농림위성 산림분야 식생지수 검보정 사이트 설계

임중빈 ( Joongbin Lim ),차성은 ( Sungeun Cha ),원명수 ( Myoungsoo Won ),김준 ( Joon Kim ),박주한 ( Juhan Park ),류영렬 ( Youngryel Ryu ),이우균 ( Woo-kyun Lee ) 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.3

우리나라 산림의 효율적인 관리와 산림 모니터링을 위해 산림청은 농림위성을 개발 중이며 2025년 발사 예정이다. 농림위성을 효율적으로 활용하기 위해 산림청 국립산림과학원은 36종의 농림위성 산림분야 활용산출물 개발을 진행 중이다. 원격탐사 기법을 활용하여 도출된 산출물들은 지상검증이 요구되며 해당 산출물들에 대한 품질 모니터링 결과를 지속적으로 보고해야 한다. 국내 최초로 산림분야 활용 위성이 개발되는 상황이라 국내에는 공식적인 산림분야 활용 산출물 검보정 사이트가 부재하다. 이에 저자들은 국제기준에 맞춰 농림위성 산림분야 활용산출물 검보정을 위한 검보정 사이트를 설계하였다. 또한 전국적으로 검보정 사이트를 설치하기 위해 적정 센서를 선택하여 해당 센서의 활용 가능성을 평가하였다. 평가 결과 지상 관측데이터와 Sentinel-2 영상과의 산림 산출물에 대한 오차가 ±5% 이내로 관측되어 해당 센서를 활용하여 전국적으로 확장이 가능함을 확인하였다. The Compact Advanced Satellite 500-4 (CAS500-4) is under development to efficiently manage and monitor forests in Korea and is scheduled to launch in 2025. The National Institute of Forest Science is developing 36 types of forestry applications to utilize the CAS500-4 efficiently. The products derived using the remote sensing method require validation with ground reference data, and the quality monitoring results for the products must be continuously reported. Due to it being the first time developing the national forestry satellite, there is no official calibration and validation site for forestry products in Korea. Accordingly, the author designed a calibration and validation site for the forestry products following international standards. In addition, to install calibration and validation sites nationwide, the authors selected appropriate sensors and evaluated the applicability of the sensors. As a result, the difference between the ground observation data and the Sentinel-2 image was observed to be within ±5%, confirming that the sensor could be used for nationwide expansion.

데이터 마이닝을 활용한 북한 산림과학 연구 동향 분석(1962~2016)

임중빈 ( Joongbin Lim ),김경민 ( Kyoung-min Kim ),김명길 ( Myung-kil Kim ),이종민 ( Jong Min Yi ),박진우 ( Jin Woo Park ) 한국산림과학회(구 한국임학회) 2020 한국산림과학회지 Vol.109 No.1

본 연구에서는 남북한 산림과학기술협력에 대비하여 북한의 산림과학 관련 학술지 분석을 통해 북한 산림과학 연구 동향을 분석하였다. 한국과학기술정보연구원(KISTI) 북한과학기술네트워크(NKtech)의 소장 논문을 대상으로 우리나라 국가과학기술표준분류체계를 토대로 임학 관련 주제어를 검색하여 분석 대상 논문을 수집하였다. 총1,389편의 논문을 수집하였으며 북한 산림업 계획 지표를 토대로 산림조성, 보호 및 이용 분야로 분류하여 각 분야별 북한 산림과학 연구 동향을 분석하였다. 2012년 김정은 집권 이후 산림조성 분야에서는 나무모 기르기(양묘)와 임농복합경영 분야의 연구 활동이 활발하였으며 사름률(활착률)을 강조하고 있었다. 산림보호 분야에서는 산림병해충에 대한 노력이 크며 임농복합경영과 풀뚝다락밭을 통해 토양침식을 감소시키려는 노력을 하고 있는 것으로 판단된다. 산림이용 분야에서는 주로 펄프/종이와 버섯 분야에서 연구가 활발함을 확인하였다. 산림경영 분야에서는 ‘생태정보’ 분야의 활동이 눈에 띄게 나타났으며 탄소감축노력을 확인할 수 있었다. 북한 산림과학 동향을 살펴본 결과 북한의 산림에 대한 관점이 기존의 ‘자연 개조’ 대상에서 ‘자연 보호’를 함께 수반하는 관리의 대상으로 변화되었다고 보여 진다. 향후 세부 분야별 남북 산림과학 기술 비교 연구와 시기별 북한의 정책방향이 연구 방향에 미친 영향에 대한 분석을 진행할 계획이다. 특히, 남북산림협력사업을 원할히 하기 위해서 남북산림용어사전 편찬이 필요할 것으로 사료된다. In this study, forest-related research papers published in North Korean journals were analyzed to understand the research trends in North Korean forest science. The Korea Science and Technology Information Institute (KISTI) North Korea Science and Technology Network (NKtech) is constructing a database related to science and technology in North Korea. From this, a total of 1,389 articlespublished from 1962 to 2016 were collected with forest science key words based on the South Korean National Science and Technology Standard Classification System. The topics were divided into four categories: afforestation, forest protection, forest use, and forest management. In the field of afforestation, research activities on nursery and agroforestry were active, and the survival rate was emphasized. In the forest protection field, there was a significant research effort into forest pests, and efforts were being made to reduce soil erosion through agroforestry. In the field of forest use, research activities on pulp/paper and mushrooms were active. In the forest management field, activities related to "ecological information" were conspicuous, and efforts were being made to reduce carbon. These results suggest that the perspective of North Korean forest research has changed from nature reorganization to nature protection. Thus, a comparative study on forest science and technology in each sub-sector of the forest research field, along with analysis of the relationship between policy direction and research direction of North Korea over time, would be worthwhile future investigations. To overcome the problem of technical terminology, a compilation/dictionary of inter-Korean forestry terminology would be useful for effective communication between the two Koreas.

남북 산림과학협력 대응 북한 지리정보체계 분야 연구 동향 분석

임중빈(Joongbin Lim),김경민(Kyoung-min Kim) 대한공간정보학회 2018 한국공간정보학회 학술대회 Vol.2018 No.11

음영기복 알고리즘을 활용한 한반도 촬영 위성영상에서의 지형그림자 탐지

김형규,임중빈,김경민,원명수,김태정,Hyeong-Gyu Kim,Joongbin Lim,Kyoung-Min Kim,Myoungsoo Won,Taejung Kim 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5

In recent years, the number of users has been increasing with the rapid development of earth observation satellites. In response, the Committee on Earth Observation Satellites (CEOS) has been striving to provide user-friendly satellite images by introducing the concept of Analysis Ready Data (ARD) and defining its requirements as CEOS ARD for Land (CARD4L). In ARD, a mask called an Unusable Data Mask (UDM), identifying unnecessary pixels for land analysis, should be provided with a satellite image. UDMs include clouds, cloud shadows, terrain shadows, etc. Terrain shadows are generated in mountainous terrain with large terrain relief, and these areas cause errors in analysis due to their low radiation intensity. previous research on terrain shadow detection focused on detecting terrain shadow pixels to correct terrain shadows. However, this should be replaced by the terrain correction method. Therefore, there is a need to expand the purpose of terrain shadow detection. In this study, to utilize CAS500-4 for forest and agriculture analysis, we extended the scope of the terrain shadow detection to shaded areas. This paper aims to analyze the potential for terrain shadow detection to make a terrain shadow mask for South and North Korea. To detect terrain shadows, we used a Hill-shade algorithm that utilizes the position of the sun and a surface's derivatives, such as slope and aspect. Using RapidEye images with a spatial resolution of 5 meters and Sentinel-2 images with a spatial resolution of 10 meters over the Korean Peninsula, the optimal threshold for shadow determination was confirmed by comparing them with the ground truth. The optimal threshold was used to perform terrain shadow detection, and the results were analyzed. As a qualitative result, it was confirmed that the shape was similar to the ground truth as a whole. In addition, it was confirmed that most of the F1 scores were between 0.8 and 0.94 for all images tested. Based on the results of this study, it was confirmed that automatic terrain shadow detection was well performed throughout the Korean Peninsula.

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 F₁ 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).

북한의 생태지리구획을 활용한 임농복합경영 적정 수종 및 작물 고찰 연구

박소희 ( Sohee Park ),임중빈 ( Joongbin Lim ),김은희 ( Eun-hee Kim ),양아람 ( A-ram Yang ) 한국산림과학회 2021 한국산림과학회지 Vol.110 No.3

임농복합경영에 관한 기술협력은 남북 산림협력 분과회담 주요의제 중 하나이다. 본 연구는 북한의 생태지리 및 입지 특성을 반영한 임농복합경영 대상지에 따른 적정 수종과 작물을 선정하기 위해 수행되었다. 생태지리구획도는 북한의 위성자료와 문헌자료를 사용하여 북한 전역을 4지대 13지역으로 구분하여 작성되었다. 북한 전역을 세 가지 입지 특성(토심, 경사 및 지형적 위치)에 따라 18개 입지 조건으로 구분하고, 북한의 임농복합경영의 경영기간에 따라 단기와 장기 임농복합경영 대상지로 분류하여 지도로 작성하였다. 최종적으로 생태지리구획도와 임농복합경영 대상지 지도를 중첩하여 수목 30종과 작물 19종을 대상으로 지역별 적정 수종 및 작물을 분류하였다. 수종과 작물은 동일 지대 내 지역별로는 대부분 유사하였으나, 지대별로 비교했을 때는 차이를 보였다. 이는 생태지리구획에 반영된 지리 기후적 특성이 지대별로 큰 차이를 보이기 때문인 것으로 보인다. 향후 본 연구 결과를 기반으로 임농복합경영 분야 남북 산림협력 시 경영 목적과 경영 유형을 모두 고려한 수종 및 작물을 제안할 수 있을 것으로 판단된다. This study aims to identify appropriate tree species and crops for agroforestry target sites in North Korea based on ecological geography and site properties. To this end, an ecological geographic map (13 regions and 4 zones) of North Korea was made using satellite images and North Korean academic journal articles. The target agroforestry sites were selected and mapped according to 18 site conditions depending on 3 site characteristics, and the sites were divided into short-term and long-term target sites depending on the agroforestry management period. Finally, optimal combinations of 30 tree species and 19 crops were selected by overlapping the ecological geographic map and agroforestry target site map. For regions within the same zone, tree species and crops were almost similar; however, compared to regions in other zones, they differed. This is likely because the geographical climatic characteristics reflected in the ecological geographic map vary greatly from zone to zone. These results will be used to propose a combination of suitable tree species and crops that takes into account both management purposes and management types for inter-Korean forest cooperation in the agroforestry sector.

정오표

강유진,조동진,한대현,임정호,임중빈,오금희,권언혜,Kang, Yoojin,Cho, Dongjin,Han, Daehyeon,Im, Jungho,Lim, Joongbin,Oh, Kum-hui,Kwon, Eonhye 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.2

농림위성 활용을 위한 산불 피해지 분류 딥러닝 알고리즘 평가

차성은,원명수,장근창,김경민,김원국,백승일,임중빈,Cha, Sungeun,Won, Myoungsoo,Jang, Keunchang,Kim, Kyoungmin,Kim, Wonkook,Baek, Seungil,Lim, Joongbin 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

RapidEye 위성영상과 구글 어스를 활용한 북한 DMZ의 산림현황 및 산림황폐지 변화 분석

권수경 ( Sookyung Kwon ),김은희 ( Eunhee Kim ),임중빈 ( Joongbin Lim ),양아람 ( A-ram Yang ) 한국지리정보학회 2021 한국지리정보학회지 Vol.24 No.4

본 연구는 위성영상을 바탕으로 북한 DMZ의 산림현황을 분석하고 산림황폐지의 변화를 분석하기 위해 수행되었다. 생육기·비생육기 RapidEye 위성영상을 활용하여 북한 DMZ의 토지피복을 분류하였다. 토지피복을 입목지(침엽수림, 활엽수림, 혼효림), 산림황폐지(산간나지, 개간산지, 무립목지), 비산림지로 분류하고 산림 및 산림황폐지 면적을 생태지리구획에 따라 집계하였다. 연안-배천지구, 법동-평강지구, 회양-금강지구, 통천-고성지구의 산림황폐율은 14.24%, 16.75%, 5.98%, 16.63%로 각각 산출되었으며, 북한 DMZ 내 산림황폐화 주요 원인은 산불과 산지전용으로 판단되었다. 또한, 구글 어스 영상을 통해 산림황폐지 변화를 분석하였으며, 산림황폐지 면적은 감소하고 있는 것으로 확인되었다. 향후 본 연구 결과는 북한 DMZ의 산림공간정보를 제공함으로써 남북접경지역의 산림협력 전략 수립을 위한 기초자료로 활용될 수 있을 것이다. This study was conducted to analyze the forest status and deforestation area changes of the DMZ region in North Korea based on satellite images. Using growing and non-growing season’s RapidEye satellite images, land cover of the North Korean DMZ was classified into stocking land(conifer, deciduous, mixed), deforested land(unstocked mountain, cultivated mountain, bare mountain), and non-forest areas. Deforestation rates in the Yeonan-baecheon, Beopdong-Pyeonggang, Heoyang-Geumgang and Tongcheon-Goseong district were calculated as 14.24%, 16.75%, 5.98%, and 16.63% respectively. Forest fire and land use change of forest were considered as the main causes of deforestation of DMZ. Changes in deforestation area were analyzed through Google Earth images. As a results, it was shown that the area of deforestation was on a decreasing trend. This study can be used as basic data for establishing inter-Korean border region’s forest cooperation strategies by providing forest spatial information on the North Korea’s DMZ.

농림위성 활용 수종분류 가능성 평가를 위한 래피드아이 영상 기반 시험 분석

권수경 ( Soo-kyung Kwon ),김경민 ( Kyoung-min Kim ),임중빈 ( Joongbin Lim ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.2

기후변화나 여러 환경문제들로부터 지속 가능한 산림자원 관리 및 모니터링을 위해 임상도의 지속적인 갱신은 필수적이다. 따라서 효율적이고 광역적인 산림 원격탐사의 필요성에 따라 차세대 중형위성 4호의 사업이 확정되어 2023년 발사 예정에 있다. 농림위성(차세대 중형위성 4호)는 5 m급 공간해상도와 Blue, Green, Red, Red Edge, Near Infra Red 총 5개 밴드를 가진다. 본 연구는 농림위성의 발사 및 활용에 앞서 농림위성과 유사한 사양을 가지는 RapidEye를 이용하여 위성 기반 수종분류의 가능성을 모의 평가하기 수행되었다. 본 연구는 춘천 선도산림경영단지를 연구 대상지로 하였으며, RapidEye 위성 영상기반 모의 수종분류는 생육기 영상으로부터 추출한 분광정보와 생육기와 비생육기의 NIR 밴드로부터 추출한 GLCM 질감특성 정보가 활용되었고, 이를 입력데이터로 하여 랜덤 포레스트(Random Forest) 기법을 적용하였다. 본 연구에서는 침엽수종 3종(소나무, 잣나무, 낙엽송), 활엽수종 5종(신갈나무, 굴참나무, 자작나무, 밤나무, 기타활엽수), 침활혼효림 총 9종으로 임상을 분류하였다. 분류 정확도는 임상도와 분류 결과를 대조하여 산출하였으며, 분류 정확도는 분광정보만 사용한 경우 39.41%, 분광정보과 질감정보를 모두 사용한 경우 69.29%의 정확도를 보였으며, 다중시기 분광정보 및 질감정보의 활용을 통해 5 m 해상도의 위성영상으로부터 수종분류의 가능성이 있음을 확인하였다. 향후 식생의 생태적 특성을 더욱 효과적으로 반영한 추가 변수를 대입하여 농림위성 활용 가능성을 제고하고자 한다. Updating a forest type map is essential for sustainable forest resource management and monitoring to cope with climate change and various environmental problems. According to the necessity of efficient and wide-area forestry remote sensing, CAS500-4 (Compact Advanced Satellite 500-4; The agriculture and forestry satellite) project has been confirmed and scheduled for launch in 2023. Before launching and utilizing CAS500-4, this study aimed to pre-evaluation the possibility of satellite-based tree species classification using RapidEye, which has similar specifications to the CAS500-4. In this study, the study area was the Chuncheon forest management complex, Gangwon-do. The spectral information was extracted from the growing season image. And the GLCM texture information was derived from the growing and non-growing seasons NIR bands. Both information were used to classification with random forest machine learning method. In this study, tree species were classified into nine classes to the coniferous tree (Korean red pine, Korean pine, Japanese larch), broad-leaved trees (Mongolian oak, Oriental cork oak, East Asian white birch, Korean Castanea, and other broadleaved trees), and mixed forest. Finally, the classification accuracy was calculated by comparing the forest type map and classification results. As a result, the accuracy was 39.41% when only spectral information was used and 69.29% when both spectral information and texture information was used. For future study, the applicability of the CAS500-4 will be improved by substituting additional variables that more effectively reflect vegetation’s ecological characteristics.