基于GIS邻域分析的无人机倾斜影像阔叶林树高提取方法研究  

Research on Extraction Method of Single Tree Height from UAVOblique Images Broad-Leaved Forest based on GIS NeighborhoodAnalysis

作　　者：廖孟光[1] 李猛 褚楠 李少宁 LIAO Mengguang;LI Meng;CHU Nan;LI Shaoning(School of Earth Sciences and Spatial lnformation Engineering,Hunan University of Science and Technology,Xiangtan 411201,China;National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology,Hunan University of Science and Technology,Xiangtan 411201,China;Instiute for Local Sustainable Development Goals,Hunan University of Science and Technology,Xiangtan 411201,China)

机构地区：[1]湖南科技大学地球科学与空间信息工程学院,湖南湘潭411201 [2]湖南科技大学地理空间信息技术国家地方联合工程实验室,湖南湘潭411201 [3]湖南科技大学区域可持续发展研究院,湖南湘潭411201

出　　处：《遥感技术与应用》2023年第5期1203-1214,共12页Remote Sensing Technology and Application

基　　金：湖南省自然科学基金项目(2022JJ30254);湖南省教育厅基金项目(19C0744);湖南省自然资源科技计划项目(2022-29)资助。

摘　　要：无人机遥感技术可快速获取测区冠层高度模型(CHM),如何从CHM中更加准确识别树顶点,是树高提取的关键。分析了不同窗口类型、窗口大小以及林分郁闭度对树顶点提取的影响,以高校校区为研究区,根据郁闭度选取密集林地和稀疏林地2块局部区域,分别利用GIS矩形邻域分析、GIS圆形邻域分析和局部最大值算法提取树顶点。结果表明:树顶点提取精度不仅受窗口大小、林分郁闭度影响,而且和窗口类型密切相关,且GIS矩形邻域分析提取树顶点的结果更加稳定,精度更高,其F测度值在密集林地最高为78.13%、稀疏林地为96.94%。将基于该结果得到的树顶点对应的提取树高与实地测量的树高值对比,密集林地的均方根误差为37 cm,稀疏林地的均方根误差为39 cm。结果证明了基于小型无人机可见光遥感技术提取较高郁闭度阔叶林树高的可行性,为后续基于冠层高度模型识别树顶点提供方法借鉴,提高树高提取精度。UAV remote sensing technology can quickly obtain the Canopy Height Model(CHM)of the survey area.How to identify tree vertices more accurately from CHM is key to tree height extraction.This paper discusses the influence of different window types,window sizes,and stand canopy density on the extraction of tree vertices.Using the university campus as the study area,two local areas of dense and sparse forest land were selected based on canopy density.GIS rectangular neighborhood analysis,GIS circular neighborhood analysis,and local maximum algorithm are used to extract tree vertices.The results show that the accuracy of tree vertex extraction is not only affected by the window size and canopy density,but also closely related to the window type,and the result of GIS rectangular neighborhood analysis to extract tree vertices is more stable and accurate,and the highest F-Measure value is 78.13%in dense forest,and 96.94%in sparse forest.Comparing the extracted tree heights corresponding to the tree vertices obtained based on this result with the tree height values measured in the field,the RMSE is 37cm for dense forest and 39cm for sparse forest.The results proved the feasibility of extracting tree heights of broad-leaved forests with higher canopy density based on the visible light remote sensing technology of small UAVs,which provided a reference for the subsequent identification of tree vertices based on the canopy height model and improved the accuracy of tree height extraction.

关 键 词：消费级无人机 倾斜测量 树高 局部最大值算法 邻域分析 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]