• Spatial Information Research
• /
• v.20 no.3
• /
• pp.27-38
• /
• 2012

As the necessity of forest conservation and management has been increased, various forest studies using LIDAR data have been actively performed. These studies often utilize the tree height as an important parameter to measure the forest quantitatively. This study thus attempt to apply two representative methods to estimate tree height from airborne LIDAR data and compare the results. The first method based on the detection of the individual trees using a local maximum filter estimates the number of trees, the position and heights of the individual trees, and the mean tree height. The other method estimates the maximum and mean tree height, and the crown mean height for each grid cell or the entire area from the canopy height model (CHM) and height histogram. In comparison with the field measurements, 76.6% of the individual trees are detected correctly; and the estimated heights of all trees and only conifer trees show the RMSE of 1.91m and 0.75m, respectively. The tree mean heights estimated from CHM retain about 1~2m RMSE, and the histogram method underestimates the tree mean height with about 0.6m. For more accurate derivation of diverse forest information, we should select and integrate the complimentary methods appropriate to the tree types and estimation parameters.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_4
• /
• pp.1145-1153
• /
• 2023

Urban trees play an important role in absorbing carbon dioxide from the atmosphere, improving air quality, mitigating the urban heat island effect, and providing ecosystem services. To effectively manage and conserve urban trees, accurate spatial information on their location, condition, species, and population is needed. In this study, we propose an algorithm that uses a high-resolution urban tree cover map constructed from deep learning approach to separate trees from the urban land surface and accurately detect tree locations through local maximum filtering. Instead of using a uniform filter size, we improved the tree detection performance by selecting the appropriate filter size according to the tree height in consideration of various urban growth environments. The research output, the location and height of individual trees in human settlement over Suwon, will serve as a basis for sustainable management of urban ecosystems and carbon reduction measures.

• Journal of Environmental Impact Assessment
• /
• v.30 no.3
• /
• pp.155-163
• /
• 2021

The number of trees to be removed trees (ART) in the environmental impact assessment is an environmental indicator used in various parts such as greenhouse gas emissions and waste of forest trees calculation. Until now, the ART has depended on the forest tree density of the vegetation survey, and the uncertainty of estimating the amount of removed trees has increased due to the sampling bias. A full-scale survey can be offered as an alternative to improve the accuracy of ART, but the reality is that it is impossible. As an alternative, there is an individual tree detection using aerial image (ITD), and in this study, we compared the ARTs estimated by full-scale survey, sample survey, and ITD. According to the research results, compared to the result of full-scale survey, the result of ITD was overestimated by 25. While 58 were overestimated by the sample survey (average). However, as the sample survey is an estimate based on random samples, ART will be overestimated or underestimated depending on the number and size of quadrats.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1633-1641
• /
• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• Spatial Information Research
• /
• v.18 no.3
• /
• pp.23-32
• /
• 2010

LIDAR data are useful for forest applications such as bare-earth DEM generation for forest areas, and estimation of tree height and forest biomass. As a core preprocessing procedure for most forest applications, this study attempts to develop an efficient method to detect forest areas from LIDAR data. First, we suggest three perceptual cues based on multiple return characteristics, height deviation and spatial distribution, being expected as reliable perceptual cues for forest area detection from LIDAR data. We then classify the potential forest areas based on the individual cue and refine them with a bi-morphological process to eliminate falsely detected areas and smoothing the boundaries. The final refined forest areas have been compared with the reference data manually generated with an aerial image. All the methods based on three types of cues show the accuracy of more than 90%. Particularly, the method based on multiple returns is slightly better than other two cues in terms of the simplicity and accuracy. Also, it is shown that the combination of the individual results from each cue can enhance the classification accuracy.