사진

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Im, Jungho (임정호)

Department
Department of Urban and Environmental Engineering(도시환경공학과)
Website
http://iris.unist.ac.kr/
Lab
Intelligent Remote sensing and geospatial Information Science Lab. (환경원격탐사/인공지능 연구실)
Research Keywords
환경원격탐사, 인공지능, Remote sensing, Geospatial modeling, Disaster monitoring and management, artificial intelligence
Research Interests
The IRIS lab utilizes remote sensing, GIS modeling, and artificial intelligence techniques to broaden and deepen our understanding of the Earth science under climate variability/change, and leverages this knowledge to better manage and control critical functions related to terrestrial, coastal, and polar ecosystems, natural and man-made disasters, water resources, and carbon sequestration.
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Issue DateTitleAuthor(s)TypeViewAltmetrics
2015-07Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observationsKe, Yinghai; Im, Jungho; Lee, Junghee, et alARTICLE680 Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations
2015-03Landfast sea ice monitoring using multisensor fusion in the AntarcticKim, Miae; Im, Jungho; Han, Hyangsun, et alARTICLE877 Landfast sea ice monitoring using multisensor fusion in the Antarctic
2014-10Building type classification using spatial and landscape attributes derived from LiDAR remote sensing dataLu, Zhenyu; Im, Jungho; Rhee, Jinyoung, et alARTICLE894 Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data
2014-09The MODIS ice surface temperature product as an indicator of sea ice minimum over the Arctic OceanKang, Daehyun; Im, Jungho; Lee, Myong-In, et alARTICLE807 The MODIS ice surface temperature product as an indicator of sea ice minimum over the Arctic Ocean
2014-08Estimating High Spatial Resolution Air Temperature for Regions with Limited in situ Data Using MODIS ProductsRhee, Jinyoung; Im, JunghoARTICLE667 Estimating High Spatial Resolution Air Temperature for Regions with Limited in situ Data Using MODIS Products
2014-07Forest Biomass and Carbon Stock Quantification Using Airborne LiDAR Data: A Case Study Over Huntington Wildlife Forest in the Adirondack ParkLi, Manqi; Im, Jungho; Quackenbush, Lindi J., et alARTICLE804 Forest Biomass and Carbon Stock Quantification Using Airborne LiDAR Data: A Case Study Over Huntington Wildlife Forest in the Adirondack Park
2014-05Prediction of the Arctic Oscillation in boreal winter by dynamical seasonal forecasting systemsKang, Daehyun; Lee, Myong-In; Im, Jungho, et alARTICLE760 Prediction of the Arctic Oscillation in boreal winter by dynamical seasonal forecasting systems
2014-03Machine learning approaches to coastal water quality monitoring using GOCI satellite dataKim, Yong Hoon; Im, Jungho; Ha, Ho Kyung, et alARTICLE619 Machine learning approaches to coastal water quality monitoring using GOCI satellite data
2013-09Remote sensing-based house value estimation using an optimized regional regression modelLu, Zhenyu; Im, Jungho; Quackenbush, Lindi J., et alARTICLE664 Remote sensing-based house value estimation using an optimized regional regression model
2013-08Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife ForestLi, Manqi; Im, Jungho; Beier, ColinARTICLE781 Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest
2013-07Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithmJung, Jaehoon; Kim, Sangpil; Hong, Sungchul, et alARTICLE825 Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm
2012-10Forest biomass estimation from airborne LiDAR data using machine learning approachesGleason, Colin J.; Im, JunghoARTICLE765 Forest biomass estimation from airborne LiDAR data using machine learning approaches
2012-09Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NYYoo, Sanglim; Im, Jungho; Wagner, John E.ARTICLE710 Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY
2012-09Indicators for separating undesirable and well-delineated tree crowns in high spatial resolution imagesZhang, Wenhua; Quackenbush, Lindi J.; Im, Jungho, et alARTICLE663 Indicators for separating undesirable and well-delineated tree crowns in high spatial resolution images
2012-08Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapesIm, Jungho; Lu, Z.; Rhee, J., et alARTICLE514 Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes
2012-07A Fusion Approach for Tree Crown Delineation from Lidar DataGleason, Colin J.; Im, JunghoARTICLE1066
2012-07Characterization of Forest Crops with a Range of Nutrient and Water Treatments Using AISA Hyperspectral ImageryGong, Binglei; Im, Jungho; Jensen, John R., et alARTICLE682 Characterization of Forest Crops with a Range of Nutrient and Water Treatments Using AISA Hyperspectral Imagery
2012-02Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor dataIm, Jungho; Lu, Zhenyu; Rhee, Jinyoung, et alARTICLE852 Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data
2012-02Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote SensingIm, Jungho; Jensen, John R.; Jensen, Ryan R., et alARTICLE638 Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote Sensing
2011-11A Volumetric Approach to Population Estimation Using Lidar Remote SensingLu, Zhenyu; Im, Jungho; Quackenbush, LindiARTICLE640

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