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- 实验方案
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SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning
摘要： Precise structural information collected from plots is significant in the management of and decision-making regarding forest resources. Currently, laser scanning is widely used in forestry inventories to acquire three-dimensional (3D) structural information. There are three main data-acquisition modes in ground-based forest measurements: single-scan terrestrial laser scanning (TLS), multi-scan TLS and multi-single-scan TLS. Nevertheless, each of these modes causes specific difficulties for forest measurements. Due to occlusion effects, the single-scan TLS mode provides scans for only one side of the tree. The multi-scan TLS mode overcomes occlusion problems, however, at the cost of longer acquisition times, more human labor and more effort in data preprocessing. The multi-single-scan TLS mode decreases the workload and occlusion effects but lacks the complete 3D reconstruction of forests. These problems in TLS methods are largely avoided with mobile laser scanning (MLS); however, the geometrical peculiarity of forests (e.g., similarity between tree shapes, placements, and occlusion) complicates the motion estimation and reduces mapping accuracy. Therefore, this paper proposes a novel method combining single-scan TLS and MLS for forest 3D data acquisition. We use single-scan TLS data as a reference, onto which we register MLS point clouds, so they fill in the omission of the single-scan TLS data. To register MLS point clouds on the reference, we extract virtual feature points that are sampling the centerlines of tree stems and propose a new optimization-based registration framework. In contrast to previous MLS-based studies, the proposed method sufficiently exploits the natural geometric characteristics of trees. We demonstrate the effectiveness, robustness, and accuracy of the proposed method on three datasets, from which we extract structural information. The experimental results show that the omission of tree stem data caused by one scan can be compensated for by the MLS data, and the time of the field measurement is much less than that of the multi-scan TLS mode. In addition, single-scan TLS data provide strong global constraints for MLS-based forest mapping, which allows low mapping errors to be achieved, e.g., less than 2.0 cm mean errors in both the horizontal and vertical directions.
关键词： MLS,Single-scan TLS,Forest mapping,SLAM,LiDAR
更新于2025-09-23 15:21:01
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Tree crown segmentation based on a tree crown density model derived from Airborne Laser Scanning
摘要： This letter describes a new algorithm for automatic tree crown delineation based on a model of tree crown density, and its validation. The tree crown density model was first used to create a correlation surface, which was then input to a standard watershed segmentation algorithm for delineation of tree crowns. The use of a model in an early step of the algorithm neatly solves the problem of scale selection. In earlier studies, correlation surfaces have been used for tree crown segmentation, involving modelling tree crowns as solid geometric shapes. The new algorithm applies a density model of tree crowns, which improves the model’s suitability for segmentation of Airborne Laser Scanning (ALS) data because laser returns are located inside tree crowns. The algorithm was validated using data acquired for 36 circular (40 m radius) field plots in southern Sweden. The algorithm detected high proportions of field-measured trees (40–97% of live trees in the 36 field plots: 85% on average). The average proportion of detected basal area (cross-sectional area of tree stems, 1.3 m above ground) was 93% (range: 84–99%). The algorithm was used with discrete return ALS point data, but the computation principle also allows delineation of tree crowns in ALS waveform data.
关键词： Tree crown segmentation,tree crown density model,Airborne Laser Scanning,forest mapping,watershed segmentation
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Tropical Forest Tree Species Classification Using Meter-Scale Image Data
摘要： Accurate identification of tropical tree species is critical for studies of forest habitat, composition, biomass, and ultimately a better understanding of the role trees play in climate variability through carbon uptake. The aim of this study was to derive an accurate classification of a tropical forest study site in Costa Rica using high-resolution imagery. A series of corrections for look and view angle, and the utilization of the DigitalGlobe atmospheric compensation procedure (AComp) provided the study with an accurate surface reflectivity dataset from WorldView-3 imagery. A rule-set object-oriented classification schema defined trees in the study area using ENVI-defined tree canopies through a segmentation of the multispectral image. The results show that select WorldView-3 bands, and WorldView-3-specific vegetation indices, can produce an accurate species-level tree classification in a complex tropical forest.
关键词： tree spectra,WorldView-3,Tropical forest mapping,object-oriented classification
更新于2025-09-10 09:29:36