• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.121-131
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• 2018

Climate change has caused changes in environmental factors that have a direct impact on agriculture such as temperature and precipitation. The meteorological disaster that has the greatest impact on agriculture is drought, and its forecasts are closely related to agricultural production and water supply. In the case of terrestrial data, the accuracy of the spatial map obtained by interpolating the each point data is lowered because it is based on the point observation. Therefore, acquisition of various meteorological data through satellite imagery can complement this terrestrial based drought monitoring. In this study, Evaporative Stress Index (ESI) was used as satellite data for drought determination. The ESI was developed by NASA and USDA, and is calculated through thermal observations of GOES satellites, MODIS, Landsat 5, 7 and 8. We will identify the difference between ESI and other satellite-based drought assessment indices (Vegetation Health Index, VHI, Leaf Area Index, LAI, Enhanced Vegetation Index, EVI), and use it to analyze the drought in South Korea, and examines the applicability of ESI as a new indicator of agricultural drought monitoring.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.201-211
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• 2017

The estuary in Nakdong River has changes by the construction of harbors, land reclamation and artificial waterway changes. These resultslead to changes of extinction and creation of deltaic barrier island. The deltaic barrier island changes in the Nakdong River estuary affect the function of the barrier islands and cause environmental changes. Therefore, it is important to monitor the changes in the area of the Nakdong estuary. In this study, long-term changes of the area and suspended sediment of deltaic barrier island in the Nakdong River estuary were analyzed using Landsat TM/ETM+ images. As a result, end point rate (EPR) values of shoreline in Jinwoodo and Sinjado are about 5m/yr and about 50 m/yr, respectively. The EPR values of north-south and east-west direction in Doyodeung are 20 m/yr and -20 ~ 10 m/yr. The suspended sediment concentration (SSC) has a maximum value of $25g/m^3$ in the vicinity of Jinwoodo and Sinjado, while it has a maximum concentration of $40g/m^3$ in the vicinity of Shinjido and Doyodeung. In other words, the area and the SSC change are small in Jinwoodo, and the area change and the SSC variation are large in Sinjado and Doyodeung. As a result of analysis of correlation between area change and SSC variation using all data, the Pearson coefficient value (r) is 0.36 and it is 0.32 in winter data. In other words, it is considered that the SSC variation affectsthe deltatic barrier island area change. However, verification using advanced altimetry data is necessary in the future. These studies can be used for coastal monitoring and environmental monitoring.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.311-320
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• 2009

This study was carried out to estimate forest biomass and to produce forest biomass thematic map for Muju county by combining field data from the 5$^{th}$ National Forest Inventory (2006-2007) and satellite data. For estimating forest biomass, two methods were examined using a Landsat TM-5(taken on April 28th, 2005) and field data: multi-variant regression modeling and t-Nearest Neighbor (k-NN) technique. Estimates of forest biomass by the two methods were compared by a cross-validation technique. The results showed that the two methods provide comparatively accurate estimation with similar RMSE (63.75$\sim$67.26ton/ha) and mean bias ($\pm$1ton/ha). However, it is concluded that the k-NN method for estimating forest biomass is superior in terms of estimation efficiency to the regression model. The total forest biomass of the study site is estimated 8.4 million ton, or 149 ton/ha by the k-NN technique.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.295-300
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• 2018

Net radiation is the total amount of radiation energy used as a heat flux for the Earth's energy cycle, and net radiation from the surface is an important factor in areas such as hydrology, climate, meteorological studies and agriculture. It is very important to monitoring the net radiation through remote sensing to be able to understand the trend of heat island and urbanization phenomenon. However, net radiation estimation using only remote sensing data is generally causes difference in accuracy depending on cloud. Therefore, in this paper, we retrieved and monitored high resolution surface net radiation at 1 hour interval in Eunpyeong New Town where urbanization using Communication, Ocean and Meteorological Satellite (COMS), Landsat-8 satellite and Computational Fluid Dynamics (CFD) model data reflecting the difference in building height. We compared the observed and estimated net radiation at the flux tower. As a result, estimated net radiation was similar trend to the observed net radiation as a whole and it had the accuracy of RMSE $54.29Wm^{-2}$ and Bias $27.42Wm^{-2}$. In addition, the calculated net radiation showed well the meteorological conditions such as precipitation, and showed the characteristics of net radiation for the vegetation and artificial area in the spatial distribution.

• Journal of Environmental Impact Assessment
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• v.8 no.3
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• pp.77-90
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• 1999

The degree of green naturality (DGN) has played a key role for maintaining the environmental quality from inappropriate developments, although the quality and effectiveness of the mapping of DGN has been under debate. In this study, spatial distribution of degree of green naturality was initially estimated from forest stand maps that were produced from the aerial photo interpretation and extensive field survey. Once the boundary of initial classes of DGN were defined, it were overlaid with normalized difference vegetation index (NDVI) data that were derived from the recently obtained Landsat Thematic Mapper data. NDVI was calculated for each pixel from the radiometrically corrected satellite image. There were no significant differences in mean values of vegetation index among the initial DGN classes. However, the satellite derived vegetation index was very effective to delineate the developed and damaged forest lands and to adjust the initial value of DGN according to the distribution of NDVI within each class.

• Journal of The Geomorphological Association of Korea
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• v.28 no.1
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• pp.83-99
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• 2021

Burn severity analysis using satellite imagery has high capabilities for research and management in inaccessible areas. We extracted the forest fire area of the DMZ (Demilitarized Zone) in the western Imjin Estuary which is restricted to access due to the confrontation between South and North Korea. Then we analyzed the forest fire severity and recoverability using atmospheric corrected Surface Reflectance Level-2 data collected from Landsat-8 OLI (Operational Land Imagery) / TIRS (Thermal Infrared Sensor). Normalized Burn Ratio (NBR), differenced NBR (dNBR), and Relative dNBR (RdNBR) were analyzed based on changes in the spectral pattern of satellite images to estimate burn severity area and intensity. Also, we evaluated the recoverability after a forest fire using a land cover map which is constructed from the NBR, dNBR, and RdNBR analyzed results. The results of dNBR and RdNBR analysis for the six years (during May 30, 2014 - May 30, 2020) showed that the intensity of monthly burn severity was affected by seasonal changes after the outbreak and the intensity of annual burn severity gradually decreased after the fire events. The regrowth of vegetation was detected in most of the affected areas for three years (until May 2020) after the forest fire reoccurred in May 2017. The monthly recoverability (from April 2014 to December 2015) of forests and grass fields was increased and decreased per month depending on the vegetation growth rate of each season. In the case of annual recoverability, the growth of forest and grass field was reset caused by the recurrence of a forest fire in 2017, then gradually recovered with grass fields from 2017 to 2020. We confirmed that remote sensing was effectively applied to research of the burn severity and recoverability in the DMZ. This study would also provide implications for the management and construction statistics database of the forest fire in the DMZ.

• The Korean Journal of Air & Space Law and Policy
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• v.35 no.1
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• pp.203-319
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• 2020

The regulation of satellite remote sensing is generally included with the scope of statutes governing outer space activities. But not all states opted for dedicated satellite remote sensing regulation. The decision whether to do so depends in part on the specific capabilities of national satellite remote sensing programs. Five states that have dedicated statutes governing operations with remote sensing data are the United States, with its developed Landsat regime (the Land Remote Sensing Policy Act of 1992, LRSPA), Canada, with its Remote Sensing Systems Act, Germany, with its Satellite Data Securities Protection Act (SatDSiG), France, with its Law on Space Operations (LOS), Japan, with its Act on Ensuring Appropriate Handling of Satellite Remote Sensing Data. The major purpose of this article is to shed light on some legal issues surrounding remote sensing activities by comparative review. The paper analyzes international conventions or soft law and national law and policies relating to satellite remote sensing. It also offers some implications and suggestions for regulations of satellite remote sensing operations and satellite data.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.21-32
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• 2022

The importance of the classical theme of how the Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance of high-resolution satellite images match the actual atmospheric reflectance and surface reflectance has been emphasized. Based on the Radiometric Calibration Network (RadCalNet) BTCN and BSCN data, this study compared the accuracy of TOA and TOC reflectance products of the currently available optical satellites, including KOMPSAT-3, WorldView-2, and Pléiades-1A image sets calculated using the absolute atmospheric correction function of the Orfeo Toolbox (OTB) tool. The comparison experiment used data in 2018 and 2019, and the Landsat-8 image sets from the same period were applied together. The experiment results showed that the product of TOA and TOC reflectance obtained from the three sets of images were highly consistent with RadCalNet data. It implies that any imagery may be applied when high-resolution reflectance products are required for a certain application. Meanwhile, the processed results of the OTB tool and those by the Apparent Reflection method of another tool for WorldView-2 images were nearly identical. However, in some cases, the reflectance products of Landsat-8 images provided by USGS sometimes showed relatively low consistency than those computed by the OTB tool, with the reference of RadCalNet BTCN and BSCN data. Continuous experiments on active vegetation areas in addition to the RadCalNet sites are necessary to obtain generalized results.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.593-598
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• 2004

본 연구에서는 위성영상자료 Landsat TM(1999.8.16), ETM+(2002.9.17)을 활용하여 북한 서한만 지역의 NDVI, 토지피복, 지표온도 분포도를 작성하여 경년에 따른 환경변화를 탐지 및 분석하였으며 ISODATA Clustering 기법을 적용하여 북한 서한만 일대의 간석지 분포도를 작성하였다. 북한 서해안 간석지 면적변화 탐지를 위하여 고지형도 (1918)를 디지털 자료로 변환하여 북한 서해안 전역의 간석지 GIS DB를 구축하였으며 위성영상자료를 이용하여 작성된 간석지 공간 분포도와의 비교ㆍ분석을 통하여 북한 서한만 일대의 84년간의 간석지 면적변화를 탐지하였다. 이러한 연구 결과를 바탕으로 북한 서해안 지역의 간석지 퇴적 환경정보 및 다양한 연안 환경정보를 구축할 수 있었으며 북한 서해안 지역과 남한 서해안 지역의 간석지 연안환경 비교 분석 등을 위한 기초자료로 활용될 것으로 판단된다.