• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.1
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• pp.38-47
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• 2002

This paper demonstrates a regional analysis of landslide occurrence potential by applying geographic information system to the Kumi City selected as a pilot study area. The estimate criteria related to natural and humane environmental factors which affect landslides were first established. A slope map and a aspect map were extracted from DEM, which was generated from the contour layers of digital topographic maps, and a NDVI vegetation map and a land cover map were obtained through satellite image processing. After the spatial database was constructed, indexes of landslide occurrence potential were computed and then a few landslide-potential areas were extracted by an overlay method. It was ascertained that there are high landslide-potential at areas of about 30% incline, aspects including either south or east at least, adjacent to water areas or pointed end of the water system, in or near fault zones, covered with medium vegetable. For more synthetic and accurate analysis, soil data, forest data, underground water level data, meteorological data and so on should be added to the spatial database.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.100-116
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• 2019

Urban green spaces, as a means to mitigate social problems and environmental risks, are getting more attention in evaluating urban environment. The inequity of green space distribution is becoming a major issue in urban planning and management. This study investigated the characteristics of green space in 3 districts (Jung-gu, Dongdaemun-gu, Seongdong-gu), that are composed of 46 administrative divisions in central Seoul, to analyze the environmental equity of urban green spaces. The correlations between the amount of green space, including the coverage of street trees, and the socioeconomic status of each administrative division were analyzed. To deduce the effects of plant coverage on the urban temperature regime, the relationship between the normalized difference of vegetation index (NDVI) and land surface temperature (LST) was analyzed. The research revealed that the mean NDVI of an administrative division was negatively correlated with the percentage of basic living recipients and disabled people. The LST of a division with low NDVI was higher due to the lack of green coverage. Such environmental inequities were closely related to residential building type, which was strongly affected by the economic status of residents. The LST of an apartment area was $2.0^{\circ}C$ lower than that of single-family houses and multi-housing areas. This is expected as the average NDVI of the apartment area was more than twice as high as the other environments considered in this study. The inequity can be exacerbated without urban planning which is deliberately designed to reduce it.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1447-1460
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• 2021

Urbanization increases the amount of impervious surface and artificial heat emission, resulting in urban heat island (UHI) effect. Local climate zones (LCZ) are a classification scheme for urban areas considering urban land cover characteristics and the geometry and structure of buildings, which can be used for analyzing urban heat island effect in detail. This study aimed to examine the UHI effect by urban structure in Suwon and Daegu using the LCZ scheme. First, the LCZ maps were generated using Landsat 8 images and convolutional neural network (CNN) deep learning over the two cities. Then, Surface UHI (SUHI), which indicates the land surface temperature (LST) difference between urban and rural areas, was analyzed by LCZ class. The results showed that the overall accuracies of the CNN models for LCZ classification were relatively high 87.9% and 81.7% for Suwon and Daegu, respectively. In general, Daegu had higher LST for all LCZ classes than Suwon. For both cities, LST tended to increase with increasing building density with relatively low building height. For both cities, the intensity of SUHI was very high in summer regardless of LCZ classes and was also relatively high except for a few classes in spring and fall. In winter the SUHI intensity was low, resulting in negative values for many LCZ classes. This implies that UHI is very strong in summer, and some urban areas often are colder than rural areas in winter. The research findings demonstrated the applicability of the LCZ data for SUHI analysis and can provide a basis for establishing timely strategies to respond urban on-going climate change over urban areas.

• Spatial Information Research
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• v.8 no.2
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• pp.275-288
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• 2000

The purpose of this study is to analyze relationship between the NDVI change pattern and landcover change pattern in the City of Sungnam during 1985 and 1996. The results of this study are as follows; (1) NDVI of the level 6 and 7 is decreased and the level 5 is increased in the area where Forst area changed to the other land cover during 1985 and 1996. (2) In the area where Agricultural-Pasture changed to forest, NDVI level became higher certainly during that time. But in the area where there has been changed from Agricultural-Pasture to Urban or built-up, Agricultural-Pasture to Barren land, the level of NDVI is decreased. (3) In the Urban or built-up to other land, or built-up the level of NDVI is increased. (4) In the area where Barren land changed to other land cover, the level of NDVI is increased.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.471-479
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• 2011

Evapotranspiration (ET) including evaporation from a land surface and transpiration from photosynthesis of vegetation is a hydrological factor that has an important role in water cycle. However, there is a limitation to understand it due to heterogeneity of land cover and vegetation. In this study, Mapping EvapoTRanspiration with Internalized Calibration (METRIC) model, one of the energy balance models, and MODerate resolution Imaging Spectroradiometer (MODIS) satellite based well-known Penman-Monteith algorithm were compared. Two ET maps were categorized and compared by land cover classification. The results represented overall applicability of the two models with the highest correlation coefficients in needleleaf and broadleaf forests. This study will be useful to estimate remote sensing based ET maps with high resolution and to figure out spatio-temporal variability and seasonal changes.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.6
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• pp.1-12
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• 2006

Remote sensing cannot provide a direct measurement of vegetation index (VI) but it can provide a reasonably good estimate of vegetation index, defined as the ratio of satellite bands. The monitoring of vegetation in nearby urban regions is made difficult by the low spatial resolution and temporal resolution image captures. In this study, enhancing spatial resolution method is adapted as to improve a low spatial resolution. Recent studies have successfully estimated normalized difference vegetation index (NDVI) using improved resolution method such as from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra satellite. Image enhancing spatial resolution is an important tool in remote sensing, as many Earth observation satellites provide both high-resolution and low-resolution multi-spectral images. Examples of enhancement of a MODIS multi-spectral image and a MODIS NDVI image of Cheongju using a Landsat TM high-resolution multi-spectral image are presented. The results are compared with that of the IHS technique is presented for enhancing spatial resolution of multi-spectral bands using a higher resolution data set. To provide a continuous monitoring capability for NDVI, in situ measurements of NDVI from paddy field was carried out in 2004 for comparison with remotely sensed MODIS data. We compare and discuss NDVI estimates from MODIS sensors and in-situ spectroradiometer data over Ochang plain region. These results indicate that the MODIS NDVI is underestimated by approximately 50%.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1097-1101
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• 2008

본 연구에서는 SLURP 수문모형을 이용하여 미래기후와 예측된 토지이용자료 및 식생의 활력도를 고려한 상태에서 하천유역의 유출 및 증발산량에 미치는 영향을 분석하였다. 경안천 상류유역($260.04\;km^2$)을 대상유역으로 선정하여 4개년(1999-2002) 동안의 일별 유출량 자료를 바탕으로 모형의 보정(1999-2000)과 검증(2001-2002)을 실시하였다. 모형의 보정 및 검정 결과 Nash-Sutcliffe 모형효율은 0.79에서 060의 범위로 나타났다. 미래 기후자료는 IPCC(Intergovernmental Panel on Climate Change)에서 제공하는 A1B 기후변화시나리오의 MIROC3.2 hires, ECHAM5-OM, HadCM3 모델의 결과값을 이용하였다. 먼저 과거 30년 기후자료(1977-2006, baseline)를 바탕으로 각 모델별 20C3M(20th Century Climate Coupled Model)의 모의 결과값을 이용하여 강수와 온도를 보정한 뒤 Change Factor Method로 Downscaling하였다. 미래 기후자료는 2020s(2010-2039), 2050s(2040-2069), 2080s(2070-2099)의 세 기간으로 나누어 분석하였다. 미래 토지이용은 과거 시계열 Landsat 토지이용도를 이용하여 CA-Markov기법으로 예측된 토지이용을 사용하였으며, 미래의 식생정보 예측을 위하여 NOAA/AVHRR 위성영상으로부터 추출된 월별 NDVI(1998-2002)와 월평균기온간의 선형 회귀식을 도출하여 미래의 식생지수 정보를 추정하였다. 모형의 적용결과, 미래기후변화에 따른 연평균 하천유출은 현재보다 최대 2020s는 23.9%, 2050s는 40.7%, 2080s는 39.5% 증가하였다. 봄 강수량 패턴의 변화로 유출량 증가하는 것으로 나타났으며 여름에는 유출량은 감소하고 증발산량은 증가하는 결과를 보였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.472-472
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• 2011

탁도는 물의 상대적인 흐림 정도를 나타내는 척도로서 이는 빛이 물을 통과할 때 산란시키는 부유고형물질 때문에 발생한다. 토양침식에 의하여 지표수로 유입된 토사와 광물질은 하천의 수송 및 퇴적 과정을 거치며 이동한다. 이때 하상 퇴적물은 바닥으로부터 먹이를 찾는 유기체에 의해 뒤섞이며 입자들은 일정기간동안 물의 흐름에 의해 부유상태로 남아 있게 되고, 유입되는 영양소와 빛에 의하여 성장하는 조류 또한 탁도를 증가시키는 원인이 된다. 이러한 부유물질의 증가는 수중에 태양복사에너지 전달을 방해하여 수중생태계의 먹이사슬과 저서생물의 서식환경에 많은 영향을 미치고, 수표면 온도 또한 태양으로부터 열을 흡수하는 표면 근처의 부유물질에 의해 증가하여 용존산소의 양에도 영향을 미친다. 따라서 수체내 분포하고 있는 부유물질의 종류와 양 및 공간적 분포 파악은 수질문제와 재난 예방 및 생물의 서식환경 문제를 파악하고 해결하는 데 매우 중요하다. 그러나 부유물질에 부착되어 있는 영양소, 금속, 살충제 등은 물 순환 시스템을 통하여 끊임없이 운반되고 상류유역의 흐름 조건에 따라 시공간적으로 변화하기 때문에 이를 규명하는 것이 매우 어려운 실정이다. 이러한 문제를 해결하기 위하여 광역적인 탁수환경의 분석방법으로 원격탐사(Remote Sensing, 이후 RS) 기법을 이용한 방법이 제안되고 있다. 이미 선진국에서는 광역수계의 수질관리를 위해 RS 기법을 이용하여 신속하고 정확한 수질상태 파악을 시도하고 있으며, 우리나라에서도 KOMPSAT 발사를 계기로 RS 관련 기술이 비약적으로 진화하고 있다. 그러나 RS 데이터를 활용하는데 필수적인 분광학적 특성 규명에 대한 연구는 대부분 식생과 토양에 한정되어 있으며 수체에 대한 연구는 현장조사의 어려움으로 인하여 상당히 제한적인 수준이다. 따라서 본 연구에서는 탁도의 변화에 따른 분광반사 특성을 휴대용 분광복사계를 이용하여 규명하고, 이를 Landsat 위성영상에 적용하여 새만금 유역을 대상으로 완공 직후인 2006년부터 2010년까지의 탁수환경을 모니터링 하였다. 그 결과 새만금 유역 탁수환경을 정성적으로 확인할 수 있었으며, 이를 이용하여 탁수환경 연구에 RS 기법이 효과적임을 제시하고자 한다.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.2
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• pp.108-120
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• 2008

This paper discusses the prediction of deforestation areas using probability models from forest census database, Geographic information system (GIS) database and the land cover database. The land cover data was analyzed using remotely-sensed (RS) data of the Landsat TM data from 1989 to 2001. Over the analysis period of 12 years, the deforestation area was about 40ha. Most of the deforestation areas were attributable to road construction and residential development activities. About 80% of the deforestation areas for residential development were found within 100m of the road network. More than 20% of the deforestation areas for forest road construction were within 100m of the road network. Geographic factors and vegetation change detection (VCD) factors were used in probability models to construct deforestation occurrence map. We examined the size effect of area partition as training area and validation area for the probability models. The Bayes model provided a better deforestation prediction rate than that of the regression model.