Estimating Canopy Fuel Parameters with In-Situ and Remote Sensing Data



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Crown fires, the fastest spreading of all forest fires, can occur in any forest type throughout the United States and the world. The occurrence of crown fires has become increasingly frequent and severe in recent years. The overall aim of this study is to estimate the forest canopy fuel parameters including crown base height (CBH) and crown bulk density (CBD), and to investigate the potential of using airborne lidar data in east Texas. The specific objectives are to: (1) propose allometric estimators of CBD and CBH and compare the results of using those estimators to those produced by the CrownMass/FMAPlus software at tree and stand levels for 50 loblolly pine plots in eastern Texas, (2) develop a methodology for using airborne light detection and ranging (lidar) to estimate CBD and CBH canopy fuel parameters and to simulate fire behavior using estimated forest canopy parameters as FARSITE inputs, and (3) investigate the use of spaceborne ICEsat /GLAS (Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System) lidar for estimating canopy fuel parameters. According to our results from the first study, the calculated average CBD values, across all 50 plots, were 0.18 kg/m? and 0.07 kg/m?, respectively, for the allometric equation proposed herein and the CrownMass program. Lorey?s mean height approach was used in this study to calculate CBH at plot level. The average height values of CBH obtained from Lorey?s height approach was 10.6 m and from the CrownMass program was 9.1 m. The results obtained for the two methods are relatively close to each other; with the estimate of CBH being 1.16 times larger than the CrownMass value. According to the results from the second study, the CBD and CBH were successfully predicted using airborne lidar data with R? values of 0.748 and 0.976, respectively. The third study demonstrated that canopy fuel parameters can be successfully estimated using GLAS waveform data; an R? value of 0.84 was obtained. With these approaches, we are providing practical methods for quantifying these parameters and making them directly available to fire managers. The accuracy of these parameters is very important for realistic predictions of wildfire initiation and growth.