Inventorying trees in an urban landscape using small-footprint discrete return imaging lidar
Automation of urban tree inventory using remote sensing is needed not only to reduce inventory costs but also to support carbon accounting for urban planners and policy-makers. However, urban areas are heterogeneous and complex, and a more sophisticated approach is needed for using remote-sensing technology like lidar for tree inventory in urban areas than is required for forested environments. Based on remote sensing and field data from a suburban residential area in the central United States, this dissertation presents a methodology for utilizing airborne small-footprint lidar data to inventory urban trees. This dissertation proposes approaches that have the potential to automate three main activities of urban tree inventory -- identifying the locations of trees, classifying the trees into taxonomic categories, and estimating biophysical parameters of individual trees -- using airborne lidar data.
Mathematical morphological operations followed by a marker-controlled watershed segmentation were found to perform well (r = 0.82 to 0.92) to delineate individual tree crowns in urban areas, especially when the trees occur in relatively isolated conditions. Using various distribution metrics of lidar returns, random forests were used to classify individual trees into different taxonomic classes (broadleaves/conifers, genera, and species). A classification accuracy of 80.5% was obtained when separating trees only into broadleaf and conifer classes, 50.0% for genera, and 51.3% for species. Using spectral metrics from high-resolution satellite imagery in addition to lidar-derived predictors improved the classification accuracies by 10.4% (to 90.9%) for broadleaf and conifer, 8.4% (to 58.4%) for genera and 8.8% (to 60.1%) for species compared to using lidar metrics alone. Prediction models to estimate several biophysical parameters such as height, crown area, diameter at breast height, and biomass were developed using lidar point cloud distributional metrics from individual trees. A high level of accuracy was attained for estimating tree height (R2=0.89, RMSE=1.3m), diameter at breast height (R2=0.82, RMSE=9.1cm), crown diameter (R2=0.90, RMSE=0.7m) and biomass (R2=0.67, RMSE=1.2t).
Our results indicate that, while using lidar data alone can achieve the automation of major urban forest inventory tasks to an acceptable level of accuracy, a synergistic use of lidar data with other spectral data such as hyperspectral or orthoimagery, which are usually available at least in the United States for most urban areas, can considerably improve the performance of the lidar-based method.