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联合资源三号与机载LiDAR的林分平均树高估测 被引量:4

Mean canopy height estimation by combing ZY-3 data and airborne LiDAR
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摘要 提出一种将资源三号(ZY-3)立体影像的空间连续测量特性与LiDAR数据的高精度定位测高优势相结合的林分平均树高估测方法。首先从LiDAR离散点云提取地面点并内插生成分辨率为1m的林区DEM,同时根据点云强度提取与DEM同源且分辨率为1m的正射影像,分别作为ZY-3数据定向处理的高程控制基准和平面控制基准。通过ZY-3多类像对组合提取研究区DSM,其中三视DSM较二视DSM高程精度最佳。基于三视DSM,林区DEM,ZY-3多光谱数据提取的植被指数和野外实测树高数据,利用回归分析方法及高程误差修正方法分别建立了四个树高估测模型,实验表明,经高程误差修正后的改进树高估测模型精度最高,模型Adj R^2=0.913,其精度达到93.29%,是最佳树高估测模型。 A mean canopy height estimation method was proposed by combing the ZY-3 three linear array stereo imagery and airborne LiDAR data.Terrain points extracted from LiDAR discrete point clouds are interpolated into a forestry digital elevation model(DEM).Meanwhile,the orthographic image that has same source as DEM is extracted according to the intensity of LiDAR discrete point clouds.During the directional processing of ZY-3 data,the DEM and the orthographic image is respectively used as elevation control datum and plane control datum.Four DSMs are extracted by matching multi-class stereo imagery pairs.The elevation accuracy of the trifocal DSM is best than two perspectives DSMs.Finally,based on the trifocal DSM,the DEM,vegetation indexes generated and canopy height measured value.Four mean canopy height estimation models were established by using linear regression analysis method and elevation error correction method.The research shows that the improved model corrected by trifocal DSM elevation error is the best model with 93.29%of the highest accuracy and 0.913 of the adjust R2.
作者 邢艳秋 张锦绣 陈世培 高立 关雷 郭慧宇 XING Yanqiu;ZHANG Jinxiu;CHEN Shipei;GAO Li;GUAN Lei;GUO Huiyu(Center for Forest Operations and Environment,Northeast Forestry University,Harbin 150040,Heilongjiang,China;The Third Surveying and Mapping Engineering Institute of Heilongjiang,Harbin 150025,Heilongjiang,China)
出处 《中南林业科技大学学报》 CAS CSCD 北大核心 2018年第11期10-16,共7页 Journal of Central South University of Forestry & Technology
基金 国家林业公益性行业科研专项(201504319)
关键词 资源三号(ZY-3) 机载LIDAR 平均树高 立体像对 植被指数 ZY-3 airborne LiDAR mean canopy height stereo imagery pairs vegetation index
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