• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.233-243
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• 2021

Photovoltaic panels are hazardous electronic waste that has heavy metal as one of the hazardous components. Each year, hazardous electronic waste is increasing worldwide and every heavy rainfall exposes the photovoltaic panel to become the source of heavy metal soil contamination. the development needs a monitoring technology for this hazardous exposure. this research use relationships between SAR temporal baseline and coherence of Sentinel-1 satellite to detected photovoltaic panel. Also, the photovoltaic plant detection tested using the difference between that photovoltaic panel and the other difference surface of coherence. The author tested the photovoltaic panel and its environment to calculate differences in coherence relationships. As a result of the experiment, the coherence of the photovoltaic panel, which is assumed to be a permanent scatterer, shows a bias that is biased toward a median value of 0.53 with a distribution of 0.50 to 0.65. Therefore, further research is needed to improve errors that may occur during processing. Additionally, the author found that the change detection using a temporal baseline is possible as the rate of reduction of coherence of photovoltaic panels differs from those of artificial objects such as buildings. This result could be an efficient way to continuously monitor regardless of weather conditions, which was a limitation of the existing optical satellite image-based photovoltaic panel detection research and to understand the spatial distribution in situations such as photovoltaic panel loss.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.51-63
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• 2022

Up-to-date statistics of crop cultivation status is essential for farm land management planning and the advancement in remote sensing technology allows for rapid update of farming information. The objective of this study was to develop a classification model of rice paddy or winter crop fields based on NDWI, NDVI, and HSV indices using Sentinel-2 satellite images. The 18 locations in central Korea were selected as target areas and photographed once for each during summer and winter with a eBee drone to identify ground truth crop cultivation. The NDWI was used to classify summer paddy fields, while the NDVI and HSV were used and compared in identification of winter crop cultivation areas. The summer paddy field classification with the criteria of -0.195

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1911-1923
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• 2022

The availability of high-resolution satellite images provides precise information without direct observation of the research target. Korea Multi-Purpose Satellite (KOMPSAT), also known as the Arirang satellite, has been developed and utilized for earth observation. The machine learning model was continuously proven as a good classifier in classifying remotely sensed images. This study aimed to compare the performance of the support vector machine (SVM) model in classifying the land cover of the Delaware River port area on high and medium-resolution images. Three optical images, which are KOMPSAT-2, KOMPSAT-3A, and Sentinel-2B, were classified into six land cover classes, including water, road, vegetation, building, vacant, and shadow. The KOMPSAT images are provided by Korea Aerospace Research Institute (KARI), and the Sentinel-2B image was provided by the European Space Agency (ESA). The training samples were manually digitized for each land cover class and considered the reference image. The predicted images were compared to the actual data to obtain the accuracy assessment using a confusion matrix analysis. In addition, the time-consuming training and classifying were recorded to evaluate the model performance. The results showed that the KOMPSAT-3A image has the highest overall accuracy and followed by KOMPSAT-2 and Sentinel-2B results. On the contrary, the model took a long time to classify the higher-resolution image compared to the lower resolution. For that reason, we can conclude that the SVM model performed better in the higher resolution image with the consequence of the longer time-consuming training and classifying data. Thus, this finding might provide consideration for related researchers when selecting satellite imagery for effective and accurate image classification.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.27-27
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• 2022

본 연구는 본 연구에서는 내륙에 위치한 하천(백제보)과 호소(남양호)를 대상으로 수체반사도를 활용하여 유역특성에 따른 반사도의 변화를 확인하고 Chlorophyll-a(Chl-a)의 농도를 추정하고자 하였다. 각 유역별 특성분석을 위해 제원자료, 토지피복도 및 11개(수소이온농도, 용존산소, BOD, COD, 부유물질, 총질소, 총인, 수온, 전기전도도 및 Chlorophyll-a)의 수질인자 자료를 구축하였다. 백제보는 2016-2017년 유인항공기에 탑재된 초분광센서를 이용하여 반사도를 측정하였고, 남양호는 2020-2021년 초분광센서가 탑재된 Drone과 Sentinel-2 MSI영상으로부터 반사도를 측정하였으며 두 유역 모두 촬영 범위에 대하여 현장샘플링을 실시하였다. 유역특성, 수질인자간 상관성 및 밴드별 상관성 분석을 실시하였다. 수질인자 간 상관성 분석 결과 Chl-a와 광학적 특징이 있는 SS, TOC가 상관성이 높게 나타났으며, 반사도의 경우 Chl-a가 고농도일수록 Near-Infrared, Blue 파장과 상관성이 높게 나타났다. 해당 분석결과를 기반으로 각 유역에 대해 Chl-a Machine-learning 기법과 원격탐사자료를 이용하여 Chl-a의 농도를 산정하였으며 백제보, 남양호 각각 결정계수(R²) 0.80, 0.88의 성능을 보였다. 추후 고해상도 광학위성영상을 통해 유역특성을 고려한 광범위한 지역 규모의 Chl-a의 시공간적 분석이 가능할 것으로 판단된다.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1773-1784
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• 2021

In order to minimize damage to oil spill accidents in the ocean, it is essential to collect a spilled area as soon as possible. Thus satellite-based remote sensing is a powerful source to detect oil spills in the ocean. With the recent rapid increase in the number of available satellites, it has become possible to generate a status report of marine oil spills soon after the accident. In this study, the oil spill area was calculated using various satellite images for the Symphony oil spill accident that occurred off the coast of Qingdao Port, China, on April 27, 2021. In particular, improving the accuracy of oil spill area determination was applied using high-resolution commercial satellite images with a spatial resolution of 2m. Sentinel-1, Sentinel-2, LANDSAT-8, GEO-KOMPSAT-2B (GOCI-II) and Skysat satellite images were collected from April 27 to May 13, but five images were available considering the weather conditions. The spilled oil had spread northeastward, bound for coastal region of China. This trend was confirmed in the Skysat image and also similar to the movement prediction of oil particles from the accident location. From this result, the look-alike patch observed in the north area from the Sentinel-1A (2021.05.01) image was discriminated as a false alarm. Through the survey period, the spilled oil area tends to increase linearly after the accident. This study showed that high-resolution optical satellites can be used to calculate more accurately the distribution area of spilled oil and contribute to establishing efficient response strategies for oil spill accidents.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1125-1137
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• 2020

This study aimed to propose the method of linking satellite image-based forest fire data to supplement the limitation of forest fire survey data that records only the ignition location and area of forest fire. For this purpose, a method was derived to link the fire survey data provided by the Korea Forest Service between January 2012 and December 2019 with MODIS and VIIRS image-based forest fire data. As a result, MODIS and VIIRS-based forest fire data out of 191 wildfires in the forest fire survey data were able to identify 11% and 44% of fire damage area, respectively. An average of 56% of forest damage area was extracted from VIIRS-based forest fire data compared to forest fire areas identified by high-resolution Sentinel-2A satellites. Therefore, for large-scale forest fires, VIIRS wildfire data can be used to compensate for the limitations of forest fire survey data that records only the ignition location and area.

• Journal of Environmental Science International
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• v.31 no.9
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• pp.735-741
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• 2022

Global warming affects forests and their ecology. Diversity in the forest is a buffer that reduces the damage due to global warming. Mixed forests are ecologically more valuable as versatile habitats and are effective in preventing landslides. In Korea, most forests were created by simple afforestation with trees of evergreen species. Typically, evergreen trees are shallow-rooted, and deciduous trees are deep-rooted. Mixed forest tree roots grip the soil effectively, which reduces the occurrence of landslides. Therefore, improving the distribution of tree types is essential to reduce damage due to global warming. For this improvement, the investigation of tree types of the forest is needed. However, determining the tree type distribution of forests that are spread over wide areas is labor-intensive and time-consuming. This study suggests effective methods for determining the distribution of tree types in a forest that is spread across a relatively wide area. Using normalized difference vegetation index and RGB images from unmanned aerial vehicles, each evergreen and deciduous tree, and grassland area can be distinguished. The distinguished image determines the distribution of tree type. This method is effective compared to directly determining the tree type distribution in the forest by the use of manpower. The data from these methods could be applied to plan a mixed forest or to prepare for future damage due to global warming.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.118-118
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• 2021

최근 지구 온난화 현상으로 인한 기후변화로 이상기후 현상이 발생하고 있으며 이로 인해 장기적으로 폭염의 빈도 및 강도 상승에 따른 가뭄 피해 우려가 증가하고 있다. 농업 가뭄은 강수량 부족, 토양 수분 부족, 저수량 부족 등 농업분야에 영향을 주는 인자들과 관련되어 있어 농작물 생육 및 수확량 감소를 야기한다. 우리나라는 논농사가 주를 이루고 있어 국내 농업 가뭄은 주수원공인 농업용 저수지의 가용저수용량으로 판단 가능하다. 따라서 안정적인 농업용수 공급을 위해 수리시설물의 모니터링, 공급량 등의 분석이 이루어져야 하며, 농업 가뭄에 대비하기 위해 농업용 저수지의 가용저수용량 파악이 필요하다. 수자원 분야에서 지점자료의 시·공간적 한계점을 보완하기 위해 인공위성 자료를 활용한 연구가 활발히 이루어지고 있으며, 본 연구에서는 위성영상 자료 및 딥러닝 기반 알고리즘을 적용하여 농업용 저수지 수표면 탐지 및 시계열 분석을 목적으로 한다. 위성영상 자료는 5일 주기 및 10 m 공간해상도를 가진 Sentinel-2 위성영상 자료를 활용하고자 하였으며, 딥러닝에 적용하기 위하여 100장 이상의 영상 이미지를 구축하였다. 딥러닝 기반 알고리즘으로는 Convolutional Neural Network (CNN)을 활용하였으며, CNN은 주로 이미지 분류나 객체 검출 문제를 해결하기 위해 제안된 모델로 최근 픽셀 단위로 분류가 가능한 알고리즘이 개발되어 높은 정확도의 수표면 탐지가 가능할 것으로 판단된다. 따라서 본 연구에서는 CNN 기반 수표면 탐지 알고리즘을 개발하여 Sentinel-2 영상 기준 경기도 안성시를 대상으로 소규모 농업용 저수지의 수표면적에 대한 시계열 데이터를 분석하고자 한다.