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Mapping Forest Structure Using UAS inside Flight Capabilities
摘要： We evaluated two unmanned aerial systems (UASs), namely the DJI Phantom 4 Pro and DJI Mavic Pro, for 3D forest structure mapping of the forest stand interior with the use of close-range photogrammetry techniques. Assisted flights were performed within two research plots established in mature pure Norway spruce (Picea abies (L.) H. Karst.) and European beech (Fagus sylvatica L.) forest stands. Geotagged images were used to produce georeferenced 3D point clouds representing tree stem surfaces. With a flight height of 8 m above the ground, the stems were precisely modeled up to a height of 10 m, which represents a considerably larger portion of the stem when compared with terrestrial close-range photogrammetry. Accuracy of the point clouds was evaluated by comparing field-measured tree diameters at breast height (DBH) with diameter estimates derived from the point cloud using four different fitting methods, including the bounding circle, convex hull, least squares circle, and least squares ellipse methods. The accuracy of DBH estimation varied with the UAS model and the diameter fitting method utilized. With the Phantom 4 Pro and the least squares ellipse method to estimate diameter, the mean error of diameter estimates was ?1.17 cm (?3.14%) and 0.27 cm (0.69%) for spruce and beech stands, respectively.
关键词： point cloud,diameter at breast height (DBH),photogrammetry,obstacle sensing,forestry,unmanned aerial system (UAS),vision positioning system
更新于2025-09-23 15:23:52
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Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data
摘要： Changes in forest areas have great impact on a range of ecosystem functions, and monitoring forest change across di?erent spatial and temporal resolutions is a central task in forestry. At the spatial scales of municipalities, forest properties and stands, local inventories are carried out periodically to inform forest management, in which airborne laser scanner (ALS) data are often used to estimate forest attributes. As local forest inventories are repeated, the availability of bitemporal ?eld and ALS data is increasing. The aim of this study was to assess the utility of bitemporal ALS data for classi?cation of dominant height change, aboveground biomass change, forest disturbances, and forestry activities. We used data obtained from 558 ?eld plots and four repeated ALS-based forest inventories in southeastern Norway, with temporal resolutions ranging from 11 to 15 years. We applied the k-nearest neighbor method for classi?cation of: (i) increasing versus decreasing dominant height, (ii) increasing versus decreasing aboveground biomass, (iii) undisturbed versus disturbed forest, and (iv) forestry activities, namely untouched, partial harvest, and clearcut. Leave-one-out cross-validation revealed overall accuracies of 96%, 95%, 89%, and 88% across districts for the four change classi?cations, respectively. Thus, our results demonstrate that various changes in forest structure can be classi?ed with high accuracy at plot level using data from repeated ALS-based forest inventories.
关键词： classi?cation,dominant height,forest change,ALS,forestry activity,aboveground biomass,disturbance,forest
更新于2025-09-23 15:19:57
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Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region
摘要： We compared the performance of Sentinel-2 and Landsat 8 data for forest variable prediction in the boreal forest of Southern Finland. We defined twelve modelling setups to train multivariable prediction models with either multilayer perceptron (MLP) or regression tree models with the brute force forward selection method. The reference data consisted of 739 circular field plots that had been collected by the Finnish Forest Centre concurrently with the Sentinel-2 and Landsat 8 acquisitions. The input data were divided into training, validation and test sets of equal sizes for 100 iterations in each modelling setup. The predicted forest variables were stem volume (V), stem diameter (D), tree height (H) and basal area (G), and their species-wise components for pine (Pine), spruce (Spr) and broadleaved (BL) trees. We recorded the performance figures and the best predictive image bands for each modelling setup. The best average performance over the 100 modelling iterations was obtained using all Sentinel-2 bands. The plot-level relative root mean square errors (RMSE%) of the field observed mean were 38.4% for average stem diameter, 42.5% for stem basal area/ha, 30.4% for average tree height, and 59.3% for growing stock volume/ha with variables including all tree species. The corresponding best figures with all Landsat 8 bands were RMSE % = 44.6%, 50.2%, 36.6% and 72.2%, respectively. The Sentinel-2 outperformed Landsat 8 also when using near-equivalent image bands and Sentinel-2 data down-sampled to 30 m pixel resolution. The relative systematic error (bias%) did not show any significant differences between Sentinel-2 and Landsat 8 data: the average of the absolute value of bias% was 0.8% for Sentinel-2 and 1.2% for Landsat 8. The best predictive Sentinel-2 image band was the red-edge 1 (B05_RE1), when variable totals including all species were estimated. The short-wave infrared bands (B11_SWIR1 & B12_SWIR2) and the visible green band (B03_Green) were also among the best predictors. The median number of predictors in the best performing models was 4–6 for the Sentinel-2 and 4–5 for the Landsat 8 models, respectively. We conclude that Sentinel-2 Multispectral Instrument (MSI) data can be recommended as the principal Earth observation data source in forest resources assessment.
关键词： Boreal forest,Forestry,Landsat 8,Sentinel-2,Forest variables
更新于2025-09-19 17:15:36
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Forest Road Status Assessment Using Airborne Laser Scanning
摘要： Forest roads allow access for silvicultural operations, harvesting, recreational activities, wildlife management, and fire suppression. In British Columbia, Canada, roads that are no longer required must be deactivated (temporarily, semipermanently, or permanently) in order to minimize the impact on the overall forested ecosystem. However, the remoteness and size of the road network present challenges for monitoring. Our aim was to examine the utility of airborne laser scanning data to assess the status and quality of forest roads across 52,000 hectares of coastal forest in British Columbia. Within the forest estate, roads can be active or deactivated, or have an unknown status. We classified road segments based on the vegetation growth on the road surface, and edges, by classifying the height distribution of airborne laser scanning returns within each road segment into four groups: no vegetation, minor vegetation, dense understory vegetation, and dense overstory vegetation. Validation indicated that 73 percent of roads were classified correctly when compared to independent field observations. The majority were classified as active roads with no vegetation or deactivated with dense vegetation. The approach presented herein can aid forest managers in verifying the status of the roads in their management area, especially in remote areas where field assessments are costly and time-consuming.
关键词： forest management,forestry,road quality,ALS,land use,LiDAR,road classification
更新于2025-09-19 17:13:59
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A Case Study of UAS Borne Laser Scanning for Measurement of Tree Stem Diameter
摘要： Diameter at breast height (DBH) is one of the most important parameter in forestry. With increasing use of terrestrial and airborne laser scanning in forestry, new exceeding possibilities to directly derive DBH emerge. In particular, high resolution point clouds from laser scanners on board unmanned aerial systems (UAS) are becoming available over forest areas. In this case study, DBH estimation from a UAS point cloud based on modeling the relevant part of the tree stem with a cylinder, is analyzed with respect to accuracy and completeness. As reference, manually measured DBHs and DBHs from terrestrial laser scanning point clouds are used for comparison. We demonstrate that accuracy and completeness of the cylinder fit are depending on the stem diameter. Stems with DBH > 20 cm feature almost 100% successful reconstruction with relative differences to the reference DBH of 9% (DBH 20–30 cm) down to 1.8% for DBH > 40 cm.
关键词： forest inventory,cylinder,diameter at breast height,forestry,point cloud,Unmanned Aerial Systems,LiDAR,DBH
更新于2025-09-16 10:30:52
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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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A Single-Tree Processing Framework Using Terrestrial Laser Scanning Data for Detecting Forest Regeneration
摘要： Direct assessment of forest regeneration from remote sensing data is a previously little-explored problem. This is due to several factors which complicate object detection of small trees in the understory. Most existing studies are based on airborne laser scanning (ALS) data, which often has insufficient point densities in the understory forest layers. The present study uses plot-based terrestrial laser scanning (TLS) and the survey design was similar to traditional forest inventory practices. Furthermore, a framework of methods was developed to solve the difficulties of detecting understory trees for quantifying regeneration in temperate montane forest. Regeneration is of special importance in our montane study area, since large parts are declared as protection forest against alpine natural hazards. Close to nature forest structures were tackled by separating 3D tree stem detection from overall tree segmentation. In support, techniques from 3D mathematical morphology, Hough transformation and state-of-the-art machine learning were applied. The methodical framework consisted of four major steps. These were the extraction of the tree stems, the estimation of the stem diameters at breast height (DBH), the image segmentation into individual trees and finally, the separation of two groups of regeneration. All methods were fully automated and utilized volumetric 3D image information which was derived from the original point cloud. The total amount of regeneration was split into established regeneration, consisting of trees with a height > 130 cm in combination with a DBH < 12 cm and unestablished regeneration, consisting of trees with a height < 130 cm. Validation was carried out against field-based expert estimates of percentage ground cover, differentiating seven classes that were similar to those used by forest inventory. The mean absolute error (MAE) of our method for established regeneration was 1.11 classes and for unestablished regeneration only 0.27 classes. Considering the metrical distances between the class centres, the MAE amounted 8.08% for established regeneration and 2.23% for unestablished regeneration.
关键词： TLS,forestry,3D image segmentation,understory,tree detection,tree stems,DBH
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 - An Unsupervised Change Detection Method for Lidar Data in Forest Areas Based on Change Vector Analysis in the Polar Domain
摘要： This paper presents a Change Detection method for bitemporal Light Detection And Ranging (LiDAR) data based on Change Vector Analysis in the polar domain. The method first extracts a suite of LiDAR metrics from the two LiDAR point clouds using a 2-D grid based approach. Second, it transforms the change in these metrics into a polar representation to examine variations in terms of magnitude and direction. The analysis of the magnitude discriminates between small magnitude changes or unchanged areas and areas affected by large disturbances related to forest removal. The analysis of the direction of change allows us to identify dominant directions to discriminate between the various types of forest change. The method has been tested on a multitemporal dataset acquired in a high productivity evergreen conifer forest in British Columbia, Canada. Experimental results indicated that the method effectively discriminates between the different types of forest change trought the analysis of the change direction.
关键词： Multitemporal,Change Vector Analysis,LiDAR,Forestry,Change Detection
更新于2025-09-10 09:29:36