Geospatial Information Science and Engineering (GIScE)

Geospatial Information Science and Engineering (GIScE)
Environmental and Resource Engineering M.S. & Ph.D.

The GIScE program offers the opportunity to study a wide range of environmental phenomena using remote sensing and GIS methods. The list of current projects and recent publications below provides a more detailed insight on our research activities.

Current projects (ordered by year awarded)

  • Development of Tools Synthesizing Advanced Machine Learning Approaches for Remote Sensing Classification, Dr. Im, AmericaView, Duration: 2011-2012.
  • Characterization of Montane Forest Ecosystems Using Advanced Remote Sensing Technology, Dr. Im (PI with Lindi Quackenbush, Martin Dovciak, and Colin Beier), USDA McIntire-Stennis Cooperative Forestry Research Program, Duration: 2010-2012.
  • Forest Change in the Adirondacks Over 40 Years of Multiple Stresses, Dr. Im (Co-PI with Martin Dovciak (PI) and Colin Beier), USDA McIntire-Stennis Cooperative Forestry Research Program, Duration: 2010-2011.
  • Using LIDAR to assess the roles of climate and land-cover dynamics as drivers of change in biodiversity, Dr. Mountrakis (PI with Bill Porter, Colin Beier, Lianjun Zhang, Bryan Blair and Ben Zuckerberg), NASA Biodiversity Program, $809,000, Duration: 2009-2012. More Information
  • SUNY Sustainability Climate Changes Solution Curriculum, Dr. Quackenbush (Co-PI with Dave Johnson, Rick Beal, Bob Malmsheimer, and Charles Spuches), NASA, Duration: 2009-2011.
  • Satellite-derived anthropogenic land use/land cover changes: Integrating detection, modeling and educational approaches, Dr. Mountrakis (PI), NASA New Investigator Program, $359,000, Duration: 2008-2011. More Information
  •  Remote Sensing Based Classification of Forests Infested by Sirex Woodwasp, Dr. Quackenbush (PI with Stephen Teale), USDA McIntire-Stennis Cooperative Forestry Research Program, Duration: 2008-2011.
  • Establishing a Novel Forest Assessment Method: The Forestless Volume Indicator. Dr. Mountrakis (PI), USDA Forest Service, $120,000 (including 50% ESF match), Duration: 2008-2010. More Information

Recently completed projects

  • NYView Web Site Development and Digital Pamphlet Production for Partner Recruitment, Dr. Im, AmericaView, Duration: 2010-2011.
  • Analysis of Closure Cap Remote Sensing, Dr. Im (PI), University of South Carolina and US Department of Energy, Duration: 2010-2011.
  • Impacts of Green Infrastructure on Directly Connected Impervious Cover and Spectral Signatures: Drs. Quackenbush and Im (Co-PI with Ted Endreny (PI)), Syracuse Center of Excellence, $7,564, Duration: 2008-2009.
  • Investigating New Advances in Forest Species Classification, Dr. Quackenbush (PI with Chuck Kroll), USDA McIntire-Stennis Cooperative Forestry Research Program, $78,200, Duration: 2005-2008.
  • Remote Sensing-assisted Hazardous Waste Site Monitoring Decision Support System: Dr. Im (Participant), NASA REASoN, $2,400,000, Duration: 2003-2008.
  • Bridging the temporal mismatch between remotely-sensed land use changes and field-based water quality/quantity observations, Dr. Mountrakis (PI with Karin Limburg, Myrna Hall and Bongghi Hong), Syracuse Center of Excellence, $100,000, Duration: 2008-2009. More Information
  • An Integrated Monitoring/Modeling Framework for Assessing Human-Nature Interactions in Urbanizing Watersheds: Wappinger and Onondaga Creek Watersheds, Dr. Mountrakis (Co-PI with Karin Limburg (PI), Myrna Hall, Bongghi Hong and Peter Groffman), Syracuse Center of Excellence, $300,000, Duration: 2006-2008. More Information
  • Incorporating Spatially-Explicit Uncertainty Metrics in Image-Derived Classification of Impervious Surfaces: Dr. Mountrakis (PI), National Science Foundation, $50,000, Duration: 2007-2008. More Information
  • Applying Remote Sensing to Forest Health Issues Related to Invasive Species: Dr. Quackenbush (Co-PI) with James Hassett (PI), NASA, $500,000, Duration: 2006-2007 (5th and final year of project)
  • Monitoring Human-Induced Land Use Changes along the Great Lakes: Dr. Mountrakis (PI), Great Lakes Research Consortium, $10,000, Duration: 2006-2007. More Information
  • Dynamic Nonpoint Pollution Model Development For the Carmans River; Phase II: Dr. Quackenbush (Co-PI) with Lee Herrington (PI), New York State Department of State, $107,414, Duration: 2005-2006
  • Synergetic Use of Satellite Imagery and Ancillary Data for Impervious Surface Estimation in the contiguous US: Dr. Mountrakis (PI), National Academies of Science and US Geological Survey, ~$80,000, Duration: 2004-2005

Selected Publications (within 5 years)

In Press

  • Gong, B., J. Im, J.R. Jensen, M. Coleman, and E. Nelson, Characterization of forest crops with a range of nutrient and water treatments using AISA hyperspectral imagery, GIScience and Remote Sensing.
  • Gleason, C. and J. Im. A fusion approach for tree crown delineation from LiDAR data, Photogrammetric Engineering & Remote Sensing.
  • Im J., J. Rhee, and L.J. Quackenbush.  Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data.  Remote Sensing of Environment.
  • Im, J., Z. Lu, J. Rhee, and J. Jensen. Hyperspectral classification of urban landscape through the fusion of feature selection and optimized immune networks, Geocarto International.
  • Jin, H., G. Mountrakis, P. Li. A super-resolution mapping method using local indicator variograms.  International Journal of Remote Sensing.
  • Luo, L., G. Mountrakis. A multi-process model of adaptable complexity for impervious surface detection. International Journal of Remote Sensing, in press.
  • Luo, L., G. Mountrakis. Converting local spectral and spatial information from a priori classifiers into contextual knowledge for impervious surface classification. ISPRS Journal of Photogrammetry and Remote Sensing.
  • Mountrakis, G., D. Triantakonstantis. Inquiry-based learning in remote sensing: A space balloon educational experiment. Journal of Geography in Higher Education.
  • Triantakonstantis, D.,  G. Mountrakis, J. Wang. A Spatially Heterogeneous Expert Based (SHEB) Urban Growth Model using Model Regionalization. Journal of Geographic Information System.
  • Mountrakis, G., A. Stefanidis. Moving Towards Personalized Geospatial Queries. Journal of Geographic Information System.
  • Hong, B., K. Limburg, M. Hall, G. Mountrakis, P. Groffman, K. Hyde, L. Luo, V. Kelly, S. Myers. An integrated monitoring/modeling framework for assessing human-nature interactions in urbanizing watersheds: Wappinger and Onondaga Creek watersheds, New York, USA. Environmental Modelling & Software.
  • Zhang, W., L.J. Quackenbush, J. Im, and L. Zhang.  Indicators for separating undesirable and well-delineated tree crowns in high spatial resolution imagery.  International Journal of Remote Sensing.


  • 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.
  • Gong, B., J. Im, and G. Mountrakis. An artificial immune network approach to multi-sensor land use/land cover classification, Remote Sensing of Environment, 115(2):600-614.
  • Gunson, K., G. Mountrakis, L.J. Quackenbush. Spatial wildlife-vehicle collision models: A review of current work and their application to transportation mitigation projects, Journal of Environmental Management, 92(4):1074-1082.
  • 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.

  • Ke, Y., and L.J. Quackenbush. A review of methods for automatic individual tree crown detection and delineation. International Journal of Remote Sensing, 32(17): 4725-4747.
  • Ke, Y., and L.J. Quackenbush. A comparison of three methods for automatic tree crown detection and delineation from high spatial resolution imagery. International Journal of Remote Sensing, 32(13): 3625-3647.
  • Lu, Z., J. Im, and L.J. Quackenbush, 2011. A volumetric approach to population estimation using LiDAR remote sensing, Photogrammetric Engineering & Remote Sensing, 77(11): 1145-1156.
  • Wang, J., G. Mountrakis. Developing a multi-network urbanization (MuNU) model: A case study of urban growth in Denver, Colorado. International Journal of Geographical Information Science, 25(2):229-253 .
  • Mountrakis G., and K. Gunson (2011). Multi-scale spatiotemporal analyses of moose-vehicle collisions: A case study in northern Vermont. International Journal of Geographical Information Science, 92(4):1074-1082.
  • Mountrakis G., L. Luo (2011). Enhancing and replacing spectral information with intermediate structural inputs: A case study on impervious surface detection. Remote Sensing of Environment, 115(5):1162-1170.
  • Mountrakis, G.J. Im, C. Ogole (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3):247-259.


  • Im, J. Spectral Characteristics of Terrestrial Surfaces, In P. Jankowski (eds.): Sage Encyclopedia of Geography.
  • 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.
  • 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.
  • Ke, Y., W. Zhang, and L.J. Quackenbush. Active contour and hill-climbing for tree crown detection and delineation. Photogrammetric Engineering and Remote Sensing, 76(10): 1169-1181.
  • Luo, L., G. Mountrakis (2010). Integrating intermediate inputs from partially classified images within a hybrid classification framework: An impervious surface estimation example. Remote Sensing of Environment, 114(6):1220-1229. pdf
  • Rhee, J., J. Im, and G.J. Carbone, 2010. Monitoring agricultural drought for humid regions using multi-sensor remote sensing data, Remote Sensing of Environment,, 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.
  • Zhang, W., Y. Ke, L.J. Quackenbush, and L. Zhang. Using Error-in-Variable Regression to Predict Tree Diameter and Crown Width from Remotely Sensed Imagery. Canadian Journal of Forest Research, 40(6): 1095-1108.



  • Im, J. and J.R. Jensen, 2008. Hyperspectral remote sensing of vegetation, Geography Compass, in press.
  • 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, 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.
  • Jensen, J.R., J. Im, P. Hardin, and R.R. Jensen, 2008. Chapter 18. Image Classification, in Handbook of Remote Sensing, in press
  • Jensen, J.R., M.E. Hodgson, M. Garcia-Quijano, and J. Im, 2008. A remote sensing and GIS-assisted spatial decision support system for hazardous waste site monitoring, Photogrammetric Engineering & Remote Sensing, in press.
  • Mountrakis G. (2008). Next generation classifiers: Focusing on integration frameworks. Highlight article for October, 2008 issue of Photogrammetric Engineering and Remote Sensing.
  • 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. 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.
  • 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.): Geospatial Technologies in Urban Environments: Policy, Practice, & Pixels, Second Edition, Berlin: Springer-Verlag, pp.7-32.
  • Quackenbush, L.J., 2007.  Separating Types of Impervious Land Cover Using Fractals, in Remote Sensing of Impervious Surfaces (Q. Weng, Ed.), CRC Press, Boca Raton, Florida, pp. 119-142.


  • 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.
  • 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.
  • Wu, W., C.A. Hall, F.N. Scatena, L.J. Quackenbush, 2006.  Spatial modelling of evapotranspiration in the Luquillo Experimental Forest of Puerto Rico using remotely-sensed data.  Journal of Hydrology, 328(3-4): 733-752.

For additional information on publications, research and teaching activities please visit the corresponding faculty webpage (Dr. Im, Dr. Mountrakis, Dr. Quackenbush).

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