Extraction method of mangrove single tree growth parameters based on combinational Airborne-Ground LiDAR

ZHANG Jingrui; REN Guangbo; WU Peiqiang; ZHANG Chengfei; LIU Shanwei; MA Yi

doi:10.11834/jrs.20233148

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Extraction method of mangrove single tree growth parameters based on combinational Airborne-Ground LiDAR
Pages: 1-14(2023)
Published Online： 03 November 2023 ，
DOI： 10.11834/jrs.20233148

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张菁锐，任广波，吴培强，张程飞，刘善伟，马毅.XXXX.联合空地LiDAR的红树林单木生长参数提取方法.遥感学报，XX（XX）： 1-14

ZHANG Jingrui，REN Guangbo，WU Peiqiang，ZHANG Chengfei，LIU Shanwei，MA Yi. XXXX. Extraction method of mangrove single tree growth parameters based on combinational Airborne-Ground LiDAR. National Remote Sensing Bulletin， XX（XX）：1-14
张菁锐，任广波，吴培强，张程飞，刘善伟，马毅.XXXX.联合空地LiDAR的红树林单木生长参数提取方法.遥感学报，XX（XX）： 1-14 DOI： 10.11834/jrs.20233148.

ZHANG Jingrui，REN Guangbo，WU Peiqiang，ZHANG Chengfei，LIU Shanwei，MA Yi. XXXX. Extraction method of mangrove single tree growth parameters based on combinational Airborne-Ground LiDAR. National Remote Sensing Bulletin， XX（XX）：1-14 DOI： 10.11834/jrs.20233148.

摘要

红树林是生长于热带、亚热带海岸潮间带的木本植物群落，是重要碳汇生态系统。激光雷达（Light Detection And Ranging，LiDAR）是获取林木三维结构参数进行生物量估算的重要技术手段。针对仅利用机载LiDAR难以完整描述出红树林三维结构的问题，本文以广东湛江英罗港和广西茅尾海红树林保护区为研究区，利用无人机载和手持式LiDAR获取的点云数据，提出了一种红树冠层下部约束聚类分割方法，实现了对木榄、红海榄、桐花树等不同类型红树的单木分割以及树高、冠幅的提取，并与传统单木分割算法进行了对比分析。结果表明：联合空地LiDAR数据，本文提出的单木分割算法在不同类型红树单木分割中均取得了最高的单木检出率，较传统的冠层高度模型分割法提升了13.4%~26.7%。有效提高了红树树高的提取精度，三种红树树高参数提取值与实测值之间的R

提高了1.8%~42.2%，RMSE减少了3.4%~55.3%。红树冠幅分割结果存在提取值偏小的规律，将能够表征红树冠层交叠密集程度的点云密度变量作为修正因子，经修正，RMSE降低了45.25%~53.33%。

Abstract

Objective Mangrove is a woody plant community growing in the tropical and subtropical coastal intertidal zone and an important carbon sink ecosystem. Light Detection And Ranging (LiDAR) is an important technical mean for obtaining 3D structural parameters of forest trees for biomass estimation. Aiming at the problem that it is difficult to fully describe the 3D structure of mangroves using only airborne LiDAR

the research on the method of mangrove single tree segmentation and parameter extraction based on combinational Airborne-Ground LiDAR helps to explore the applicability of LiDAR in the protection of coastal ecosystems

and provides technical and data support for mangrove biomass estimation and carbon sink capacity assessment.Method This article takes the mangrove nature reserve in Yingluo Port

Zhanjiang

Guangdong and Maowei Sea

Guangxi as the research areas. Propose a clustering segmentation method constrained by the lower part of the mangrove canopy based on point cloud data obtained from UAV and handheld LiDAR. Through the registration of two kinds of data

the positioning error is eliminated. The single tree trunk point cloud obtained by handheld LiDAR was extracted by threshold method. Point cloud fitting was carried out by Hough transformation to extract the relative position information of single tree. Using this information

the crown vertex generated by airborne LiDAR point cloud is constrained

thus improving the segmentation accuracy of single wood. Implemented single tree segmentation and extraction of tree height and crown width for different types of mangroves

and compared them with traditional single tree segmentation algorithms.Result Combined with Airborne-Ground LiDAR

the total detection rate of single tree has increased by 13.4%~26.7% compared to the segmentation method based on the CHM. The accuracy of single tree segmentation of fusion point cloud was the highest

with a total detection rate of 62.7%

and a total of 47 correct single trees were detected

among which the detection rate of three kinds of mangroves was more than 50%. The R

between the extracted and measured values of mangrove height parameters increased by 1.8%~42.2%

and the RMSE decreased by 3.4% ~55.3%. Based on the segmentation results

it is found that the extracted values of mangrove canopy are generally small. By extracting the point cloud density variable that can represent the density of mangrove canopy overlap and evaluating its linear correlation with the mean absolute error of the extracted values

a crown error correction formula is proposed

and the RMSE after correction is reduced by 45.25%~53.33%.Conclusion The results show that combined with Airborne-Ground LidDAR data

the single tree segmentation algorithm proposed in this paper has the highest single tree detection rate. The segmentation method can remove the highest point redundancy more accurately

and effectively improve the extraction accuracy of mangrove tree height

crown width and other 3D spatial structure parameters. The extraction value of mangrove crown width is generally small

and the fitting analysis of density variables and errors can effectively correct the crown width of mangrove.The combined use of handheld and airborne LiDAR data can obtain more accurate and comprehensive structural information such as tree height and crown width than single data

and can be better applied to the study of mangrove ecosystem 3D structure and biomass parameter acquisition.

关键词

遥感红树林激光雷达Hough变换点云聚类算法单木分割三维结构参数树冠偏冠性

Keywords

remote sensingmangroveLiDARHough transformationpoint cloud clustering algorithm3D structure parametersingle tree segmentationcrown slant

references

Burt A, Disney M and Calders K. 2019. Extracting individual trees from lidar point clouds using Treeseg. Methods in Ecology and Evolution, 10,438-445 [DOI:10.1111/2041-210X.13121http://dx.doi.org/10.1111/2041-210X.13121]

Cheng J L. 2016. Study on mangrove afforestation technology on deep-water mudflat Shuidong Habor. Southwest University

成家隆. 2016. 水东湾深水裸滩红树林造林技术研究. 西南大学

Deng J W, Tian Y C, Zhang Q, Tao J, Zhang Y L and Huang S G. 2022. Application of airborne LiDAR in the estimation of the mean height of mangrove stand. Remote Sensing for Natural Resources, 34(3): 129-137

邓静雯, 田义超, 张强, 陶进, 张亚丽, 黄升光. 2022. 机载LiDAR在红树林林分平均高估算中的应用. 自然资源遥感, 34(03): 129-137 [DOI:10.6046/zrzyyg. 2021237http://dx.doi.org/10.6046/zrzyyg.2021237]

Fan G, Xu Z, Wang J, Nan L, Xiao H, Xin Z and Chen F. 2022. Plot-level reconstruction of 3D tree models for aboveground biomass estimation. Ecological Indicators, 142,109211 [DOI: 10.1016/j.ecolind. 2022.109211http://dx.doi.org/10.1016/j.ecolind.2022.109211]

Geng L, Li M Z, Fan W Y and Wang B. 2018. Individual tree structure parameters and effective crown of the stand extraction base on airborn LiDAR data. Forestry Science, 54(07): 62-72

耿林, 李明泽, 范文义, 王斌. 2018. 基于机载LiDAR的单木结构参数及林分有效冠的提取. 林业科学, 54(07): 62-72 [DOI: 10.11707/j.1001-7488. 20180707http://dx.doi.org/10.11707/j.1001-7488.20180707]

Han W D and Gao X M. 1998. Strategy of the mangroves conservation in Zhanjiang. Guangdong Forestry Technology, 1998(03): 18-22

韩维栋, 高秀梅. 1998. 湛江红树林的保护策略. 广东林业科技, 1998(03): 18-22

Hu Y B, Ren G B, Ma Y, Yang J F, Wang J B, An J B, Liang J, Ma Y Q and Song X K. 2022. Coastal wetland hyperspectral classification under the collaborative of subspace partition and infinite probabilistic latent graph ranking. Sci China Tech Sci, 65:

Huo D. 2015. Study on inversion of forest parameters based on airborne LiDAR. Northeast Forestry University

霍达. 2015. 基于机载LiDAR反演森林参数的研究. 东北林业大学

Hyyppa J, Kelle O, Lehikoinen M and Inkinen M. 2001. A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on geoscience and remote sensing, 39(5),969-975 [DOI:10.1109/36.921414http://dx.doi.org/10.1109/36.921414]

Lei L, Yin T, Chai G, Li Y, Wang Y, Jia X and Zhang X. 2022. A novel algorithm of individual tree crowns segmentation considering three-dimensional canopy attributes using UAV oblique photos. International Journal of Applied Earth Observation and Geoinformation, 112,102893 [DOI:10.1016/j.jag. 2022.102893http://dx.doi.org/10.1016/j.jag.2022.102893]

Lei P. 2022. Research on individual tree structure parameter estimation of mangrove based on UAV LiDAR. Guilin University of Technology

雷鹏. 2022. 基于无人机激光雷达的红树林单木结构参数估测研究. 桂林理工大学 [DOI:10.27050/d.cnki.gglgc. 2022.000284http://dx.doi.org/10.27050/d.cnki.gglgc.2022.000284]

Li J, Cheng X and Xiao Z. 2022. A branch-trunk-constrained hierarchical clustering method for street trees individual extraction from mobile laser scanning point clouds. Measurement, 189: 110440 [DOI:10.1016/j.measurement. 2021.110440http://dx.doi.org/10.1016/j.measurement.2021.110440]

Li N, Chen P M and Qin C X. 2014. Study on the relationship among growth factors and biomass of common mangroves in Guangdong province. Guangdong Agricultural Science, 41 (09): 63-68

李娜, 陈丕茂, 秦传新. 2014. 广东省常见红树植物生长因子之间的关系及生物量研究. 广东农业科学, 41(09): 63-68 [DOI:10.16768/j.issn.1004-874x. 2014.09.044http://dx.doi.org/10.16768/j.issn.1004-874x.2014.09.044]

Li W, Guo Q, Jakubowski M K and Kelly M. 2012. A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering and Remote Sensing, 78(1),75-84 [DOI:10.14358/PERS.78.1.75http://dx.doi.org/10.14358/PERS.78.1.75]

Li X J, Jing Y X, Chen G Z and Ren Y L. 2005. Advances on polluted ecology and purification effect of mangrove wetland system. Wetland Science, 2005(04): 315-320

李晓菊, 靖元孝, 陈桂珠, 任延丽. 2005. 红树林湿地系统污染生态及其净化效果的研究概况. 湿地科学, 2005(04): 315-320 [DOI:10.13248/j.cnki.wetlandsci. 2005.04.012http://dx.doi.org/10.13248/j.cnki.wetlandsci.2005.04.012]

Lin P. 2001. A review on the mangrove research in China. Journal of Xiamen University (Natural Science Edition), 2001(02): 592-603

林鹏. 2001. 中国红树林研究进展. 厦门大学学报(自然科学版), 2001(02): 592-603

Lin P, Lu C Y, Wang G L and Chen H X. 1990. Biomass and productivity of Bruguiera Sexangula mangrove forest in Hailian Island, China. Journal of Xiamen University (Natural Science Edition), 29(2): 209-213

林鹏, 卢昌义, 王恭礼, 陈焕雄. 1990. 海莲红树林的生物量和生产力. 厦门大学学报(自然科学版), 29(2): 209-213

Mongus D and Zalik B. 2015. An efficient approach to 3D single tree-crown delineation in LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 108,219-233 [DOI:10.1016/j.isprsjprs. 2015.08.004http://dx.doi.org/10.1016/j.isprsjprs.2015.08.004]

Pan L H, Shi X F, Fan H Q, Li B, Tao Y C, Song C, Su Z N and Tan X R. 2021. Analysis of Mangrove Mortality Due to Rapid Kaolinite Deposition Caused by Reclamation in Tieshan Bay, Guangxi. Journal of Guangxi Academy of Sciences, 37 (03): 270-278

潘良浩, 史小芳, 范航清, 李斌, 陶艳成, 宋超, 苏治南, 谭星儒. 2021. 广西铁山港围填海导致的高岭土快速沉积致红树林死亡原因分析. 广西科学院学报, 37(03): 270-278 [DOI:10.13657/j.cnki.gxkxyxb.20210928.012http://dx.doi.org/10.13657/j.cnki.gxkxyxb.20210928.012]

Popescu S C, Wynne R H and Nelson R F. 2003. Estimating plot-level tree heights with LiDAR: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37,71-95 [DOI:10.1016/S0168-1699(02)00121-7http://dx.doi.org/10.1016/S0168-1699(02)00121-7]

Qiu Q, Zhang W G, Wang L, Cao S S and Sun W. 2021. Estimation of single wood factors of Picea schrenkiana var. tianshanica Forest Based on Backpack LiDAR. Forestry Resource Management, 2021(02): 99-109

邱琴, 张文革, 王蕾, 曹姗姗, 孙伟. 2021. 基于背包式激光雷达的天山云杉林单木因子估测. 林业资源管理, 2021(02): 99-109 [DOI:10.13466/j.cnki.lyzygl. 2021.02.014http://dx.doi.org/10.13466/j.cnki.lyzygl.2021.02.014]

Shendryk I, Broich M, Tulbure M G and Alexandrov S V. 2016. Bottom-up delineation of individual trees from full-waveform airborne laser scans in a structurally complex eucalypt forest. Remote Sensing of Environment, 173,69-83 [DOI:10.1016/j.rse. 2015.11.008http://dx.doi.org/10.1016/j.rse.2015.11.008]

Su Z N, Qiu G L, Fan H Q and Fang C. 2020. Seagrass beds store less carbon but support more macrobenthos than mangrove forests. Marine Environmental Research, 162. [DOI: 10.1016/j.marenvres.2020.105162http://dx.doi.org/10.1016/j.marenvres.2020.105162]

Taureau F, Robin M, Proisy C, Fromard F, Imbert D and Debaine F. 2019. Mapping the mangrove forest canopy using spectral unmixing of very high spatial resolution satellite images. Remote Sensing, 11(3): 367. [DOI:10.3390/rs11030367http://dx.doi.org/10.3390/rs11030367]

Thorsten D, Norbert H, Gerhard K and Rubén J. 2006. Mangroves, a major source of dissolved organic carbon to the oceans. Global Biogeochemical Cycles, 20(1): 72-76. [DOI:10.1029/2005GB002570http://dx.doi.org/10.1029/2005GB002570]

Wang L, Jia M M, Yin D M and Tian J Y. 2019. A review of remote sensing for mangrove forests: 1956–2018. Remote Sensing of Environment, 231. [DOI: 10.1016/j.rse.2019.111223http://dx.doi.org/10.1016/j.rse.2019.111223]

Wu P Q, Ren G B, Zhang C F, Wang H, Liu S W and Ma Y. 2022. Fine identification and biomass estimation of mangroves based on UAV multispectral and LiDAR. National Remote Sensing Bulletin, 26(06): 1169-1181

吴培强, 任广波, 张程飞, 王浩, 刘善伟, 马毅. 2022. 无人机多光谱和LiDAR的红树林精细识别与生物量估算. 遥感学报, 26(06): 1169-1181 [DOI:10.11834/jrs.0221484http://dx.doi.org/10.11834/jrs.0221484]

Wu P Q, Zhang J, Ma Y and Li X M. 2013. Remote sensing monitoring and analysis of the changes of mangrove resources in China in the past 20 years. Advances in marine science, 31(3): 406-414

吴培强, 张杰, 马毅, 李晓敏. 2013. 近20a来我国红树林资源变化遥感监测与分析. 海洋科学进展, 31(3): 406-414

Xu W M, Yang H, Li Z H, Cheng J P, Lin H T and Yang G J. 2022. Single tree segmentation in close-planting orchard using UAV digital image. Journal of Wuhan University (Information Science Edition), 47(11): 1906-1916

徐伟萌, 杨浩, 李振洪, 程金鹏, 林哈特, 杨贵军. 2022. 利用无人机数码影像进行密植型果园单木分割. 武汉大学学报(信息科学版), 47(11): 1906-1916 [DOI:10.13203/j.whugis20220024http://dx.doi.org/10.13203/j.whugis20220024]

Zhao X, Guo Q, Su Y and Xue B. 2016. Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas. ISPRS Journal of Photogrammetry and Remote Sensing, 117,79-91 [DOI:10.1016/j.isprsjprs. 2016.03.016http://dx.doi.org/10.1016/j.isprsjprs.2016.03.016]

）（ J F, Liu Q W, Cui X M and Zhang W B. 2022. Extraction of individual tree parameters by combining terrestrial and UAV LiDAR. Transactions of the Chinese Society of Agricultural Engineering, 38(14): 51-58

朱俊峰, 刘清旺, 崔希民, 张文博. 2022. 地基与无人机激光雷达结合提取单木参数. 农业工程学报, 38(14): 51-58 [DOI:10.11975/j.issn.1002-6819. 2022.14.007http://dx.doi.org/10.11975/j.issn.1002-6819.2022.14.007]

Zhu K F, Liao B W and Zhang J E. 2011. Studies on the biomass of mangrove plantation of Sonneratia apetala and Bruguiera gymnorrhiza in the wetland of Nansha in Guangzhou city. Forestry Science Research, 24(04): 531-536

朱可峰, 廖宝文, 章家恩. 2011. 广州市南沙红树植物无瓣海桑、木榄人工林生物量的研究. 林业科学研究, 24(04): 531-536 [DOI:10.13275/j.cnki.lykxyj. 2011.04.016http://dx.doi.org/10.13275/j.cnki.lykxyj.2011.04.016]

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