为解决传统田间植株叶面积参数测量精度低、破坏性大和劳动强度大等问题,文章选取烟草作为研究对象,利用无人机摄影测量进行特征点匹配生成密集点云构建烟草点云表型模型,并利用Lambert球面坐标系将三维坐标转换为球面坐标,分别计算孔...为解决传统田间植株叶面积参数测量精度低、破坏性大和劳动强度大等问题,文章选取烟草作为研究对象,利用无人机摄影测量进行特征点匹配生成密集点云构建烟草点云表型模型,并利用Lambert球面坐标系将三维坐标转换为球面坐标,分别计算孔隙度、有效叶面积指数和丛生指数进而得到真实叶面积指数,以半球摄影法计算的结果作为参考值分析不同空间分辨率下单株和地块尺度叶面积指数的计算精度。结果表明:无人机摄影测量中0.43、0.86、1.29、2.15 cm 4种分辨率点云模型计算的结果和数字半球摄影法计算得到的结果的决定系数R²分别为0.959、0.931、0.967和0.985,相对误差RE分别为11.87%、19.74%、14.96%和11.79%,均方根误差RMSE分别为0.150、0.195、0.136和0.094,相对均方根误差rRMSE分别为20.81%、26.97%、18.87%和13.10%。4种分辨率模型的整体计算精度较高,其中单株烟草和地块尺度烟草最佳计算精度模型均为分辨率2.15 cm的点云模型,计算精度分别为87.29%、94.24%,研究结果表明通过无人机摄影测量获取三维点云能够对喀斯特山区石漠化较为严重地区的烟草LAI进行估算,同时证明无人机点云表型模型估算田间植株叶面积指数具有可行性、准确性和高效性的特点。展开更多
对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据,机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类,但现有的机载MS-LiDAR数...对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据,机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类,但现有的机载MS-LiDAR数据的土地覆盖分类研究所需特征维度过高、算法复杂度高。因此,提出了一种整合空间相关性和归一化差分比率指数(Normalized Difference Ratio Index,NDRI)特征的逐步分类算法。该算法首先融合机载MS-LiDAR数据的多波段独立点云,获取兼具空间位置及其多光谱信息的单一点云数据;然后利用空间邻域增长下的地面滤波算法分离地面和非地面点;接着基于不同目标的激光反射特性差异设计将草地(树木)自地面(非地面)中分离的NDRI指数,并利用类间方差最大原则下的自适应最优NDRI指数实现地面和非地面点的精细分类;最后利用3D多数投票法优化分类结果。采用加拿大Optech Titan实测MS-LiDAR数据测试提出算法的有效性及可行性,实验结果表明:算法的平均总体精度和Kappa系数分别可达90.17%和0.861,可有效实现城区MS-LiDAR数据的三维土地覆盖分类;分步处理的方式更有利于针对具体的分离目标的特点设计简单且有效的规则,算法设计更简单、复杂度低;NDRI可为其他机器学习算法的显著性特征的设计和选择提供理论支撑。展开更多
Leaf area index (LAI) is a key parameter for studying global terrestrial ecology and environment and has great ecological significance. How to accurately measure and calculate structural parameters of trees has become...Leaf area index (LAI) is a key parameter for studying global terrestrial ecology and environment and has great ecological significance. How to accurately measure and calculate structural parameters of trees has become an urgent matter. This paper reports the use of terrestrial laser scanning (TLS) as a measurement tool to achieve accurate LAI estimation through point cloud preprocessing measures, the LeWos algorithm, and voxel methods. The accuracy and feasibility of this indirect measurement method were explored. It is found that the single wood structure parameters extracted from TLS have a good linear relationship with manual measurement, and the extraction errors meet the requirements of real-scene conversion. The study also found when the voxel size is consistent with the minimum distance of the point cloud set by TLS instrument, it has a strong correlation with the measured value of canopy analyser. These results lay the foundation for conveniently and quickly obtaining structural parameters of trees, tree growth state detection, and canopy ecological benefit assessment.展开更多
文摘为解决传统田间植株叶面积参数测量精度低、破坏性大和劳动强度大等问题,文章选取烟草作为研究对象,利用无人机摄影测量进行特征点匹配生成密集点云构建烟草点云表型模型,并利用Lambert球面坐标系将三维坐标转换为球面坐标,分别计算孔隙度、有效叶面积指数和丛生指数进而得到真实叶面积指数,以半球摄影法计算的结果作为参考值分析不同空间分辨率下单株和地块尺度叶面积指数的计算精度。结果表明:无人机摄影测量中0.43、0.86、1.29、2.15 cm 4种分辨率点云模型计算的结果和数字半球摄影法计算得到的结果的决定系数R²分别为0.959、0.931、0.967和0.985,相对误差RE分别为11.87%、19.74%、14.96%和11.79%,均方根误差RMSE分别为0.150、0.195、0.136和0.094,相对均方根误差rRMSE分别为20.81%、26.97%、18.87%和13.10%。4种分辨率模型的整体计算精度较高,其中单株烟草和地块尺度烟草最佳计算精度模型均为分辨率2.15 cm的点云模型,计算精度分别为87.29%、94.24%,研究结果表明通过无人机摄影测量获取三维点云能够对喀斯特山区石漠化较为严重地区的烟草LAI进行估算,同时证明无人机点云表型模型估算田间植株叶面积指数具有可行性、准确性和高效性的特点。
文摘对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据,机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类,但现有的机载MS-LiDAR数据的土地覆盖分类研究所需特征维度过高、算法复杂度高。因此,提出了一种整合空间相关性和归一化差分比率指数(Normalized Difference Ratio Index,NDRI)特征的逐步分类算法。该算法首先融合机载MS-LiDAR数据的多波段独立点云,获取兼具空间位置及其多光谱信息的单一点云数据;然后利用空间邻域增长下的地面滤波算法分离地面和非地面点;接着基于不同目标的激光反射特性差异设计将草地(树木)自地面(非地面)中分离的NDRI指数,并利用类间方差最大原则下的自适应最优NDRI指数实现地面和非地面点的精细分类;最后利用3D多数投票法优化分类结果。采用加拿大Optech Titan实测MS-LiDAR数据测试提出算法的有效性及可行性,实验结果表明:算法的平均总体精度和Kappa系数分别可达90.17%和0.861,可有效实现城区MS-LiDAR数据的三维土地覆盖分类;分步处理的方式更有利于针对具体的分离目标的特点设计简单且有效的规则,算法设计更简单、复杂度低;NDRI可为其他机器学习算法的显著性特征的设计和选择提供理论支撑。
文摘Leaf area index (LAI) is a key parameter for studying global terrestrial ecology and environment and has great ecological significance. How to accurately measure and calculate structural parameters of trees has become an urgent matter. This paper reports the use of terrestrial laser scanning (TLS) as a measurement tool to achieve accurate LAI estimation through point cloud preprocessing measures, the LeWos algorithm, and voxel methods. The accuracy and feasibility of this indirect measurement method were explored. It is found that the single wood structure parameters extracted from TLS have a good linear relationship with manual measurement, and the extraction errors meet the requirements of real-scene conversion. The study also found when the voxel size is consistent with the minimum distance of the point cloud set by TLS instrument, it has a strong correlation with the measured value of canopy analyser. These results lay the foundation for conveniently and quickly obtaining structural parameters of trees, tree growth state detection, and canopy ecological benefit assessment.
