基于无人机影像的树顶点和树高提取及其影响因素分析

doi:10.13466/j.cnki.lyzygl.2019.04.016

林业资源管理 ›› 2019›› Issue (4): 107-116.doi: 10.13466/j.cnki.lyzygl.2019.04.016

基于无人机影像的树顶点和树高提取及其影响因素分析

刘江俊¹^,²(), 高海力³, 方陆明¹^,²(), 郑辛煜¹^,⁴, 姜广宇¹^,²

1.浙江农林大学信息工程学院,杭州 311300
2.浙江省林业智能监测与信息技术研究重点实验室,杭州 311300
3.浙江省公益林和国有林场管理总局,杭州 311300
4.国家林业与草原局林业感知技术与智能装备重点实验室,杭州 311300

收稿日期:2019-06-27 修回日期:2019-07-19 出版日期:2019-08-28 发布日期:2020-10-20
通讯作者: 方陆明
作者简介:刘江俊(1994-),男,浙江江山人,在读硕士,主要从事林业信息技术方面的研究工作。Email: 904300315@qq.com
基金资助:
浙江省科技重点研发计划资助项目(2018C02013);国家自然科学基金面上项目(31670641)

Tree Vertex and Height Extraction Based on UAV Imagery and Analysis on its Influencing Factors

LIU Jiangjun¹^,²(), GAO Haili³, FANG Luming¹^,²(), ZHENG Xinyu¹^,⁴, JIANG Guangyu¹^,²

1. School of Information Engineering,Zhejiang A&F University,Hangzhou,Zhejiang 311300,China
2. Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou, Zhejiang 311300, China
3. Hangzhou Public Welfare Forests and State-owned Forest Farm Mamagement Bureau, Hangzhou, Zhejiang 311300, China
4. Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment,Hangzhou, Zhejiang 311300, China

Received:2019-06-27 Revised:2019-07-19 Online:2019-08-28 Published:2020-10-20
Contact: FANG Luming

摘要/Abstract

摘要：

对基于无人机影像生成的树冠高度模型(Canopy Height Model,CHM),采用局部最大值算法进行树顶点和树高提取的可行性进行了探讨。此外,还探讨了分辨率、窗口大小对于树顶点提取的影响。以密集的针阔混交林为样地,利用SfM(Structure from Motion)算法结合无人机影像对研究区进行三维重建,得到点云、数字表面模型(Digital Surface Model,DSM)、数字高程模型(Digital Elevation Model,DEM)等一系列三维数据并生成CHM。然后,对不同分辨率的CHM使用不同的平滑窗口大小、移动窗口大小组合进行树顶点的提取并对结果进行精度评价。当CHM分辨率为0.4m,平滑窗口大小为3×3像元,移动窗口大小为3×3像元时,树顶点的提取精度最高,F测度为77.08%。将基于该组合提取正确的37个树顶点对应的提取树高与实地测量得到的树高对比,R ²为0.966 9,RMSE为1.411 4m,rRMSE=10.69%。研究结果表明:利用无人机影像可以较好地提取复杂树林的树顶点和树高;基于局部最大值算法提取树顶点,需要根据实际情况确定CHM的分辨率、平滑窗口大小和移动窗口大小,以获得最佳提取结果。

关键词: 无人机, 树顶点, 树高, 分辨率, 窗口大小

Abstract:

In this paper,the feasibility of using local maximum algorithm to extract tree vertices and tree height based on the canopy height model(CHM) generated by UAV imagery was discussed.In addition,the effect of resolution and window size on tree vertex extraction was also discussed.In this study,a dense conifer-broadleaf forest is used as sample plot.The SfM(Structure from Motion) algorithm was combined with the UAV images to reconstruct the research area in three dimensions,and a series of three-dimensional data such as point cloud,digital surface model(DSM),digital elevation model(DEM) and CHM were generated.Then,for different resolutions of CHM,different smoothing window size and moving window size combinations were used to extract the tree vertices and the accuracies of results were evaluated.When the CHM resolution is 0.4m,the size of smoothing window is 3×3 pixels,and the size of moving window is 3×3 pixels,the extraction accuracy of tree vertex is the highest,and the F-Measure is 77.08%.The extracted tree heights corresponding to the 37 tree vertices extracted based on the combination was compared with the tree heights measured in the field,and the R ²,RMSE,rRMSE is 0.9669,1.4114m,10.69% respectively.The results showed that UAV imagery can be used to extract tree vertices and tree heights of complex forests.Extracting the tree vertices based on the local maximum algorithm needs to determine the resolution of the CHM,the smoothing window size and the moving window size according to the actual situation to obtain the best extraction result.

Key words: unmanned aerial vehicle, tree vertex, tree height, resolution, window size

中图分类号:

S758

刘江俊, 高海力, 方陆明, 郑辛煜, 姜广宇. 基于无人机影像的树顶点和树高提取及其影响因素分析[J]. 林业资源管理, 2019,(4): 107-116.

LIU Jiangjun, GAO Haili, FANG Luming, ZHENG Xinyu, JIANG Guangyu. Tree Vertex and Height Extraction Based on UAV Imagery and Analysis on its Influencing Factors[J]. FOREST RESOURCES WANAGEMENT, 2019,(4): 107-116.

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参考文献 23

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树高	最小值	中位数	最大值	平均数	标准差
实测树高/m	7.5	14.1	18.3	13.2	2.6330
提取树高/m	6.6	12.5	16.4	11.9	2.4402

基于无人机影像的树顶点和树高提取及其影响因素分析

Tree Vertex and Height Extraction Based on UAV Imagery and Analysis on its Influencing Factors

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图/表 12

参考文献 23

相关文章 15

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Metrics

本文评价

移动窗口大小/像元	平滑窗口大小/像元
移动窗口大小/像元	3×3	5×5	7×7	9×9	11×11	13×13	15×15	17×17
3×3	580	293	168	130	59	65	54	43
5×5	430	234	150	110	78	58	49	40
7×7	297	188	127	93	69	54	45	38
9×9	260	144	110	80	64	52	40	37
11×11	144	115	94	70	53	43	38	33
13×13	113	97	81	59	51	43	34	28
15×15	86	77	66	52	43	40	34	27
17×17	74	64	57	48	41	37	31	27
19×19	57	51	45	42	38	34	28	26
21×21	46	40	40	36	33	31	26	25

CHM	参考/株	提取/株	FP/株	FN/株	TP/株	R/%	P/%	F/%
CHM_0.1m	48	48	13	13	35	72.91	72.91	72.91
CHM_0.2m	48	50	13	11	37	74.00	77.08	75.51
CHM_0.3m	48	48	12	12	36	75.00	75.00	75.00
CHM_0.4m	48	48	11	11	37	77.08	77.08	77.08

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