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6SV2.1과 GK2A AOD를 이용한 기계학습 기반의 Sentinel-2 영상 대기보정
김서연,윤유정,강종구,정예민,최소연,임윤교,서영민,박찬원,이경도,나상일,안호용,류재현,이양원,Seoyeon Kim,Youjeong Youn,Jonggu Kang,Yemin Jeong,Soyeon Choi,Yungyo Im,Youngmin Seo,Chan-Won Park,Kyung-Do Lee,Sang-Il Na,Ho-Yong Ahn,Jae-Hyun Ryu 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
In this letter, we simulated an atmospheric correction for Sentinel-2 images, of which spectral bands are similar to Compact Advanced Satellite 500-4 (CAS500-4). Using the second simulation of the satellite signal in the solar spectrum - vector (6SV)2.1 radiation transfer model and random forest (RF), a type of machine learning, we developed an RF-based atmospheric correction model to simulate 6SV2.1. As a result, the similarity between the reflectance calculated by 6SV2.1 and the reflectance predicted by the RF model was very high.
위성기반 산불피해지수를 이용한 북한지역 산불피해지 분석
김서연,윤유정,정예민,권춘근,서경원,이양원,Kim, Seoyeon,Youn, Youjeong,Jeong, Yemin,Kwon, Chunguen,Seo, Kyungwon,Lee, Yangwon 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Recent climate change can increase the frequency and damage of wildfires worldwide. It can also lead to the deterioration of the forest ecosystem and increase casualties and economic loss. Satellite-based indices for forest damage can facilitate an objective and rapid examination of burned areas and help analyze inaccessible places like North Korea. In this letter, we conducted a detection of burned areas in North Korea using the traditional Normalized Burn Ratio (NBR), the Normalized Difference Vegetation Index (NDVI) to represent vegetation vitality, and the Fire Burn Index (FBI) and Forest Withering Index (FWI) that were recently developed. Also, we suggested a strategy for the satellite-based detection of burned areas in the Korean Peninsula as a result of comparing the four indices. Future work requires the examination of small-size wildfires and the applicability of deep learning technologies.
산불위험지수 지역최적화를 통한 2022년 북한산불 사례분석
윤유정,김서연,최소연,박강현,강종구,김근아,권춘근,서경원,이양원,Youn, Youjeong,Kim, Seoyeon,Choi, Soyeon,Park, Ganghyun,Kang, Jonggu,Kim, Geunah,Kwon, Chunguen,Seo, Kyungwon,Lee, Yangwon 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Wildfires in North Korea can have a directly or indirectly affect South Korea if they go south to the Demilitarized Zone. Therefore, this study calculates the regional optimized Forest Fire Danger Index (FFDI) based on Local Data Assessment and Prediction System (LDAPS) weather data to obtain forest fire risk in North Korea, and applied it to the cases in Goseong-gun and Cheorwon-gun, North Korea in April 2022. As a result, the suitability was confirmed as the FFDI at the time of ignition corresponded to the risk class Extreme and Severe sections, respectively. In addition, a qualitative comparison of the risk map and the soil moisture map before and after the wildfire, the correlation was grasped. A new forest fire risk index that combines drought factors such as soil moisture, Standardized Precipitation Index (SPI), and Normalized Difference Water Index (NDWI) will be needed in the future.
기온감률 보정과 최적크리깅을 이용한 산악기상관측망 기온자료의 우리나라 500미터 격자화
윤유정 ( Youjeong Youn ),김서연 ( Seoyeon Kim ),강종구 ( Jonggu Kang ),정예민 ( Yemin Jeong ),최소연 ( Soyeon Choi ),임윤교 ( Yungyo Im ),서영민 ( Youngmin Seo ),원명수 ( Myoungsoo Won ),천정화 ( Junghwa Chun ),김경민 ( Kyungmin K 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
산악지역의 기상정보를 상세하고 적절히 제공하기 위해 산림청에서는 2012년부터 전국 주요 산악지역을 대상으로 산악기상관측망(Automatic Mountain Meteorology Observation Station, AMOS)을 구축하여, 2022년 현재 464개의 관측소가 운영되고 있다. 본 연구에서는 AMOS 지점 관측을 이용하여 우리나라 산림에 적합한 기온 격자자료를 산출하기 위해서, 기온감률 보정을 적용한 최적의 크리깅(kriging) 기법을 제안하고 그 가용성을 평가하였다. 우선 통계적 처리를 통해 AMOS 기온자료의 이상치를 제거하였고, 이 자료를 이용하여 경험 베리오그램(variogram)에 가장 근사하는 이론 베리오그램을 도출하여 최적화 크리깅을 수행하였다. 이 때 기온감률 보정(lapse rate correction)을 적용하여 산악지형의 고도 변이가 반영되는 500 m 해상도의 기온격자지도를 생성하였다. 공간적으로 치우치지 않은 검증샘플을 이용한 암맹평가를 통해 본 기법의 가용성을 평가한 결과, 0.899-0.953의 상관계수 및 0.933-1.230℃의 오차를 나타내 기온감률 보정을 적용하지 않은 정규크리깅에 비해 정확도가 다소 향상되었다. 또한 기온감률 크리깅은 우리나라 산림의 복잡지형을 잘 표현하여, 강원도 산간지역과 해안산림지역의 국지적인 변이 및 지리산·내장산과 그 주변 산림의 지형적 차이와 같은 미세한 지역특성을 살릴 수 있다는 것이 가장 큰 장점이라고 할 수 있다. To provide detailed and appropriate meteorological information in mountainous areas, the Korea Forest Service has established an Automatic Mountain Meteorology Observation Station (AMOS) network in major mountainous regions since 2012, and 464 stations are currently operated. In this study, we proposed an optimal kriging technique with lapse rate correction to produce gridded temperature data suitable for Korean forests using AMOS point observations. First, the outliers of the AMOS temperature data were removed through statistical processing. Then, an optimized theoretical variogram, which best approximates the empirical variogram, was derived to perform the optimal kriging with lapse rate correction. A 500-meter resolution Kriging map for temperature was created to reflect the elevation variations in Korean mountainous terrain. A blind evaluation of the method using a spatially unbiased validation sample showed a correlation coefficient of 0.899 to 0.953 and an error of 0.933 to 1.230℃, indicating a slight accuracy improvement compared to regular kriging without lapse rate correction. However, the critical advantage of the proposed method is that it can appropriately represent the complex terrain of Korean forests, such as local variations in mountainous areas and coastal forests in Gangwon province and topographical differences in Jirisan and Naejangsan and their surrounding forests.
YOLO를 이용한 SAR 영상의 선박 객체 탐지: 편파별 모델 구성과 정확도 특성 분석
임윤교,윤유정,강종구,김서연,정예민,최소연,서영민,이양원,Yungyo Im,Youjeong Youn,Jonggu Kang,Seoyeon Kim,Yemin Jeong,Soyeon Choi,Youngmin Seo,Yangwon Lee 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
Ship detection at sea can be performed in various ways. In particular, satellites can provide wide-area surveillance, and Synthetic Aperture Radar (SAR) imagery can be utilized day and night and in all weather conditions. To propose an efficient ship detection method from SAR images, this study aimed to apply the You Only Look Once Version 5 (YOLOv5) model to Sentinel-1 images and to analyze the difference between individual vs. integrated models and the accuracy characteristics by polarization. YOLOv5s, which has fewer and lighter parameters, and YOLOv5x, which has more parameters but higher accuracy, were used for the performance tests (1) by dividing each polarization into HH, HV, VH, and VV, and (2) by using images from all polarizations. All four experiments showed very similar and high accuracy of 0.977 ≤ AP@0.5 ≤ 0.998. This result suggests that the polarization integration model using lightweight YOLO models can be the most effective in terms of real-time system deployment. 19,582 images were used in this experiment. However, if other SAR images,such as Capella and ICEYE, are included in addition to Sentinel-1 images, a more flexible and accurate model for ship detection can be built.