Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach

Yun, Ting; Jiang, Kang; Li, Guangchao; Eichhorn, Markus P.; Fan, Jiangchuan; Liu, Fangzhou; Chen, Bangqian; An, Feng; Cao, Lin

doi:10.1016/j.rse.2021.112307

Cited by 107 publications

(62 citation statements)

References 47 publications

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“…The specific method of estimation of tree heights from LiDAR data we use was chosen for its proven wide applicability and use. While other methods of tree height estimation might be better suited under specific situations [35][36][37], there is no indication that alternatively derived tree height estimates would have altered our main conclusion about the disagreement in the CDL and the NLCD impacting the area-total AGFB estimates.…”

Section: Discussionmentioning

confidence: 70%

“…The canopy height information combined with stand density or canopy cover has been used for biomass estimation [33,34]. The challenges and performance of the methods to detect and delineate individual trees from LiDAR data are a subject of continuous research [35][36][37]. In this study, we apply a widely used method proposed by [38].…”

Section: Tree Heights and Lidar Analysismentioning

confidence: 99%

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Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests

et al. 2021

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Mapping and quantifying forest inventories are critical for the management and development of forests for natural resource conservation and for the evaluation of the aboveground forest biomass (AGFB) technically available for bioenergy production. The AGFB estimation procedures that rely on traditional, spatially sparse field inventory samples constitute a problem for geographically diverse regions such as the state of North Carolina in the southeastern U.S. We propose an alternative AGFB estimation procedure that combines multiple geospatial data. The procedure uses land cover maps to allocate forested land areas to alternative forest types; uses the light detection and ranging (LiDAR) data to evaluate tree heights; calculates the area-total AGFB using region- and tree-type-specific functions that relate the tree heights to the AGFB. We demonstrate the procedure for a selected North Carolina region, a 2.3 km2 area randomly chosen in Duplin County. The tree diameter functions are statistically estimated based on the Forest Inventory Analysis (FIA) data, and two publicly available, open source land cover maps, Crop Data Layer (CDL) and National Land Cover Database (NLCD), are compared and contrasted as a source of information on the location and typology of forests in the study area. The assessment of the consistency of forestland mapping derived from the CDL and the NLCD data lets us estimate how the disagreement between the two alternative, widely used maps affects the AGFB estimation. The methodology and the results we present are expected to complement and inform large-scale assessments of woody biomass in the region.

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Section: Discussionmentioning

confidence: 70%

Section: Tree Heights and Lidar Analysismentioning

confidence: 99%

Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests

et al. 2021

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“…With the development of light detection and ranging (lidar), highly precise estimations for crown shapes are available [22,23]. Canopy gaps are also a significant indicator of light conditions [24,25]. However, its scope is usually for multiple trees in the forest.…”

Section: Conventional Methods Of Assessing Light Condition In Sample Plotsmentioning

confidence: 99%

Design of a Generic Virtual Measurement Workflow for Processing Archived Point Cloud of Trees and Its Implementation of Light Condition Measurements on Stems

et al. 2021

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Virtual measurement workflow (VMW) was a generic data mining method developed in this study. It was used to extract tree information from archived point clouds under limited conditions by applying virtual measurements in virtual reality. As an example of how to use VMW for a specific topic, the VMW implementation of light condition measurement was further developed. This implementation could measure the temporal and spatial distribution of sunlight on virtual trees (stems). The output was expected as a new type of raw measurement data for tree morphology and phycological studies. At a single tree scale, it facilitated the quantitative interpretation of the growth strategy of branches. By measuring a single tree, it was found that only 4.34% of the stem surface could be illuminated throughout the day (8 h). Meanwhile, 35.87% of the stem surfaces were exposed to sunlight for less than one hour a day. A further mathematical processing of the output, i.e., γ (a ratio between relative area of triangles and relative quantities of triangles in each exposure duration group) improved the sensitivity of identifying differences in lighting conditions. Furthermore, we measured virtual trees of four species from an additional data source using a standardized setting. These include the sessile oak, gemu tree, Masson’s pine, and cherry tree. It was found that the shape of the crown was also significant for the distribution of solar energy on stems. For instance, the gemu tree had a cylindrical tree crown with narrow tree skeleton. A percentage of 10.38 of the surface on the gemu tree was illuminated throughout the day (8 h). The Masson’s pine had similar height and DBH with the gemu tree. However, the elliptical tree crown of the Masson’s pines prevented more lights. The area on the stem that was exposed to sunlight (8 h) dropped from 10.38% to 5.71%. This good differentiation of different crown structures might help this VMW implementation to continue to develop as a tool for identifying the effect of various crown shapes on radiosity for different tree species. The successful development of this VMW implementation had several practical applications for tree studies. Meanwhile, it demonstrated the overall feasibility of VMW and provided a paradigm for further development of other VMW implementations.

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“…Owing to the top-down scanning mechanism and the large flight coverage, ALS has the capability of gathering highly accurate and dense point clouds and, thus, is well suited for larger areas and cities [26]. Numerous studies have demonstrated that ALS data can be employed for urban 3D morphology investigations [27], building rooftops extractions, density information acquisitions [28][29][30], urban green volume estimations [31,32], and individual tree detections [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Tree Extraction from Airborne Laser Scanning Data in Urban Areas

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et al. 2021

Remote Sensing

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Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.

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Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach

Cited by 107 publications

References 47 publications

Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests

Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests

Design of a Generic Virtual Measurement Workflow for Processing Archived Point Cloud of Trees and Its Implementation of Light Condition Measurements on Stems

Tree Extraction from Airborne Laser Scanning Data in Urban Areas

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