- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Using Tree Detection Based on Airborne Laser Scanning to Improve Forest Inventory Considering Edge Effects and the Co-Registration Factor
摘要： The estimation of forest biophysical attributes improves when airborne laser scanning (ALS) is integrated. Individual tree detection methods (ITD) and traditional area-based approaches (ABA) are the two main alternatives in ALS-based forest inventory. This study evaluated the performance of the enhanced area-based approach (EABA), an edge-correction method based on ALS data that combines ITD and ABA, at improving the estimation of forest biophysical attributes, while testing its efficiency when considering co-registration errors that bias remotely sensed predictor variables. The study was developed based on a stone pine forest (Pinus pinea L.) in Central Spain, in which tree spacing and scanning conditions were optimal for the ITD approach. Regression modeling was used to select the optimal predictor variables to estimate forest biophysical attributes. The accuracy of the models improved when using EABA, despite the low-density of the ALS data. The relative mean improvement of EABA in terms of root mean squared error was 15.2%, 17.3%, and 7.2% for growing stock volume, stand basal area, and dominant height, respectively. The impact of co-registration errors in the models was clear in the ABA, while the effect was minor and mitigated under EABA. The implementation of EABA can highly contribute to improve modern forest inventory applications.
关键词： forest modeling,3D point clouds,positioning,precision forestry,remote sensing
更新于2025-09-12 10:27:22
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AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees
摘要： Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. However, some works cannot guarantee sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct detailed 3D tree models from laser scans. We first extract an initial tree skeleton from the input point cloud by establishing a minimum spanning tree using the Dijkstra shortest-path algorithm. Then, the initial tree skeleton is pruned by iteratively removing redundant components. After that, an optimization-based approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The overall fitting error (i.e., the distance between the input points and the output model) is less than 10 cm. The reconstructed tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc. The source code of this work is freely available at https://github.com/tudelft3d/adtree.
关键词： point cloud,precision forestry,laser scanning,tree modelling
更新于2025-09-11 14:15:04
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A comparison of UAV laser scanning, photogrammetry and airborne laser scanning for precision inventory of small-forest properties
摘要： This study addresses the use of multiple sources of auxiliary data from unmanned aerial vehicles (UAVs) and airborne laser scanning (ALS) data for inference on key biophysical parameters in small forest properties (5–300 ha). We compared the precision of the estimates using plot data alone under a design-based inference with model-based estimates that include plot data and the following four types of auxiliary data: (1) terrain-independent variables from UAV photogrammetric data (UAV-SfM); (2) variables obtained from UAV photogrammetric data normalized using external terrain data (UAV-SfMDTM); (3) UAV-LS and (4) ALS data. The inclusion of remotely sensed data increased the precision of DB estimates by factors of 1.5–2.2. The optimal data sources for top height, stem density, basal area and total stem volume were: UAV-LS, UAV-SfM, UAV-SfMDTM and UAV-SfMDTM. We conclude that the use of UAV data can increase the precision of stand-level estimates even under intensive ?eld sampling conditions.
关键词： UAV,laser scanning,photogrammetry,forest inventory,precision forestry,ALS
更新于2025-09-11 14:15:04
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Modelos para estimativa de volume de árvores individuais pela morfometria da copa obtida com lidar
摘要： The volumetric estimate from digital scanning of the forests through the use of LIDAR increases the precision of forest management techniques in planning tropical forest logging operations. The use of this remote detection technology allows the incorporation of crown morphometric variables which are still little known and little used due to the difficulty of collecting field data for volume equations. The objective of this study was to build equations capable of estimating the stem volume of dominant and codominant individual trees from the crown’s morphometry obtained by airborne LIDAR, considering two forest inventory situations: a) with the collection of diameter at breast height (DBH), and crown morphometric variables obtained from LIDAR data and b) using only the crown morphometry variables. For the selection of models the factors considered were: the correlation matrix of predictor variables and the combination of variables that generates the best results by statistical criteria Syx, Syx (%) and Pressp, and that were homoscedastic and had a normal and independent distribution of errors. The influence analysis was performed for the best equations. The results for the statistical fit of the equations to the two situations allowed the selection of models with and without DBH, with R2aj.(%) values of a) 92.92 and b) 79.44, Syx(%) values of a) 16.73 and b) 27.47, and, Pressp criterion values of a) 201.15 m6 and b) 537.47 m6, respectively. Through morphometric variables it was possible to develop equations capable of accurately estimating the stem volume of dominant and codominant trees in tropical forests.
关键词： Regression analysis,Laser profiling,Amazon,Precision forestry
更新于2025-09-04 15:30:14