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Dr. Lin Cao
Department of Forest Resources Management, Faculty of Forestry, Nanjing Forestry University, No. 159 Lonpang Road, Nanjing 210037, China

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Research Keywords & Expertise

0 ecological modelling
0 tree species classification
0 wildlife habitat
0 LiDAR applications in forest inventory and management
0 UAV and their point cloud processing

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UAV and their point cloud processing
tree species classification
Forest biomass estimation

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Journal article
Published: 29 April 2021 in ISPRS International Journal of Geo-Information
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A holistic strategy is established for automated UAV-LiDAR strip adjustment for plantation forests, based on hierarchical density-based clustering analysis of the canopy cover. The method involves three key stages: keypoint extraction, feature similarity and correspondence, and rigid transformation estimation. Initially, the HDBSCAN algorithm is used to cluster the scanned canopy cover, and the keypoints are marked using topological persistence analysis of the individual clusters. Afterward, the feature similarity is calculated by considering the linear and angular relationships between each point and the pointset centroid. The one-to-one feature correspondence is retrieved by solving the assignment problem on the similarity score function using the Kuhn–Munkres algorithm, generating a set of matching pairs. Finally, 3D rigid transformation parameters are determined by permutations over all conceivable pair combinations within the correspondences, whereas the best pair combination is that which yields the maximum count of matched points achieving distance residuals within the specified tolerance. Experimental data covering eighteen subtropical forest plots acquired from the GreenValley and Riegl UAV-LiDAR platforms in two scan modes are used to validate the method. The results are extremely promising for redwood and poplar tree species from both the Velodyne and Riegl UAV-LiDAR datasets. The minimal mean distance residuals of 31 cm and 36 cm are achieved for the coniferous and deciduous plots of the Velodyne data, respectively, whereas their corresponding values are 32 cm and 38 cm for the Riegl plots. Moreover, the method achieves both higher matching percentages and lower mean distance residuals by up to 28% and 14 cm, respectively, compared to the baseline method, except in the case of plots with extremely low tree height. Nevertheless, the mean planimetric distance residual achieved by the proposed method is lower by 13 cm.

ACS Style

Reda Fekry; Wei Yao; Lin Cao; Xin Shen. Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover. ISPRS International Journal of Geo-Information 2021, 10, 284 .

AMA Style

Reda Fekry, Wei Yao, Lin Cao, Xin Shen. Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover. ISPRS International Journal of Geo-Information. 2021; 10 (5):284.

Chicago/Turabian Style

Reda Fekry; Wei Yao; Lin Cao; Xin Shen. 2021. "Marker-Less UAV-LiDAR Strip Alignment in Plantation Forests Based on Topological Persistence Analysis of Clustered Canopy Cover." ISPRS International Journal of Geo-Information 10, no. 5: 284.

Journal article
Published: 13 February 2021 in Forests
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Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.

ACS Style

Renjie Xu; Haifeng Lin; Kangjie Lu; Lin Cao; Yunfei Liu. A Forest Fire Detection System Based on Ensemble Learning. Forests 2021, 12, 217 .

AMA Style

Renjie Xu, Haifeng Lin, Kangjie Lu, Lin Cao, Yunfei Liu. A Forest Fire Detection System Based on Ensemble Learning. Forests. 2021; 12 (2):217.

Chicago/Turabian Style

Renjie Xu; Haifeng Lin; Kangjie Lu; Lin Cao; Yunfei Liu. 2021. "A Forest Fire Detection System Based on Ensemble Learning." Forests 12, no. 2: 217.

Journal article
Published: 23 January 2021 in Remote Sensing of Environment
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Accurate segmentation of individual tree crowns (ITCs) from airborne light detection and ranging (LiDAR) data remains a challenge for forest inventories. Although many ITC segmentation methods have been developed to derive tree crown information from airborne LiDAR data, these algorithms contain uncertainty in processing false treetops because of foliage clumps and lateral branches, overlapping canopies without clear valley-shape areas, and sub-canopy crowns with neighbouring trees that obscure their shapes from an aerial perspective. Here, we propose an approach to crown segmentation using computer vision theories applied in different forest types. First, a dual Gaussian filter was designed with automated adaptive parameter assignment and a screening strategy for false treetops. This preserved the geometric characteristics of sub-canopy trees while eliminating false treetops. Second, anisotropic water expansion controlled by the energy function was applied for accurate crown segmentation. This utilized gradient information from the digital surface model and explored the morphological structures of tree crown boundaries as analogous to the maximal valley height difference from surrounding treetops. We demonstrate the generality of our approach in the subtropical forests within China. Our approach enhanced the detection rate of treetops and ITC segmentation relative to the marker-controlled watershed method, especially in complicated intersections of multiple crowns. A high performance was demonstrated for three pure Eucalyptus plots (a treetop detection rate r ≥ 0.95 and crown width estimation R2 ≥ 0.90 for canopy trees; r ≥ 0.85 and R2 ≥ 0.88 for sub-canopy trees) and three plots dominated by Chinese fir (r ≥ 0.95 and R2 ≥ 0.87 for canopy trees; r ≥ 0.79 and R2 ≥ 0.83 for sub-canopy trees). Finally, in a relatively complex forest park containing a wide range of tree species and sizes, a high performance was also achieved (r = 0.93 and R2 ≥ 0.85 for canopy trees; r = 0.70 and R2 ≥ 0.80 for sub-canopy trees). Our method demonstrates that methods inspired by the computer vision theory can improve on existing approaches, providing the potential for accurate crown segmentation even in mixed forests with complex structures

ACS Style

Ting Yun; Kang Jiang; Guangchao Li; Markus P. Eichhorn; Jiangchuan Fan; Fangzhou Liu; Bangqian Chen; Feng An; Lin Cao. Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach. Remote Sensing of Environment 2021, 256, 112307 .

AMA Style

Ting Yun, Kang Jiang, Guangchao Li, Markus P. Eichhorn, Jiangchuan Fan, Fangzhou Liu, Bangqian Chen, Feng An, Lin Cao. Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach. Remote Sensing of Environment. 2021; 256 ():112307.

Chicago/Turabian Style

Ting Yun; Kang Jiang; Guangchao Li; Markus P. Eichhorn; Jiangchuan Fan; Fangzhou Liu; Bangqian Chen; Feng An; Lin Cao. 2021. "Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach." Remote Sensing of Environment 256, no. : 112307.

Journal article
Published: 20 January 2021 in Remote Sensing of Environment
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Assessing changes in forest structure over time is crucial for monitoring forest resources, supporting sustainable forest management practices, and providing key insights into changes in the terrestrial carbon cycle. Recent research interest and rapid growth of unmanned aerial vehicle (UAV)-based digital aerial photogrammetry (DAP) technology principally due to its low cost and timeliness, is providing high-spatial resolution data for enhancing forest inventories and forest dynamics monitoring. The increasing prevalence of these UAV acquired datasets in forestry promotes a need for better understanding of how DAP-based point clouds change over time, and how these changes may relate to changes in forest structure. In this study, we utilized bi-temporal DAP data to investigate changes in forest structure over a 3-year period in subtropical planted forest stands throughout Dongtai Yellow Sea National Forest Park, Jiangsu Province, China. To do so, we evaluated both direct (i.e., structural parameter changes estimated using the differences between bi-temporal DAP metrics) and indirect (i.e., structural parameters were modelled for each date, and their changes predicted as their differences) methods to estimate the changes in forest structure. In addition, once models were developed, changes in Lorey's mean height and volume were extrapolated across the entire study site and examined related to the forest type and age. Our assessments of the different approaches showed that the direct approach (R2 = 0.54–0.78) resulted in improved accuracy compared to the indirect method (R2 = 0.51–0.73). The distributional metrics, namely, height percentiles (e.g., H75 and H95) and canopy return density (e.g., D7 and D5), and the Weibull-fitted metrics (e.g., α) were found to be sensitive to changes in structural parameters, whereas canopy volume-related metrics had relatively low predictive capabilities. Overall, the predicted changes in Lorey's mean height and volume were mapped over the entire study area, and indicated that Lorey's mean height increased mostly in middle-aged and young forest stands. Over-mature stands showed the lowest height increment. This study proved the capability of using bi-temporal point clouds from UAV-based DAP for enhancing forest inventories and promoting sustainable forest management.

ACS Style

Xiaoyao Fu; Zhengnan Zhang; Lin Cao; Nicholas C. Coops; Tristan R.H. Goodbody; Hao Liu; Xin Shen; Xiangqian Wu. Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds. Remote Sensing of Environment 2021, 255, 112300 .

AMA Style

Xiaoyao Fu, Zhengnan Zhang, Lin Cao, Nicholas C. Coops, Tristan R.H. Goodbody, Hao Liu, Xin Shen, Xiangqian Wu. Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds. Remote Sensing of Environment. 2021; 255 ():112300.

Chicago/Turabian Style

Xiaoyao Fu; Zhengnan Zhang; Lin Cao; Nicholas C. Coops; Tristan R.H. Goodbody; Hao Liu; Xin Shen; Xiangqian Wu. 2021. "Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds." Remote Sensing of Environment 255, no. : 112300.

Journal article
Published: 29 December 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Remote-sensing-assisted estimates of Moso bamboo forest structure are imperative for supporting sustainable forest management (SFM). Passive sensors have limited usages in characterizing continuous vertical structures of Moso bamboos since they have difficulty in penetrating dense upper canopies. Airborne full-waveform (FWF) Light Detection and Ranging (LiDAR) technology has shown significant potentials in providing an improved representation of 3-D structures in much denser canopies, while the capability of FWF to assess the structure of the unique Moso bamboo forests is less known. Many previous studies commonly assume perfectly vertical waveforms and do not account for slanted penetrations of waveforms in canopies. This study presented a framework for investigating the availability of FWF to assess subtropical Moso bamboo forest structures. We used a physical-based approach for radiometric calibration and an advanced voxel-based pseudo-vertical waveform approach to assign waveforms into voxels and reconstruct composite waveforms for extracting FWF metrics. Second, the differences in FWF-derived metrics in response to canopy structures across various management strategies were compared. Finally, the capability of FWF metrics for estimating bamboo structural parameters was evaluated by parametric and nonparametric models. The results showed that the FWF metrics varied significantly (p-values < 0.05) in response to different management strategies. In general, nonparametric approaches (locally weighted linear regression and k-nearest neighbor) outperformed parametric approaches (multiple linear regression). This study demonstrated that the pseudo-vertical waveform approach could improve the performances of FWF metrics for characterizing 3-D Bamboo forest structure and provide new insights on accurately estimating bamboo structural parameters across different management strategies.

ACS Style

Zhengnan Zhang; Lin Cao; Hao Liu; Xiaoyao Fu; Xin Shen. Assessing the 3-D Structure of Bamboo Forests Using an Advanced Pseudo-Vertical Waveform Approach Based on Airborne Full-Waveform LiDAR Data. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -24.

AMA Style

Zhengnan Zhang, Lin Cao, Hao Liu, Xiaoyao Fu, Xin Shen. Assessing the 3-D Structure of Bamboo Forests Using an Advanced Pseudo-Vertical Waveform Approach Based on Airborne Full-Waveform LiDAR Data. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-24.

Chicago/Turabian Style

Zhengnan Zhang; Lin Cao; Hao Liu; Xiaoyao Fu; Xin Shen. 2020. "Assessing the 3-D Structure of Bamboo Forests Using an Advanced Pseudo-Vertical Waveform Approach Based on Airborne Full-Waveform LiDAR Data." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-24.

Journal article
Published: 21 December 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Accurately estimating and mapping forest structural parameters are essential for monitoring forest resources and understanding ecological processes. The novel deep learning algorithm has the potential to be a promising approach to improve the estimation accuracy while combining with advanced remote sensing technology. Airborne light detection and ranging (LiDAR) has the preferable capability to characterize 3-D canopy structure and estimate forest structural parameters. In this study, we developed a deep learning-based algorithm (Deep-RBN) that combined the fully connected network (FCN) deep learning algorithm with the optimized radial basis neural network (RBN) algorithm for forest structural parameter estimation using airborne LiDAR data. The multiple iterations were used to constantly update the internal weights to achieve the optimized accuracy of model fitting, and the optimized RBN algorithm was developed for the limited training sets. We assessed the efficiency and capability of the Deep-RBN in the estimation of forest structural parameters in a subtropical planted forest of southern China, by comparing the traditional FCN algorithm and multiple linear regression. We found that Deep-RBN had the strongest capability in estimates of forest structural parameters (R 2 = 0.67–0.86, rRMSE = 6.95%–20.34%). The sensitivity analysis of the key hyperparameters of Deep-RBN algorithm showed that the learning rate is one of the most important parameters that influence the performance of predictive models, and while its value equal is to 0.001, the predictive models had the highest accuracy (mean DBH: RMSE = 1.01, mean height: RMSE = 1.45, volume: RMSE = 26.49, stem density: RMSE = 121.06). With the increase of training samples added in Deep-RBN model, the predictive models performed better; however, no significant improvements of accuracy were observed while the number of training set is larger than 80. This study demonstrates the benefits of jointly using the Deep-RBN algorithm and airborne LiDAR data to improve the accuracy of forest structural parameter estimation and mapping, which provides a promising methodology for sustainable forest resources monitoring.

ACS Style

Hao Liu; Xin Shen; Lin Cao; Ting Yun; Zhengnan Zhang; Xiaoyao Fu; Xinxin Chen; Fangzhou Liu. Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 1603 -1618.

AMA Style

Hao Liu, Xin Shen, Lin Cao, Ting Yun, Zhengnan Zhang, Xiaoyao Fu, Xinxin Chen, Fangzhou Liu. Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 ():1603-1618.

Chicago/Turabian Style

Hao Liu; Xin Shen; Lin Cao; Ting Yun; Zhengnan Zhang; Xiaoyao Fu; Xinxin Chen; Fangzhou Liu. 2020. "Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. : 1603-1618.

Journal article
Published: 14 August 2020 in Remote Sensing of Environment
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Biochemical traits in forest vegetation are key indicators of leaf physiological processes, specifically photosynthetic and other photochemical light pathways, and are critical to the quantification of the terrestrial carbon cycle. Advances in remote sensing sensors and platforms are allowing multi-dimensional and continuous-spatial information to be acquired in a fast and non-destructive way to quantify forest biochemical traits at multiple spatial scales. Here we demonstrate the use of high spectral resolution, hyperspectral data combined with high density three-dimensional information from Light Detection and Ranging (LiDAR) both acquired from an unmanned aerial system (UAS) platform, to quantify and assess the three-dimensional distribution of biochemical pigments on individual tree canopy surfaces. To do so, a DSM based fusion method was developed to integrate the 3D LiDAR point cloud with hyperspectral reflectance data. Regression-based models were then developed to predict a number of biochemical traits (i.e., chlorophyll (Chl) a, b, total Chl and total carotenoids (Cars) content) from a suite of common spectral indices at three vertical canopy levels, and were evaluated using a leave-one-out cross-validation approach. One-way ANOVA and Duncan's multiple comparison post hoc tests were used to investigate the vertical distribution of biochemical pigments on individual tree canopy surfaces, and in response to age and species. Our results demonstrated that a number of vegetation indices, derived from the hyperspectral data, were strongly correlated with a number of biochemical traits (Adj-R2 = 0.85–0.91; rRMSE = 5.19–6.38%). In general, models fitted using leaf samples from the upper, middle and lower canopies separately (Adj-R2 = 0.85–0.91; rRMSE = 5.19–6.38%) had similar accuracy to the models developed with pooled data (Adj-R2 = 0.87–0.90; rRMSE = 5.21–6.11%). The differences between separate models and global models were not statistically significant (P > 0.05). However, the distribution of biochemical pigments across vertical layers varied significantly. For dawn redwood (Metasequoia glyptostroboides) and poplar (Populus deltoides), the results were consistent in that the lower component of the canopy (least light impacted) had the highest chlorophyll and carotenoids biochemical traits. Moreover, the vertical distribution of biochemical traits on individual tree canopy surfaces changed with age likely due to the growth variation from the photosynthetic activity of the canopy. This study indicates the potential of using fused 3D point cloud information with spectral data to monitor physiological activities of forest canopy for carbon accumulation estimation as well as precision forestry applications such as nutrition diagnosis, water regulation and subsequent productivity enhancement of these planted forest systems.

ACS Style

Xin Shen; Lin Cao; Nicholas C. Coops; Hongchao Fan; Xiangqian Wu; Hao Liu; Guibin Wang; Fuliang Cao. Quantifying vertical profiles of biochemical traits for forest plantation species using advanced remote sensing approaches. Remote Sensing of Environment 2020, 250, 112041 .

AMA Style

Xin Shen, Lin Cao, Nicholas C. Coops, Hongchao Fan, Xiangqian Wu, Hao Liu, Guibin Wang, Fuliang Cao. Quantifying vertical profiles of biochemical traits for forest plantation species using advanced remote sensing approaches. Remote Sensing of Environment. 2020; 250 ():112041.

Chicago/Turabian Style

Xin Shen; Lin Cao; Nicholas C. Coops; Hongchao Fan; Xiangqian Wu; Hao Liu; Guibin Wang; Fuliang Cao. 2020. "Quantifying vertical profiles of biochemical traits for forest plantation species using advanced remote sensing approaches." Remote Sensing of Environment 250, no. : 112041.

Journal article
Published: 01 July 2020 in International Journal of Applied Earth Observation and Geoinformation
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Tree species composition of forest stand is an important indicator of forest inventory attributes for assessing ecosystem health, understanding successional processes, and digitally displaying forest biodiversity. In this study, we acquired high spatial resolution multispectral and RGB imagery over a subtropical natural forest in southwest China using a fixed-wing UAV system. Digital aerial photogrammetric (DAP) technique was used to generate multi-spectral and RGB derived point clouds, upon which individual tree crown (ITC) delineation algorithms and a machine learning classifier were used to identify dominant tree species. To do so, the structure-from-motion method was used to generate RGB imagery-based DAP point clouds. Then, three ITC delineation algorithms (i.e., point cloud segmentation (PCS), image-based multiresolution segmentation (IMRS), and advanced multiresolution segmentation (AMRS)) were used and assessed for ITC detection. Finally, tree-level metrics (i.e., multispectral, texture and point cloud metrics) were used as metrics in the random forest classifier used to classify eight dominant tree species. Results indicated that the accuracy of the AMRS ITC segmentation was highest (F1-score = 82.5 %), followed by the segmentation using PCS (F1-score = 79.6 %), the IMRS exhibited the lowest accuracy (F1-score = 78.6 %); forest types classification (coniferous and deciduous) had a higher accuracy than the classification of all eight tree species, and the combination of spectral, texture and structural metrics had the highest classification accuracy (overall accuracy = 80.20 %). In the classification of both eight tree species and two forest types, the classification accuracies were lowest when only using spectral metrics, indicated that the texture metrics and point cloud structural metrics had a positive impact on the classification (the overall accuracy and kappa accuracy increased by 1.49–4.46 % and 2.86–6.84 %, respectively).

ACS Style

Zhong Xu; Xin Shen; Lin Cao; Nicholas C. Coops; Tristan R.H. Goodbody; Tai Zhong; Weidong Zhao; Qinglei Sun; Sang Ba; Zhengnan Zhang; Xiangqian Wu. Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests. International Journal of Applied Earth Observation and Geoinformation 2020, 92, 102173 .

AMA Style

Zhong Xu, Xin Shen, Lin Cao, Nicholas C. Coops, Tristan R.H. Goodbody, Tai Zhong, Weidong Zhao, Qinglei Sun, Sang Ba, Zhengnan Zhang, Xiangqian Wu. Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests. International Journal of Applied Earth Observation and Geoinformation. 2020; 92 ():102173.

Chicago/Turabian Style

Zhong Xu; Xin Shen; Lin Cao; Nicholas C. Coops; Tristan R.H. Goodbody; Tai Zhong; Weidong Zhao; Qinglei Sun; Sang Ba; Zhengnan Zhang; Xiangqian Wu. 2020. "Tree species classification using UAS-based digital aerial photogrammetry point clouds and multispectral imageries in subtropical natural forests." International Journal of Applied Earth Observation and Geoinformation 92, no. : 102173.

Journal article
Published: 22 April 2020 in 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.

ACS Style

Zhixian Huang; Xiao Huang; Jiangchuan Fan; Markus Eichhorn; Feng An; Bangqian Chen; Lin Cao; Zhengli Zhu; Ting Yun. Retrieval of Aerodynamic Parameters in Rubber Tree Forests Based on the Computer Simulation Technique and Terrestrial Laser Scanning Data. Remote Sensing 2020, 12, 1318 .

AMA Style

Zhixian Huang, Xiao Huang, Jiangchuan Fan, Markus Eichhorn, Feng An, Bangqian Chen, Lin Cao, Zhengli Zhu, Ting Yun. Retrieval of Aerodynamic Parameters in Rubber Tree Forests Based on the Computer Simulation Technique and Terrestrial Laser Scanning Data. Remote Sensing. 2020; 12 (8):1318.

Chicago/Turabian Style

Zhixian Huang; Xiao Huang; Jiangchuan Fan; Markus Eichhorn; Feng An; Bangqian Chen; Lin Cao; Zhengli Zhu; Ting Yun. 2020. "Retrieval of Aerodynamic Parameters in Rubber Tree Forests Based on the Computer Simulation Technique and Terrestrial Laser Scanning Data." Remote Sensing 12, no. 8: 1318.

Journal article
Published: 26 February 2020 in Forests
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The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the characteristics of the broad-leaved tree point clouds. An observation procedure down-samples the point clouds with the partial Fourier matrix. The regularized orthogonal matching pursuit algorithm (ROMP) finally reconstructs the original point clouds. The experimental results illustrate that the proposed scheme can preserve morphological attributes of the broad-leaved tree within a range of relative error: 0.0010%–3.3937%, and robustly extend to plot-level within a range of mean square error (MSE): 0.0063–0.2245.

ACS Style

Renjie Xu; Ting Yun; Lin Cao; Yunfei Liu. Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing. Forests 2020, 11, 257 .

AMA Style

Renjie Xu, Ting Yun, Lin Cao, Yunfei Liu. Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing. Forests. 2020; 11 (3):257.

Chicago/Turabian Style

Renjie Xu; Ting Yun; Lin Cao; Yunfei Liu. 2020. "Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing." Forests 11, no. 3: 257.

Journal article
Published: 05 February 2020 in Remote Sensing
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Unmanned aerial vehicles using light detection and ranging (UAV LiDAR) with high spatial resolution have shown great potential in forest applications because they can capture vertical structures of forests. Individual tree segmentation is the foundation of many forest research works and applications. The tradition fixed bandwidth mean shift has been applied to individual tree segmentation and proved to be robust in tree segmentation. However, the fixed bandwidth-based segmentation methods are not suitable for various crown sizes, resulting in omission or commission errors. Therefore, to increase tree-segmentation accuracy, we propose a self-adaptive bandwidth estimation method to estimate the optimal kernel bandwidth automatically without any prior knowledge of crown size. First, from the global maximum point, we divide the three-dimensional (3D) space into a set of angular sectors, for each of which a canopy surface is simulated and the potential tree crown boundaries are identified to estimate average crown width as the kernel bandwidth. Afterwards, we use a mean shift with the automatically estimated kernel bandwidth to extract individual tree points. The method is iteratively implemented within a given area until all trees are segmented. The proposed method was tested on the 7 plots acquired by a Velodyne 16E LiDAR system, including 3 simple plots and 4 complex plots, and 95% and 80% of trees were correctly segmented, respectively. Comparative experiments show that our method contributes to the improvement of both segmentation accuracy and computational efficiency.

ACS Style

Wanqian Yan; Haiyan Guan; Lin Cao; Wilson Yu; Cheng Li; Jianyong Lu. A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data. Remote Sensing 2020, 12, 515 .

AMA Style

Wanqian Yan, Haiyan Guan, Lin Cao, Wilson Yu, Cheng Li, Jianyong Lu. A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data. Remote Sensing. 2020; 12 (3):515.

Chicago/Turabian Style

Wanqian Yan; Haiyan Guan; Lin Cao; Wilson Yu; Cheng Li; Jianyong Lu. 2020. "A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data." Remote Sensing 12, no. 3: 515.

Journal article
Published: 24 November 2019 in International Journal of Applied Earth Observation and Geoinformation
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Forest plantations are an important source of terrestrial carbon sequestration. The forest of Robinia pseudoacacia in the Yellow River Delta (YRD) is the largest artificial ecological protection forest in China. However, more than half of the forest has appeared different degrees of dieback and even death since the 1990s. Timely and accurate estimation of the forest aboveground biomass (AGB) is a basis for studying the carbon cycle of forests. Light Detecting and Ranging (LiDAR) has been proved to be one of the most powerful methods for forest biomass estimation. However, because of an irregular and overlapping shape of the broadleaved forest canopy in a growing season, it is difficult to segment individual trees and estimate the tree biomass from airborne LiDAR data. In this study, a new method was proposed to solve this problem of individual tree detection in the Robinia pseudoacacia forest based on a combination of the Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) with the Backpack-LiDAR. The proposed method mainly consists of following steps: (i) at a plot level, trees in the UAV-LiDAR data were detected by seed points obtained by an individual tree segmentation (ITS) method from the Backpack-LiDAR data; (ii) height and diameter at breast height (DBH) of an individual tree would be extracted from UAV and Backpack LiDAR data, respectively; (iii) the individual tree AGB would be calculated through an allometric equation and the forest AGB at the plot level was accumulated; and (iv) the plot-level forest AGB was taken as a dependent variable, and various metrics extracted from UAV-LiDAR point cloud data as independent variables to estimate forest AGB distribution in the study area by using both multiple linear regression (MLR) and random forest (RF) models. The results demonstrate that: (1) the seed points extracted from Backpack-LiDAR could significantly improve the overall accuracy of individual tree detection (F = 0.99), and thus increase the forest AGB estimation accuracy; (2) compared with MLR model, the RF model led to a higher estimation accuracy (p < 0.05); and (3) LiDAR intensity information selected by both MLR and RF models and laser penetration rate (LP) played an important role in estimating healthy forest AGB.

ACS Style

Jinbo Lu; Hong Wang; Shuhong Qin; Lin Cao; Ruiliang Pu; Guilin Li; Jing Sun. Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds. International Journal of Applied Earth Observation and Geoinformation 2019, 86, 102014 .

AMA Style

Jinbo Lu, Hong Wang, Shuhong Qin, Lin Cao, Ruiliang Pu, Guilin Li, Jing Sun. Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds. International Journal of Applied Earth Observation and Geoinformation. 2019; 86 ():102014.

Chicago/Turabian Style

Jinbo Lu; Hong Wang; Shuhong Qin; Lin Cao; Ruiliang Pu; Guilin Li; Jing Sun. 2019. "Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds." International Journal of Applied Earth Observation and Geoinformation 86, no. : 102014.

Journal article
Published: 11 September 2019 in 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.

ACS Style

Jiamin Wang; Chen; Lin Cao; Feng An; Lianfeng Xue; Ting Yun; Wang; Cao; An; Xue; Yun; Xinxin Chen; Bangqian Chen. Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning. Forests 2019, 10, 793 .

AMA Style

Jiamin Wang, Chen, Lin Cao, Feng An, Lianfeng Xue, Ting Yun, Wang, Cao, An, Xue, Yun, Xinxin Chen, Bangqian Chen. Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning. Forests. 2019; 10 (9):793.

Chicago/Turabian Style

Jiamin Wang; Chen; Lin Cao; Feng An; Lianfeng Xue; Ting Yun; Wang; Cao; An; Xue; Yun; Xinxin Chen; Bangqian Chen. 2019. "Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning." Forests 10, no. 9: 793.

Journal article
Published: 20 June 2019 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Developing an accurate model for estimating the forest structural parameters of planted forests is crucial for forest productivity predictions and can provide a better understanding of the carbon cycle under climate change. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represents a promising active remote sensing technology that has the potential to be used for forest inventories. In addition, the process-based model, physiological principles predicting growth (3-PG), which is based on physiological principles and environmental factors, has been applied to estimate the growth of even-aged, mono-specific forests under the effect of different management levels, site conditions, and climate change. In this study, the performance of UAV-LiDAR metrics was assessed and applied to estimate forest structural parameters using a multivariate linear regression (MLR) method. The 3-PG was parameterized and used to simulate the diameter at breast height, stem density, volume and above-ground biomass of a planted ginkgo forest in eastern China. In addition, a sensitivity analysis was conducted on the 3-PG model's input parameters. The results demonstrated that both the MLR based on UAV-LiDAR data and a progress model of the 3-PG have a promising potential for estimating forest structural parameters (R2 > 0.70, relative root squared error >20%). A sensitivity analysis of the 3-PG parameters also confirmed that the parameter “age at canopy cover” (fullCanAge) is vital for the 3-PG model, and positively correlation with the simulated results. The method presented here represents an improvement on traditional methods for estimating forest structural parameters because it more explicitly accounts for climatic effects included in the 3-PG model.

ACS Style

Lin Cao; Kun Liu; Xin Shen; Xiangqian Wu; Hao Liu. Estimation of Forest Structural Parameters Using UAV-LiDAR Data and a Process-Based Model in Ginkgo Planted Forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 4175 -4190.

AMA Style

Lin Cao, Kun Liu, Xin Shen, Xiangqian Wu, Hao Liu. Estimation of Forest Structural Parameters Using UAV-LiDAR Data and a Process-Based Model in Ginkgo Planted Forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (11):4175-4190.

Chicago/Turabian Style

Lin Cao; Kun Liu; Xin Shen; Xiangqian Wu; Hao Liu. 2019. "Estimation of Forest Structural Parameters Using UAV-LiDAR Data and a Process-Based Model in Ginkgo Planted Forests." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 11: 4175-4190.

Journal article
Published: 14 April 2019 in Remote Sensing
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Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R2 = 0.92, rRMSE = 3.5%), followed by the CHMM (R2 = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R2 = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.

ACS Style

Xiangqian Wu; Xin Shen; Lin Cao; Guibin Wang; Fuliang Cao. Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests. Remote Sensing 2019, 11, 908 .

AMA Style

Xiangqian Wu, Xin Shen, Lin Cao, Guibin Wang, Fuliang Cao. Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests. Remote Sensing. 2019; 11 (8):908.

Chicago/Turabian Style

Xiangqian Wu; Xin Shen; Lin Cao; Guibin Wang; Fuliang Cao. 2019. "Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests." Remote Sensing 11, no. 8: 908.

Journal article
Published: 03 April 2019 in Remote Sensing
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Forest structural attributes are key indicators for parameterization of forest growth models, which play key roles in understanding the biophysical processes and function of the forest ecosystem. In this study, UAS-based multispectral and RGB imageries were used to estimate forest structural attributes in planted subtropical forests. The point clouds were generated from multispectral and RGB imageries using the digital aerial photogrammetry (DAP) approach. Different suits of spectral and structural metrics (i.e., wide-band spectral indices and point cloud metrics) derived from multispectral and RGB imageries were compared and assessed. The selected spectral and structural metrics were used to fit partial least squares (PLS) regression models individually and in combination to estimate forest structural attributes (i.e., Lorey’s mean height (HL) and volume(V)), and the capabilities of multispectral- and RGB-derived spectral and structural metrics in predicting forest structural attributes in various stem density forests were assessed and compared. The results indicated that the derived DAP point clouds had perfect visual effects and that most of the structural metrics extracted from the multispectral DAP point cloud were highly correlated with the metrics derived from the RGB DAP point cloud (R2 > 0.75). Although the models including only spectral indices had the capability to predict forest structural attributes with relatively high accuracies (R2 = 0.56–0.69, relative Root-Mean-Square-Error (RMSE) = 10.88–21.92%), the models with spectral and structural metrics had higher accuracies (R2 = 0.82–0.93, relative RMSE = 4.60–14.17%). Moreover, the models fitted using multispectral- and RGB-derived metrics had similar accuracies (∆R2 = 0–0.02, ∆ relative RMSE = 0.18–0.44%). In addition, the combo models fitted with stratified sample plots had relatively higher accuracies than those fitted with all of the sample plots (∆R2 = 0–0.07, ∆ relative RMSE = 0.49–3.08%), and the accuracies increased with increasing stem density.

ACS Style

Xin Shen; Lin Cao; Bisheng Yang; Zhong Xu; Guibin Wang. Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries. Remote Sensing 2019, 11, 800 .

AMA Style

Xin Shen, Lin Cao, Bisheng Yang, Zhong Xu, Guibin Wang. Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries. Remote Sensing. 2019; 11 (7):800.

Chicago/Turabian Style

Xin Shen; Lin Cao; Bisheng Yang; Zhong Xu; Guibin Wang. 2019. "Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries." Remote Sensing 11, no. 7: 800.

Journal article
Published: 25 March 2019 in Remote Sensing
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Automatic 3D forest mapping and individual tree characteristics estimation are essential for forest management and ecosystem maintenance. The low-cost unmanned aerial vehicle (UAV) laser scanning (ULS) is a newly developed tool for cost-effectively collecting 3D information and attempts to use it for 3D forest mapping have been made, due to its capability to provide 3D information with a lower cost and higher flexibility than the standard ULS and airborne laser scanning (ALS). As the direct georeferenced point clouds may suffer from distortion caused by the poor performance of a low-cost inertial measurement unit (IMU), and 3D forest mapping using low-cost ULS poses a great challenge. Therefore, this paper utilized global navigation satellite system (GNSS) and IMU aided Structure-from-Motion (SfM) for trajectory estimation, and, hence, overcomes the poor performance of low-cost IMUs. The accuracy of the low-cost ULS point clouds was compared with the ground truth data collected by a commercial ULS system. Furthermore, the effectiveness of individual trees segmentation and tree characteristics estimation derived from the low-cost ULS point clouds were accessed. Experiments were undertaken in Dongtai forest farm, Yancheng City, Jiangsu Province, China. The results showed that the low-cost ULS achieved good point clouds quality from visual inspection and comparable individual tree segmentation results (P = 0.87, r = 0.84, F= 0.85) with the commercial system. Individual tree height estimation performed well (coefficient of determination (R2 ) = 0.998, root-mean-square error (RMSE) = 0.323 m) using the low-cost ULS. As for individual tree crown diameter estimation, low-cost ULS achieved good results (R2 = 0.806, RMSE = 0.195 m) after eliminating outliers. In general, such results illustrated the high potential of the low-cost ULS in 3D forest mapping, even though 3D forest mapping using the low-cost ULS requires further research.

ACS Style

Jianping Li; Bisheng Yang; Yangzi Cong; Lin Cao; Xiaoyao Fu; Zhen Dong. 3D Forest Mapping Using A Low-Cost UAV Laser Scanning System: Investigation and Comparison. Remote Sensing 2019, 11, 717 .

AMA Style

Jianping Li, Bisheng Yang, Yangzi Cong, Lin Cao, Xiaoyao Fu, Zhen Dong. 3D Forest Mapping Using A Low-Cost UAV Laser Scanning System: Investigation and Comparison. Remote Sensing. 2019; 11 (6):717.

Chicago/Turabian Style

Jianping Li; Bisheng Yang; Yangzi Cong; Lin Cao; Xiaoyao Fu; Zhen Dong. 2019. "3D Forest Mapping Using A Low-Cost UAV Laser Scanning System: Investigation and Comparison." Remote Sensing 11, no. 6: 717.

Journal article
Published: 10 February 2019 in Forests
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Estimating forest structural attributes of planted forests plays a key role in managing forest resources, monitoring carbon stocks, and mitigating climate change. High-resolution and low-cost remote-sensing data are increasingly available to measure three-dimensional (3D) canopy structure and model forest structural attributes. In this study, we compared two suites of point cloud metrics and the accuracies of predictive models of forest structural attributes using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) data, in a subtropical coastal planted forest of East China. A comparison between UAV-LiDAR and UAV-DAP metrics was performed across plots among different tree species, heights, and stem densities. The results showed that a higher similarity between the UAV-LiDAR and UAV-DAP metrics appeared in the dawn redwood plots with greater height and lower stem density. The comparison between the UAV-LiDAR and DAP metrics showed that the metrics of the upper percentiles (r for dawn redwood = 0.95–0.96, poplar = 0.94–0.95) showed a stronger correlation than the lower percentiles (r = 0.92–0.93, 0.90–0.92), whereas the metrics of upper canopy return density (r = 0.21–0.24, 0.14–0.15) showed a weaker correlation than those of lower canopy return density (r = 0.32–0.68, 0.31–0.52). The Weibull α parameter indicated a higher correlation (r = 0.70–0.72) than that of the Weibull β parameter (r = 0.07–0.60) for both dawn redwood and poplar plots. The accuracies of UAV-LiDAR (adjusted (Adj)R2 = 0.58–0.91, relative root-mean-square error (rRMSE) = 9.03%–24.29%) predicted forest structural attributes were higher than UAV-DAP (Adj-R2 = 0.52–0.83, rRMSE = 12.20%–25.84%). In addition, by comparing the forest structural attributes between UAV-LiDAR and UAV-DAP predictive models, the greatest difference was found for volume (△Adj-R2 = 0.09, △rRMSE = 4.20%), whereas the lowest difference was for basal area (△Adj-R2 = 0.03, △rRMSE = 0.86%). This study proved that the UAV-DAP data are useful and comparable to LiDAR for forest inventory and sustainable forest management in planted forests, by providing accurate estimations of forest structural attributes.

ACS Style

Lin Cao; Hao Liu; Xiaoyao Fu; Zhengnan Zhang; Xin Shen; Honghua Ruan. Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests. Forests 2019, 10, 145 .

AMA Style

Lin Cao, Hao Liu, Xiaoyao Fu, Zhengnan Zhang, Xin Shen, Honghua Ruan. Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests. Forests. 2019; 10 (2):145.

Chicago/Turabian Style

Lin Cao; Hao Liu; Xiaoyao Fu; Zhengnan Zhang; Xin Shen; Honghua Ruan. 2019. "Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests." Forests 10, no. 2: 145.

Journal article
Published: 04 February 2019 in Forests
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Tree diameter distributions are essential for the calculation of stem volume and biomass, as well as simulation of growth and yield and to understand timber assortments. Accurate and reliable prediction of tree diameter distributions is critical for optimizing forest structure compositions, scheduling silvicultural operations and promoting sustainable management. In this study, we investigated the potential of airborne Light Detection and Ranging (LiDAR) data for predicting tree diameter distributions using a bimodal finite mixture model (FMM) and a multimodal k-nearest neighbor (KNN) model (compared to the unimodal Weibull model (UWM)) over a subtropical planted forest in southern China. To do so, we first evaluated the capability of various LiDAR predictions (i.e., the bimodality coefficient (BC) and Lorenz-based indicators) to stratify forest structural types into unimodal and multimodal stands. Once the best LiDAR prediction for the differentiation was determined, the parameters of UWM (in non-specific and species-specific models) and FMM (in structure-specific models) were estimated by LiDAR-derived metrics and the tree diameter distributions of stands were generated by the estimated LiDAR parameters. When KNN was applied for constructing diameter distributions, optimal KNN strategies, including number of neighbors k, response configurations and imputation methods (i.e., Most Similar Neighbor (MSN) and Random Forest (RF)) for different species were heuristically determined. Finally, the predictive performance of estimated LiDAR the parameters of UWM, FMM and KNN for predicting diameter distributions were assessed. The results showed that LiDAR-predicted Lorenz-based indicators performed best for differentiation. Parameters of UWM and FMM were predicted well and the species-specific models had higher accuracies than the non-specific models. Overall, RF imputation from KNN with an optimal response set (i.e., DBH) were was stable than MSN imputation when k = 5 neighbors. In addition, the inclusion of bimodal FMM for differentiated all plots generally produced a more accurate result (Mean eR = 40.85, Mean eP = 0.20) than multimodal KNN (Mean eR = 52.19, Mean eP = 0.26), whereas the UWM produced the lowest performance (Mean eR = 52.31, Mean eP = 0.26). This study demonstrated the benefits of multimodal models with LiDAR for estimating diameter distributions for supporting forest inventory and sustainable forest management in subtropical planted forests.

ACS Style

Zhengnan Zhang; Lin Cao; Christopher Mulverhill; Hao Liu; Yong Pang; Zengyuan Li. Prediction of Diameter Distributions with Multimodal Models Using LiDAR Data in Subtropical Planted Forests. Forests 2019, 10, 125 .

AMA Style

Zhengnan Zhang, Lin Cao, Christopher Mulverhill, Hao Liu, Yong Pang, Zengyuan Li. Prediction of Diameter Distributions with Multimodal Models Using LiDAR Data in Subtropical Planted Forests. Forests. 2019; 10 (2):125.

Chicago/Turabian Style

Zhengnan Zhang; Lin Cao; Christopher Mulverhill; Hao Liu; Yong Pang; Zengyuan Li. 2019. "Prediction of Diameter Distributions with Multimodal Models Using LiDAR Data in Subtropical Planted Forests." Forests 10, no. 2: 125.

Journal article
Published: 08 January 2019 in Remote Sensing
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Accurate and reliable information on tree volume distributions, which describe tree frequencies in volume classes, plays a key role in guiding timber harvest, managing carbon budgets, and supplying ecosystem services. Airborne Light Detection and Ranging (LiDAR) has the capability of offering reliable estimates of the distributions of structure attributes in forests. In this study, we predicted individual tree volume distributions over a subtropical forest of southeast China using airborne LiDAR data and field measurements. We first estimated the plot-level total volume by LiDAR-derived standard and canopy metrics. Then the performances of three Weibull parameter prediction methods, i.e., parameter prediction method (PPM), percentile-based parameter recover method (PPRM), and moment-based parameter recover method (MPRM) were assessed to estimate the Weibull scale and shape parameters. Stem density for each plot was calculated by dividing the estimated plot total volume using mean tree volume (i.e., mean value of distributions) derived from the LiDAR-estimated Weibull parameters. Finally, the individual tree volume distributions were generated by the predicted scale and shape parameters, and then scaled by the predicted stem density. The results demonstrated that, compared with the general models, the forest type-specific (i.e., coniferous forests, broadleaved forests, and mixed forests) models had relatively higher accuracies for estimating total volume and stem density, as well as predicting Weibull parameters, percentiles, and raw moments. The relationship between the predicted and reference volume distributions showed a relatively high agreement when the predicted frequencies were scaled to the LiDAR-predicted stem density (mean Reynolds error index eR = 31.47–54.07, mean Packalén error index eP = 0.14–0.21). In addition, the predicted individual tree volume distributions predicted by PPRM of (average mean eR = 37.75) performed the best, followed by MPRM (average mean eR = 40.43) and PPM (average mean eR = 41.22). This study demonstrated that the LiDAR can potentially offer improved estimates of the distributions of tree volume in subtropical forests.

ACS Style

Lin Cao; Zhengnan Zhang; Ting Yun; Guibin Wang; Honghua Ruan; Guanghui She. Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data. Remote Sensing 2019, 11, 97 .

AMA Style

Lin Cao, Zhengnan Zhang, Ting Yun, Guibin Wang, Honghua Ruan, Guanghui She. Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data. Remote Sensing. 2019; 11 (1):97.

Chicago/Turabian Style

Lin Cao; Zhengnan Zhang; Ting Yun; Guibin Wang; Honghua Ruan; Guanghui She. 2019. "Estimating Tree Volume Distributions in Subtropical Forests Using Airborne LiDAR Data." Remote Sensing 11, no. 1: 97.