Abstract:In precision forestry, tree species identification is key to evaluating the role of forest ecosystems in the provision of ecosystem services, such as carbon sequestration and assessing their effects on climate regulation and climate change. In this study, we investigated the effectiveness of tree species classification of urban forests using aerial-based HyMap hyperspectral imagery and light detection and ranging (LiDAR) data. First, we conducted an object-based image analysis (OBIA) to segment individual tree crowns present in LiDAR-derived Canopy Height Models (CHMs). Then, hyperspectral values for individual trees were extracted from HyMap data for band reduction through Minimum Noise Fraction (MNF) transformation which allowed us to reduce the data to 20 significant bands out of 118 bands acquired. Finally, we compared several different classifications using Random Forest (RF) and Multi Class Classifier (MCC) methods. Seven tree species were classified using all 118 bands which resulted in 46.3% overall classification accuracy for RF versus 79.6% for MCC. Using only the 20 optimal bands extracted through MNF, both RF and MCC achieved an increase in overall accuracy to 87.0% and 88.9%, respectively. Thus, the MNF band selection process is a preferable approach for tree species classification when using hyperspectral data. Further, our work also suggests that RF is heavily disadvantaged by the high-dimensionality and noise present in hyperspectral data, while MCC is more robust when handling high-dimensional datasets with small sample sizes. Our overall results indicated that individual tree species identification in urban forests can be accomplished with the fusion of object-based LiDAR segmentation of crowns and hyperspectral characterization.

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The drop-weight impacts on a thin layer of cyclotetramethylene tetranitramine (HMX) explosive particles have been performed. The drop-weight impact machine is equipped with high-speed photographic techniques. Effects of drop heights and the amount of particles on compaction, deformation, and thermal responses were investigated. Considering the contact plasticity, friction, melting, fracture, as well as chemical reaction, an analytical model based on heating and kinematics equations for a single layer of explosive particles has been developed. The calculated average pressure, loading time and time-to-ignition agree well with experimental measured ones. Threshold conditions for ignition can be derived from the predicted temperature history curves. Individual particles' pressure-strain exhibited stress-strain-like relationships, from which close relations can be found between experimental phenomena and the material's softening/hardening characteristics.