A Novel Tree Height Extraction Approach for Individual Trees by Combining TLS and UAV Image-Based Point Cloud Integration

Tian, Jiarong; Dai, Tingting; Li, Haidong; Liao, Chengrui; Teng, Wenxiu; Hu, Qingwu; Ma, Weibo; Xu, Yueren

doi:10.3390/f10070537

Cited by 44 publications

(28 citation statements)

References 39 publications

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“…There are several methods for the automatic detection and parameters extraction on 3D models using crop height models [37], combining terrestrial laser scanner and UAV photogrammetric point clouds [38], fusing RGB and multispectral point clouds to extract individual tree parameters [39], computing 3D vegetation indices in olive groves [40], and obtaining forest structural attributes [41]. However, given the complexity and the unique characteristics of vineyard plots, such methods are not suitable to be applied.…”

Section: Individual Grapevine Detectionmentioning

confidence: 99%

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

et al. 2020

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The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant’s structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.

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Section: Individual Grapevine Detectionmentioning

confidence: 99%

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

et al. 2020

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“…However, the lack of canopy information makes it difficult to estimate the tree structure (tree height); hence the point cloud generated by the SfM method was used to integrate with the TLS point clouds. Points can be measured from the ground with TLS, and UAS photogrammetry can provide additional information on the tops of the canopies [86]. Moreover, the TLS data were provided with geographical coordinates because the SfM model already had coordinate information that was collected by the GNSS embedded in the UAS platform.…”

Section: Data Processing Of Point Cloudsmentioning

confidence: 99%

Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

et al. 2020

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The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry.

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“…To further understand if the proposed scheme sufficiently preserves the original characteristics of a single tree, we quantify the morphological attributes of the reconstructed trees at different compression ratios. The morphological attributes considered include crown size (east-west, south-north, height) [4], diameter at breast height (DBH, diameter of the trunk at 1.30 m above the ground-surface) [2][3][4], ground diameter (diameter of the trunk at 0.20 m above the ground-surface, only for Papaya tree), tree height [2,4,5], which are important parameters for tree growth [1][2][3][4][5]. These morphological attributes are estimated with the software CloudCompare [42], a professional tool for processing point cloud.…”

Section: Experiments On Morphological Attributesmentioning

confidence: 99%

“…They have high measurement accuracy and data acquisition efficiency, and are suitable for capturing large scenes with relatively low expenditure. TLS has been used to obtain 3D observations on tree surfaces to study morphological attributes [1][2][3][4][5]. However, the sampled data from TLS is very dense and with considerable redundancy.…”

Section: Introductionmentioning

confidence: 99%

Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

Yun

Cao

et al. 2020

Forests

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The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the characteristics of the broad-leaved tree point clouds. An observation procedure down-samples the point clouds with the partial Fourier matrix. The regularized orthogonal matching pursuit algorithm (ROMP) finally reconstructs the original point clouds. The experimental results illustrate that the proposed scheme can preserve morphological attributes of the broad-leaved tree within a range of relative error: 0.0010%–3.3937%, and robustly extend to plot-level within a range of mean square error (MSE): 0.0063–0.2245.

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A Novel Tree Height Extraction Approach for Individual Trees by Combining TLS and UAV Image-Based Point Cloud Integration

Cited by 44 publications

References 39 publications

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

Automatic Grapevine Trunk Detection on UAV-Based Point Cloud

Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

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