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Individual tree extraction from terrestrial laser scanning data via graph pathing

Forest Ecosystems 2021年第4期8卷 903-913页

作者： Di Wang xinlian liang Gislain II Mofack Olivier Martin-Ducup Department of Remote Sensing Science and Technology School of Electronic EngineeringXidian University710077 Xi’anChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University430079 WuhanChina Department of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute02430 MasalaFinland Plant Systematics and Ecology Laboratory Higher Teacher’s Training CollegeUniversity of Yaounde IBP 337 YaoundeCameroon AMAP Univ MontpellierIRDCNRSCIRADINRAEF-34000 MontpellierFrance

Background:Individual tree extraction from terrestrial laser scanning(TLS)data is a prerequisite for tree-scale estimations of forest biophysical *** task currently is undertaken through laborious and time-consuming manual assistance and quality *** study presents a new fully automatic approach to extract single trees from large-area TLS *** data-driven method operates exclusively on a point cloud graph by path finding,which makes our method computationally efficient and universally applicable to data from various forest ***:We demonstrated the proposed method on two openly available ***,we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest ***,we successfully extracted 270 trees from one hectare temperate *** validation resulted in a mean Intersection over Union(mIoU)of 0.82 for single crown segmentation,which further led to a relative root mean square error(RMSE%)of 21.2% and 23.5% for crown area and tree volume estimations,***:Our method allows automated access to individual tree level information from TLS point *** proposed method is free from restricted assumptions of forest *** is also computationally efficient with an average processing time of several seconds for one million *** is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications,ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.

关键词： Point cloud Segmentation Tree extraction Graph pathing

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Cloth simulation-based construction of pitfree canopy height models from airborne LiDAR data

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Forest Ecosystems 2020年第1期7卷 1-13页

作者： Wuming Zhang Shangshu Cai xinlian liang Jie Shao Ronghai Hu Sisi Yu Guangjian Yan State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of SciencesBeijing Engineering Research Center for Global Land Remote Sensing ProductsInstitute of Remote Sensing Science and EngineeringFaculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Department of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute02431 MasalaFinland College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China ICube Laboratory UMR 7357 CNRS-University of Strasbourg300 bd Sebastien BrantCS10413F-67412 Illkirch CedexFrance Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory ***:We develop an algorithm based on cloth simulation for constructing a pit-free ***:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy *** pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free ***,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

关键词： Data pits Tree crown Canopy height models Cloth simulation Pit-free

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Quantification of occlusions influencing the tree stem curve retrieving from single-scan terrestrial laser scanning data

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Forest Ecosystems 2019年第4期6卷 285-297页

作者： Peng Wan Tiejun Wang Wuming Zhang xinlian liang Andrew K.Skidmore Guangjian Yan State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing Engineering Research Centre for Global Land Remote Sensing Products Institute of Remote Sensing Science and Engineering Faculty of Geographical Science Beijing Normal University

Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem *** study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning(TLS) data,evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-scan TLS to estimate stem ***: We proposed an index, occlusion rate, to quantify the occlusion level in TLS data. We then analyzed three influencing factors for the occlusion rate: the percentage of basal area near the scanning center, the scanning distance and the source of occlusions. Finally, we evaluated the effects of occlusions on stem curve estimates from single-scan TLS ***: The results showed that the correlations between the occlusion rate and the stem curve estimation accuracies were strong(r = 0.60–0.83), so was the correlations between the occlusion rate and its influencing factors(r = 0.84–0.99). It also showed that the occlusions from tree stems were the main factor of the low detection rate of stems, while the non-stem components mainly influenced the completeness of the retrieved stem ***: Our study demonstrates that the occlusions significantly affect the accuracy of stem curve retrieval from the single-scan TLS data in a typical-size(32 m × 32 m) forest plot. However, the single-scan mode has the capacity to accurately estimate the stem curve in a small forest plot(< 10 m × 10 m) or a plot with a lower occlusion rate, such as less than 35% in our tested datasets. The findings from this study are useful for guiding the practice of retrieving forest parameters using single-scan TLS data.

关键词： Stem curve Stem volume Terrestrial laser scanning Scan mode

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Fast registration of forest terrestrial laser scans using key points detected from crowns and stems

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International Journal of Digital Earth 2020年第12期13卷 1585-1603页

作者： Wenxia Dai Bisheng Yang xinlian liang Zhen Dong Ronggang Huang Yunsheng Wang Jiri Pyörälä Antero Kukko State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China Department of Remote Sensing and Photogrammetry Finnish Geospatial Research InstituteFGI(Former Finnish Geodetic Institute)MasalaFinland Institute of Geodesy and Geophysics Chinese Academy of SciencesWuhanPeople’s Republic of China

Registration of TLS data is an important prerequisite to overcome the limitations of *** existing registration methods rely on stems to determine the transformation ***,the complexity of the registration problem increases dramatically as the number of stems *** is tricky to reduce the stems and determine the valid ones that can provide reliable registration transformation without a knowledge of the two *** paper presents an automatic and fast registration of TLS point clouds in forest *** reduces stems by selecting from the overlap areas,which are recovered from the mode-based key points that are detected from *** proposed method was tested in a managed forest in Finland,and was compared with the stem-based registration method without reducing *** experiments demonstrated that the mean rotation error was 2.09′,and the mean errors in horizontal and vertical translation were 1.13 and 7.21 cm,*** with the stem-based method,the proposed method improves the registration efficiency significantly(818 s vs 96 s)and achieves similar results in terms of the mean registration errors(1.94′for rotation error,0.83 and 7.38 cm for horizontal and vertical translation error,respectively).

关键词： Terrestrial laser scanning(TLS) forest point clouds registration

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Automated registration of wide-baseline point clouds in forests using discrete overlap search

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Forest Ecosystems 2022年第6期9卷 852-877页

作者： Onni Pohjavirta xinlian liang Yunsheng Wang Antero Kukko Jiri Pyorala Eric Hyyppa Xiaowei Yu Harri Kaartinen Juha Hyyppa Department of Remote Sensing and Photogrammetry Finnish Geospatial Research InstituteVuorimiehentie 5Espoo02150Finland The State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan430070China Department of Forest Sciences University of HelsinkiHelsinkiFI-00014Finland Department of Geography and Geology University of TurkuTurkuFI-20500Finland

Forest is one of the most challenging environments to be recorded in a three-dimensional(3D)digitized geometrical representation,because of the size and the complexity of the environment and the data-acquisition constraints brought by on-site *** studies have indicated that the data-acquisition pattern can have more influence on the registration results than other *** practice,the ideal short-baseline observations,i.e.,the dense collection mode,is rarely feasible,considering the low accessibility in forest environments and the commonly limited labor and time *** wide-baseline observations that cover a forest site using a few folds less observations than short-baseline observations,are therefore more preferable and commonly ***,the wide-baseline approach is more challenging for data registration since it typically lacks the required sufficient overlaps between *** now,a robust automated registration solution that is independent of special hardware requirements has still been *** is,the registration accuracy is still far from the required level,and the information extractable from the merged point cloud using automated registration could not match that from the merged point cloud using manual *** paper proposes a discrete overlap search(DOS)method to find correspondences in the point clouds to solve the low-overlap problem in the wide-baseline point *** proposed automatic method uses potential correspondences from both original data and selected feature points to reconstruct rough observation geometries without external knowledge and to retrieve precise registration parameters at *** extensive experiment was carried out with 24 forest datasets of different conditions categorized in three difficulty *** performance of the proposed method was evaluated using various accuracy criteria,as well as based on data acquired from different hardware,platforms,viewing persp

关键词： Close-range sensing Forest Registration Point cloud Wide-baseline Terrestrial laser scanning Unmanned aerial vehicle Drone In situ Discrete overlap search

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TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest

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International Journal of Digital Earth 2017年第7期10卷 701-718页

作者： Yi Lin Tian Wei Bin Yang Yuri Knyazikhin Yuhu Zhang Hisashi Sato Xing Fang xinlian liang Lei Yan Shanlin Sun Institute of Remote Sensing and GIS Beijing Key Lab of Spatial Information Integration and Its ApplicationsSchool of Earth and Space SciencesPeking UniversityBeijingPeople’s Republic of China North China University of Science and Technology TangshanPeople’s Republic of China Department of Earth and Environment Boston UniversityBostonUSA College of Resources Environment and Tourism Capital Normal UniversityBeijingPeople’s Republic of China Research Institute for Global Change JAMSTECKanagawaJapan School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China Finnish Geospatial Research Institute MasalaFinland Guilin University of Aerospace Technology GuilinPeople’s Republic of China

In forest ecosystem studies,tree stem structure variables(SSVs)proved to be an essential kind of parameters,and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon *** this newly emerging task,satellite imagery such as WorldView-2 panchromatic images(WPIs)is used as a potential solution for co-prediction of tree-level multifarious SSVs,with static terrestrial laser scanning(TLS)assumed as a‘bridge’.The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters,and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models(termed as Model1s and Model2s).In the case of Picea abies,Pinus sylvestris,Populus tremul and Quercus robur in a boreal forest,tests showed that Model1s and Model2s for different tree species can be derived(*** maximum R^(2)=0.574 for ***).Overall,this study basically validated the algorithm proposed for co-prediction of multifarious SSVs,and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling,which is useful for large-scale investigations of forest understory,macroecosystem ecology,global vegetation dynamics and global carbon cycle.

关键词： Tree stem structure variable(SSV) WorldView-2 panchromatic image(WPI) static terrestrial laser scanning(TLS) allometric relationship co-prediction model

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Seamless integration of above-and undercanopy unmanned aerial vehicle laser scanning for forest investigation

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Forest Ecosystems 2021年第1期8卷 124-138页

作者： Yunsheng Wang Antero Kukko Eric Hyyppä Teemu Hakala Jiri Pyörälä Matti Lehtomäki Aimad El Issaoui Xiaowei Yu Harri Kaartinen xinlian liang Juha Hyyppä Department of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute FGIThe National Land Survey of Finland02431 MasalaFinland Department of Forest Sciences University of HelsinkiFI-00014 HelsinkiFinland Department of Geography and Geology University of TurkuFI-20500 TurkuFinland

Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor *** study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single *** advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low ***:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to *** GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve ***:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial ***,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest *** studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.

关键词： Forest In situ Inventory Above canopy Under canopy Unmanned aerial vehicle Laser scanning Point cloud Close range remote sensing

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点云信息提取研究进展和展望

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测绘学报 2017年第10期46卷 1460-1469页

作者：张继贤林祥国梁欣廉国家测绘产品质量检验测试中心北京100830 中国测绘科学研究院北京100830 芬兰地理信息研究所芬兰基尔科努米02431

点云是目前摄影测量、遥感、计算机视觉等多个领域广泛应用的数据源之一,而信息提取是点云处理、分析和应用的必经环节。为此,学术界已经提出了大量点云信息提取方法。本文从基元类型、提取特征、特征选择与分类器等3个视角概括了点云... 详细信息

点云是目前摄影测量、遥感、计算机视觉等多个领域广泛应用的数据源之一,而信息提取是点云处理、分析和应用的必经环节。为此,学术界已经提出了大量点云信息提取方法。本文从基元类型、提取特征、特征选择与分类器等3个视角概括了点云信息提取的相关研究现状,总结出点云信息提取存在的5个主要问题,点明了点云信息提取的6个主要发展趋势,并着重介绍了"融合多基元的点云信息提取范式"。

关键词：激光雷达点云摄影测量点云滤波分类多基元融合信息提取

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基于地面激光扫描仪的树干削度方程提取

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南京林业大学学报（自然科学版） 2014年第5期38卷 6-10页

作者：梁子瑜孙圆梁欣廉李卫正徐建南京林业大学林学院江苏南京210037 Department of Remote Sensing and Photogrammetry Finnish Geodetic Institute Geodeetinrinne 2 Masala Fl-02430Finland 南京林业大学现代分析测试中心江苏南京210037

应用高精度三维激光扫描仪及点云数字处理技术,建立点云数据与树干削度之间的反演关系,拟合树干削度方程,并以江苏省南方型杨树林分为例,对树干曲线反演,提取了适合该树种生长情况的林分削度方程,同时进行样木的树干解析对拟合方程进行... 详细信息

应用高精度三维激光扫描仪及点云数字处理技术,建立点云数据与树干削度之间的反演关系,拟合树干削度方程,并以江苏省南方型杨树林分为例,对树干曲线反演,提取了适合该树种生长情况的林分削度方程,同时进行样木的树干解析对拟合方程进行了检验与分析。研究表明,地面激光扫描可以大幅提高地面每木调查的效率,所建立的树干削度方程有较高的估计精度和较好的拟合特性。

关键词：树干削度方程地面激光扫描点云数字处理技术杨树人工林

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建筑物自动变化检测方法的测试

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地壳构造与地壳应力 2012年第2期 8-16页

作者： Nicolas Champion Franz Rottensteiner Leena Matikainen xinlian liang Juha Hyypp Brian P.Olsen 李成龙 IGN MATISSaint-MandéFrance Institute of Photogrammetry and Geo Information Leibniz Universitt Hannover FGI Dept.of Remote Sensing and PhotogrammetryMasalaFinland National Survey and Cadastre(KMS) CopenhagenDenmark

数据库的更新(尤其是二维建筑物数据库)已经成为一个热门话题,特别是在较为发达的国家。在这些国家里,二维建筑物数据库已经在近十年来完成。主要问题集中在长期及费时的变化检测步骤,这些步骤可能通过最近获取的传感器数据实现自动化... 详细信息

数据库的更新(尤其是二维建筑物数据库)已经成为一个热门话题,特别是在较为发达的国家。在这些国家里,二维建筑物数据库已经在近十年来完成。主要问题集中在长期及费时的变化检测步骤,这些步骤可能通过最近获取的传感器数据实现自动化。目前在此领域的自动化与经验的缺乏已经促使EuroSDR开展一项当前最先进的不同变化检测方法比较的实验。本文的主要目的是介绍实验的背景和三种不同数据(航拍图像、卫星图像和激光雷达)获得的结果。此外,我们给出了实验中的一些发现以及未来为了建立一个有效的系统所要努力的方向。

关键词：变化检测建筑物基础 Champion 卫星图像传感器数据检测步骤新建筑物标记图对象检测矢量数据

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