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School of Urban & Environmental Engineering
IRIS LAB

Intelligent Remote sensing and geospatial Information Systems

IRIS Lab

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Office: Engineering BLDG 2. Rm. 801-8

Tel: +82-52-217-2824

Fax: +82-52-217-2309

 

▶ 2014-present: Associate Professor, UNIST
▶ 2012-2014: Assistant Professor, UNIST
▶ 2007-2012: Assistant Professor, Environmental Resources Engineering, State University of New York, College of Environmental Science and Forestry

▶ 2006-2007: Research Associate, Center for GIS and Remote Sensing, University of South Carolina

▶ 2006: Ph.D. in Geography, University of South Carolina

▶ 2000: M.C.P. in Environmental Management, Seoul National University

▶ 1998: B.S. in Oceanography, Seoul National University

members

Research Assistant Professors

이주형(Juhyung Lee) julee@unist.ac.kr

Research Topic

Active passive microwave satellite retrieval

 

Post Doctors

박혜미(Haemi Park) haemipark@unist.ac.kr

Research Topic

Environmental remote sensing / Carbon cycle modeling

Favorite Quote

“The truth will set you free”

 

Combined MS + PhD

이상균(Sanggyun Lee) sglee@unist.ac.kr
Graduate Research Topic
Atmospheric / Polar Remote Sensing
Favorite Quote
“Everything you need is already inside”

김미애(Miae Kim) toa0710@naver.com
Graduate Research Topic
Atmospheric / Polar Remote Sensing
Favorite Quote
“The journey is the reward”

박선영(Seonyoung Park) qkrtjsdud5@unist.ac.kr
Research Topic
Remote sensing / GIS of disasters
Drought / Multi-sensor data fusion
Favorite Quote
‘coram Deo’

이정희(Junghee Lee) olive7861@unist.ac.kr
Research Topic
Coastal remote sensing / Multi-sensor data fusion
Favorite Quote
Only I can change my life. No one can do it for me.

하성현(Sunghyun Ha)
Research Topic
Atmospheric / Remote sensing of ocean and water qualiy
Favorite Quote
“As you grow older, you will find the only things you regret are the things you didn’t do.”

신민소 (Minso Shin) msshin1125@unist.ac.kr
Research Topic
Polar remote Sensing / Multi-sensor data fusion
Favorite Quote
Where there is a will there is a way

장예은(Yeeun Jang) jye610@unist.ac.kr
Research Topic
Remote sensing of disasters (Drought) / Multi-sensor data fusion

Favorite Quote
Not Why, But How!

장은나(Eunna Jang) dmssk1005@unist.ac.kr
Research Topic
Ocean, and water quality / Atmospheric
Favorite Quote
“Shine Your Light!”

박수민(Sumin Park) smpark113@unist.ac.kr
Research Topic
Remote sensing of disasters (Drought)
Favorite Quote
Perpetual optimism is a force multiplier. -Colin Powell-

심성문(Seongmun Shim) smsim@unist.ac.kr
Research Topic
Polar Remote Sensing
Favorite Quote
“I AM ALIVE !”

 

Alumni

Fang Fang (MS)
fangfang0829@unist.ac.kr
Graduate Research Topic
Forest characterization using remote sensing
Favorite Quote
“Keep your cherish first mood in your mind”

Manqi Li (Post-Master Researcher)
mli.manqi@gmail.com
Research Topic
Forest remote sensing; Geospatial modeling
Favorite Quote
“crowd wisdom” – James Surowieki –

한향선(Hyangsun Han) (Research Assistant Professor)
hyangsun@unist.ac.kr
Research Topic
Remote sensing / Interferometric SAR applications
Favorite Quote
Life is a matter of direction, not speed

research

Over the coming decades, society will face a range of emerging problems driven by climate variability and change and the stresses that the growth and migration of human populations pose. My research seeks to broaden and deepen our understanding of the Earth systems on which society depends using remote sensing and GIS technologies, and leverage this knowledge to better manage and control critical functions related to urban ecology, terrestrial and coastal ecosystems, water resources, natural and man-made disasters, and carbon sequestration. Specific interests include: remote sensing of terrestrial and coastal ecosystem responses to climate variability, polar remote sensing, remote sensing data assimilation and applications, and natural and man-made disaster (e.g., floods, droughts, forest fires, landslides, and oil spills) monitoring and assessment using remote sensing and GIS.

 

On-going research projects

● Development of a drought monitoring system through integrated modeling of multi-sensor data

● Development of a remote sensing-based integrated modeling and monitoring system for sustainable management of forest ecosystems

● Forest parameter estimation using airborn LiDAR remote sensing

● Development of the land-atmosphere carbon cycle model for the GAIA climate prediction simulator

● Assessment of impacts of climate change and complex disasters

 

Past research projects

● Spatiotemporal variation of forest carbon stocks by land use change in urban areas

● Characterization of montane forest ecosystems using advanced remote sensing technology

● Development of tools synthesizing advanced machine learning approaches for remote sensing classification

● Forest change in the Adirondacks over 40 years of multiple stresses

● Impacts of green infrastructure on directly connected impervious cover and spectral signatures

publications

  1. Rhee, J., Im, J. (2014). Estimating high-resolution air temperature for regions with limited in-situ data.Remote Sensing, 6, 7360-7378.
  2. Lu, Z., Im, J., Rhee, J., Hodgson, M.E. (2014). Building type classification using spatial attributes derived from LiDAR remote sensing data, Landscape and Urban Planning, 130, 134-148.
  3. Kang, D., Im, J., Lee, M., Quackenbush, L.J. (2014). The MODIS ice surface temperature product as an indicator of sea ice minimum over the Arctic Ocean. Remote Sensing of Environment, 152, 99-108.
  4. Kang, D., Lee, M., Im, J., Kim, D., Kim, H., Kang, H., Schubert, S., Arribas, A., MacMachlan, C. (2014). Prediction of the Arctic Oscillation in boreal winter by dynamical seasonal forecasting systems. Geophysical Research Letters, 41, 3577-3585.
  5. Kim, Y., Im, J., Ha, H., Choi, J., Ha, S. (2014). Machine learning approaches to coastal water quality monitoring using GOCI satellite data, GIScience and Remote Sensing, 51(2), 158-174.
  6. Li, M., Im, J., Liu, T., Quackenbush, L.J. (2014). Forest biomass and carbon stock quantification using full waveform LiDAR data in montane forests, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, in press.
  7. Li, M., Im, J., Beier, C. (2013). Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest, GIScience and Remote Sensing, 50(4), 361-384.
  8. Jung, J., Kim, S., Hong, S., Kim, K., Kim, E., Im, J., and Heo, J. (2013). Effects of national forest inventory location error on forest carbon stock estimation using k-nearest neighbor algorithm, ISPRS Journal of Photogrammetry and Remote Sensing, 81:82-92.
  9. Lu, Z., Im, J., Quackenbush, L.J., Yoo, S. (2013). Remote sensing based house value estimation using an optimized regional regression model, Photogrammetric Engineering & Remote Sensing, in press.
  10. Yoo, S., Im, J., Wagner, J.E. (2012). Variable selection for hedonic modeling using machine learning approaches: A case study in Onondaga County, NY, USA, Landscape and Urban Planning, 107: 293-306.
  11. Gleason, C., Im, J. (2012). Forest biomass estimation from airborne LiDAR data using machine learning approaches, Remote Sensing of Environment, 125: 80-91.
  12. Gleason, C., Im, J. (2012). A fusion approach for tree crown delineation from LiDAR data,Photogrammetric Engineering & Remote Sensing, 78(7): 679-692.
  13. Gong, B., Im, J., Jensen, J.R., Coleman, M., Rhee, J., Nelson, E. (2012). Characterization of forest crops with a range of nutrient and water treatments using AISA hyperspectral imagery, GIScience and Remote Sensing, 49(4): 463-497.
  14. Zhang, W., Quackenbush, L.J., Im, J., Zhang, L. (2012). Indicators for separating undesirable and well-delineated tree crowns from high spatial resolution imagery, International Journal of Remote Sensing, 33(17): 5451-5472.
  15. Im, J., Lu, Z., Rhee, J., Jensen, J.R. (2012). Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes, Geocarto International, 27(5): 373-393.
  16. Im, J., Jensen, J.R., Jensen, R.R., Gladden, J., Waugh, J., Serrato, M. (2012). Vegetation cover analysis of hazardous waste sites in Utah and Arizona using hyperspectral remote sensing, Remote Sensing, 4(2), 327-353.
  17. Im, J., Lu, Z., Rhee, J., Quackenbush, L.J. (2012). Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data, Remote Sensing of Environment, 117, 102-113.
  18. Lu, Z., Im, J., Quackenbush, L.J. (2011). A volumetric approach to population estimation using LiDAR remote sensing, Photogrammetric Engineering & Remote Sensing, 77(11): 1145-1156.
  19. Gleason, C., Im, J. (2011). A review of remote sensing of forest biomass and biofuel: options for small area applications, GIScience and Remote Sensing, 48(2): 141-170.
  20. Mountrakis, G., Im, J., Ogole, C. (2011). A review of support vector machines in remote sensing,ISPRS Photogrammetry and Remote Sensing, 66: 247-259.
  21. Im, J., Lu, Z., Jensen, J.R. (2011). A genetic algorithm approach to moving threshold optimization for binary change detection, Photogrammetric Engineering & Remote Sensing, 77(2): 167-180.
  22. Gong, B., Im, J., Mountrakis, G. (2011). An artificial immune network approach to multi-sensor land use/land cover classification, Remote Sensing of Environment, 115: 600-614.
  23. Lu, Z., Im, J., Quackenbush, L.J., Halligan, K. (2010). Population estimation based on multi-sensor data fusion, International Journal of Remote Sensing, 31(21): 5587-5604.
  24. Rhee, J., Im, J., Carbone, G.J. (2010). Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data, Remote Sensing of Environment, 114: 2875-2887.
  25. Wang, Z., Jensen, J.R., Im, J. (2010). An automatic region-based image segmentation algorithm for remote sensing applications, Environmental Modelling and Software, 25: 1149-1165.
  26. Ke, Y., Quackenbush, L.J., Im, J. (2010). Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification, Remote Sensing of Environment, 114: 1141-1154.
  27. Im, J., Hodgson, M.E. (2009). Characteristics of search spaces for identifying optimum thresholds in change detection studies, GIScience and Remote Sensing, 46(3): 249-272.
  28. Im, J., Rhee, J., Jensen, J.R. (2009). Enhancing binary change detection performance using a moving threshold window (MTW) approach, Photogrammetric Engineering & Remote Sensing, 75(8): 951-962.
  29. Im, J., Jensen, J.R., Coleman, M., Nelson, E. (2009). Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments, Geocarto International, 24(4): 293-312.
  30. Jensen, J.R., Hodgson, M.E., Garcia-Quijano, M., Im, J., Tullis, J.A. (2009). A remote sensing and GIS-assisted spatial decision support system for hazardous waste site monitoring, Photogrammetric Engineering & Remote Sensing, 75(2): 169-178.
  31. Im, J., Jensen, J.R. (2008). Hyperspectral remote sensing of vegetation, Geography Compass, 2(6): 1943-1961.
  32. Rhee, J., Im, J., Carbone, G.J., Jensen, J.R. (2008). Delineation of climate regions using in-situ and remotely-sensed data for the Carolinas, Remote Sensing of Environment, 112: 3099-3111.
  33. Im, J., Jensen, J.R., Hodgson, M.E. (2008). Optimizing the binary discriminant function in change detection applications, Remote Sensing of Environment, 112: 2761-2776.
  34. Im, J., Jensen, J.R., Hodgson, M.E. (2008). Object-based classification using high posting density lidar data, GIScience and Remote Sensing, 45(2): 209-228.
  35. Im, J., Jensen, J.R., Tullis, J.A. (2008). Object-based change detection using correlation image analysis and image segmentation techniques, International Journal of Remote Sensing, 29(2): 399-423.
  36. Im, J., Rhee, J., Jensen, J.R., Hodgson, M.E. (2007). An automated binary change detection model using a calibration approach, Remote Sensing of Environment, 106: 89-105.
  37. Rhee, J. Im, J. (2006). Non-point source critical area analysis and embedded RUSLE model development for soil loss management in the Congaree river basin in South Carolina, USA, The Journal of GIS Association of Korea, 14(4): 363-377.
  38. Im, J., 2006. Neighborhood correlation image analysis for change detection using different spatial resolution imagery, Korean Journal of Remote Sensing, 22(5): 337-350.
  39. Jensen, J.R., Garcia-Quijano, M., Hadley, B., Im, J., Wang, W., Nel, A.L., Teixeira, E., Davis, B.A. (2006). Remote sensing agricultural crop type for sustainable development in South Africa, Geocarto International, 21(2): 5-18.
  40. Im, J., Jensen, J.R. (2005). A change detection model based on neighborhood correlation image analysis and decision tree classification, Remote Sensing of Environment. 99: 326-340.
  41. Im, J., Park, C.H. (2004). Neural networks approach to fire severity mapping from a single post-fire Landsat 7 ETM+ imagery, Korean Journal of Remote Sensing, 20(1): 23-38.
  42. Im, J., Jeong, J.C. (1999). Comparison between neural network and conventional statistical analysis methods for estimation of water quality using remote sensing, Journal of the Korean Society of Remote Sensing, 15(2): 107-118.

IRIS LAB

+82-52-217-2824