Extraction of tree crown parameters from high-density pure Chinese fir plantations based on UAV images

Sun Zhao; Pan Lei; Sun Yujun

doi:10.12171/j.1000-1522.20190386

Journal of Beijing Forestry University > 2020 > 42(10): 20-26. > DOI: 10.12171/j.1000-1522.20190386

Sun Zhao, Pan Lei, Sun Yujun. Extraction of tree crown parameters from high-density pure Chinese fir plantations based on UAV images[J]. Journal of Beijing Forestry University, 2020, 42(10): 20-26. DOI: 10.12171/j.1000-1522.20190386

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Extraction of tree crown parameters from high-density pure Chinese fir plantations based on UAV images

National Forestry and Grassland Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing 100083, China

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Received Date: October 08, 2019
Revised Date: November 27, 2019
Available Online: January 07, 2020
Published Date: October 24, 2020

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Abstract

Abstract

Objective Crown width is an important characteristic factor of canopy structure, which directly affects the productivity and vitality of trees. The forest canopy density is one of the important indexes to reflect forest canopy structure and density and to evaluate forest management and logging intensity. UAV has the advantages of easily getting high-resolution remote sensing images with high precision and low cost. Studying the method of extracting canopy parameters using UAV images is of great significance for improving the accuracy and efficiency of forest resource inventory and monitoring.
Method Taking Chinese fir plantation in Jiangle Forest Farm of Fujian Province, eastern China as the research object, using the quadrotor UAV CCD image data as the data source, based on the object-oriented classification method, the canopy parameters of the Chinese fir plantation were extracted from the UAV images. Then the canopy objects were grouped into one group according to the segmentation results of the images, and the number of raster pixels of each canopy object was counted to calculate the canopy width area and canopy density.
Result The object-oriented classification effectively extracted the crown of high canopy density stand. When the segmentation scale was 70, the segmentation of single tree had the best effect. Some single trees were lost during the segmentation process because of over-segmentation and under-segmentation. After completing the segmentation, optimizing the feature space of the segmented object and selecting appropriate classification features, finally the study area was divided into two types: canopy and forest gap. By counting the number of grid points of each object, the calculated stand factors included canopy density and crown area. With the measured data on the ground as reference, the crown area extraction accuracy was 0.829 1, and the forest canopy density measurement accuracy was 0.973 1.
Conclusion The results show that the canopy parameter extraction based on high-resolution image of UAV is also applicable in high-canopy closed forest stands, which can effectively improve the efficiency and accuracy of forest resource survey.
- UAV image,
- object-oriented classification,
- extraction of crown parameter,
- Chinese fir

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References (26)

References

[1]	李赟, 温小荣, 佘光辉, 等. 基于UAV高分影像的杨树冠幅提取及相关性研究[J]. 林业科学研究, 2017, 30(4):653−658. Li Y, Wen X R, She G H, et al. Study on poplar crown extraction and correlation based on UAV high resolution image[J]. Forest Research, 2017, 30(4): 653−658.
[2]	于旭宅, 王瑞瑞, 陈伟杰. 改进分水岭算法在无人机遥感影像树冠分割中的应用[J]. 福建农林大学学报(自然科学版), 2018, 47(4):428−434. Yu X Z, Wang R R, Chen W J. Forest canopy segmentation of UAV remote sensing images using improved watershed algorithm[J]. Journal of Fujian Agricultural and Forestry University (Natural Science Edition), 2018, 47(4): 428−434.
[3]	唐晏. 基于无人机采集图像的植被识别方法研究[D]. 成都: 成都理工大学, 2014. Tang Y. Research on the vegetation identification method based on UAV image acquisition[D]. Chengdu: Chengdu University of Technology, 2014.
[4]	付凯婷. 无人机遥感技术估算桉树蓄积量的研究[D]. 南宁: 广西大学, 2015. Fu K T. UAV remote sensing technology to estimate the research of eucalyptus volume[D]. Nanning: Guangxi University, 2015.
[5]	Brandtberg T, Walter F. Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis[J]. Machine Vision and Applications, 1998, 11(2): 64−73. doi: 10.1007/s001380050091
[6]	Fritz A, Kattenborn T, Koch B. UAV-based photogrammetric point clouds-tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds[C]//International archives of the photogrammetry, remote sensing and spatial information sciences. Rostock: ISPRS, 2013.
[7]	Grznárová A, Mokroš M, Surový P, et al. The crown diameter estimation from fixed wing type of UAV imagery [C]// International archives of the photogrammetry, remote sensing and spatial information sciences. Enschede: ISPRS, 2019.
[8]	李丹, 张俊杰, 赵梦溪. 基于FCM和分水岭算法的无人机影像中林分因子提取[J]. 林业科学, 2019, 55(5):180−187. doi: 10.11707/j.1001-7488.20190520 Li D, Zhang J J, Zhao M X. Extraction of stand factors in UAV image based on FCM and watershed algorithm[J]. Scientia Silvae Sinicae, 2019, 55(5): 180−187. doi: 10.11707/j.1001-7488.20190520
[9]	王枚梅, 林家元, 林沂, 等. 基于无人机可见光影像的亚高山针叶林树冠参数信息自动提取[J]. 林业资源管理, 2017(4):82−88. Wang M M, Lin J Y, Lin Y, et al. Subalpine coniferous forest crown information automatic extraction based on optical UAV remote sensing imagery[J]. Forest Resources Management, 2017(4): 82−88.
[10]	毛学刚, 陈文曲, 魏晶昱, 等. 分割尺度对面向对象树种分类的影响及评价[J]. 林业科学, 2017, 53(12):73−83. doi: 10.11707/j.1001-7488.20171208 Mao X G, Chen W Q, Wei J Y, et al. Effect and evaluation of segmentation scale on object-based forest species classification[J]. Scientia Silvae Sinicae, 2017, 53(12): 73−83. doi: 10.11707/j.1001-7488.20171208
[11]	冯静静, 张晓丽, 刘会玲. 基于灰度梯度图像分割的单木树冠提取研究[J]. 北京林业大学学报, 2017, 39(3):16−23. Feng J J, Zhang X L, Liu H L. Single tree crown extraction based on gray gradient image segmentation[J]. Journal of Beijing Forestry University, 2017, 39(3): 16−23.
[12]	史洁青, 冯仲科, 刘金成. 基于无人机遥感影像的高精度森林资源调查系统设计与试验[J]. 农业工程学报, 2017, 33(11):82−90. doi: 10.11975/j.issn.1002-6819.2017.11.011 Shi J Q, Feng Z K, Liu J C. Design and experiment of high precision forest resource investigation system based on UAV remote sensing images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(11): 82−90. doi: 10.11975/j.issn.1002-6819.2017.11.011
[13]	穆喜云, 张秋良, 刘清旺, 等. 基于机载LiDAR数据的林分平均高及郁闭度反演[J]. 东北林业大学学报, 2015, 43(9):84−89. doi: 10.3969/j.issn.1000-5382.2015.09.017 Mu X Y, Zhang Q L, Liu Q W, et al. Inversion of forest height and canopy closure using airborne LiDAR data[J]. Journal of Northeast Forestry University, 2015, 43(9): 84−89. doi: 10.3969/j.issn.1000-5382.2015.09.017
[14]	李崇贵, 蔡体久. 森林郁闭度对蓄积量估测的影响规律[J]. 东北林业大学学报, 2006, 34(1):15−17. doi: 10.3969/j.issn.1000-5382.2006.01.006 Li C G, Cai T J. Effect of forest canopy density on stock volume estimation[J]. Journal of Northeast Forestry University, 2006, 34(1): 15−17. doi: 10.3969/j.issn.1000-5382.2006.01.006
[15]	郭昱杉, 刘庆生, 刘高焕, 等. 基于标记控制分水岭分割方法的高分辨率遥感影像单木树冠提取[J]. 地球信息科学学报, 2016, 18(9):1259−1266. Guo Y S, Liu Q S, Liu G H, et al. Individual tree crown extraction of high resolution image based on marker-controlled watershed segmentation method[J]. Journal of Geo-Information Science, 2016, 18(9): 1259−1266.
[16]	付尧. 杉木人工林生态系统生物量及碳储量定量估测[D]. 北京: 北京林业大学, 2016. Fu Y. Quantitative estimation of biomass and carbon storage for Chinese fir plantation[D]. Beijing: Beijing Forestry University, 2016.
[17]	孙鸿博, 杨扬, 郭可贵, 等. 基于无人机多源遥感的输电线下树冠分割方法研究[J]. 中南林业调查规划, 2018, 37(2):30−33, 35. Sun H B, Yang Y, Guo K G, et al. Research on the method of subdivision canopy segmentation based on UAV multi-source remote sensing[J]. Central South Forest Inventory and Planning, 2018, 37(2): 30−33, 35.
[18]	何艺, 周小成, 黄洪宇, 等. 基于无人机遥感的亚热带森林林分株数提取[J]. 遥感技术与应用, 2018, 33(1):168−176. He Y, Zhou X C, Huang H Y, et al. Counting tree number in subtropical forest districts based on UAV remote sensing images[J]. Remote Sensing Technology and Application, 2018, 33(1): 168−176.
[19]	王伟. 无人机影像森林信息提取与模型研建[D]. 北京: 北京林业大学, 2015. Wang W. Forest information extraction and model building based on UAV image[D]. Beijing: Beijing Forestry University, 2015.
[20]	肖武, 任河, 吕雪娇, 等. 基于无人机遥感的高潜水位采煤沉陷湿地植被分类[J]. 农业机械学报, 2019, 50(2):177−186. doi: 10.6041/j.issn.1000-1298.2019.02.020 Xiao W, Ren H, Lü X J, et al. Vegetation classification by using UAV remote sensing in coal mining subsidence wetland with high ground-water level[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(2): 177−186. doi: 10.6041/j.issn.1000-1298.2019.02.020
[21]	李赟. 基于UAV高分影像的林木冠幅提取与蓄积量估测研究[D]. 南京: 南京林业大学, 2017. Li Y. Study on crown extraction and forest volume estimation based on UAV high resolution image[D]. Nanjing: Nanjing Forestry University, 2017.
[22]	穆亚南, 丁丽霞, 李楠, 等. 基于面向对象和随机森林模型的杭州湾滨海湿地植被信息提取[J]. 浙江农林大学学报, 2018, 35(6):1088−1097. doi: 10.11833/j.issn.2095-0756.2018.06.012 Mu Y N, Ding L X, Li N, et al. Classification of coastal wetland vegetation in Hangzhou Bay with an object-oriented, random forest model[J]. Journal of Zhejiang A&F University, 2018, 35(6): 1088−1097. doi: 10.11833/j.issn.2095-0756.2018.06.012
[23]	陈济才, 文学虎, 李国明. 基于面向对象的高分影像地表覆盖典型要素快速提取对比研究[J]. 遥感信息, 2014, 29(4):37−40. doi: 10.3969/j.issn.1000-3177.2014.04.008 Chen J C, Wen X H, Li G M. Fast extraction of typical features of land-cover based on object-oriented technique with high-resolution remote sensing imagery[J]. Remote Sensing Information, 2014, 29(4): 37−40. doi: 10.3969/j.issn.1000-3177.2014.04.008
[24]	Trimble Germany GmbH. eCognition developer 9.0 reference book[Z]. Munich: Trimble Germany GmbH, 2014.
[25]	吴见, 彭道黎. 基于面向对象的QuickBird影像退耕地树冠信息提取[J]. 光谱学与光谱分析, 2010, 30(9):2533−2536. doi: 10.3964/j.issn.1000-0593(2010)09-2533-04 Wu J, Peng D L. Tree-crown information extraction of farmland returned to forests using QuickBird image based on object-oriented approach[J]. Spectroscopy and Spectral Analysis, 2010, 30(9): 2533−2536. doi: 10.3964/j.issn.1000-0593(2010)09-2533-04
[26]	毛学刚, 邢秀丽, 李佳蕊, 等. 基于航空正射影像的面向对象林隙识别[J]. 林业科学, 2019, 55(2):87−96. doi: 10.11707/j.1001-7488.20190209 Mao X G, Xing X L, Li J R, et al. Object-oriented recognition of forest gap based on aerial orthophoto[J]. Scientia Silvae Sinicae, 2019, 55(2): 87−96. doi: 10.11707/j.1001-7488.20190209

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Extraction of tree crown parameters from high-density pure Chinese fir plantations based on UAV images

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