%0 Journal Article %J Remote Sensing of Environment %D 1995 %T Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE %A Friedl, M. A. %A Davis, F. W. %A Michaelsen, J. %A Moritz, M. A. %K Documentation %K General–Field Apparatus) (Mathematical Biology and Statistical Methods) (Ecology Environmental Biology–Bioclimatology and Biometeorology) (Ecology Environmental Biology–Plant) (Biophysics–Biocybernetics (1972- )) (Forestry and Forest Products) Pl %K General–Field Methods) (Methods %K Materials and Apparatus %K Nomenclature and Terminology) (General Biology–Information %K Plantae-Unspecified (General Biology–Taxonomy %K Retrieval and Computer Applications) (Methods %X Biophysical inversion of remotely sensed data is constrained by the complexity of the remote sensing process. Variations in sensor response associated with solar and sensor geometries, surface directional reflectance, topography, atmospheric absorption and scattering, and sensor electrical-optical engineering interact in complex manners that are difficult to deconvolve and quantify in individual images or in time series of images. We have developed a model of the remote sensing process to allow systematic examination of these factors. The model is composed of three main components, including a ground scene model, an atmospheric model, and a sensor model, and may be used to simulate imagery produced by instruments such as the Landsat Thematic Mapper and the Advanced Very High Resolution Radiometer. Using this model, we examine the effect of subpixel variance in leaf area index (LAI) on relationships among LAI, the fraction of absorbed photosynthetically active radiation (FPAR), and the normalized difference vegetation index (NDVI). To do this, we use data from the first ISLSICP Field Experiment (FIFE) to parameterize ground scene properties within the model. Our results demonstrate interactions between sensor spatial resolution and spatial autocorrelation in ground scenes that produce a variety of effects in the relationship between both LAI and FPAR and NDVI. Specifically, sensor regularization, nonlinearity in the relationship between LAI and NDVI, and scaling the NDVI all influence the range, variance, and uncertainty associated with estimates of LAI and FPAR inverted from simulated NDVI data. These results have important implications for parameterization of land surface process models using biophysical variables such as LAI and FPAR estimated from remotely sensed data. %B Remote Sensing of Environment %V 54 %P 233-246 %G eng %0 Book Section %B Ecological Studies Analysis and Synthesis, Vol. 117. Global change and mediterranean-type ecosystems; International Symposium, Valencia, Spain, September 1992 %D 1995 %T Sensitivity of fire regime in chaparral ecosystems in climate change %A Davis, F. W. %A Michaelsen, J. %E Moreno, J. M. %E Oechel, W. C. %K Documentation %K Plantae-Unspecified (General Biology–Information %K Retrieval and Computer Applications) (Mathematical Biology and Statistical Methods) (Ecology Environmental Biology–Bioclimatology and Biometeorology) (Ecology Environmental Biology–Plant) (Biophysics–Biocybernetics (1972- )) Plants Book Chapter Mee %B Ecological Studies Analysis and Synthesis, Vol. 117. Global change and mediterranean-type ecosystems; International Symposium, Valencia, Spain, September 1992 %I Springer-Verlag New York, Inc. %C New York, New York, USA; Berlin, Germany %P 435-456 %G eng %0 Book Section %B Ecological Studies Analysis and Synthesis, Vol. 117. Global change and mediterranean-type ecosystems; International Symposium, Valencia, Spain, September 1992 %D 1995 %T Sensitivity of fire regime in chaparral ecosystems in climate change %A Davis, F. W. %A Michaelsen, J. %E Moreno, J. M. %E Oechel, W. C. %K (Biophysics--Biocybernetics (1972- )) %K (Ecology %K (General Biology--Information, Documentation, Retrieval and Computer Applications) %K (Mathematical Biology and Statistical Methods) %K Book Chapter %K California %K Computer Simulation %K Ecology %K Environmental Biology--Bioclimatology and Biometeorology) %K Environmental Biology--Plant) %K Fire History %K Mathematical Model %K Meeting Paper %K Plantae-Unspecified %K Plants %K Usa %K vegetation %B Ecological Studies Analysis and Synthesis, Vol. 117. Global change and mediterranean-type ecosystems; International Symposium, Valencia, Spain, September 1992 %I Springer-Verlag New York, Inc. %C New York, New York, USA; Berlin, Germany %P 435-456 %8 1995 %G eng %0 Journal Article %J International Journal of Remote Sensing %D 1994 %T Estimating grassland biomass and Leaf Area Index using ground and satellite data %A Friedl, M. A. %A Michaelsen, J. %A Davis, F. W. %A Walker, H. %A Schimel, D. S. %K 675 COMMONWEALTH AVE %K BOSTON %K CTR REMOTE SENSING %K MA 02215. %K Remotely sensed data. Tallgrass prairie. Canopy reflectance. Noaa-avhrr. Vegetation. Photosynthesis. Transpiration. Images. Fife. Earth sciences. Reprint available from: Friedl MA. BOSTON UNIV %X 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 %B International Journal of Remote Sensing %V 15 %P 1401-1420 %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 %0 Journal Article %J Ecology %D 1989 %T Interactions of factors affecting seedling recruitment of Blue oak (Quercus douglasii) in California %A Borchert, M. I. %A Davis, F. W. %A Michaelsen, J. %A Oyler, L. D. %K acorn blue oak cattle exclosures gophers interaction mice predation seedling woodland classification and regression tree (CART) hierarchical cluster analysis stepwise logistic regression American Canyon Agua Escondido San Luis Obispo County northness inde %B Ecology %V 70 %P 389-404 %G eng