Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Sačkov, Ivan; Hlásny, Tomáš; Bucha, Tomáš; Juriš, M.

doi:10.3832/ifor2093-010

Cited by 21 publications

(16 citation statements)

References 42 publications

(41 reference statements)

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“…Vauhkonen et al [42] stated that the accuracy of tree detection in Scandinavia and Central Europe forests should be around 70%. Regarding algorithms used in this study, the accuracy of tree detection is found within the interval of 52%-84% according to the results from this study ( Table 2) and previous research for the same study area [26,27]. However, the commission rate of the raster-based method may exceed 45%, while the commission rate of multisource-based method is usually within 8%.…”

Section: Stand Density Indexsupporting

confidence: 63%

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A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

2019

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Estimation of biophysical variables based on airborne laser scanning (ALS) data using tree detection methods concentrates mainly on delineation of single trees and extraction of their attributes. This study provides new insight regarding the potential and limits of two detection methods and underlines some key aspects regarding the choice of the more appropriate alternative. First, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia), which uses the information contained in the point cloud and a priori information. Second, we applied the raster-based method implemented in OPALS software (Vienna University of Technology, Austria), which extracts information from several ALS-derived height models. A comparative study was conducted for a part of the university forest in Zvolen (Slovakia, Central Europe). ALS-estimated variables of both methods were compared (1) to the ground reference data within four heterogonous stands with an area size of 7.5 ha as well as (2) to each other within a comprehensive forest unit with an area size of 62 ha. We concluded that both methods can be used to evaluate forest stand and ecological variables. The overall performance of both methods achieved a matching rate within the interval of 52%–64%. The raster-based method provided faster and slightly more accurate estimate of most variables, while the total volume was more precisely estimated using the multisource-based method. Specifically, the relative root mean square errors did not exceed 7.2% for mean height, 8.6% for mean diameter, 21.4% for total volume, 29.0% for stand density index, and 7.2% for Shannon’s diversity index. Both methods provided estimations with differences that were statistically significant, relative to the ground data as well as to each other (p < 0.05).

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Section: Stand Density Indexsupporting

confidence: 63%

“…Finally, the outputs of all procedures were exported to point and polygon vector files in an ESRI shapefile format. All details of the algorithm are listed in the study of Sačkov et al [26].…”

Section: Individual Tree and Tree Height Detectionmentioning

confidence: 99%

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

2019

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“…The laser data had an average density of laser hits of 4.3 pt/m 2 . In further processing, the point clouds were classified in accordance with the American Society for Photogrammetry and Remote Sensing classes, and noise points were removed using reFLex software [25].…”

Section: Ground Reference Datamentioning

confidence: 99%

Inventory of Close-to-Nature Forests Based on the Combination of Airborne LiDAR Data and Aerial Multispectral Images Using a Single-Tree Approach

Sačkov

Sedliak

Kulla

et al. 2017

Forests

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This study is concerned with the assessment of application possibilities for remote sensing data within a forest inventory in close-to-nature forests. A combination of discrete airborne laser scanning data and multispectral aerial images separately evaluated main tree and forest stand characteristics (i.e., the number of trees, mean height and diameter, tree species, tree height, tree diameter, and tree volume). We used eCognition software (Trimble GeoSpatial, Munich, Germany) for tree species classification and reFLex software (National Forest Centre, Zvolen, Slovakia) for individual tree detection as well as for forest inventory attribute estimations. The accuracy assessment was conducted at the ProSilva demo site Smolnícka Osada (Eastern Slovakia, Central Europe), which has been under selective management for more than 60 years. The remote sensing data were taken using a scanner (Leica ALS70-CM) and camera (Leica RCD30) from an average height of 1034 m, and the ground reference data contained the measured positions and dimensions of 1151 trees in 45 plots distributed across the region. This approach identified 73% of overstory and 28% of understory trees. Tree species classification within overstory trees resulted in an overall accuracy slightly greater than 65%. We also found that the mean difference between the remote-based results and ground data was −0.3% for tree height, 1.1% for tree diameter, and 1.9% for stem volume. At the stand level, the mean difference reached values of 0.4%, 17.9%, and −21.4% for mean height, mean diameter, and growing stock, respectively.

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“…Because of the increased spatial resolution of UAV LiDAR data, some studies, to improve segmentation accuracy, segmented individual trees directly from point clouds. The existing point-based clustering methods (such as region growing [17], k-means clustering [26], normalized cut [28], and mean shift [29,30]) have been widely used for individual tree segmentation in terms of 3D data characteristics, geometric structures, and height variation of trees [31][32][33]. Compared with other clustering methods, mean shift does not require seed points or number of clusters before clustering.…”

Section: Introductionmentioning

confidence: 99%

A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data

et al. 2020

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Unmanned aerial vehicles using light detection and ranging (UAV LiDAR) with high spatial resolution have shown great potential in forest applications because they can capture vertical structures of forests. Individual tree segmentation is the foundation of many forest research works and applications. The tradition fixed bandwidth mean shift has been applied to individual tree segmentation and proved to be robust in tree segmentation. However, the fixed bandwidth-based segmentation methods are not suitable for various crown sizes, resulting in omission or commission errors. Therefore, to increase tree-segmentation accuracy, we propose a self-adaptive bandwidth estimation method to estimate the optimal kernel bandwidth automatically without any prior knowledge of crown size. First, from the global maximum point, we divide the three-dimensional (3D) space into a set of angular sectors, for each of which a canopy surface is simulated and the potential tree crown boundaries are identified to estimate average crown width as the kernel bandwidth. Afterwards, we use a mean shift with the automatically estimated kernel bandwidth to extract individual tree points. The method is iteratively implemented within a given area until all trees are segmented. The proposed method was tested on the 7 plots acquired by a Velodyne 16E LiDAR system, including 3 simple plots and 4 complex plots, and 95% and 80% of trees were correctly segmented, respectively. Comparative experiments show that our method contributes to the improvement of both segmentation accuracy and computational efficiency.

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Integration of tree allometry rules to treetops detection and tree crowns delineation using airborne lidar data

Cited by 21 publications

References 42 publications

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

Inventory of Close-to-Nature Forests Based on the Combination of Airborne LiDAR Data and Aerial Multispectral Images Using a Single-Tree Approach

A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data

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