Performance and Sensitivity of Individual Tree Segmentation Methods for UAV-LiDAR in Multiple Forest Types

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.

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Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽

10.3390/f12020250 ◽

2021 ◽

Vol 12 (2) ◽

pp. 250

Author(s):

Wade T. Tinkham ◽

Neal C. Swayze

Keyword(s):

Structure From Motion ◽

Ponderosa Pine ◽

Point Cloud ◽

Image Resolution ◽

Canopy Height ◽

Computer Hardware ◽

Forest Monitoring ◽

Individual Tree ◽

Ponderosa Pine Forests ◽

Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

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Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0418 ◽

2020 ◽

Vol 50 (10) ◽

pp. 1012-1024

Author(s):

Meimei Wang ◽

Jiayuan Lin

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Tree Height ◽

Absolute Error ◽

Coefficient Of Determination ◽

Surface Model ◽

Individual Tree ◽

Aerial Vehicle ◽

The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

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Canopy Extraction and Height Estimation of Trees in a Shelter Forest Based on Fusion of an Airborne Multispectral Image and Photogrammetric Point Cloud

Journal of Sensors ◽

10.1155/2021/5519629 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Xuewen Wang ◽

Qingzhan Zhao ◽

Feng Han ◽

Jianxin Zhang ◽

Ping Jiang

Keyword(s):

Point Cloud ◽

Object Oriented ◽

Tree Height ◽

Multispectral Image ◽

Support Vector ◽

Surface Model ◽

Object Oriented Method ◽

3D Point Cloud ◽

Individual Tree ◽

Height Estimation

To reduce data acquisition cost, this study proposed a novel method of individual tree height estimation and canopy extraction based on fusion of an airborne multispectral image and photogrammetric point cloud. A fixed-wing drone was deployed to acquire the true color and multispectral images of a shelter forest. The Structure-from-Motion (SfM) algorithm was used to reconstruct the 3D point cloud of the canopy. The 3D point cloud was filtered to acquire the ground point cloud and then interpolated to a Digital Elevation Model (DEM) using the Radial Basis Function Neural Network (RBFNN). The DEM was subtracted from the Digital Surface Model (DSM) generated from the original point cloud to get the canopy height model (CHM). The CHM was processed for the crown extraction using local maximum filters and watershed segmentation. Then, object-oriented methods were employed in the combination of 12 bands and CHM for image segmentation. To extract the tree crown, the Support Vector Machine (SVM) algorithm was used. The result of the object-oriented method was vectorized and superimposed on the CHM to estimate the tree height. Experimental results demonstrated that it is efficient to employ point cloud and the proposed approach has great potential in the tree height estimation. The proposed object-oriented method based on fusion of a multispectral image and CHM effectively reduced the oversegmentation and undersegmentation, with an increase in the F -score by 0.12–0.17. Our findings provided a reference for the health and change monitoring of shelter forests as well.

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Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

Remote Sensing ◽

10.3390/rs12050885 ◽

2020 ◽

Vol 12 (5) ◽

pp. 885 ◽

Cited By ~ 4

Author(s):

Juan Picos ◽

Guillermo Bastos ◽

Daniel Míguez ◽

Laura Alonso ◽

Julia Armesto

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Single Point ◽

Tree Height ◽

Individual Tree ◽

Eucalyptus Plantation ◽

Local Maxima ◽

Eucalypt Plantations ◽

Aerial Vehicle ◽

Precision Forestry

The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations.

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Individual Tree Detection and Qualitative Inventory of a Eucalyptus sp. Stand Using UAV Photogrammetry Data

Remote Sensing ◽

10.3390/rs13183655 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3655

Author(s):

André Almeida ◽

Fabio Gonçalves ◽

Gilson Silva ◽

Adriano Mendonça ◽

Maria Gonzaga ◽

...

Keyword(s):

Point Cloud ◽

Digital Terrain Model ◽

Tree Height ◽

Northeastern Brazil ◽

Maximum Height ◽

Individual Tree ◽

Terrain Model ◽

Aerial Photogrammetry ◽

Individual Trees

Digital aerial photogrammetry (DAP) data acquired by unmanned aerial vehicles (UAV) have been increasingly used for forest inventory and monitoring. In this study, we evaluated the potential of UAV photogrammetry data to detect individual trees, estimate their heights (ht), and monitor the initial silvicultural quality of 1.5-year-old Eucalyptus sp. stand in northeastern Brazil. DAP estimates were compared with accurate tree locations obtained with real time kinematic (RTK) positioning and direct height measurements obtained in the field. In addition, we assessed the quality of a DAP-UAV digital terrain model (DTM) derived using an alternative ground classification approach and investigated its performance in the retrieval of individual tree attributes. The DTM built for the stand presented an RMSE of 0.099 m relative to the RTK measurements, showing no bias. The normalized 3D point cloud enabled the identification of over 95% of the stand trees and the estimation of their heights with an RMSE of 0.36 m (11%). However, ht was systematically underestimated, with a bias of 0.22 m (6.7%). A linear regression model, was fitted to estimate tree height from a maximum height metric derived from the point cloud reduced the RMSE by 20%. An assessment of uniformity indices calculated from both field and DAP heights showed no statistical difference. The results suggest that products derived from DAP-UAV may be used to generate accurate DTMs in young Eucalyptus sp. stands, detect individual trees, estimate ht, and determine stand uniformity with the same level of accuracy obtained in traditional forest inventories.

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UAV-Based Canopy Height Modeling under Leaf-on and Leaf-off Conditions for Determining Tree Height and Crown Diameter (Case study: Hyrcanian mixed Forest)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0125 ◽

2021 ◽

Author(s):

Vahid Nasiri ◽

Ali.A. Darvishsefat ◽

Hossein Arefi ◽

Marc Pierrot-Deseilligny ◽

Manochehr Namiranian ◽

...

Keyword(s):

Mixed Forest ◽

Digital Terrain Model ◽

Local Maximum ◽

Tree Height ◽

Field Measurements ◽

Canopy Height ◽

Aerial Images ◽

Surface Model ◽

Individual Tree ◽

Crown Diameter

Tree height and crown diameter are two common individual tree attributes that can be estimated from Unmanned Aerial Vehicles (UAVs) images thanks to photogrammetry and structure from motion. This research investigates the potential of low-cost UAV aerial images to estimate tree height and crown diameter. Two successful flights were carried out in two different seasons corresponding to leaf-off and leaf-on conditions to generate Digital Terrain Model (DTM) and Digital Surface Model (DSM), which were further employed in calculation of a Canopy Height Model (CHM). The CHM was used to estimate tree height using low pass and local maximum filters, and crown diameter was estimated based on an Invert Watershed Segmentation (IWS) algorithm. UAV-based tree height and crown diameter estimates were validated against field measurements and resulted in 3.22 m (10.1%) and 0.81 m (7.02%) RMSE, respectively. The results showed high agreement between our estimates and field measurements, with R2=0.808 for tree height and R2=0.923 for crown diameter. Generally, the accuracy of the results was considered acceptable and confirmed the usefulness of this approach for estimating tree heights and crown diameter.

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The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data

Remote Sensing ◽

10.3390/rs11232880 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2880 ◽

Cited By ~ 5

Author(s):

Qiuli Yang ◽

Yanjun Su ◽

Shichao Jin ◽

Maggi Kelly ◽

Tianyu Hu ◽

...

Keyword(s):

Forest Type ◽

The United States ◽

Airborne Lidar ◽

Canopy Height ◽

Lidar Data ◽

Individual Tree ◽

Segmentation Methods ◽

Vegetation Characteristics ◽

Canopy Height Model ◽

Height Model

This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics.

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Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

Remote Sensing ◽

10.3390/rs12050863 ◽

2020 ◽

Vol 12 (5) ◽

pp. 863 ◽

Cited By ~ 14

Author(s):

Ana Paula Dalla Corte ◽

Franciel Eduardo Rex ◽

Danilo Roberti Alves de Almeida ◽

Carlos Roberto Sanquetta ◽

Carlos A. Silva ◽

...

Keyword(s):

Forest Inventory ◽

Point Cloud ◽

Tree Height ◽

High Density ◽

Forest Plantations ◽

Density Point ◽

Individual Tree ◽

Total Height ◽

Area Of Interest ◽

Mean Square Errors

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

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Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

Open Geosciences ◽

10.1515/geo-2020-0266 ◽

2021 ◽

Vol 13 (1) ◽

pp. 705-716

Author(s):

Qiuji Chen ◽

Xin Wang ◽

Mengru Hang ◽

Jiye Li

Keyword(s):

Point Cloud ◽

Clustering Algorithm ◽

Tree Height ◽

Mining Area ◽

Research Area ◽

Airborne Lidar ◽

Shaanxi Province ◽

Point Cloud Data ◽

Individual Tree ◽

Cloud Data

Abstract The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for K-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.

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A Novel Tree Height Extraction Approach for Individual Trees by Combining TLS and UAV Image-Based Point Cloud Integration

Forests ◽

10.3390/f10070537 ◽

2019 ◽

Vol 10 (7) ◽

pp. 537 ◽

Cited By ~ 11

Author(s):

Jiarong Tian ◽

Tingting Dai ◽

Haidong Li ◽

Chengrui Liao ◽

Wenxiu Teng ◽

...

Keyword(s):

Point Cloud ◽

Coniferous Forest ◽

Tree Height ◽

High Density ◽

Point Cloud Data ◽

Individual Tree ◽

Cloud Data ◽

Tree Farms ◽

Uav Image ◽

Structure Evaluation

Research Highlights: This study carried out a feasibility analysis on the tree height extraction of a planted coniferous forest with high canopy density by combining terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) image–based point cloud data at small and midsize tree farms. Background and Objectives: Tree height is an important factor for forest resource surveys. This information plays an important role in forest structure evaluation and forest stock estimation. The objectives of this study were to solve the problem of underestimating tree height and to guarantee the precision of tree height extraction in medium and high-density planted coniferous forests. Materials and Methods: This study developed a novel individual tree localization (ITL)-based tree height extraction method to obtain preliminary results in a planted coniferous forest plots with 107 trees (Metasequoia). Then, the final accurate results were achieved based on the canopy height model (CHM) and CHM seed points (CSP). Results: The registration accuracy of the TLS and UAV image-based point cloud data reached 6 cm. The authors optimized the precision of tree height extraction using the ITL-based method by improving CHM resolution from 0.2 m to 0.1 m. Due to the overlapping of forest canopies, the CSP method failed to delineate all individual tree crowns in medium to high-density forest stands with the matching rates of about 75%. However, the accuracy of CSP-based tree height extraction showed obvious advantages compared with the ITL-based method. Conclusion: The proposed method provided a solid foundation for dynamically monitoring forest resources in a high-accuracy and low-cost way, especially in planted tree farms.

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