Objectives:

Our global objective is to develop, test, and demonstrate an integrated remote sensing and geostatistical approach for the estimation and broad-area mapping of coniferous forest biophysical parameters. Spatially-explicit estimation of forest biophysical parameters (height, density, basal area, LAI, and biomass) at landscape to regional scales is a critical information component for animal habitat management, insect infestation assessment, fire behavior and spread modeling, and biodiversity assessment. 

In this research, we are addressing several critical questions that must be resolved in order for the integrated remote sensing/geostatistical approach to be usable on an operational basis by forest and natural resource managers in both the commercial and governmental sectors. Key among these questions are the critical spatial scale for geostatistical estimation of forest biophysical parameters and the critical minimum number of field-sampled points required for accurate model outputs.

Our specific objectives are:

• Development of geostatistical models for forest biophysical parameters (height, density, basal area, leaf area index, and biomass) using multiscale satellite imagery and field data.
   
• Calibration and verification of the models by field data and statistical means to determine the critical number of field points and critical spatial scales for estimation of forest biophysical parameters using remote sensing and geostatistics.
   
• Testing the models in two specific forest characterization and inventory applications, specifically forest cover type mapping and insect damage assessment.
   
• Dissemination of the algorithms and procedures to the user community via online tutorials and porting of the algorithms to image processing systems cuurently used by the remote sensing community.