@article {419,
	title = {Estimating grassland biomass and Leaf Area Index using ground and satellite data},
	journal = {International Journal of Remote Sensing},
	volume = {15},
	number = {7},
	year = {1994},
	note = {English Article Current Contents/Physical, Chemical \& Earth Sciences. Reprint available from: Friedl MA BOSTON UNIV CTR REMOTE SENSING 675 COMMONWEALTH AVE BOSTON, MA 02215 USA UNIV CALIF SANTA BARBARA CTR REMOTE SENSING \& ENVIRONM OPT SANTA BARBARA, CA 93106 USA LAWRENCE LIVERMORE NATL LAB LIVERMORE, CA 94550 USA NATL CTR ATMOSPHER RES BOULDER, CO 80307 USA 0004},
	pages = {1401-1420},
	abstract = {We compared estimates of regional biomass and LAI for a tallgrass prairie site derived from ground data versus estimates derived from satellite data. Linear regression models were estimated to predict LAI and biomass from Landsat-TM data for imagery acquired on three dates spanning the growing season of 1987 using co-registered TM data and ground measurements of LAI and biomass collected at 27 grassland sites. Mapped terrain variables including burning treatment, land-use, and topographic position were included as indicator variables in the models to acccount for variance in biomass and LAI not captured in the TM data. Our results show important differences in the relationships between Kauth-Thomas greenness (from TM), LAI, biomass and the various terrain variables. In general, site-wide estimates of biomass and LAI derived from ground versus satellite-based data were comparable. However, substantial differences were observed in June. In a number of cases, the regression models exhibited significantly higher explained variance due to the incorporation of terrain variables, suggesting that for areas encompassing heterogeneous land-cover the inclusion of categorical terrain data in calibration procedures is a useful technique. [References: 46] 46},
	keywords = {675 COMMONWEALTH AVE, BOSTON, CTR REMOTE SENSING, MA 02215., Remotely sensed data. Tallgrass prairie. Canopy reflectance. Noaa-avhrr. Vegetation. Photosynthesis. Transpiration. Images. Fife. Earth sciences. Reprint available from: Friedl MA. BOSTON UNIV},
	author = {Friedl, M. A. and Michaelsen, J. and Davis, F. W. and Walker, H. and Schimel, D. S.}
}
@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.}
}
@inbook {500,
	title = {Spatial information for extrapolation of canopy processes: examples from FIFE},
	booktitle = {Scaling Physiological Processes: Leaf to Globe},
	year = {1993},
	pages = {21-38},
	publisher = {Academic Press},
	organization = {Academic Press},
	address = {New York},
	author = {Schimel, D. S. and Davis, F. W. and Kittel, G. T.},
	editor = {Ehleringer, J. R. and Fields, C. B.}
}
@inbook {746,
	title = {Spatial information for extrapolation of canopy processes: examples from FIFE},
	booktitle = {Scaling Physiological Processes: Leaf to Globe},
	year = {1993},
	month = {1993},
	pages = {21-38},
	publisher = {Academic Press},
	organization = {Academic Press},
	address = {New York},
	author = {Schimel, D. S. and Davis, F. W. and Kittel, G. T.},
	editor = {Ehleringer, J. R. and Fields, C. B.}
}
@article {385,
	title = {Covariance of biophysical data with digital topographic and land use maps over the FIFE site},
	journal = {Journal of Geophysical Research-Atmospheres},
	volume = {97},
	number = {ND17},
	year = {1992},
	pages = {19009-19021},
	author = {Davis, F. W. and Schimel, D. S. and Friedl, M. A. and Michaelsen, J. C. and Kittel, T. G. F. and Dubayah, R. and Dozier, J.}
}
