Jungho Im
Ulsan National Institute of Science and Technology · 環境科学
研究室紹介
Professor Jungho Im's research lab specializes in remote sensing and environmental monitoring, focusing on the development and application of advanced machine learning techniques for Earth observation. The lab emphasizes land surface and coastal water quality monitoring, change detection, evapotranspiration estimation, and crop classification using multi-source satellite data such as Landsat, MODIS, GOCI, and SAR sensors. Key research directions include improving the accuracy of environmental parameter estimation through hybrid modeling, data fusion, and contextual analysis, particularly in urban and coastal regions of South Korea. The lab integrates optical and radar remote sensing with in-situ measurements to support operational environmental monitoring and climate resilience strategies.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15This 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
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
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 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