spermatophytes %K Synthetic Aperture Radar %K Usa %K Vascular plants %B International Journal of Remote Sensing %V 16 %P 503-513 %8 1995 %G eng %0 Journal Article %J Remote Sensing of Environment %D 1994 %T The effects of changes in loblolly pine biomass and soil moisture on ERS-1 SAR backscatter %A Wang, Y. %A Kasischke, E. S. %A Melack, J. M. %A Davis, F. W. %A Christensen, N. L., Jr. %K (Aerospace and Underwater Biological Effects--General %K (Biochemistry--Physiological Water Studies (1970- )) %K (Biophysics--Biocybernetics (1972- )) %K (Ecology %K (Forestry and Forest Products) %K (Mathematical Biology and Statistical Methods) %K (Plant Physiology, Biochemistry and Biophysics--Growth, Differentiation) %K (Plant Physiology, Biochemistry and Biophysics--Water Relations) %K (Soil Science--Fertility and Applied Studies (1970- )) %K (Soil Science--Physics and Chemistry (1970- )) %K Coniferopsida %K Environmental Biology--Plant) %K European Remote Sensing Satellite Synthetic Aperture Radar %K Gymnosperms %K Mathematical Model %K Methods) %K North Carolina %K Plants %K Research Article %K Spermatophytes %K Usa %K Vascular plants %B Remote Sensing of Environment %V 49 %P 25-31 %8 1994 %G eng %0 Journal Article %J Journal of Vegetation Science %D 1994 %T Regression tree analysis of satellite and terrain data to guide vegetation sampling and surveys %A Michaelsen, J. %A Schimel, D. S. %A Friedl, M. A. %A Davis, F. W. %A Dubayah, R. C. %K (Aerospace and Underwater Biological Effects--General %K (Ecology %K (General Biology--Institutions, Administration and Legislation) %K (Methods, Materials and Apparatus, General--Field Methods) %K (Methods, Materials and Apparatus, General--Photography) %K Angiosperms %K Biophysical Properties %K Ecological Classification %K Environmental Biology--Bioclimatology and Biometeorology) %K Environmental Biology--Plant) %K Gramineae %K International Satellite Land Surface Climatology Program %K Methods) %K monitoring %K Monocots %K Plants %K Research Article %K Satellite Imagery %K Spermatophytes %K Tall Grass Prairie Landscape %K Vascular plants %X Monitoring of regional vegetation and surface biophysical properties is tightly constrained by both the quantity and quality of ground data. Stratified sampling is often used to increase sampling efficiency, but its effectiveness hinges on appropriate classification of the land surface. A good classification must he sufficiently detailed to include the important sources of spatial variability, but at the same time it should be as parsimonious as possible to conserve scarce and expensive degrees of freedom in ground data. As part of the First ISLSCP (International Satellite Land Surface Climatology Program) Field Experiment (FIFE), we used Regression Tree Analysis to derive an ecological classification of a tail grass prairie landscape. The classification is derived from digital terrain, land use, and land cover data and is based on their association with spectral vegetation indices calculated from single-date and multi-temporal satellite imagery. The regression tree analysis produced a site stratification that is similar to the a priori scheme actually used in FIFE, but is simpler and considerably more effective in reducing sample variance in surface measurements of variables such as biomass, soil moisture and Bowen Ratio. More generally, regression tree analysis is a useful technique for identifying and estimating complex hierarchical relationships in multivariate data sets. %B Journal of Vegetation Science %V 5 %P 673-686 %8 1994 %G eng