전천에서의 위성-모델 융합 에어로졸 광학두께 추정

임정호(Jungho Im),이정희(Junghee Lee),박서희(Seohui Park),신민소(Minso Shin),김미애(Miae Kim) 대한공간정보학회 2018 한국지형공간정보학회 학술대회 Vol.2018 No.11

원격탐사를 이용한 수질평가시의 인공신경망에 의한 분석과 기존의 회귀분석과의 비교

임정호 ( Jung Ho Im ),정종철 ( Jong Chul Jeong ) 大韓遠隔探査學會 1999 大韓遠隔探査學會誌 Vol.15 No.2

본 연구에서는 원격탐사를 이용하여 수질 파라미터들을 평가하는데 기존의 다중 회귀나 밴드비 회귀분석을 이용한 통계적인 방법과 신경망을 이용한 방법을 비교하였다. 사용된 영상은 1996년 3월 18일 대청호 유역의 Landsat TM 영상이며, 30개의 현장 실측치가 위성이 통과하는 시간대에 샘플링되었다. 적용된 신경망은 3개의 층으로 구성된 전향 신경망이며 훈련방법으로는 역전파를 사용하였다. 본 연구에서는 가용한 훈련 데이터 셀이 작으므로 cross-validation 방법이 적용되었다. 비록 기존의 희귀분석에 의한 결과도 어느 정도 유의하게 나왔지만, 신경망에 의한 결과가 훨씬 성공적인 수행을 보여주었다. 신경망을 이용한 수질평가는 신경망이 자료의 비선형적 속성을 잘 반영해주기 때문에 기존의 통계적 기법보다 훨씬 나은 결과를 제공한다고 판단된다. A comparison of a neural network approach with the conventional statistical methods, multiple regression and band ratio analyses, for the estimation of water quality parameters is presented in this paper. The Landsat TM image of Lake Daechung acquired on March 18, 1996 and the thirty in-situ sampling data sets measured during the satellite overpass were used for the comparison. We employed a three-layered and feedforward network trained by backpropagation algorithm. A cross validation was applied because of the small number of training pairs available for this study. The neural network showed much more successful performance than the conventional statistical analyses, although the results of the conventional statistical analyses were significant. The superiority of a neural network to statistical methods in estimating water quality parameters is strictly because the neural network modeled non-linear behaviors of data sets much better.

항공라이다를 활용한 개체목 수관추출 방법 개발

임정호(Im, Jungho),Tao Liu,Fang Fang,이정희(Lee, Junghee),김경민(Kim, Kyungmin),김은숙(Kim, Eunsook) 한국측량학회 2013 한국측량학회 학술대회자료집 Vol.2013 No.4

K-Means Clustering 기법과 원격탐사 자료를 활용한 탄소기반 글로벌 해양 생태구역 분류

김영준,배덕원,임정호,정시훈,추민기,한대현,Young Jun Kim,Dukwon Bae,Jungho Im,Sihun Jung,Minki Choo,Daehyeon Han 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5

An acceleration of climate change in recent years has led to increased attention towards 'blue carbon' which refers to the carbon captured by the ocean. However, our comprehension of marine ecosystems is still incomplete. This study classified and analyzed global marine eco-provinces using k-means clustering considering carbon cycling. We utilized five input variables during the past 20 years (2001-2020): Carbon-based Productivity Model (CbPM) Net Primary Production (NPP), particulate inorganic and organic carbon (PIC and POC), sea surface salinity (SSS), and sea surface temperature (SST). A total of nine eco-provinces were classified through an optimization process, and the spatial distribution and environmental characteristics of each province were analyzed. Among them, five provinces showed characteristics of open oceans, while four provinces reflected characteristics of coastal and high-latitude regions. Furthermore, a qualitative comparison was conducted with previous studies regarding marine ecological zones to provide a detailed analysis of the features of nine eco-provinces considering carbon cycling. Finally, we examined the changes in nine eco-provinces for four periods in the past (2001-2005, 2006-2010, 2011-2015, and 2016-2020). Rapid changes in coastal ecosystems were observed, and especially, significant decreases in the eco-provinces having higher productivity by large freshwater inflow were identified. Our findings can serve as valuable reference material for marine ecosystem classification and coastal management, with consideration of carbon cycling and ongoing climate changes. The findings can also be employed in the development of guidelines for the systematic management of vulnerable coastal regions to climate change.

GOCI-II 대기상한 반사도와 기계학습을 이용한 남한 지역 시간별 에어로졸 광학 두께 산출

양세영,최현영,임정호,Seyoung Yang,Hyunyoung Choi,Jungho Im 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

항공 라이다와 딥러닝 기반 도시 수목 면적 지도를 이용한 개별 도시 수목 탐지

이연수,손보경,임정호,Yeonsu Lee,Bokyung Son,Jungho Im 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5

Urban trees play an important role in absorbing carbon dioxide from the atmosphere, improving air quality, mitigating the urban heat island effect, and providing ecosystem services. To effectively manage and conserve urban trees, accurate spatial information on their location, condition, species, and population is needed. In this study, we propose an algorithm that uses a high-resolution urban tree cover map constructed from deep learning approach to separate trees from the urban land surface and accurately detect tree locations through local maximum filtering. Instead of using a uniform filter size, we improved the tree detection performance by selecting the appropriate filter size according to the tree height in consideration of various urban growth environments. The research output, the location and height of individual trees in human settlement over Suwon, will serve as a basis for sustainable management of urban ecosystems and carbon reduction measures.

딥러닝 기법을 활용한 위성영상 기반의 가뭄 중장기예측

박수민(Sumin Park),임정호(Jungho Im) 한국항공우주학회 2022 한국항공우주학회 학술발표회 논문집 Vol.2022 No.4

단일 시기의 Landsat 7 ETM+ 영상을 이용한 산불피해지도 작성

원강영 ( Kang Yung Won ),임정호 ( Jung Ho Im ) 大韓遠隔探査學會 2001 大韓遠隔探査學會誌 Vol.17 No.1

인공위성영상(ETM+)을 이용하여 산불피해지역을 분석하기 위해 KT(Kauth-Thomas)변환기법과 IHS(Intensity-Hue-Saturation)변환기법을 적용하여 비교해 보고 산불피해등급지도를 작성하였다. 이 연구는 두 부분으로 나누어 수행되었는데, 그 첫 번째는 기하보정만 수행한 영상의 7, 4, 1밴드를 어용하여 IHS변환을 적용하여 단순 슬라이싱 기법으로 산불피해지역을 피해 정도별로 등급화 하는 것이 가능한가를 분석하였다. 그 결과 각 컴포넌트에서 클래스의 분광 특성이 서로 겹쳐서 단순 슬라이싱 기법으로는 적절한 분류가 이루어지지 않았다. 두 번째는 방사 및 지형보정을 한 영상을 각각 IHS와 KT변환기법으로 변환시킨 후 최대우도법을 이용해 분류하였다. 현장데이타가 부족하여 cross-validation을 수행하였으며, 일관되게 KT변환기법에 의한 분류가 IHS기법에 의한 분류보다 더 좋은 결과를 보여주었다. 또한 KT feature space와 IHS 컴포넌트의 분광분포를 그래프 상에서 분석해 보았다. 이 연구에서는 KT변환기법이 IHS변환가법보다 산불피해지역을 추출함에 있어 더 높은 정확도를 나타내고, 산불과 관련된 지표의 물리적 특성을 더 잘 반영함을 볼 수 있었다. The KT(Kauth-Thomas) and IHS(Intensity-Hue-Saturation) transformation techniques were introduced and compared to investigate fire-scarred areas with a single post-fire Landsat 7 ETM+ image. This study consists of two parts. First, using only geometrically corrected imagery, it was examined whether or not the different level of fire-damaged areas could be detected by simple slicing method within the image enhanced by the IHS transform. As a result, since the spectral distribution of each class on each IHS component was overlaid, the simple slicing method did not seem appropriate for the delineation of the areas of the different level of fire severity. Second, the image rectified by both radiometrically and topographically was enhanced by the KT transformation and the IHS transformation, respectively. Then, the images were classified by the maximum likelihood method. The cross-validation was performed for the compensation of relatively small set of ground truth data. The results showed that KT transformation produced better accuracy than IHS transformation. In addition, the KT feature spaces and the spectral distribution of IHS components were analyzed on the graph. This study has shown that, as for the detection of the different level of fire severity, the KT transformation reflects the ground physical conditions better than the IHS transformation.

빙점 강하제를 첨가한 다진 홍고추가 김치 품질 특성에 미치는 영향

성정민 ( Jung Min Sung ),임정호 ( Jeong Ho Lim ),김순임 ( Sun Im Kim ),정진웅 ( Jin Woong Jeong ) 한국식품저장유통학회 ( 구 한국농산물저장유통학회 ) 2009 한국식품저장유통학회지 Vol.16 No.6

GOCI 위성영상과 기계학습을 이용한 한반도 연안 수질평가지수 추정

장은나 ( Eunna Jang ),임정호 ( Jungho Im ),하성현 ( Sunghyun Ha ),이상균 ( Sanggyun Lee ),박영규 ( Young Gyu Park ) 대한원격탐사학회 2016 大韓遠隔探査學會誌 Vol.32 No.3

우리나라는 대규모 산업단지와 대도시들이 연안에 집중되면서 연안의 오염이 날로 심각해지고 있다. 이러한 연안 오염을 모니터링하기 위해서 위성 영상을 이용한 연안 수질평가지수 모니터링 연구가 수행 될 필요가 있다. 수질평가지수란 저층 산소포화도, 엽록소 농도, 투명도, 용존무기질소 및 용존무기인 농도를 수질평가 항목으로 구성하여 해양환경관리법에 따른 해양환경기준을 통해 해역별로 기준을 설정하여 산출하는 지수이다. 이 연구는 한반도 주변의 연안지역을 대상으로 2011년부터 2013년까지의 현장관측 자료 및 Geostationary Ocean Color Imager (GOCI) 위성 영상을 이용하여 연안 표층 해수에 대한 기계학습 기반의 두 가지 수질평가지수 추정 기법을 개발하였다. 첫 번째 방법으로는 GOCI 반사도를 이용하여 추정된 수질평가 항목들로 수질평가지수를 계산하였고, 두 번째 방법은 GOCI 반사도 및 산출물(엽록소 농도, 총 부유물질, 용존유기물)을 이용하여 수질평가지수를 추정하였다. 기계학습으로는 Random Forest(RF), Support Vector Regression (SVR), Cubist를 사용하였다. 수질평가 항목 추정에서 투명도의 정확도가 가장 높게 나타났으며, 모든 수질평가 항목 추정에서 세 가지 기계학습 중 RF의 정확도가 가장 높았다. 하지만 추정된 수질평가 항목들로 계산한 수질평가지수는 추정된 수질평가 항목들의 오차와 저층 산소포화도의 불확실성으로 인해 정확도가 높지는 않았다. 반면 GOCI 반사도와 산출물을 이용하여 추정한 수질평가지수는 현장 관측 기반 수질평가지수와 비교했을 때 첫 번째 방법보다 정확도가 높게 나타났다. 또한 엽록소 농도가 수질평가지수 추정에 가장 중요한 변수로 나타났다. In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.