Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances

Yun, Ting; Jiang, Kang; Hou, Hu; An, Feng; Chen, Bangqian; Jiang, Anna; Li, Weizheng; Liu, Xue

doi:10.3390/rs11080903

Cited by 16 publications

(13 citation statements)

References 34 publications

(37 reference statements)

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“…For example, the random sample consensus (RANSAC) algorithm and Hough transform of circle detection have been adopted to recognize the horizontal cross section of a trunk and determine the trunk location [18,19]. The topological method (e.g., comparative shortest-path algorithm [20] or marked neighbourhood searching at voxel-scale from root points [21]) has been used to depict the structures of the non-photosynthetic components of trees, while least square fitting based on point clouds of trunks has been used to retrieve tree growth directions and the centres of tree crowns [22]. Finally, the revolving door schematic mode of the morphological method [23] has been used to automatically recognize tree crowns from MLS data while simultaneously precluding the interference from poles and buildings.…”

Section: Introductionmentioning

confidence: 99%

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Wang

Chen

Cao

et al. 2019

Forests

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Rubber trees in southern China are often impacted by natural disturbances that can result in a tilted tree body. Accurate crown segmentation for individual rubber trees from scanned point clouds is an essential prerequisite for accurate tree parameter retrieval. In this paper, three plots of different rubber tree clones, PR107, CATAS 7-20-59, and CATAS 8-7-9, were taken as the study subjects. Through data collection using ground-based mobile light detection and ranging (LiDAR), a voxelisation method based on the scanned tree trunk data was proposed, and deep images (i.e., images normally used for deep learning) were generated through frontal and lateral projection transform of point clouds in each voxel with a length of 8 m and a width of 3 m. These images provided the training and testing samples for the faster region-based convolutional neural network (Faster R-CNN) of deep learning. Consequently, the Faster R-CNN combined with the generated training samples comprising 802 deep images with pre-marked trunk locations was trained to automatically recognize the trunk locations in the testing samples, which comprised 359 deep images. Finally, the point clouds for the lower parts of each trunk were extracted through back-projection transform from the recognized trunk locations in the testing samples and used as the seed points for the region’s growing algorithm to accomplish individual rubber tree crown segmentation. Compared with the visual inspection results, the recognition rate of our method reached 100% for the deep images of the testing samples when the images contained one or two trunks or the trunk information was slightly occluded by leaves. For the complicated cases, i.e., multiple trunks or overlapping trunks in one deep image or a trunk appearing in two adjacent deep images, the recognition accuracy of our method was greater than 90%. Our work represents a new method that combines a deep learning framework with point cloud processing for individual rubber tree crown segmentation based on ground-based mobile LiDAR scanned data.

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Section: Introductionmentioning

confidence: 99%

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Wang

Chen

Cao

et al. 2019

Forests

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“…The single-leaf area and the angles between the trunk and branches were measured using an LI-3000C portable area metre and an angle measurement device, respectively. In a previous study of rubber tree property retrieval [6], we proved that rubber trees belonging to the same clone have similar topological skeleton structures and phenotypic characteristics. Therefore, two typical rubber trees of each clone (PR107 and CATAS 7-20-59) were selected as the references for constructing the corresponding tree models.…”

Section: Study Site and Data Collectionmentioning

confidence: 80%

“…2. Crown volume and foliage clump volume: A 3D convex hull algorithm [6] was used to deduce the tree crown volume and volume of each foliage clump. 3.…”

Section: Retrieving the Foliage Clump Propertiesmentioning

confidence: 99%

“…For example, in 2011 and 2014, the losses of tapping rubber trees reached 3.5 million and 4.7 million Yuan on the Leizhou Peninsula due to hurricanes Nasha and Rammasun, respectively. In 2016, tropical storm Dianmu (August 18th), severe tropical storm Mirinae (July 26th) and super hurricane Sarika (October 18th) caused extensive wind damage in the rubber plantations and led to relatively low temperatures [6]. To better predict wind damage to rubber trees, it is necessary to qualitatively and quantitatively measure the wind resistance capability of rubber trees of different clones under high-wind conditions [7].…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Aerodynamic Parameters in Rubber Tree Forests Based on the Computer Simulation Technique and Terrestrial Laser Scanning Data

Huang

Fan

et al. 2020

Remote Sensing

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Rubber trees along the southeast coast of China always suffer severe damage from hurricanes. Quantitative assessments of the capacity for wind resistance of various rubber tree clones are currently lacking. We focus on a vulnerability assessment of rubber trees of different clones under wind disturbance impacts by employing multidisciplinary approaches incorporating scanned points, aerodynamics, machine learning and computer graphics. Point cloud data from two typical rubber trees belonging to different clones (PR107 and CATAS 7-20-59) were collected using terrestrial laser scanning, and a connection chain of tree skeletons was constructed using a clustering algorithm of machine learning. The concept of foliage clumps based on the trunk and first-order branches was first proposed to optimize rubber tree plot 3D modelling for simulating the wind field and assessing the wind-related parameters. The results from the obtained phenotypic traits show that the variable leaf area index and included angle between the branches and trunk result in variations in the topological structure and gap fraction of tree crowns, respectively, which are the major influencing factors relevant to the rubber tree’s capacity to resist hurricane strikes. The aerodynamics analysis showed that the maximum dynamic pressure, wind velocity and turbulent intensity of the wind-related parameters in rubber tree plots of clone PR107 (300 Pa, 30 m/s and 15%) are larger than that in rubber tree plots of clone CATAS-7-20-59 (120 Pa, 18 m/s and 5%), which results in a higher probability of local strong cyclone occurrence and a higher vulnerability to hurricane damage.

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“…9 Remote sensing technologies have unique advantages for capturing forest characteristic parameters, which have been widely utilized over the last few decades. [10][11][12] With continuous advances in sensors and associated platforms, as well as the enrichment and improvement of various spectral, spatial, and temporal resolutions, forestry researchers can use remote sensing data in combination with ground survey data to obtain accurate profiles of spatial structures of forests.…”

mentioning

confidence: 99%

Estimation of secondary forest parameters by integrating image and point cloud-based metrics acquired from unmanned aerial vehicle

et al. 2019

J. Appl. Rem. Sens.

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Estimation of secondary forest parameters by integrating image and point cloud-based metrics acquired from unmanned aerial vehicle," Abstract. To better assess spatial variabilities in subtropical secondary forest biomass, the goal of the present study was to employ small-footprint, discrete-return light detection and ranging and unmanned aerial vehicles, integrated with structure from motion (UAV-SFM) data, to accurately estimate the stand characteristic parameters of subtropical forests. The Lorey's height, density, basal area, volume, and aboveground biomass (AGB) of 30 plots (∅10 m) were modeled using an array of SFM point cloud metrics and image color metrics (forestry vegetation coverage and color index set). Further, the individual models developed using stepwise multiregression analysis and a branch-and-bound algorithm were employed to examine the best models. Overall, the results indicated that the coefficient of determination (R 2 ) for Lorey's height (R 2 ¼ 0.58 to 0.95), volume (R 2 ¼ 0.29 to 0.71), and AGB (R 2 ¼ 0.27 to 0.64) were significantly enhanced compared with the density (R 2 ¼ 0.18 to 0.48), and basal area (R 2 ¼ 0.24 to 0.52). Utilizing independent stand-level data from ground inventory, our results revealed that, overall, the model fit was significant for most stand characteristic parameters, with relationships close to a 1:1 line, thereby indicating no significant bias [relative root mean square error ðrRMSEÞ ¼ 5.32% to 27.04%]. Our image metrics had a statistically significant association with Lorey's height (rRMSE decreased by 0.08%), volume (rRMSE decreased by 0.58%), and AGB (rRMSE decreased by 3.11%), which provided additional explanatory power in the regression analysis. This research demonstrated the potential of UAV-SFM as a technology to accurately assess subtropical forest carbon while providing an improved elucidation of the positive effects of image color metrics.

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Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances

Cited by 16 publications

References 34 publications

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Retrieval of Aerodynamic Parameters in Rubber Tree Forests Based on the Computer Simulation Technique and Terrestrial Laser Scanning Data

Estimation of secondary forest parameters by integrating image and point cloud-based metrics acquired from unmanned aerial vehicle

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