Automatic Forest Mapping at Individual Tree Levels from Terrestrial Laser Scanning Point Clouds with a Hierarchical Minimum Cut Method

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.

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SLAM-BASED BACKPACK LASER SCANNING FOR FOREST PLOT MAPPING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-267-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 267-271

Author(s):

J. Shao ◽

W. Zhang ◽

L. Luo ◽

S. Cai ◽

H. Jiang

Keyword(s):

Laser Scanning ◽

Structural Information ◽

Three Dimensional ◽

Vertical Direction ◽

Point Clouds ◽

Forest Plot ◽

Individual Tree ◽

Forest Mapping ◽

Localization And Mapping ◽

The Individual

Abstract. Acquisition of three-dimensional (3D) structural information is significant for forest measurements. To achieve faster data collection in forests, we design a backpack laser scanning (BLS) system using a single mobile laser scanning (MLS) scanner and specific to forest environments. The simultaneous localization and mapping (SLAM) approach based on the natural geometric characteristics of trees is used for BLS-based forest mapping, in which the skeleton line of the individual tree is extracted for scan matching and the incremental maps are adopted for global optimization of all the BLS point clouds. The final experimental results show that the SLAM-based BLS system achieves accurate forest plots mapping and allows reaching low mapping errors, in which the mean errors are approximately 3 cm in the horizontal and 2 cm in the vertical direction.

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3D Forest Mapping Using A Low-Cost UAV Laser Scanning System: Investigation and Comparison

Remote Sensing ◽

10.3390/rs11060717 ◽

2019 ◽

Vol 11 (6) ◽

pp. 717 ◽

Cited By ~ 12

Author(s):

Jianping Li ◽

Bisheng Yang ◽

Yangzi Cong ◽

Lin Cao ◽

Xiaoyao Fu ◽

...

Keyword(s):

Laser Scanning ◽

Low Cost ◽

Poor Performance ◽

Point Clouds ◽

Individual Tree ◽

Forest Mapping ◽

The Poor ◽

3D Information ◽

Tree Characteristics ◽

Characteristics Estimation

Automatic 3D forest mapping and individual tree characteristics estimation are essential for forest management and ecosystem maintenance. The low-cost unmanned aerial vehicle (UAV) laser scanning (ULS) is a newly developed tool for cost-effectively collecting 3D information and attempts to use it for 3D forest mapping have been made, due to its capability to provide 3D information with a lower cost and higher flexibility than the standard ULS and airborne laser scanning (ALS). As the direct georeferenced point clouds may suffer from distortion caused by the poor performance of a low-cost inertial measurement unit (IMU), and 3D forest mapping using low-cost ULS poses a great challenge. Therefore, this paper utilized global navigation satellite system (GNSS) and IMU aided Structure-from-Motion (SfM) for trajectory estimation, and, hence, overcomes the poor performance of low-cost IMUs. The accuracy of the low-cost ULS point clouds was compared with the ground truth data collected by a commercial ULS system. Furthermore, the effectiveness of individual trees segmentation and tree characteristics estimation derived from the low-cost ULS point clouds were accessed. Experiments were undertaken in Dongtai forest farm, Yancheng City, Jiangsu Province, China. The results showed that the low-cost ULS achieved good point clouds quality from visual inspection and comparable individual tree segmentation results (P = 0.87, r = 0.84, F= 0.85) with the commercial system. Individual tree height estimation performed well (coefficient of determination (R2 ) = 0.998, root-mean-square error (RMSE) = 0.323 m) using the low-cost ULS. As for individual tree crown diameter estimation, low-cost ULS achieved good results (R2 = 0.806, RMSE = 0.195 m) after eliminating outliers. In general, such results illustrated the high potential of the low-cost ULS in 3D forest mapping, even though 3D forest mapping using the low-cost ULS requires further research.

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

Remote Sensing ◽

10.3390/rs10081215 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1215 ◽

Cited By ~ 8

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.

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

Remote Sensing ◽

10.3390/rs13030507 ◽

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.

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Individual tree extraction from urban mobile laser scanning point clouds using deep pointwise direction embedding

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2021.03.002 ◽

2021 ◽

Vol 175 ◽

pp. 326-339

Author(s):

Haifeng Luo ◽

Kourosh Khoshelham ◽

Chongcheng Chen ◽

Hanxian He

Keyword(s):

Laser Scanning ◽

Point Clouds ◽

Individual Tree ◽

Tree Extraction

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

Forests ◽

10.3390/f12070835 ◽

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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Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0433 ◽

2021 ◽

Cited By ~ 1

Author(s):

Matthew B. Creasy ◽

Wade Travis Tinkham ◽

Chad M. Hoffman ◽

Jody C. Vogeler

Keyword(s):

Forest Structure ◽

Ponderosa Pine ◽

Laser Scanning ◽

Point Clouds ◽

System Structure ◽

Detection Methods ◽

Unmanned Aerial System ◽

Individual Tree ◽

Detection Rates ◽

Ponderosa Pine Forests

Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS)-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5,000 stem-mapped trees in ponderosa pine-dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable-window detection methods with UAS-based canopy height models have greater tree detection rates compared to fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.

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Automatic Assessment of Crown Projection Area on Single Trees and Stand-Level, Based on Three-Dimensional Point Clouds Derived from Terrestrial Laser-Scanning

Forests ◽

10.3390/f9050237 ◽

2018 ◽

Vol 9 (5) ◽

pp. 237 ◽

Cited By ~ 5

Author(s):

Tim Ritter ◽

Arne Nothdurft

Keyword(s):

Laser Scanning ◽

Three Dimensional ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Projection Area ◽

Automatic Assessment ◽

Crown Projection Area

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Measuring the Contribution of Leaves to the Structural Complexity of Urban Tree Crowns with Terrestrial Laser Scanning

Remote Sensing ◽

10.3390/rs13142773 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2773

Author(s):

Georgios Arseniou ◽

David W. MacFarlane ◽

Dominik Seidel

Keyword(s):

Laser Scanning ◽

Structural Complexity ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Urban Trees ◽

Box Dimension ◽

State University ◽

Leaf Removal ◽

Quercus Macrocarpa ◽

Methodological Considerations

Trees have a fractal-like branching architecture that determines their structural complexity. We used terrestrial laser scanning technology to study the role of foliage in the structural complexity of urban trees. Forty-five trees of three deciduous species, Gleditsia triacanthos, Quercus macrocarpa, Metasequoia glyptostroboides, were sampled on the Michigan State University campus. We studied their structural complexity by calculating the box-dimension (Db) metric from point clouds generated for the trees using terrestrial laser scanning, during the leaf-on and -off conditions. Furthermore, we artificially defoliated the leaf-on point clouds by applying an algorithm that separates the foliage from the woody material of the trees, and then recalculated the Db metric. The Db of the leaf-on tree point clouds was significantly greater than the Db of the leaf-off point clouds across all species. Additionally, the leaf removal algorithm introduced bias to the estimation of the leaf-removed Db of the G. triacanthos and M. glyptostroboides trees. The index capturing the contribution of leaves to the structural complexity of the study trees (the ratio of the Db of the leaf-on point clouds divided by the Db of the leaf-off point clouds minus one), was negatively correlated with branch surface area and different metrics of the length of paths through the branch network of the trees, indicating that the contribution of leaves decreases as branch network complexity increases. Underestimation of the Db of the G. triacanthos trees, after the artificial leaf removal, was related to maximum branch order. These results enhance our understanding of tree structural complexity by disentangling the contribution of leaves from that of the woody structures. The study also highlighted important methodological considerations for studying tree structure, with and without leaves, from laser-derived point clouds.

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