REGRESSION TREE ANALYSIS OF SATELLITE AND TERRAIN DATA TO GUIDE VEGETATION SAMPLING AND SURVEYS

被引:134
作者
MICHAELSEN, J
SCHIMEL, DS
FRIEDL, MA
DAVIS, FW
DUBAYAH, RC
机构
[1] Department of Geography, Center for Computational Earth System Science, University of California, Santa Barbara, California
[2] National Center for Atmospheric Research, Boulder, Colorado, 80307
[3] Department and Center for Remote Sensing, Boston University, Boston, Massachusetts
[4] Department of Geography, University of Maryland, College Park, Maryland
关键词
GIS; KONZA PRAIRIE; REMOTE SENSING; SPECTRAL VEGETATION INDEX; TERRAIN STRATIFICATION;
D O I
10.2307/3235882
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
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 be 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 tall 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.
引用
收藏
页码:673 / 686
页数:14
相关论文
共 27 条
[1]  
Anderson T.W., An Introduction to Multivariate Statistical Analysis., (1984)
[2]  
Asrar G., Theory and Applications of Optical Remote Sensing., (1989)
[3]  
Becker R.A., Chambers J.M., Wilks A.R., The New S Language., (1988)
[4]  
Borchert M., Davis F.W., Michaelsen J., Oyler L.D., Interactions of factors affecting seedling recruitment of blue oak Quercus douglasii in California, Ecology, 70, pp. 389-404, (1989)
[5]  
Breiman L., Friedman J., Olshen R., Stone C., Classification and Regression Trees., (1984)
[6]  
Brewer K.R.W., Hanif M., Sampling with unequal probabilities., (1982)
[7]  
Chambers J.M., Hastie T.J., Statistical Models in S., (1992)
[8]  
Cleveland W.S., Robust locally weighted regression and smoothing scatter plots, Journal of the American Statistical Association, 74, pp. 829-836, (1979)
[9]  
Cochran W.G., Sampling Techniques, (1963)
[10]  
Crist E., Cicone R., A physically‐based transformation of thematic mapper data ‐ the TM tasseled cap, IEEE Trans. Geosci. Remote Sens., 22, pp. 256-263, (1984)