• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.3
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• pp.269-277
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• 2012

Street trees are one of useful urban facilities that reduce carbon dioxide and provide green space in urban areas. They are usually managed by local government, and it is effective to use aerial LIDAR data in order to acquire information such as the location, height and crown width of street tree systematically. In this research, algorithm was proposed that improves the accuracy of extracting top points of street trees and separates the region of individual street trees from aerial LIDAR data. In order to verify the proposed algorithm, a simulated aerial LIDAR data that exactly knows the number, height and crown width of street trees was created. As for the procedure of data processing, filtering that separates ground and non-ground points from LIDAR data was first conducted in order to separate the region of individual street trees. An estimated non-street tree points were then removed from non-ground points, and the top points of street trees were estimated. Region of individual street trees was determined by using the intersecting point of straight line that connects top point and ground point of street tree. Through the experiment by using simulated data, it was possible to refine wrongly estimated points occurred by determining tree tops and to determine the positional information, height, crown width of street trees through the determination of region of street trees.

• 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 Forest and Environmental Science
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• v.32 no.4
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• pp.367-376
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• 2016

Urban greenery is an important component of urban environment and is fast gaining prominence especially in the developing countries. The destruction of urban trees has resulted to the degradation of the environment, thus the introduction of green Kumasi project by Kumasi Metropolitan Assembly, Ashanti Region of Ghana. The composition and diversity of urban trees gives rise to adequate management and monitoring, thus an inventory of urban trees of the Metropolis was conducted to document complete information on its density, diversity, composition and distribution. A total tree population of 1,101 was enumerated in the principal roads of the Metropolis. The ten most encountered tree species accounted for 61.04% of all the individual tree populations with Mangifera indica being dominant. The dominant families: Fabaceae, Moraceae and Arecaceae constitute 38.57% of the tree population. Diversity of the tree species was very high. The minimum diversity criteria were met on analysis of the diversity of this population. The proportion of exotic species was high with 65.71% of the trees belonging to the introduced species. It is recommended that greater emphasis should be placed on the planting of indigenous trees in future tree planting exercise.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.543-551
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• 2006

Recently efforts to extract information about forests by using remote sensing techniques for efficient forest management have progressed actively. In terms of extraction of tree information using single remote sensing data, however, the accuracy of tree recognition and the quantity of extracted information is limited. The objective of this study is to carry out tree modeling in domestic environment applying the latest core technique for tree modeling using color aerial photographs and LiDAR data and to estimate the result of tree modeling. A small-scale coniferous forest was investigated in Daejeon. It was 0.77 that the $R^2$ of accuracy test of tree numbers that estimated with color aerial photography and LiDAR data. In terms of tree height, there was no difference between the estimated value and the field measurements in the case of the group accuracy test of the recently unchanged area. Moreover $R^2$ was 0.83 in the case of the individual accuracy test.

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

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.9-19
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• 2004

The selection of comparative standard parcels should be objective and reasonable, which is an important task in the individual land price appraisal procedure. However, the current procedure is mainly done manually by government officials. Therefore, the efficiency and objectiveness of this selection procedure is not guaranteed and questionable. In this study, we first defined the problem by analyzing the current comparative standard land parcel selection method. In addition, we devised a decision tree-based method using a machine learning algorithm that is considered to be efficient and objective compared to the current selection procedure. Finally the proposed method is then applied to the study area for evaluating the appropriateness and accuracy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.98-103
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• 2000

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

• Korean Journal of Plant Resources
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• v.19 no.3
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• pp.381-384
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• 2006

This study was conducted to selected Korean black raspberry (Rubus coreanus Miq.) for high productivity. The eight major agronomic traits were investigated from 198 clones of the clone bank established in Korea Forest Research Institute, Suwon, Korea. The selection levels based on number of fruit per fructify lateral (NFFL) over 20, and fruit weight (FW) over 1.3g, and yield of individual per fructify lateral (YIFL) over 25g, were applied on 198 clones, resulted in 17 clones selected. The selected superior trees, 17 clones, appeared regional differences for amount of fruiting among 4 different test sites. When number of fruit per fruit petiole (NRFP), fruit weight (FW), yield of individual (YI) and sugar content were satisfied over 20, 1.4g, 6kg and 9.5 brix, respectively, as a select condition, 5 clones were reselected as the superior trees among 17 clones. for 3 years.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.211-218
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• 2005

This study presents an integrated data mining model for the credit evaluation of the customers of a capital company. Based on customer information and financing processes in capital market, we derived individual models from multi-layered perceptrons(MLP), multivariate discrimination analysis(MDA), and decision tree. Further, the results from the existing models were compared with the results from the integrated model using genetic algorithm. The integrated model presented by this study turned out to be superior to the existing models. This study contributes not only to verifying the existing individual models but also to overcoming the limitations of the existing approaches.