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박정묵,심우담,이정수 강원대학교 산림과학연구소 2018 Journal of Forest Science Vol.34 No.6
In this study, the deforestation and forest degradation areas have been obtained in Myanmar using a land cover lamp (LCM) and a tree cover map (TCM) to get the CO2 potential reduction and the strength of occurrence was evaluated by using the geostatistical technique. By applying a multiple criteria decision-making method to the regions having high strength of occurrence for the CO2 potential reduction for the deforestation and forest degradation areas, the priority was selected for candidate lands for REDD+ project. The areas of deforestation and forest degradation were 609,690ha and 43,515ha each from 2010 to 2015. By township, Mong Kung had the highest among the area of deforestation with 3,069ha while Thlangtlang had the highest in the area of forest degradation with 9,213 ha. The number of CO2 potential reduction hotspot areas among the deforestation areas was 15, taking up the CO2 potential reduction of 192,000 ton in average, which is 6 times higher than that of all target areas. Especially, the township of Hsipaw inside the Shan region had a CO2 potential reduction of about 772,000 tons, the largest reduction potential among the hotpot areas. There were many CO2 potential reduction hot spot areas among the forest degradation area in the eastern part of the target region and has the CO2 potential reduction of 1,164,000 tons, which was 27 times higher than that of the total area. AHP importance analysis showed that the topographic characteristic was 0.41 (0.40 for height from surface, 0.29 for the slope and 0.31 for the distance from water area) while the geographical characteristic was 0.59 (0.56 for the distance from road, 0.56 for the distance from settlement area and 0.19 for the distance from Capital). Yawunghwe, Kalaw, and Hsi Hseng were selected as the preferred locations for the REDD+ candidate region for the deforestation area while Einme, Tiddim, and Falam were selected as the preferred locations for the forest degradation area.
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벽체의 버팀보 조합 강성에 대한 흙막이 구조물 거동해석
박정묵,정대석 한국재난정보학회 2019 한국재난정보학회 학술대회 Vol.2019 No.09
최근의 경제성장과 산업발전으로 도심지에서의 굴착공사는 대규모, 대심도의 근접시공이 불가피한 실정이며 흙막이 구조 물의 붕괴로 인한 막대한 인적 및 물적 피해를 야기 시킬 수 있다. 이에 따라 가설 흙막이 벽체의 거동에 대한 관심은 점차 증대되 고 있으며 흙막이 구조물의 안정과 경제적인 시공에 대한 연구가 꾸준히 진행되고 있다. 본 연구에서는 흙막이 벽체 강성에 따 른 굴착시 흙막이 구조물의 거동에 대해 이론적 해석과 실제 계측결과를 비교분석하여 그 적용성 여부를 고찰하고자 하였다. 또한, 버팀보 형식에 따른 변위 및 토압분포 양상, 그리고 안정율도 분석하여 향후 굴착완료시까지 안정적인 시공을 할 수 있는 가장 효과적이며 경제적인 계획 및 설계시에 활용할 수 있는 자료를 제시하고자 하였다. 연구대상 검토단면으로는 버팀보로 지지된 공사현장 사례을 선정하였으며, 다양한 지반조건의 적용과 정확도 있는 수치해석을 위하여 탄소성해석과 유한요소해 석을 비교분석 하였다.
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박정묵,심우담,이정수 강원대학교 산림과학연구소 2019 Journal of Forest Science Vol.35 No.3
This study was performed to construct tree species classification map according to three information types (spectral information, texture information, and spectral and texture information) by altitude (30 m, 60 m, 90 m) using the unmanned aerial vehicle images and the object-based classification method, and to evaluate the concordance rate through field survey data. The object-based, optimal weighted values by altitude were 176 for 30 m images, 111 for 60 m images, and 108 for 90 m images in the case of Scale while 0.4/0.6, 0.5/0.5, in the case of the shape/color and compactness/smoothness respectively regardless of the altitude. The overall accuracy according to the type of information by altitude, the information on spectral and texture information was about 88% in the case of 30 m and the spectral information was about 98% and about 86% in the case of 60 m and 90 m respectively showing the highest rates. The concordance rate with the field survey data per tree species was the highest with about 92% in the case of Pinus densiflora at 30 m, about 100% in the case of Prunus sargentii Rehder tree at 60 m, and about 89% in the case of Robinia pseudoacacia L. at 90 m.
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딥러닝 모델 기반 위성영상 데이터세트 공간 해상도에 따른 수종분류 정확도 평가
박정묵,심우담,김경민,임중빈,이정수,Park, Jeongmook,Sim, Woodam,Kim, Kyoungmin,Lim, Joongbin,Lee, Jung-Soo 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F<sub>1</sub> regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).
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지상 라이다 산림 데이터에서 개체목 분리를 위한 파이프라인 접근 방식
박정묵,임종수,강진택,조형주,김동근 한국멀티미디어학회 2024 멀티미디어학회논문지 Vol.27 No.3
LiDAR technology has revolutionized the analysis of forest structure by offering three-dimensional insights essential for effective forest management. This study proposes an innovative pipeline-based tree separation algorithm, specifically designed for autonomous segmentation of individual trees in terrestrial LiDAR-scanned forests. The algorithm encompasses a streamlined, eight-phase process encompassing duplicate and outlier point removal, ground flattening, trunk-crown segmentation, individual tree trunk separation, voxel downsampling, tree separation, and calculation of tree properties. This approach ensures a seamless transition between phases, with each phase’s output feeding directly into the next. Our empirical study, employing real-life terrestrial LiDAR datasets, demonstrates the algorithm’s effectiveness. This advancement not only elevates digital forest management tools but also has substantial implications for ecological and environmental research.
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박정묵,이용규,이정수 한국산림과학회 2022 Forest Science And Technology Vol.18 No.2
Securing reliable data on forest areas is necessary for the establishment of various policies and decision-making for forest administration. In this study, the definition of forest, min- imum partitioning criteria, purpose of production, production method, and period of update were analyzed, which were prescribed for the statistics (Forest Basic Statistics [FBS] and Cadastral Statistical Annual Report [CSAR]) and spatial data (digital forest type map, sub- divided land cover map, continuous cadastral map). Forested area was calculated according to the statistics and spatial data for Wonju, Gangwon-do, and the forest area between statis- tics and spatial data was quantitatively compared. In terms of the definition of forest and minimum partitioning criteria, the FBS and digital forest type maps were similar, and the land cover map, CSAR, and continuous cadastral maps were different in these aspects. About forest area, there was a difference in each forestry data. The highest was CSAR(61,406 ha) and the lowest was Sub-divided Land Cover Map(57,818 ha). This is thought to be because there were some types of spatial areas that were classified as forest in the digital forest type map but classified as cropland, grassland, settlement, and bare land in the sub-divided land cover map and continuous cadastral map. Moreover, in the case of the continuous cadastral map, it is thought that there was an error in the area calculation due to differences between the land category in the map and the actual land use status, which led to differences in the calculated area between different types of spatial data. For future statistics and spatial data, appropriate measures should be established to address the issue of the differences between the calculated area due to misclassification during visual reading, and the difference between the land category in the map and the actual land use status. The calculated forest area should be used for research on the definition of forest, the pur- poses of map production, and production methods for each type of information and data
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