Historical and current distribution ranges and loss of mega-herbivores and carnivores of Asia.

Ecosystem functioning is dependent a lot on large mammals, which are, however, vulnerable and facing extinction risks due to human impacts mainly. Megafauna of Asia has been declining for a long, not only in numbers but also in their distribution ranges. In the current study, we collected information on past and current occurrence and distribution records of Asia's megafauna species. We reconstructed the historical distribution ranges of the six herbivores and four carnivores for comparison with their present ranges, to quantify spatially explicit levels of mega-defaunation. Results revealed that historically the selected megafauna species were more widely distributed than at current. Severe range contraction was observed for the Asiatic lion, three rhino species, Asian elephant, tigers, and tapirs. Defaunation maps generated have revealed the vanishing of megafauna from parts of the East, Southeast, and Southwest Asia, even some protected Areas losing up to eight out of ten megafaunal species. These defaunation maps can help develop future conservation policies, to save the remaining distribution ranges of large mammals.

Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping.

Urban land cover map is essential for urban planning, environmental studies and management. This paper aims to demonstrate the potential of geometric and radiometric features derived from LiDAR waveform and point cloud data in urban land cover mapping with both parametric and non-parametric classification algorithms. Small footprint LiDAR waveform data acquired by RIEGL LMS-Q560 in Zhangye city, China is used in this study. A LiDAR processing chain is applied to perform waveform decomposition, range determination and radiometric characterization. With the synergic utilization of geometric and radiometric features derived from LiDAR data, urban land cover classification is then conducted using the Maximum Likelihood Classification (MLC), Support Vector Machines (SVM) and random forest algorithms. The results suggest that the random forest classifier achieved the most accurate result with overall classification accuracy of 91.82% and the kappa coefficient of 0.88. The overall accuracies of MLC and SVM are 84.02, and 88.48, respectively. The study suggest that the synergic utilization of geometric and radiometric features derived from LiDAR data can be efficiently used for urban land cover mapping, the non-parametric random forest classifier is a promising approach for the various features with different physical meanings.

Range determination for generating point clouds from airborne small footprint LiDAR waveforms.

This paper presents a range determination approach for generating point clouds from small footprint LiDAR waveforms. Waveform deformation over complex terrain area is simulated using convolution. Drift of the peak center position is analyzed to identify the first echo returned by the illuminated objects in the LiDAR footprint. An approximate start point of peak in the waveform is estimated and adopted as the indicator of range calculation; range correction method is proposed to correct pulse widening over complex terrain surface. The experiment was carried out on small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. The results suggest that the proposed approach generates more points than standard commercial products; based on field measurements, a comparative analysis between the point clouds generated by the proposed approach and the commercial software GeocodeWF indicates that: 1). the proposed approach obtained more accurate tree heights; 2). smooth surface can be achieved with low standard deviation. In summary, the proposed approach provides a satisfactory solution for range determination in estimating 3D coordinate values of point clouds, especially for correcting range information of waveforms containing deformed peaks.