• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.140-140
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• 2019

본 연구에서는 Terra MODIS 위성자료와 Tensorflow를 활용해 1 km 공간 해상도의 토양수분을 산정하는 알고리즘을 개발하고, 국내 관측 자료를 활용해 검증하고자 한다. 토양수분 모의를 위한 입력 자료는 Terra MODIS NDVI(Normalized Difference Vegetation Index)와 LST(Land Surface Temperature), GPM(Global Precipitation Measurement) 강우 자료를 구축하고, 농촌진흥청에서 제공하는 1:25,000 정밀토양도를 기반으로 모의하였다. 여기서, LST와 GPM의 자료는 기상청의 종관기상관측지점의 LST, 강우 자료와 조건부합성(Conditional Merging, CM) 기법을 적용해 결측치를 보간하였고, 모든 위성 자료의 공간해상도를 1 km로 resampling하여 활용하였다. 토양수분 산정 기술은 인공 신경망(Artificial Neural Network) 모형의 딥 러닝(Deep Learning)을 적용, 기계 학습기반의 패턴학습을 사용하였다. 패턴학습에는 Python 라이브러리인 TensorFlow를 사용하였고 학습 자료로는 농촌진흥청 농업기상정보서비스에서 101개 지점의 토양수분 자료(2014 ~ 2016년)를 활용하고, 모의 결과는 2017 ~ 2018년까지의 자료로 검증하고자 한다.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.

• Journal of Wetlands Research
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• v.19 no.1
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• pp.122-131
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• 2017

Remotely sensed satellite data is easier to collect and better to represent local phenomenon than a site data. So they can contribute to the activation and development of many research. However, it is necessary to improve spatial resolution suitable for application in the area of complex topography such as the Korean Peninsula. In this study, finer resolution Land Surface Temperature (LST) was downscaled from 4 km to 500 m by combining GOCI with MI data of Communication, Ocean and Meteorological Satellite (COMS). It was then statistically analyzed with LST data observed from the ASOS sites to validate its applicability. As a result, it was found that the errors decreased and correlation increased at the most validation sites, also the spatial distribution analysis showed a similar tendency but it expressed the complicated terrain better. This study suggests possibility of expanding the application range of COMS by producing finer resolution data available in various studies.

• Journal of Wetlands Research
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• v.22 no.4
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• pp.275-285
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• 2020

In this study, after estimating VCI (Vegation Condition Index), TCI (Thermal Condition Index) and VHI (Vegetation Health Index) from the NDVI (Normalized Differentiation Vegetation Index) and LST (Land Surface Temperature) remotely sensed at major sites in Korea during the 2001-1919 period, the correlation between these indices and various drought indices is analyzed for the purpose of assessing the effects of ecological drought. The relative impact of VCI and TCI on vegetation health was found to vary by region. The effects of drought on vegetation in Korea's forest areas could be more clearly identified in TCI than in VCI. It is suggested that the revised VHI, reflecting the relative influence of VCI and TCI, can better explain the effects of drought on vegetation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.2
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• pp.140-148
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• 2007

The remote sensing observations of land surface properties are inevitably influenced by the landscape heterogeneity. In this paper, we introduce a geostatistical technique to provide a quantitative interpretation of landscape heterogeneity in terms of key land surface parameters. The study areas consist of the two KoFlux sites: (1) the Gwangneung site, covered with temperate mixed forests on a complex terrain, and (2) the Haenam site with mixed croplands on a relatively flat terrain. The semivariogram and fractal analyses were performed for both sites to characterize the spatial heterogeneity of two radiation parameters, i.e., land surface temperature (LST) and albedo. These parameters are the main factors affecting the reflected longwave and shortwave radiation components from the two study sites. We derived them from the high-resolution Landsat ETM+ satellite images collected on 23 Sep. 2001 and 14 Feb. 2002. The results of our analysis show that the characteristic scales of albedo was >1 km at the Gwangneung site and approximately 0.3 km at the Haenam site. For LST, the scale of heterogeneity was also >1 km at the Gwangneung site and >0.6 to 1.0 km at the Haenam site. At both sites, there was little change in the characteristic scales of the two parameters between the two different seasons.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.143-156
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• 2019

The purpose of this study is to examine whether the Sinseo Innovation City of Daegu has been eco-friendly developed by analyzing changes in NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature) and conducting field surveys. Using Landsat satellite images, it compares NDVI and LST changes between the years of 2008 and 2018. The results of the study are as follows. First, the NDVI has decreased by 0.07 and the zLST has increased by $0.85^{\circ}C$ over the past 10 years. Second, districts with lower NDVI and higher zLST were concentrated with infrastructure with impermeable materials. Districts with higher NDVI and lower zLST were utilized urban design techniques such as permeable parking lot, green roof, and permeable pavement. Third, districts with higher NDVI and lower zLST were applied eco-friendly planning items properly by district unit plan guideline. It is meaningful to suggest planing directions and urban planning elements considering the environmental friendly development.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.741-743
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• 2003

This study focuses on monitoring the surface UHI in a tropical city of Bangkok in both spatial and temporal dimensions based on MODIS- and TM -derived land surface temperature (LST). The spatial extension and magnitude of the surface UHI are explored for days and nights as well as its variations through the dry (least-clouded) season. Surface UHI growth between 1993 and 2002 is mapped using highresolution LANDSAT TM thermal bands. UHI patterns are, then, analyzed in association with land/vegetation covers derived from high-resolution ETM+ and ASTER satellites and ancillary data.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.179-188
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• 2022

Global warming causes climate change and increases extreme weather events worldwide, and the occurrence of heatwaves and droughts is also increasing in Korea. For the monitoring of extreme weather, various satellite data such as LST (Land Surface Temperature), TCI (Temperature Condition Index), NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), and VHI (Vegetation Health Index) have been used. VHI, the combination of TCI and VCI, represents the vegetation stress affected by meteorological factors like precipitation and temperature and is frequently used to assess droughts under climate change. TCI and VCI require historical reference values for the LST and NDVI for each date and location. So, it is complicated to produce the VHI from the recent satellite GK2A (Geostationary Korea Multi-Purpose Satellite-2A). This study examined the retrieval of VHI using GK2A AMI (Advanced Meteorological Imager) by referencing the historical data from VIIRS (Visible Infrared Imaging Radiometer Suite) NDVI and LST as a proxy data. We found a close relationship between GK2A and VIIRS data needed for the retrieval of VHI. We produced the TCI, VCI, and VHI for GK2A during 2020-2021 at intervals of 8 days and carried out the interpretations of recent extreme weather events in Korea. GK2A VHI could express the changes in vegetation stress in 2020 due to various extreme weather events such as heatwaves (in March and June) and low temperatures (in April and July), and heavy rainfall (in August), while NOAA (National Oceanic and Atmospheric Administration) VHI could not well represent such characteristics. The GK2A VHI presented in this study can be utilized to monitor the vegetation stress due to heatwaves and droughts if the historical reference values of LST and NDVI can be adjusted in a more statistically significant way in the future work.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.139-139
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• 2019

지구 온난화 및 기후변화로 인해 비롯된 전 지구적인 기온 상승은 가뭄, 폭염, 한파 등의 이상 기후 현상을 야기하여 인류의 생존을 위협하는 환경 문제로 대두되고 있다. 이와 같은 기후변화 및 이상기후 현상을 이해하고 파악하기 위해서는 정확하고 상세한 기온 정보가 필수적이다. 우리나라는 기상청에서 전국 590개소의 기상관측장비로 기온 정보를 생산하고 있지만 산림이 약 70%를 차지하는 복잡한 지형을 가지고 있어 지상관측밀도의 공간적 제약이 발생해 상세하고 균일한 기온 정보 생산에 제약이 있다. 이러한 단점을 극복하기 위해 본 연구에서는 위성으로 측정한 지표면 온도(Land Surface Temperature, LST) 자료와 다중선형회귀모형(Multiple Linear Regression Model)을 활용해 두 자료간의 상관관계를 파악하고 지상기온을 예측하고자 한다. 위성자료로 Terra 및 Aqua MODIS 위성의 1000m 공간해상도를 가진 일별 LST자료 MOD11A1, MYD11A1의 Daytime 자료를 각각 2000년부터 2018년까지 총 19년의 기간에 대해 구축하였으며, 전국 92개의 기상청 관측소로부터 최고, 최저 기온 자료를 동 기간에 대해 구축하였다. LST를 이용한 이상기온 예측 알고리즘은 python을 이용해 구현하였으며 예측 결과는 실제 기온 자료를 통해 검증하였다. 또한, 예측 기온 자료의 연대별, 순별(상, 중, 하순) 분석을 실시하고, 2018년 극한 폭염 및 한파(2017년 12월~2018년 2월)의 예측 가능성을 검토하여 연구 결과에 대한 다양한 활용방안을 제시하고자 한다.

• Journal of Environmental Science International
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• v.16 no.1
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• pp.65-71
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• 2007

This study aims to estimate a urban heat island potential distribution based on the land-use types using Landsat TM(1100 LST August 2004) and AWS data in Daegu. The heat island potential is defined as a difference between surface temperature and air-temperature at each place. The study area was selected as about $900km^2$ square including Daegu metropolitan area. Land-use data obtained by dividing all of Daegu metropolitan area in- to 1-km-square three types of maps were prepared in the 1960s, 1970s and 2000s respectively. Land-use types were classified into 5 categories. Forest and farm lands have been reduced at a wide range during 40 years. Most of those changed into urban area. The heat island potential distribution presented a striking contrasts according to land-use types. For example, the heat island potential of urban area was higher than $10^{\circ}C$ in comparison to those of water or paddy rice areas.