scholarly journals Individual tree extraction from terrestrial laser scanning data via graph pathing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Di Wang ◽  
Xinlian Liang ◽  
Gislain II Mofack ◽  
Olivier Martin-Ducup
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Tree Level ◽  
Forest Types ◽  
Computationally Efficient ◽  
Large Area ◽  
Individual Tree ◽  
Single Crown ◽  
Tree Extraction

Abstract Background Individual tree extraction from terrestrial laser scanning (TLS) data is a prerequisite for tree-scale estimations of forest biophysical properties. This task currently is undertaken through laborious and time-consuming manual assistance and quality control. This study presents a new fully automatic approach to extract single trees from large-area TLS data. This data-driven method operates exclusively on a point cloud graph by path finding, which makes our method computationally efficient and universally applicable to data from various forest types. Results We demonstrated the proposed method on two openly available datasets. First, we achieved state-of-the-art performance on locating single trees on a benchmark dataset by significantly improving the mean accuracy by over 10% especially for difficult forest plots. Second, we successfully extracted 270 trees from one hectare temperate forest. Quantitative validation resulted in a mean Intersection over Union (mIoU) of 0.82 for single crown segmentation, which further led to a relative root mean square error (RMSE%) of 21.2% and 23.5% for crown area and tree volume estimations, respectively. Conclusions Our method allows automated access to individual tree level information from TLS point clouds. The proposed method is free from restricted assumptions of forest types. It is also computationally efficient with an average processing time of several seconds for one million points. It is expected and hoped that our method would contribute to TLS-enabled wide-area forest qualifications, ranging from stand volume and carbon stocks modelling to derivation of tree functional traits as part of the global ecosystem understanding.

scholarly journals Exploring the Variability of Tropical Savanna Tree Structural Allometry with Terrestrial Laser Scanning

2020 ◽  
Vol 12 (23) ◽  
pp. 3893
Author(s):  
Linda Luck ◽  
Lindsay B. Hutley ◽  
Kim Calders ◽  
Shaun R. Levick
Keyword(s):  
Laser Scanning ◽  
3D Structure ◽  
Terrestrial Laser Scanning ◽  
Tree Height ◽  
Tree Architecture ◽  
Allometric Equations ◽  
Environmental Research ◽  
Tree Level ◽  
Individual Tree ◽  
Monitoring Programs

Individual tree carbon stock estimates typically rely on allometric scaling relationships established between field-measured stem diameter (DBH) and destructively harvested biomass. The use of DBH-based allometric equations to estimate the carbon stored over larger areas therefore, assumes that tree architecture, including branching and crown structures, are consistent for a given DBH, and that minor variations cancel out at the plot scale. We aimed to explore the degree of structural variation present at the individual tree level across a range of size-classes. We used terrestrial laser scanning (TLS) to measure the 3D structure of each tree in a 1 ha savanna plot, with coincident field-inventory. We found that stem reconstructions from TLS captured both the spatial distribution pattern and the DBH of individual trees with high confidence when compared with manual measurements (R2 = 0.98, RMSE = 0.0102 m). Our exploration of the relationship between DBH, crown size and tree height revealed significant variability in savanna tree crown structure (measured as crown area). These findings question the reliability of DBH-based allometric equations for adequately representing diversity in tree architecture, and therefore carbon storage, in tropical savannas. However, adoption of TLS outside environmental research has been slow due to considerable capital cost and monitoring programs often continue to rely on sub-plot monitoring and traditional allometric equations. A central aspect of our study explores the utility of a lower-cost TLS system not generally used for vegetation surveys. We discuss the potential benefits of alternative TLS-based approaches, such as explicit modelling of tree structure or voxel-based analyses, to capture the diverse 3D structures of savanna trees. Our research highlights structural heterogeneity as a source of uncertainty in savanna tree carbon estimates and demonstrates the potential for greater inclusion of cost-effective TLS technology in national monitoring programs.

scholarly journals Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 415 ◽  
Author(s):  
Mohammad Imangholiloo ◽  
Ninni Saarinen ◽  
Lauri Markelin ◽  
Tomi Rosnell ◽  
Roope Näsi ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Hyperspectral Imagery ◽  
Digital Terrain Model ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Density ◽  
Tree Canopy ◽  
Tree Level ◽  
Species Classification ◽  
Individual Tree

Seedling stands are mainly inventoried through field measurements, which are typically laborious, expensive and time-consuming due to high tree density and small tree size. In addition, operationally used sparse density airborne laser scanning (ALS) and aerial imagery data are not sufficiently accurate for inventorying seedling stands. The use of unmanned aerial vehicles (UAVs) for forestry applications is currently in high attention and in the midst of quick development and this technology could be used to make seedling stand management more efficient. This study was designed to investigate the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions. The focus was in retrieving tree density and the height in young seedling stands in the southern boreal forests of Finland. After creating the canopy height model from photogrammetric point clouds using national digital terrain model based on ALS, the watershed segmentation method was applied to delineate the tree canopy boundary at individual tree level. The segments were then used to extract tree heights and spectral information. Optimal bands for calculating vegetation indices were analysed and used for species classification using the random forest method. Tree density and the mean tree height of the total and spruce trees were then estimated at the plot level. The overall tree density was underestimated by 17.5% and 20.2% in leaf-off and leaf-on conditions with the relative root mean square error (relative RMSE) of 33.5% and 26.8%, respectively. Mean tree height was underestimated by 20.8% and 7.4% (relative RMSE of 23.0% and 11.5%, and RMSE of 0.57 m and 0.29 m) in leaf-off and leaf-on conditions, respectively. The leaf-on data outperformed the leaf-off data in the estimations. The results showed that UAV imagery hold potential for reliably characterizing seedling stands and to be used to supplement or replace the laborious field inventory methods.

scholarly journals USING MOBILE LASER SCANNING POINT CLOUDS TO EXTRACT URBAN ROADSIDE TREES FOR ECOLOGICAL BENEFITS ESTIMATION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 211-216
Author(s):  
W. Fan ◽  
B. Yang ◽  
F. Liang ◽  
Z. Dong
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Morphological Parameters ◽  
Heat Island ◽  
Individual Tree ◽  
Ecological Benefits ◽  
Urban Heat ◽  
The City ◽  
Tree Extraction ◽  
Parameters Calculation

Abstract. Roadside trees in the city play a crucial role in addressing the issues of air pollution, urban heat island effects, road noise, and so on. This paper proposes an efficient and robust method to automatically extract individual roadside trees with morphological parameters from mobile laser scanning (MLS) point clouds for ecological benefits estimation. The proposed method consists of four steps: MLS data pre-processing, pole-like objects classification, individual tree extraction, morphological parameters calculation for ecological benefits estimation. The proposed method is verified using three complex datasets in Shanghai, China. Comprehensive experiments demonstrate that the proposed method achieves good performance in extracting individual tree in terms of average precision and recall (91.83%, 92.60%), and provides detailed information for ecological benefits estimation.

scholarly journals Airborne laser scanning applied to eucalyptus stand inventory at individual tree level

2018 ◽  
Vol 53 (12) ◽  
pp. 1373-1382 ◽  
Author(s):  
Diogo Nepomuceno Cosenza ◽  
Vicente Paulo Soares ◽  
Helio Garcia Leite ◽  
José Marinaldo Gleriani ◽  
Cibele Hummel do Amaral ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Tree Height ◽  
Point Clouds ◽  
Equation System ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Individual Tree ◽  
Local Maxima ◽  
Airborne Laser

Abstract: The objective of this work was to evaluate the application of airborne laser scanning (ALS) to a large-scale eucalyptus stand inventory by the method of individual trees, as well as to propose a new method to estimate tree diameter as a function of the height obtained from point clouds. The study was carried out in a forest area of 1,681 ha, consisting of eight eucalyptus stands with ages varying from four to seven years. After scanning, tree heights were obtained using the local maxima algorithm, and total wood stock by summing up individual volumes. To determine tree diameters, regressions fit using data measured in the inventory plots were used. The results were compared with the estimates obtained from field sampling. The equation system proposed is adequate to be applied to the tree height data derived from ALS point clouds. The tree individualization approach by local maxima filters is efficient to estimate number of trees and wood stock from ALS data, as long as the results are previously calibrated with field data.

scholarly journals Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks

2018 ◽  
Vol 10 (8) ◽  
pp. 1215 ◽  
Author(s):  
Zhouxin Xi ◽  
Chris Hopkinson ◽  
Laura Chasmer
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Visual Assessment ◽  
Common Species ◽  
Branch Points ◽  
Forest Environment ◽  
Individual Tree ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

Terrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing.

scholarly journals Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation

2021 ◽  
Vol 13 (2) ◽  
pp. 223
Author(s):  
Zhenyang Hui ◽  
Shuanggen Jin ◽  
Dajun Li ◽  
Yao Yevenyo Ziggah ◽  
Bo Liu
Keyword(s):  
Transfer Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Principal Component ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Individual Tree ◽  
Terrestrial Lidar ◽  
Initial Segmentation ◽  
Tree Extraction

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

scholarly journals Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 835
Author(s):  
Ville Luoma ◽  
Tuomas Yrttimaa ◽  
Ville Kankare ◽  
Ninni Saarinen ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Tree Growth ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Stem Volume ◽  
Accurate Information ◽  
Stem Form ◽  
Monitoring Period ◽  
Significant Difference

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

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