Russell G. Congalton
Professor, Remote Sensing & Geographics Information Systems
My research interests are divided almost equally between basic research on spatial data uncertainty/map accuracy and applied research applying the tools of remote sensing, GIS, and spatial data analysis to solving natural resource problems. These projects have included deer and bear habitat mapping, endangered plant habitat analysis, mapping forest change, fire and fuels management, and eelgrass mapping, to name just a few. Currently, I am conducting both basic and applied research on land cover/vegetation mapping and validation of New England forest cover types in Pawtuckaway State Park, NH using various sources of remotely sensed data and different automated image processing methodologies. I have recently concluded a NSF-funded environmental science and education project called the GLOBE Program. I was the principal investigator of the Land Cover component (one quarter of the GLOBE Program) for over ten years. This research is international and involves developing scientific protocols and educational learning activities for GLOBE schools to perform land cover mapping and collect scientifically valid data. Over 25,000 schools in more than 100 countries participate in this program. While not funded to work with GLOBE currently, I am still very active in educational outreach to many of the colleagues I worked with during the GLOBE Project and am actively writing proposals to continue this work. Lastly, I am the Director of the New Hampshire View Program, a part of AmericaView, that is dedicated to promoting and enhancing the use of spatial data analysis and education throughout the US. Curriculum Vitae
- 1984 Ph.D. Remote Sensing & Forest Biometrics, Virginia Polytechnic Institute and State University
- 1981 M.S. Remote Sensing & Forest Biometrics, Virginia Polytechnic Institute and State University
- 1979 B.S. Natural Resource Management, Rutgers University
- NR 458: The Science of Where
- NR 658: Introduction to Geographic Information Systems
- NR 757/857, GEOG 757: Remote Sensing of the Environment
- NR 759/859, GEOG 759: Digital Image Processing
- NR 760/860, GEOG 860: Geographic Information Systems
|Current Students||Alumni: 2005 - present||Year||Degree||Year||Degree|
|M.S. Students||Bob Champoux||2013||M.S.||Brianna Heath||2008||M.S.|
|Morgan Crowley(co-chair)||Michael Campbell||2012||M.S.||Alastair Lough||2008||Ph.D.|
|Nicholas Dowhaniak(co-chair)||Christina Czarnecki||2012||M.S.||Tina Cormier||2007||M.S|
|Heather Grybas||Daniel Maynard (co-chair)||2012||M.S.||Jesse Bishop||2006||M.S|
|Jenna Kovacs||Meghan MacLean||2012||Ph.D.||John Iiames||2006||Ph.D.|
|Lindsay Ledoux||Shawn Herrick||2011||M.S.||Peter Tardie||2005||M.S.|
|Alexis Rudko||2010||M.S.||Michael Toepfer||2005||M.S.|
|Ph.D. Students||Katie Jacques||2009||M.S.||Mark Brennan||2005||Ph.D.|
|Kamini Yadav||Meghan Graham||2008||M.S.|
Iiames, J., R. Congalton, A. Pilant, and T. Lewis. 2008. Leaf area index (LAI) change detection analysis on Loblolly Pine (Pinus taeda) following complete understory removal. Photogrammetric Engineering and Remote Sensing. Vol. 74. No. 11. pp. 1389-1400.
Iiames, J., R. Congalton, A. Pilant, and T. Lewis. 2008. Validation of an integrated estimation of Loblolly Pine (Pinus taeda L.) leaf area index (LAI) utilizing two indirect optical methods in the southeastern United States. Southern Journal of Applied Forestry Vol. 32. No. 3. pp 101 – 110.
Congalton, R. and K. Green. 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd Edition. CRC/Taylor & Francis, Boca Raton, FL 183p.
Congalton, R. 2009. Accuracy Assessment of Spatial Data Sets. IN: Manual of Geographic Information Systems. M. Madden (Editor). American Society for Photogrammetry and Remote Sensing, Bethesda, MD. pp. 225 – 233.
Congalton, R. 2009. Accuracy and Error Analysis of Global and Local Maps: Lessons Learned and Future Considerations. IN: Remote Sensing of Global Croplands for Food Security. P. Thenkabail, J. Lyon, H. Turral, and C. Biradar. (Editors). CRC/Taylor & Francis, Boca Raton, FL pp. 441-458.
Congalton, R. 2010. How to Assess the Accuracy of Maps Generated from Remotely Sensed Data. IN: Manual of Geospatial Science and Technology, 2nd Edition. John Bossler. (Editor). Taylor & Francis, Boca Raton, FL pp. 403-421.
Congalton, R. 2010. Remote sensing: An overview. GIScience and Remote Sensing. 47, No. 4. pp. 443-459.
Maclean, M. and R. Congalton. 2011. Investigating issues in map accuracy when using an object-based approach to map benthic habitats. GIScience and Remote Sensing. 48, No. 4. pp 457-477
Rodriquez-Galiano, V., B. Ghimire, E. Pardo-Iguzquiza, M. Chica-Olmo, and R. Congalton. 2012. Incorporating the Downscaled Landsat TM Thermal Band in Land-cover Classification using Random Forest. Photogrammetric Engineering and Remote Sensing. Vol. 78. No. 2. pp. 129-137.
Cormier, T., R. Congalton, and K. Babbitt. 2013. Spatial-statistical predictions of vernal pool locations in Massachusetts: Incorporating the spatial component into ecological modeling. Photogrammetric Engineering and Remote Sensing. Vol. 79. No. 1. pp. 25-35.
MacLean, M. M. Campbell, D. Maynard, M. Ducey, and R. Congalton. 2013. Requirements for labeling forest polygons in an object-based image analysis classification. International Journal of Remote Sensing. Vol. 34 No. 7. pp. 2531-2547.
MacLean, M. and R. Congalton. 2013. Applicability of multi-date land cover mapping using Landsat 5 TM imagery in the Northeastern US. Photogrammetric Engineering and Remote Sensing. Vol. 79. No. 4. pp. 359-368.
Maynard, D. M. Ducey, R. Congalton, and J. Hartter. 2013. Modeling forest canopy structure and density by combining point quadrat sampling and survival analysis. Forest Science. Vol.59., No 6. pp. 681- 692. http://dx.doi.org/10.5849/forsci.12-086.
MacLean, M. and R. Congalton. 2013. PolyFrag: A vector-based program for computing landscape metrics. GIScience and Remote Sensing. Vol. 50, No. 6. pp. 591-603. http://dx.doi.org/10.1080/15481603.2013.856537.
Iiames, J, R. Congalton and R. Lunetta. 2013. Analyst variation associated with landcover image classification of Landsat ETM+ data for the assessment of coarse spatial resolution regional/global landcover products. GIScience and Remote Sensing. Vo. 50., No. 6. pp. 604-622.
Dodge, R. and R. Congalton 2013. Meeting Environmental Challenges with Remote Sensing Imagery. American Geosciences Institute. Alexandria, VA. 82p.
Maynard, D. M. Ducey, R. Congalton, J. Kershaw, and J. Hartter. 2014. Vertical point sampling with a digital camera: Slope correction and field evaluation. Computers and Electronics in Agriculture. Vol. 100. pp. 131-138. http://dx.doi.org/10.1016/j.compag.2013.11.007
Selected Service Activities
Editor-in-Chief, Photogrammetric Engineering & Remote Sensing, January, 2008 - present
American Society for Photogrammetry and Remote Sensing
- Fellow - 2007
- National Workshop Director 1997 – 2008
- Immediate Past President 2005-2006
- President 2004-2005
- President-elect 2003 –2004
- Vice President 2002 – 2003
- Secretary/Treasurer, New England Region 2004 - present
- Board of Directors, New England Region 1995 – 1997
- National Board of Directors, 1989-1991
- National GIS Division Director, 1989-1991
- President of Northern California Region, 1990 - 1991
- Vice President of Northern California Region, 1988 - 1989
- Board of Directors, Northern California Region 1986 – 1987
Society of American Foresters, Associate Editor for Remote Sensing & GIS, Northern Journal of Applied Forestry 1996 – 2002
The Sanborn Map Company – Academic Advisory Council, Chair – 2006 – 2011.
- Board of Directors 2012 – present
- Vice Chair, Board of Directors 2013-2014
- Chair, Board of Directors 2014 - present