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  • 软件名称:基于机载LiDAR点云数据森林郁闭度估测
  • 软件大小: 0.00 B
  • 软件评级: ★★★
  • 开 发 商: 赵勋,岳彩荣,李春干,谷雷,张国飞
  • 软件来源: 《遥感技术与应用》
  • 解压密码:www.gissky.net

资源简介

摘要: 郁闭度是反映森林数量和质量的重要参数,是森林调查的重要因子之一。以广西壮族自治区高峰林场试验区获取的机载LiDAR点云数据为基础,基于二维冠层高度模型(Canopy Height Model,CHM)和三维点云开展了森林郁闭度估测研究。使用实地调查的105块样地作为验证参考数据对郁闭度估测结果进行了精度评价,结果表明:基于二维CHM估测郁闭度与实测值之间的R2=0.388,RMSE=0.17;而基于三维点云估测郁闭度采用了2种方法:第一种方法采用归一化后2 m以上高度植被点云密度与归一化后所有点云密度比值估测郁闭度,估测结果与实测值之间的R2=0.467,RMSE=0.13。第二种方法采用归一化后2 m以上高度第一次回波植被点云密度与归一化后第一次回波所有点云密度比值估测郁闭度,估测结果与实测值之间的R2=0.478,RMSE=0.12;基于三维点云的2种方法估测林分郁闭度的精度皆优于基于二维CHM的方法,基于三维点云估测林分郁闭度方法中,第二种方法的精度优于第一种方法。 关键词: 机载LiDAR点云数据;  冠层高度模型;  高峰林场;  郁闭度     Abstract: Canopy density is an important parameter reflecting the quantity and quality of forest, and also one of the important factors of forest survey. This paper is based on the airborne LiDAR point cloud data obtained from the experimental area of Gaofeng Forest Farm in Guangxi Zhuang autonomous region, the canopy height model (CHM) and three-dimensional point cloud were used to estimate forest canopy density. The accuracy of canopy density estimation results were evaluated by using 105 field samples as reference data. The results showed that R2=0.388 and RMSE=0.17 between canopy density estimation and measured values based on canopy height model(CHM).Two methods are used to estimate canopy density based on three-dimensional point cloud: In the first method, canopy density was estimated by the ratio of point cloud density of vegetation at a height of more than 2 meters after normalization to density of all point clouds after normalization, and R2=0.467 and RMSE=0.13 between the estimated results and measured values. In the second method, the density of vegetation point cloud in the first echo at a height of more than 2 meters after normalization and the density ratio of all point cloud in the first echo after normalization were used to estimate canopy density. R2=0.478 and RMSE=0.12 between the estimated results and the measured values. The accuracy of the two methods based on three-dimensional point cloud is better than that based on Canopy Height Model (CHM). Among the methods based on three-dimensional point cloud, the accuracy of the second method is better than that of the first method.

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