The majority of biofuels are produced from corn and grain. The drawback to these sources of biofuels is the vast amount of cultivated land needed to produce substantial amounts of biofuel, potentially increasing the price of food and livestock products. Mesquite trees, a type of woody plant, are a proven source of bioenergy feedstock found on semi-arid lands. The overall objectives of this study were to develop algorithms for determining woody plant biomass on rangelands in Texas at plot-level using terrestrial lidar and at the local scale by integrating reference biomass and multispectral imagery. Terrestrial lidar offers a more efficient method for estimating biomass than traditional field measurements. Variables from the terrestrial lidar point cloud 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. Regression models were developed for variables obtained through each processing technique for estimating woody plant, above-ground biomass. Regression models were able to explain 81 percent and 77 percent of the variance associated with the aboveground biomass using percentile height statistics and height bins, respectively. The aboveground biomass map was generated by using the cokriging interpolation method with NDVI and ground biomass data. According to cross-validation, ordinary cokriging estimated biomass accurately (R^2 = 0.99). The results of this study revealed that terrestrial lidar can be used to accurately and efficiently estimate the aboveground biomass of mesquite trees in a semi-arid environment at plot level. Moreover, spatial interpolation techniques proved useful in scaling up biomass estimates to local scale.