Environmental monitoring programs are increasingly linked to remotely sensed and field information in order to integrate our descriptions of small-scale processes up to regional and global scales. A major shortcoming of these links is the inability of current methods to incorporate the spatial autocorrelation inherent in remotely sensed and ground-based data while simultaneously resolving the frequently disparate scales of the two types of data. We propose to develop, calibrate, and transfer to the end user community a remote sensing-based geostatistical procedure for coniferous forest characterization and inventory. Field-sampled measurements of forest structure, seral state, and biophysical attributes will be extrapolated by cokriging and stochastic simulation with satellite data to create spatially-explicit geostatistical models of forest characterization. The goal of this research is to develop new methods for the analysis of forest canopy structure, secondary forest regrowth, and forest fire history that take advantage of both the spectral and spatial correlation of ground phenomena and remotely sensed information. The proposed project has four objectives: 1) development of cokriging and stochastic simulation models for height, basal area, leaf area index, and biomass using multiscale satellite imagery and field data; 2) calibration and verification of the models by field data and statistical means; 3) testing the model in two specific applications (insect damage assessment and cover type mapping); and 4) disseminating the algorithms and procedures to the user community by means of software modules, Internet pages, and conventional outreach methods. Initial model development will focus on the lodgepole pine (Pinus contorta var latifolia) forest of the Greater Yellowstone Ecosystem, where we have been conducting forest research for nearly a decade. Models will be analyzed to determine the optimum resolution for mapping forest structure, regrowth, and fire history. Public and private forest managers will directly benefit from the techniques developed and transferred from a NASA Regional Applications Center to a broader user community. The innovative aspects of this research are: 1), using remotely sensed data to predict canopy structure, secondary regrowth, and fire history of coniferous forests by cokriging and stochastic simulation of field-based and remotely sensed data; and 2), determination of the optimum scale for calculation of forest canopy structure, secondary regrowth, and fire history.

Funded by NASA Remote Sensing Applications Research in Agriculture, Forestry, and Range Resources Management (NRA-98-OES-09).

Mark Jakubauskas, Ph.D., P.I.
Kevin Price, Ph.D., P.I.
Edward Martinko, Ph.D., P.I.
Clayton Blodgett, Ph.D. Candidate

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Mark Jakubauskas