• Proceedings of the Korean Society of Disaster Information Conference
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• 2016.11a
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• pp.364-365
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• 2016

본 연구에서는 고해상도 위성영상을 이용하여 지난 8월 29일 북한 함경북도 지역에서 발생한 홍수에 의한 피해를 분석하였다. 북한은 접근이 불가능한 지리적 특성을 가지기 때문에 인공위성을 활용한 모니터링이 유일한 관측 수단이라고 할 수 있다. 북한측 발표내용에 의하면 이번 홍수로 인해 사망 130여명, 실종 400여명, 시설물 8,670동 등 대규모 피해가 발생하였으며, 이재민은 7만명이 넘는 것으로 나타났다. 위성영상을 이용하여 모든 피해지역을 파악하는 것은 한계가 있지만, 일부 지역의 피해분석을 통해 피해규모를 간접적으로 확인하는 것은 가능하다. 본 연구에서는 5m급 고해상도 위성영상인 플래닛스코프(PlanetScope), 래피드아이(RapidEye) 영상을 이용하여 회령, 송학, 남양, 종성 4개 지역의 홍수피해 전, 직후, 한 달 후의 변화를 분석하였다. 분석결과, 해당지역은 시설물 및 농경지 침수, 제방붕괴 등이 발생하였으며, 홍수로 인한 지형변화가 동반되었음이 확인되었다.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.291-304
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• 2021

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 broad-leaved 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.113-126
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• 2021

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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.505-513
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• 2014

The RapidEye can acquire the 6.5m spatial resolution satellite imagery with the high temporal resolution on each day, based on its constellation of five satellites. The image products are available in two processing levels of Basic 1B and Ortho 3A. The Basic 1B image have radiometric and sensor corrections and include RPCs (Rational Polynomial Coefficients) data. In Korea, the geometric accuracy of RapidEye imagery can be improved, based on the scaled national digital maps that had been built. In this paper, we present the fully automated procedures to georegister the 1B data using 1:25,000 digital maps. Those layers of map are selected if the layers appear well in the RapidEye image, and then the selected layers are RPCs-projected into the RapidEye 1B space for generating vector images. The automated edge-based matching between the vector image and RapidEye improves the accuracy of RPCs. The experimental results showed the accuracy improvement from 2.8 to 0.8 pixels in RMSE when compared to the maps.