PointCNN-Based Individual Tree Detection Using LiDAR Point Clouds

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.

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THE FUSION OF INDIVIDUAL TREE DETECTION AND VISUAL INTERPRETATION IN ASSESSMENT OF FOREST VARIABLES FROM LASER POINT CLOUDS

ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xxxviii-5-w12-157-2011 ◽

2012 ◽

Vol XXXVIII-5/W12 ◽

pp. 157-161

Author(s):

V. Kankare ◽

M. Vastaranta ◽

M. Holopainen ◽

X. Yu ◽

J. Hyyppä ◽

...

Keyword(s):

Point Clouds ◽

Visual Interpretation ◽

Individual Tree ◽

Tree Detection

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A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data

Remote Sensing ◽

10.3390/rs13020322 ◽

2021 ◽

Vol 13 (2) ◽

pp. 322

Author(s):

Melissa Latella ◽

Fabio Sola ◽

Carlo Camporeale

Keyword(s):

Point Clouds ◽

Individual Tree ◽

Tree Distribution ◽

Tree Detection ◽

Forest Modeling ◽

Point Density ◽

Potential Applications ◽

Field Campaigns ◽

The Individual ◽

Individual Trees

Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management.

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Density-Based Individual Tree Detection from Three-Dimensional Point Clouds

10.5194/egusphere-egu21-15628 ◽

2021 ◽

Author(s):

Carlo Camporeale ◽

Melissa Latella ◽

Fabio Sola

Keyword(s):

Time Series ◽

Spatial Distribution ◽

Three Dimensional ◽

Region Of Interest ◽

Point Clouds ◽

Individual Tree ◽

Tree Detection ◽

Round Shape ◽

Point Density ◽

Individual Trees

The use of three-dimensional point clouds in forestry is steadily increasing. Numerous algorithms to detect individual trees from point clouds and derive some fundamental inventory parameters have been proposed so far, but they usually provide higher accuracy in coniferous stands than in deciduous one. In the latter kind of stands, indeed, the tree identification is hampered by the geometrical round shape of the crowns, the interlacing branches of adjacent trees and the usual presence of understory vegetation.In an attempt to overcome these limitations, we developed an algorithm that is innovatively based on the areal point density of the three-dimensional cloud and that provides the height and coordinates of all the trees within a region of interest.In this work, we apply the algorithm to different situations, ranging from the regularly-arranged plantations to the very interlaced crowns of the naturally established stands, demonstrating how it is able to correctly detect most of the trees and recreate a map of their spatial distribution. We also test its capability to deal with relatively low point density and explore the possibility to use it to recreate time series of vegetation biomass. Finally, we discuss the algorithm&#8217;s limitations and potentialities, particularly focusing on its coupling to other existing tools to deal with a wider range of applications in forestry and land management.&#160;

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A Comparison of ALS and Dense Photogrammetric Point Clouds for Individual Tree Detection in Radiata Pine Plantations

Remote Sensing ◽

10.3390/rs13173536 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3536

Author(s):

Irfan A. Iqbal ◽

Jon Osborn ◽

Christine Stone ◽

Arko Lucieer

Keyword(s):

Forest Inventory ◽

Point Cloud ◽

Point Clouds ◽

Tree Level ◽

Point Cloud Data ◽

Individual Tree ◽

Detection Rates ◽

Cloud Data ◽

Tree Detection ◽

Structural Metrics

Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of ALS data. At the tree level, individual tree detection (ITD) algorithms have been developed to detect and/or delineate individual trees either from ALS point cloud data or from ALS- or DAP-based canopy height models. An examination of the application of ITDs to DAP-based point clouds has not yet been reported. In this research, we evaluate the suitability of DAP-based point clouds for individual tree detection in the Pinus radiata plantation. Two ITD algorithms designed to work with point cloud data are applied to dense point clouds generated from small- and medium-format photography and to an ALS point cloud. Performance of the two ITD algorithms, the influence of stand structure on tree detection rates, and the relationship between tree detection rates and canopy structural metrics are investigated. Overall, we show that there is a good agreement between ALS- and DAP-based ITD results (proportion of false negatives for ALS, SFP, and MFP was always lower than 29.6%, 25.3%, and 28.6%, respectively, whereas, the proportion of false positives for ALS, SFP, and MFP was always lower than 39.4%, 30.7%, and 33.7%, respectively). Differences between small- and medium-format DAP results were minor (for SFP and MFP, differences between recall, precision, and F-score were always less than 0.08, 0.03, and 0.05, respectively), suggesting that DAP point cloud data is robust for ITD. Our results show that among all the canopy structural metrics, the number of trees per hectare has the greatest influence on the tree detection rates.

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Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Remote Sensing ◽

10.3390/rs9030185 ◽

2017 ◽

Vol 9 (3) ◽

pp. 185 ◽

Cited By ~ 145

Author(s):

Olli Nevalainen ◽

Eija Honkavaara ◽

Sakari Tuominen ◽

Niko Viljanen ◽

Teemu Hakala ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Point Clouds ◽

Individual Tree ◽

Tree Detection

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Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations

International Journal of Remote Sensing ◽

10.1080/01431161.2018.1486519 ◽

2018 ◽

Vol 39 (15-16) ◽

pp. 5211-5235 ◽

Cited By ~ 37

Author(s):

Juan Guerra-Hernández ◽

Diogo N. Cosenza ◽

Luiz Carlos Estraviz Rodriguez ◽

Margarida Silva ◽

Margarida Tomé ◽

...

Keyword(s):

Point Clouds ◽

High Density ◽

Density Point ◽

Individual Tree ◽

Tree Detection ◽

Eucalyptus Plantations

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

Remote Sensing ◽

10.3390/rs13020223 ◽

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.

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Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽

10.3390/f12020250 ◽

2021 ◽

Vol 12 (2) ◽

pp. 250

Author(s):

Wade T. Tinkham ◽

Neal C. Swayze

Keyword(s):

Structure From Motion ◽

Ponderosa Pine ◽

Point Cloud ◽

Image Resolution ◽

Canopy Height ◽

Computer Hardware ◽

Forest Monitoring ◽

Individual Tree ◽

Ponderosa Pine Forests ◽

Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

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