Use of High-Resolution Remotely Sensed Data in Estimating and Crown Fire Behavior Variables

摘要

Airborne laser scanning (LIDAR) and interferometric synthetic aperture radar (IFSAR) datasets, along with standard aerial photographs, were acquired over two forested sites in Washington State: second-growth Douglas-fir stands at Capitol Forest in moist western Washington, and mixed pine/fir stands at the Mission Creek Fire/Fire Surrogates site in dry eastern Washington. Forest inventory plots were measured and carefully geo-located on both sites. High resolution orthophotographs were generated for each site. Canopy fuel variables were computed for each plot using these ground measurements. For each study site regression analysis was used to develop predictive models relating a variety of LIDAR- and IFSAR-based metrics to the canopy fuel parameters. Strong relationships were found for virtually all parameters between remotely sensed metrics and field-based fuel estimates (R2 ranging from 0.74 to 0.98). Software and algorithms were developed for processing LIDAR data into a suite of vegetation metrics. The Fusion data visualization system and LIDAR Data Visualization (LDV) system allow users to process, display, and fuse LIDAR data with other remote sensing and GIS spatial data. Working with the Remote Sensing Applications Center (RSAC), software distribution and training materials were developed and are available through the RSAC website.