UNIST · Environmental Science
이 교수의 연구실은 원격 감지 기반 환경 모니터링과 지능형 데이터 분석을 중심으로, 기후 변화 대비, 수자원 관리, 농업 생산성 향상 및 해양 환경 보호를 목표로 합니다. 주로 위성 데이터(예: Landsat, MODIS, GOCI)를 활용해 수질, 식생, 증발산, 농경지 등을 정밀하게 분석하고, 머신러닝 기반의 모델링 기법(랜덤 포레스트, SVR, Cubist 등)을 통해 정량적 예측과 변화 탐지 기술을 개발하고 있습니다. 특히, 지역 기반 기상 예측 모델의 보정 및 고해상도 환경 지표 추정에 초점을 맞추고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
This study introduces change detection based on object/neighbourhood correlation image analysis and image segmentation techniques. The correlation image analysis is based on the fact that pairs of brightness values from the same geographic area (e.g. an object) between bi‐temporal image datasets tend to be highly correlated when little change occurres, and uncorrelated when change occurs. Five different change detection methods were investigated to determine how new contextual features could imp
Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate two major water quality indicators, chlorophyll-a (chl-a) and suspended particulate matter (SPM) concentrations, in coastal environments on the west coast of South Korea using Geostationary Ocean Color Imager (GOCI) satellite data. Three machine learning approaches including random forest, Cubist, and s
This study presented a MODIS 8-day 1 km evapotranspiration (ET) downscaling method based on Landsat 8 data (30 m) and machine learning approaches. Eleven indicators including albedo, land surface temperature (LST), and vegetation indices (VIs) derived from Landsat 8 data were first upscaled to 1 km resolution. Machine learning algorithms including Support Vector Regression (SVR), Cubist, and Random Forest (RF) were used to model the relationship between the Landsat indicators and MODIS 8-day 1 k
Abstract Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi‐model ensemble (MME) to
Abstract Hyperspectral analysis of vegetation involves obtaining spectral reflectance measurements in hundreds of bands in the electromagnetic spectrum. These measurements may be obtained using hand‐held spectroradiometers or hyperspectral remote sensing instruments placed onboard aircraft or satellites. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, biomass, chlorophyll, and leaf nutrient concentration which are used to understand ecosystem fu
