• 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.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1215-1227
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• 2018

Sea ice motion is an important factor for assessing change of sea ice because the motion affects to not only regional distribution of sea ice but also new ice growth and thickness of ice. This study presents an application of multi-temporal high-resolution optical satellites images obtained from Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and Korea Multi-Purpose Satellite-3 (KOMPSAT-3) to measure sea ice motion using SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) feature tracking techniques. In order to use satellite images from two different sensors, spatial and radiometric resolution were adjusted during pre-processing steps, and then the feature tracking techniques were applied to the pre-processed images. The matched features extracted from the SIFT showed even distribution across whole image, however the matched features extracted from the SURF showed condensed distribution of features around boundary between ice and ocean, and this regionally biased distribution became more prominent in the matched features extracted from the ORB. The processing time of the feature tracking was decreased in order of SIFT, SURF and ORB techniques. Although number of the matched features from the ORB was decreased as 59.8% compared with the result from the SIFT, the processing time was decreased as 8.7% compared with the result from the SIFT, therefore the ORB technique is more suitable for fast measurement of sea ice motion.

• Spatial Information Research
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• v.17 no.1
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• pp.117-129
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• 2009

South Korea has been undergoing spring drought periodically and diverse researches using vegetation index have been carried out to monitor spring droughts. The strength of the vegetation index-based drought monitoring is that the monitoring method enables efficient spatio-temporal grasp of changes in drought events. According to the development of low resolution satellite images such as MODIS, which are characterized by outstanding temporal resolution, the use of the method is expected to increase. Drought analysis using vegetation index considered only meteorological factor as a cause that affects vitality of vegetation. But many indirect and direct factors affect vegetation stress, So many uncertainties are involved in such method of analysis. To secure objectivity of drought analysis that uses vegetation index it is therefore necessary to compare the method with most representative drought analysis tools that are used for drought management. In this study, PDSI and SPI which a meteorological drought index that quantifies drought and that is used as a basic index for drought monitoring and MODIS NDVI are compared to propose correlation among them and to show usefulness of drought assessment that uses vegetation index. This study shows changing patterns of NDVI and SPI 6-month are similar and correlation between NDVI and SPI was highest in inland vegetation cover.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.917-926
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• 2021

Lodging rice is one of critical agro-meteorological disasters. In this study, the UAV-based multispectral imageries before and after rice lodging in rice paddy field of Jeollanamdo agricultural research and extension servicesin 2020 was analyzed. The UAV imagery on 14th Aug. includesthe paddy rice without any damage. However, 4th and 19th Sep. showed the area of rice lodging. Multispectral camera of 10 bands from 444 nm to 842 nm was used. At the area of restoration work against lodging rice, the reflectance from 531 nm to 842 nm were decreased in comparison to un-lodging rice. At the area of lodging rice, the reflectance of around 668 nm had small increases. Further, the blue and NIR (Near-Infrared) wavelength had larger. However, according to the types of lodging, the change of reflectance was different. The NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge) shows dome sensitivities to lodging rice, but they were different to types of lodging. These results will be useful to make algorithm to detect the area of lodging rice using a UAV.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.603-612
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• 2022

The study aims were to evaluate a machine-learning, algorithm-based, forest biomass-estimation model to estimate subnational forest biomass and to comparatively analyze REDD+ forest reference emission levels. Time-series Landsat satellite imagery and ESA Biomass Climate Change Initiative information were used to build a machine-learning-based biomass estimation model. The k-nearest neighbors algorithm (kNN), which is a non-parametric learning model, and the tree-based random forest (RF) model were applied to the machine-learning algorithm, and the estimated biomasses were compared with the forest reference emission levels (FREL) data, which was provided by the Paraguayan government. The root mean square error (RMSE), which was the optimum parameter of the kNN model, was 35.9, and the RMSE of the RF model was lower at 34.41, showing that the RF model was superior. As a result of separately using the FREL, kNN, and RF methods to set the reference emission levels, the gradient was set to approximately -33,000 tons, -253,000 tons, and -92,000 tons, respectively. These results showed that the machine learning-based estimation model was more suitable than the existing methods for setting reference emission levels.

• Journal of Environmental Impact Assessment
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• v.18 no.3
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• pp.171-183
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• 2009

This study aims to analyze the sedimentary environment change in mid-channel bar of the lower Geum river basin after the construction of the estuary barrage using multi-temporal satellite data and GIS. The sedimentary environment changes were observed in mid-channel bar areas. The mid-channel bar F was found to have been newly formed for 10 years(1996-2006), whereas the mid-channel bar B located between mid-channel bar A and C has disappeared by erosion during the same periods. When examined by section, the areas of the mid-channel bar in the upper stream section from the Yipo's reference point generally increased due to the prevailing sedimentary environments, and those of the downstream section decreased where corrosive environments are dominant. In ternms of the centroid movement, the mid-channel bars grew up toward the downstream by switching erosion and accumulation, as sedimentation was prevailing in the downstream area of mid-channel bars and corrosion was dominant in the upper stream. Through grain size analysis, the study areas are divided into three sections according to the average grain size. In Section I, the mid-channel bars were formed as a result of sedimentary process of tides in the past. In Section II, the mid-channel bars were formed partly through the sedimentary process of rivers although the sedimentary process of tides is prevailing. In Section III, the mid-channel bars were formed mainly through the sedimentary process of rivers, even if it showed the sedimentary process of tides in the past.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.1
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• pp.73-83
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• 2016

Forest degradation reduces ecosystem services provided by forest and could lead to change in composition of species. In North Korea, there has been significant forest degradation due to conversion of forest into terrace fields for food production and cut-down of forest for fuel woods. This study analyzed the phenological changes in North Korea, in terms of vegetation and moisture in soil and vegetation, from March to Octorber 2013, using MODIS (MODerate resolution Imaging Spectroradiometer) images and indexes including NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), and NDWI (Normalized Difference Water Index). In addition, marginal farmland was derived using elevation data. Lastly, degraded terrace fields of 16 degree was analyzed using NDVI, NDSI, and NDWI indexes, and marginal farmland characteristics with slope variable. The accuracy value of land cover classification, which shows the difference between the observation and analyzed value, was 84.9% and Kappa value was 0.82. The highest accuracy value was from agricultural (paddy, field) and forest area. Terrace fields were easily identified using slope data form agricultural field. Use of NDVI, NDSI, and NDWI is more effective in distinguishing deforested terrace field from agricultural area. NDVI only shows vegetation difference whereas NDSI classifies soil moisture values and NDWI classifies abandoned agricultural fields based on moisture values. The method used in this study allowed more effective identification of deforested terrace fields, which visually illustrates forest degradation problem in North Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.428-428
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• 2023

글로벌 위성 기반의 강수량 관측에 대한 역사는 1979년에 Arkin의 의해 제안된 IR(Infra-Red) 방법에 의해 위성으로부터 강우자료를 유도하는 개념이 도입된 이후 1987년 해양에서의 비교적 정확한 강수량 추정이 가능한 다중 채널의 마이크로파(MW) 복사계를 이용한 방법에서 1997년TRMM(Tropical Rainfall Measurement Mission)위성의 PR(Precpipitation Radar)의 레이더를 이용하는 방법, 그리고 2014년 GPM(Global Precipitation Measurement Mission) 핵심 위성(GPM Core Observatory)에 탑재된 Dual PR에 의한 방법으로 위성강수의 정확도를 매우 높여가고 있다(Kim et al. 2013). 한국과 아세안의 경제협력이 증가하면서 국내 ODA 정책에서 아세안은 가장 우선적인 대상이 되었다. 정부는 2011-2015년 기간에 라오스 등 26개 국가를 중점협력국에 포함시켰고, 2021~2025년간 적용될 제3기 중점협력국에 라오스를 포함하고 있다. 본 연구는 위성영상으로부터 유도된 위성강수 자료를 이용하여 라오스의 남능강 유역에 대한홍수량을 추정하는 것을 목적으로 하였다. 분석자료인 위성강수 자료는 GSMaP 위성강수 자료를 이용하였다. 이 자료는 1시간의 시간해상도와 0.1°의 공간해상도를 갖는다. 라오스 남능강 유역 9개 지점의 2019년 8월~9월까지의 총강수량 비교 결과 9개 지점의 1일 관측강우의 경우 유역내 평균 약 699.2mm였고, 위성강수는 425.4mm로 위성강수가 과소추정되는 결과를 보이고 있으나 두 자료간의 결정계수(r²)는 약 0.79의 정확도를 보이는 것으로 분석되었다. 위성강수를 이용한 홍수량 분석 결과 같은 시기에서 남능강 유역 출구점의 첨두유출량은 약 5,786m³/s로 분석되었다. 분석도구는 한국건설기술연구원에서 개발하여 운영중인 GRM 강우-유출 모형을 이용하였다. 향후 위성강수와 지점강수의 조합에 의한 다운스케일링 기법에 대한 연구를 수행하여 계측자료가 부족한 지역에서의 홍수량을 분석하는 연구를 진행할 계획이다.

• Journal of The Geomorphological Association of Korea
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• v.17 no.2
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• pp.29-39
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• 2010

Climate change recently causes rapid rises in sea level in Baramarae intertidal area and the rises present several socio-economic impacts to the affected area. We have assessed the vulnerability of the region by the rise of the sea level. Using quantitative GIS method on multi-temporal satellite images, we have first estimated the elevation (Digital Elevation Model: DEM) of Baramarae intertidal area and hence we were possibly able to identify the flooded areas under the IPCC SRES scenarios. As sea level rises by 20cm, 30cm, 40cm, 50cm and 60 cm, the estimated flooded areas of the tidal flat are 68ha, 85ha, 103ha, 121ha and 139ha, respectively. The most affected area is the tidal flat in Gagyeongju Village (Gonam-li, Gonam-myeon, Taean, Chungnam), because it has not only lower altitude but also, perhaps more significantly smooth slope. The potential affected areas are currently populated by farming of oysters and short-necked clams and therefore the areas expect significant economic loss by rise of sea level.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.509-520
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• 2012

The objective of this study was to estimate rice yield in Korea using satellite and meteorological data such as sunshine hours or solar radiation, and rainfall. Terra and Aqua MODIS (The MOderate Resolution Imaging Spectroradiometer) products; MOD13 and MYD13 for NDVI and EVI, MOD15 and MYD15 for LAI, respectively from a NASA web site were used. Relations of NDVI, EVI, and LAI obtained in July and August from 2000 to 2011 with rice yield were investigated to find informative days for rice yield estimation. Weather data of rainfall and sunshine hours (climate data 1) or solar radiation (climate data 2) were selected to correlate rice yield. Aqua NDVI at DOY 233 was chosen to represent maximum vegetative growth of rice canopy. Sunshine hours and solar radiation during rice ripening stage were selected to represent climate condition. Multiple regression based on MODIS NDVI and sunshine hours or solar radiation were conducted to estimate rice yields in Korea. The results showed rice yield of $494.6kg\;10a^{-1}$ and $509.7kg\;10a^{-1}$ in 2011, respectively and the difference from statistics were $1.1kg\;10a^{-1}$ and $14.1kg\;10a^{-1}$, respectively. Rice yield distributions from 2002 to 2011 were presented to show spatial variability in the country.