机载激光雷达和高光谱组合系统的亚热带森林估测遥感试验

为了提高森林的类型识别及生物物理参数反演精度,采用国产机载激光雷达和高光谱组合系统(ALHIS),选择湖北典型亚热带森林开展了航空遥感试验,获取了试验区激光雷达点云、高光谱和CCD影像数据,提取了森林高度和优势树种类别信息。对数据的分析表明,激光雷达林分平均高的估测精度达到90.67%,激光雷达估测平均高与地面实测胸径加权平均高之间显著相关(R2=0.73,RMSE=1.29m)。按照优势树种分类结果进行统计,发现马尾松、栓皮栎和其它树种的林分平均高分别为9.62m、9.30m、8.79m,不同树种之间的林分平均高相差不大。高光谱优势树种识别总体精度达到82.00%(Kappa=0.70),试验区森林和非森林面积所占比例分别为60.01%和39.99%,马尾松、栓皮栎和其它树种面积在森林中所占比例分别为59.77%、24.99%和15.23%。试验证明,ALHIS能够同时获取高分辨率的植被遥感特征数据,以用于森林制图、优势树种/树种组识别、碳储量估算及生态环境建模等研究。

The remote sensing experiment on airborne LiDAR and hyperspectral integrated system for subtropical forest estimation

To improve the accuracy of forest ’ s type extraction and biophysical parameters inversion , an aviation experi-ment on the typical subtropical forest area in Hubei was conducted by using the Airborne Light detection and ran -ging and Hyperspectral Integrated System ( ALHIS) , and acquired the point cloud data and the hyperspectral and CCD ( Charge Couple Device ) images.The forest heights were extracted and the dominate tree species were identi-fied by using these data .The estimation accuracy of average height reached 90 .67%at stand level .The correlation between the average height estimated by using the light detection and ranging ( LiDAR) and the average height of field measurements weighted by DBH (diameter at breast height) was significant (R2 =0.73, RMSE=1.29m). According to the dominant tree species classification , the average heights of Pinusmassoniana Lamb., Quercusvaria-bilis Bl.and other species were 9.62m, 9.30m and 8.79m, respectively.The variation between different species was not significant .The classification accuracy of dominant tree species using hyperspectual image was 82.00%(Kappa=0.70).The proportions of the forest area and the non-forest area were 60.01%and 39.99%respective-ly.The proportions of the areas of Pinusmassoniana Lamb., Quercusvariabilis Bl.and other species were 59.77%, 24 .99%and 15 .23%, respectively .The experiment shows that the ALHIS can acquire high resolution remote sensing data describing vegetation characteristics for forest mapping , dominant tree species /group species recogni-tion, carbon estimation, ecological environment modeling , etc.