山西典型城市地下水污染综合评价及原因分析

以山西省典型城市为重点,在集对分析方法的基础上,将水质评价标准、评价指标及其权重作为物元,建立了地下水质量评价模型,选择pH值、总硬度、矿化度、硫酸盐及氯化物5项水质指标进行评价,通过计算平均联系度确定水质等级。研究结果表明,评价区主要超标物为矿化度、总硬度和硫酸盐。地下水水质主要受到地下水开采、工业与生活废弃物的排放、农业污灌、化肥及农药的施用、采矿活动以及水文地质条件等的影响。     

Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds

Urban forest is a crucial part of urban ecological environment. The accurate estimation of its tree aboveground biomass (AGB) is of significant value to evaluate urban ecological functions and estimate urban forest carbon storage. It has a high accuracy to estimate the forest AGB with field measured canopy structure parameters, but unsuitable for large-scale operations. Limited by low spatial resolution or spectral saturation, the estimated forest AGBs based on various satellite remotely sensed data have relatively low accuracies. In contrast, Unmanned Aerial Vehicle (UAV) remote sensing provides a promising way to accurately estimate the tree AGB of fragmented urban forest. In this study, taking an artificial urban forest in Ma'anxi Wetland Park in Chongqing City, China as an example, we used UAVs equipped with a digital camera and a LiDAR to acquire two point cloud data. One was produced from overlapping images using Structure from Motion (SfM) photogrammetry, and the other was resolved from laser scanned raw data. The dual point clouds were combined to extract individual tree height (H) and canopy radius (R_c), which were then input to the newly established allometric equation with tree H and R_c as predictor variables to obtain the AGBs of all dawn redwood trees in study area. In accuracy assessment, the coefficient of determination (R²) and Root Mean Square Error (RMSE) of extracted H were 0.9341 and 0.59 m; the R² and RMSE of extracted R_c were 0.9006 and 0.28 m; the R² and RMSE of estimated AGB were 0.9452 and 17.59 kg. These results proved the feasibility and effectiveness of applying dual-source UAV point cloud data and the new allometric equation on H and R_c to accurate AGB estimation of urban forest trees.