@inbook {638,
	title = {Sensitivity of fire regime in chaparral ecosystems in climate change},
	booktitle = {Ecological Studies Analysis and Synthesis, Vol. 117. Global change and mediterranean-type ecosystems; International Symposium, Valencia, Spain, September 1992},
	year = {1995},
	month = {1995},
	pages = {435-456},
	publisher = {Springer-Verlag New York, Inc.},
	organization = {Springer-Verlag New York, Inc.},
	address = {New York, New York, USA; Berlin, Germany},
	keywords = {(Biophysics--Biocybernetics (1972- )), (Ecology, (General Biology--Information, Documentation, Retrieval and Computer Applications), (Mathematical Biology and Statistical Methods), Book Chapter, California, Computer Simulation, Ecology, Environmental Biology--Bioclimatology and Biometeorology), Environmental Biology--Plant), Fire History, Mathematical Model, Meeting Paper, Plantae-Unspecified, Plants, Usa, vegetation},
	author = {Davis, F. W. and Michaelsen, J.},
	editor = {Moreno, J. M. and Oechel, W. C.}
}
@article {722,
	title = {Regression tree analysis of satellite and terrain data to guide vegetation sampling and surveys},
	journal = {Journal of Vegetation Science},
	volume = {5},
	year = {1994},
	note = {JOURNAL ARTICLE; RESEARCH ARTICLE},
	month = {1994},
	pages = {673-686},
	abstract = {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.},
	keywords = {(Aerospace and Underwater Biological Effects--General, (Ecology, (General Biology--Institutions, Administration and Legislation), (Methods, Materials and Apparatus, General--Field Methods), (Methods, Materials and Apparatus, General--Photography), Angiosperms, Biophysical Properties, Ecological Classification, Environmental Biology--Bioclimatology and Biometeorology), Environmental Biology--Plant), Gramineae, International Satellite Land Surface Climatology Program, Methods), monitoring, Monocots, Plants, Research Article, Satellite Imagery, Spermatophytes, Tall Grass Prairie Landscape, Vascular plants},
	author = {Michaelsen, J. and Schimel, D. S. and Friedl, M. A. and Davis, F. W. and Dubayah, R. C.}
}
