• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.67-74
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• 2016

Drone images with high spatial resolution are emerging as an alternative to previous studies with extraction limits in high density forests. Individual tree in the dense forests were extracted from drone images. To detect the individual tree extracted through the image segmentation process, the image segmentation results were compared between the combination of DSM and all R,G,B band and the combination of DSM and R,G,B band separately. The changes in the tree density of a deciduous forest was experimented by time and image. Especially the image of May when the forests are dense, among the images of March, April, May, the individual tree extraction rate based on the trees surveyed on the site was 50%. The analysis results of the width of crown showed that the RMSE was less than 1.5m, which was the best result. For extraction of the experimental area, the two sizes of medium and small trees were extracted, and the extraction accuracy of the small trees was higher. The forest tree volume and forest biomass could be estimated if the tree height is extracted based on the above data and the DBH(diameter at breast height) is estimated using the relational expression between crown width and DBH.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1731-1738
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• 2021

The objective of this study was to extract individual trees and tree heights using UAV drone images. The study site was Gongju national university experiment forest, located in Yesan-gun, Chungcheongnam-do. The thinning intensity study sites consisted of 40% thinning, 20% thinning, 10% thinning and control. The image was filmed by using the "Mavic Pro 2" model of DJI company, and the altitude of the photo shoot was set at 80% of the overlay between 180m pictures. In order to prevent image distortion, a ground reference point was installed and the end lap and side lap were set to 80%. Tree heights were extracted using Digital Surface Model (DSM) and Digital Terrain Model (DTM), and individual trees were split and extracted using object-based analysis. As a result of individual tree extraction, thinning 40% stands showed the highest extraction rate of 109.1%, while thinning 20% showed 87.1%, thinning 10% showed 63.5%, and control sites showed 56.0% of accuracy. As a result of tree height extraction, thinning 40% showed 1.43m error compared with field survey data, while thinning 20% showed 1.73 m, thinning 10% showed 1.88 m, and control sites showed the largest error of 2.22 m.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.4
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• pp.65-73
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• 2018

Although the road construction in forest is increasing and there is a need for development ecological restoration on deforest area, no consideration has been given to individual trees in there. This study analyzed aerial photographs of deforest area before and after road construction for determining the degree of forest destruction by extracting individual trees. Study area was selected in the sites where are damaged by road construction in GongJu-si, YuSung-gu, and YeongDong-gun. The aerial photograph taken 1979 before construction is panchromatic image of 80cm in GSD (Ground Sample Distance) and other photograph taken 2016 after construction is multi-spectral image of 10cm in GSD. In order to minimize the difference of GSD, we conducted image re-sampling process for setting to same GSD for the two photographs. After that we carried out visual interpretation method for determining to change of individual tree. The result found that for GongJu-si of the number of individual tree was 1,014 in 1979 and 886 in 2016, which decreased by 128 (12.6%) and the average width of those decreased from 5.77m to 5.75m by 0.47%. In case of YoungDong-gun, the number of it was 761 in 1979 and 746 in 2016, which decreased by 2.0% and the average width of it decreased from 8.99mm to 8.90m by 1.1%. Lastly in case of YuSung-gu, the number of it was 1,578 in 1979 and 988 in 2016, which decreased by 37.4% and the average width of it decreased from 7.09m to 6.65m by 6.21%. these result imply that road construction causes destruction of forests. Since there are limitations such as errors due to researcher, it is necessary to construct a quantitative analysis method for the change of the deforest area. It is need to study the method of extracting individual tree in deforest area more accurately using high-resolution image of GSD 10cm or more as well. This study can be used as a basic data for the ecological restoration of the deforest area considering characteristics of individual tree such as height, diameter at breast height, and biomass.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3213-3219
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• 2014

Currently, increase of greenhouse gas has had a signigicant impact on climate change in urbanization. As a result, the government has been looking for ways to take advantage of the trees that generate oxygen and reduce carbon dioxide for the prevention of climate change. It is essential to extract individual tree for calculating the amount of carbon dioxide reduction of trees. Aerial LIDAR data have three-dimensional information of building as well as trees as form of point clouds. In this study, automated model was developed to extract individual tree using aerial LIDAR data. For this purpose, we established a methodology for extracting trees and then proceeded the process of developing it as an automated model based on model builder of ArcGIS Software. In order to evaluate the applicability of the developed model, the model was compared with commercial software in study area located in Yongin City. Through the experimental result, the proposed model was extract trees 9.91% higher than commercial software. From this results, it was found that the model effectively extracted trees.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.99-110
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• 2023

In this paper, we propose a system termed ForestLi that can accurately estimate the diameter at breast height (DBH) and tree height using LiDAR point cloud data. The ForestLi system processes LiDAR point cloud data through the following steps: downsampling, outlier removal, ground segmentation, ground height normalization, stem extraction, individual tree segmentation, and DBH and tree height measurement. A commercial system, such as LiDAR360, for processing LiDAR point cloud data requires the user to directly correct errors in lower vegetation and individual tree segmentation. In contrast, the ForestLi system can automatically remove LiDAR point cloud data that correspond to lower vegetation in order to improve the accuracy of estimating DBH and tree height. This enables the ForestLi system to reduce the total processing time as well as enhance the accuracy of accuracy of measuring DBH and tree height compared to the LiDAR360 system. We performed an empirical study to confirm that the ForestLi system outperforms the LiDAR360 system in terms of the total processing time and accuracy of measuring DBH and tree height.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.247-249
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• 2007

Crown parameters are important roles in tree species identification, because the canopy is the aggregate of all the crowns. However, crown measurements with spaceborne image data have remained more difficult than on aerial photographs since trees show more structural detail at higher resolutions. This recognized problem led to the initiation of the research to determine if high resolution satellite image data could be used to identify and classify single tree species. In this paper, shape parameters derived from pixel-based crown area measurements and texture features derived from GLCM parameters in QuickBird image were tested and compared for individual tree species identification. As expected, initial studies have shown that the crown parameters and the canopy texture parameters provided a differentiating method between coniferous trees and broad-leaved trees within the compartment(less than forest stand) for single extraction from spaceborne high resolution image.

• Journal of Korean Society of Forest Science
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• v.96 no.3
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• pp.251-258
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• 2007

Airborne Lidar (light detection and ranging) can be an effective alternative in forest inventory to overcome the limitations of conventional field survey and aerial photo interpretation. In this study, we attempt to develop methodologies to identify individual trees and to estimate tree height from airborne Lidar data. Initially, digital elevation model (DEM) data representing the exact ground surface were generated by removing non-ground returns from the multiple-return laser point clouds, obtained over the coniferous forest site of rugged terrain. Based on the canopy height model (CHM) data representing non-ground layer, individual tree heights are extracted through pseudo-grid method and moving window filtering algorithm. Comparing with field survey data and aerial photo interpretation on sample plots, the number of trees extracted from Lidar data show over 90% accuracy and tree heights were underestimated within 1.1m in average at two plantation stands of pine (Pinus koraiensis) and larch (Larix leptolepis).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.605-614
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• 2015

Drone imaging, which is more cost-effective and controllable compared to airborne LiDAR, requires a low-cost camera and is used for capturing color images. From the overlapped color images, we produced two high-resolution digital surface models over different test areas. After segmentation, we performed tree identification according to the method proposed by , and computed the tree height and the canopy crown size. Compared with the field measurements, the computed results for the tree height in test area 1 (coniferous trees) were found to be accurate, while the results in test area 2 (deciduous coniferous trees) were found to be underestimated. The RMSE of the tree height was 0.84 m, and the width of the canopy crown was 1.51 m in test area 1. Further, the RMSE of the tree height was 2.45 m, and the width of the canopy crown was 1.53 m in test area 2. The experiment results validated the use of drone images for the extraction of a tree structure.

• Journal of Cadastre & Land InformatiX
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• v.46 no.1
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• pp.87-100
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• 2016

The acquisition of the forest resource information has depended on a partial sampling method or aerial photographs which demand a lot of effort and time because of the vast areas and the difficult approach. For the acquisition of the forest resource information, there have been the optical remote-sensing and the multi-spectrum image to offer only horizontal distributions of trees, but a new technological approach, such as Airborne LiDAR, is more necessary to acquire directly three dimensional information related to the forest terrains and trees' features. This paper proposes an algorithm for the forest information extraction such as trees' individual numbers and the heights of trees by using LiDAR data. Especially, this proposed algorithm adopts a region growing method for the extraction of the vegetation-point and extracts the forest information using morphological features of trees.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.27-34
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• 2013

Due to the increased interest in global warming, interest in forest resources aimed towards reducing greenhouse gases have subsequently increased. Thus far, data related to forest resources have been obtained, through the employment of aerial photographs or satellite images, by means of plotting. However, the use of imaging data is disadvantageous; merely, due to the fact that recorded measurements such as the height of trees, in dense forest areas, lack accuracy. Within such context, the authors of this study have presented a method of data processing in which an individual tree is isolated within forested areas through the use of LIDAR data and ortho-images. Such isolation resulted in the provision of more efficient and accurate data in regards to the height of trees. As for the data processing of LIDAR, the authors have generated a normalized digital surface model to extract tree points via local maxima filtering, and have additionally, with motives to extract forest areas, applied object oriented image classifications to the processing of data using ortho-images. The final tree point was then given a figure derived from the combination of LIDAR and ortho-images results. Based from an experiment conducted in the Yongin area, the authors have analyzed the merits and demerits of methods that either employ LIDAR data or ortho-images and have thereby obtained information of individual trees within forested areas by combining the two data; thus verifying the efficiency of the above presented method.