Print PageThe woody plant encroachment is a serious problem on rangelands and cultivated lands in Texas. The encroachment directly threatens the grass forage production and reduces the area of cultivated lands. Mesquite trees, a type of woody plant, are a proven source of bioenergy feedstock found on semi-arid lands. The removal of mesquite trees can serve a dual-purpose: to mitigate the negative effects associated with woody plant encroachment and provide bioenergy feedstock. The objective of this study was to develop algorithms for determining available woody plant biomass for bioenergy feedstock on rangelands in North Texas at plot-level using terrestrial lidar remote sensing. Terrestrial lidar remote sensing offers a novel method for estimating biomass than traditional field measurements. Variables from the terrestrial lidar point cloud data were compared to ground measurements of biomass to find a best fitting regression model. Two processing methods were investigated for analyzing the lidar point cloud data, namely: 1) percentile height statistics and 2) a height bin approach.
Results/Conclusions
Regression models were developed for variables obtained through each processing technique for estimating woody plant, aboveground biomass. Regression models were able to explain 81% and 77% of the variance associated with the aboveground biomass using percentile height statistics and height bins, respectively. The comparison of the two processing methods indicated that the height bin approach provided adequate estimation of woody plant biomass. The results of this study revealed that terrestrial lidar point cloud data can be used to accurately and efficiently estimate the aboveground biomass of mesquite trees in a semi-arid environment.
