Russell G. Congalton

Professor
Carsey Author
Phone: (603) 862-4644
Office: Natural Resources & the Environment, James Hall Rm 164, Durham, NH 03824
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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, loon, 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 southeastern NH using various sources of remotely sensed data including unmanned aerial systems (UAS) and different automated image processing methodologies. I was part of an 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 the 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. In addition, I am working on an NSF-funded multi-investigator project evaluating the effectiveness of payments for ecological services in Mexico and also a NASA MEaSUREs multi-investigator project mapping agricultural crops worldwide at Landsat 30m resolution. 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.

Russell G. Congalton is a professor in natural resources and the environment at the University of New Hampshire.

Education

  • Ph.D., Forest Biometrics and Remote Sensing, Virginia Polytechnic Institute and State University
  • M.S., Forest Biometrics and Remote Sensing, Virginia Polytechnic Institute and State University
  • B.S., Natural Resources Management, Rutgers University

Research Interests

  • Earth/Terrestrial Remote Sensing
  • Geographic Information Systems (GIS)

Courses Taught

  • GEOG 658: Intro Geographic Info Systems
  • GEOG 757: Remote Sensing of Environment
  • GEOG 759: Digital Image Process Nat Res
  • GEOG 760: GIS in Natural Resources
  • NR 458: The Science of Where
  • NR 600: Work Experience
  • NR 658: Intro Geographic Info Systems
  • NR 759: Digital Image Process Nat Res
  • NR 795: Inv/Field Methods in GIS
  • NR 799: Honors Senior Thesis
  • NR 857: Remote Sensing of the Environ
  • NR 899: Master's Thesis
  • NR 995: Inv/Spatial Data Analysis
  • NR 996: Natural Resource Education
  • NR 998: Directed Research

Selected Publications

Teluguntla, P., Thenkabail, P. S., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., . . . Huete, A. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 144, 325-340. doi:10.1016/j.isprsjprs.2018.07.017

Sun, P., Congalton, R. G., & Pan, Y. (2018). Using a simulation analysis to evaluate the impact of crop mapping error on crop area estimation from stratified sampling. International Journal of Digital Earth, 1-21. doi:10.1080/17538947.2018.1499827

Fraser, B., & Congalton, R. (2018). Issues in Unmanned Aerial Systems (UAS) Data Collection of Complex Forest Environments. Remote Sensing, 10(6), 908. doi:10.3390/rs10060908

Sun, P., & Congalton, R. (2018). Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps. Remote Sensing, 10(3), 487. doi:10.3390/rs10030487

Dowhaniuk, N., Hartter, J., Ryan, S. J., Palace, M. W., & Congalton, R. G. (2018). The impact of industrial oil development on a protected area landscape: demographic and social change at Murchison Falls Conservation Area, Uganda. Population and Environment, 39(3), 197-218. doi:10.1007/s11111-017-0287-x

Macleod, R. D., & Congalton, R. G. (1998). Quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 64(3), 207-216. Retrieved from http://gateway.webofknowledge.com/

Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. doi:10.1016/0034-4257(91)90048-B

STORY, M., & CONGALTON, R. G. (1986). ACCURACY ASSESSMENT - A USERS PERSPECTIVE. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 52(3), 397-399. Retrieved from http://gateway.webofknowledge.com/

CONGALTON, R. G., ODERWALD, R. G., & MEAD, R. A. (1983). ASSESSING LANDSAT CLASSIFICATION ACCURACY USING DISCRETE MULTIVARIATE-ANALYSIS STATISTICAL TECHNIQUES. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 49(12), 1671-1678. Retrieved from http://gateway.webofknowledge.com/

CONGALTON, R. G., & MEAD, R. A. (1983). A QUANTITATIVE METHOD TO TEST FOR CONSISTENCY AND CORRECTNESS IN PHOTOINTERPRETATION. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 49(1), 69-74. Retrieved from http://gateway.webofknowledge.com/

Most Cited Publications