Publications

Journals

Remote Sensing of Environment
International Journal of Remote Sensing
Photogrammetric Engineering & Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing
GeoCarto International
GIScience and Remote Sensing
Computer and Geoscience
Environmental Modelling & Software
International Journal of Geographical Information Science

Referred Journal Articles

• Gleason, C. and J. Im, 2012. Forest biomass estimation from airborne LiDAR data using machine learning approaches, Remote Sensing of Environment, in re-review.
• Yoo, S., J. Im, and J.E. Wagner, 2012. Variable selection for hedonic modeling using machine learning approaches: A case study in Onondaga County, NY, USA, Landscape and Urban Planning, in re-review.
• Gleason, C. and J. Im, 2012. A fusion approach for tree crown delineation from LiDAR data, Photogrammetric Engineering & Remote Sensing, in press.
• Zhang, W., L.J. Quackenbush, J. Im, and L. Zhang, 2012. Indicators for separating undesirable and well-delineated tree crowns from high spatial resolution imagery, International Journal of Remote Sensing, in press.
• Gong, B., J. Im, J.R. Jensen, M. Coleman, and E. Nelson, 2012. Characterization of forest crops with a range of nutrient and water treatments using AISA hyperspectral imagery, GIScience and Remote Sensing, in press.
• Im, J., Z. Lu, J. Rhee, and J. Jensen, 2012. Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes, Geocarto International, in press, doi:10.1080/10106049.2011.642898.
• Im, J., J.R. Jensen, R.R. Jensen, J. Gladden, J. Waugh, and M. Serrato, 2012. Vegetation cover analysis of hazardous waste sites in Utah and Arizona using hyperspectral remote sensing, Remote Sensing, 4, 327-353.
• Im, J., Z. Lu, J. Rhee, and L.J. Quackenbush, 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.
• Lu, Z., J. Im, and L.J. Quackenbush, 2011. A volumetric approach to population estimation using LiDAR remote sensing, Photogrammetric Engineering & Remote Sensing, 77, 1145-1156.
• Gleason, C. and J. Im, 2011. A review of remote sensing of forest biomass and biofuel: options for small area applications, GIScience and Remote Sensing, 48(2): 141-170.
• Mountrakis, G., J. Im, and C. Ogole, 2011. Support vector machines in remote sensing: a review, ISPRS Photogrammetry and Remote Sensing, 66: 247-259.
• Im, J., Z. Lu, and J.R. Jensen, 2011. A genetic algorithm approach to moving threshold optimization for binary change detection, Photogrammetric Engineering & Remote Sensing, 77(2):167-180.
• Gong, B., J. Im, and G. Mountrakis, 2011. An artificial immune network approach to multi-sensor land use/land cover classification, Remote Sensing of Environment, 115:600-614.
• Lu, Z., J. Im, L.J. Quackenbush, and K. Halligan, 2010. Population estimation based on multi-sensor data fusion, International Journal of Remote Sensing, 31(21):5587-5604.
• Rhee, J., J. Im, and G. Carbone, 2010. Monitoring agricultural drought for humid regions using multi-sensor remote sensing data, Remote Sensing of Environment, 114:2875-2887.
• Wang, Z., J.R. Jensen, and J. Im, 2010. An automatic region-based image segmentation algorithm for remote sensing applications, Environmental Modelling and Software, 25:1149-1165.
• Ke, Y., L.J. Quackenbush, and J. Im, 2010. Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification, Remote Sensing of Environment, 114(6): 1141-1154.
• Im, J. and M.E. Hodgson, 2009. Search spaces for identifying optimum thresholds in change detection studies, GIScience and Remote Sensing, 46(3): 249-272.
• Im, J., J. Rhee, and J.R. Jensen, 2009. Enhancing binary change detection performance using a moving threshold window (MTW) approach, Photogrammetric Engineering & Remote Sensing, 75(8): 951-962.
• Im, J., J.R. Jensen, M. Coleman, and E. Nelson, 2009. Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatment, Geocarto International, 24(4):293-312.
• Jensen, J.R., M.E. Hodgson, M. Garcia-Quijano, J. Im, and J. Tullis, 2009. A remote sensing and GIS-assisted spatial decision support system for hazardous waste site monitoring, Photogrammetric Engineering & Remote Sensing, 75(2):169-178.
• Im, J. and J.R. Jensen, 2008. Hyperspectral remote sensing of vegetation, Geography Compass, 2(6):1943-1961.
• Rhee, J., J. Im, G.J. Carbone, and J.R. Jensen, 2008. Delineation of climate regions using in-situ and remotely-sensed data for the Carolinas, Remote Sensing of Environment, 112:3099-3111.
• Im, J., J.R. Jensen, and M.E. Hodgson, 2008. Optimizing the binary discriminant function in change detection applications, Remote Sensing of Environment, 112:2761-2776.
• Im, J., J.R. Jensen, and M.E. Hodgson, 2008. Object-based land cover classification using high posting density lidar data, GIScience and Remote Sensing, 45(2):209-228.
• Im, J., J.R. Jensen, and J.A. Tullis, 2008. Object-based change detection using correlation image analysis and image segmentation techniques, International Journal of Remote Sensing, 29(2):399-423.
• Im, J., J. Rhee, J.R. Jensen, and M.E. Hodgson, 2007. An automated binary change detection model using a calibration approach, Remote Sensing of Environment, 106:89-105.
• Rhee, J. and J. Im, 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.
• Im, J., 2006. Neighborhood correlation image analysis for change detection using different spatial resolution imagery, Korean Journal of Remote Sensing, 22(5):337-350.
• Jensen, J.R., M. Garcia-Quijano, B. Hadley, J. Im, Z. Wang, A.L. Nel, E. Teixeira, and B.A. Davis, 2006. Remote sensing agricultural crop type for sustainable development in South Africa, Geocarto International, 21(2):5-18.
• Im, J. and J.R. Jensen, 2005. A change detection model based on neighborhood correlation image analysis and decision tree classification, Remote Sensing of Environment, 99:326-340.
• Im, J. and C.H. Park, 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.
• Im, J. and J.C. Jeong, 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.

Books

• J. Im, 2010. Spectral Characteristics of Terrestrial Surfaces, In P. Jankowski (eds.): Sage Encyclopedia of Geography.
• Jensen, J.R., J. Im, P. Hardin, and R.R. Jensen, 2009. Chapter 19. Image Classification, In T.A. Warner, M.D. Nellis, and G.M. Foody (eds.): Sage Handbook of Remote Sensing, pp. 269-281.
• Jensen, J.R. and J. Im, 2007. Remote Sensing Change Detection in Urban Environments, In R.R. Jensen, J.D. Gatrell and D. McLean (eds.): Geo-spatial Technologies in Urban Environments: Policy, Practice, & Pixels, Second Edition, Berlin: Springer-Verlag, pp.7-32.
• Im, J., C.H. Park, and H. Sohn, 2005. Remote Sensing and Digital Image Processing - translated into Korean from "Introductory Digital Image Processing (J.R. Jensen), 3rd ed., Upper Saddle River, NJ: Prentice Hall," SigmaPress Co., South Korea, 580 p.