Publications – BASAL

PEER-REVIEWED ARTICLES

*Graduate student authors are bolded

Congalton, R. G. and R. A. Mead. 1983. A quantitative method to test for consistency and correctness in photo-interpretation. Photogrammetric Engineering and Remote Sensing. Vol. 49. No. 1, p. 69-74.

Congalton, R. G., R. G. Oderwald, and R. A. Mead. 1983. Assessing Landsat classification accuracy using discrete multivariate statistical techniques. Photogrammetric Engineering and Remote Sensing. Vol. 49, No. 12, p. 1671-1678.

Congalton, R. and R. Mead. 1986. A review of three discrete multivariate analysis techniques used in assessing the accuracy of remotely sensed data from error matrices. IEEE Transactions of Geoscience and Remote Sensing. Vol. GE-24, No 1, p. 169-174.

Story, M. and R. Congalton. 1986. Accuracy assessment: A user's perspective. Photogrammetric Engineering and Remote Sensing. Vol. 52, No. 3. pp. 397-399.

Pierce, L. and R. Congalton. 1988. A methodology for mapping forest latent heat flux densities using remote sensing. Remote Sensing of Environment . Vol 24, pp. 405-418.

Congalton, R. 1988. Using spatial autocorrelation analysis to explore errors in maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing. Vol. 54, No. 5, pp. 587-592.

Congalton, R. 1988. A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing. Vol. 54, No. 5, pp. 593-600.

Chuvieco, E. and R. Congalton. 1988. Using cluster analysis to improve the selection of training statistics in classifying remotely sensed data. Photogrammetric Engineering and Remote Sensing. Vol. 54, No. 9, pp. 1275-1281.

Chuvieco, E. and R. Congalton. 1988. Mapping and inventory of forest fires from digital processing of TM data. Geocarto International. Vol. 3, No. 4, pp. 41-53.

Chuvieco, E. and R. Congalton. 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment. Vol. 29, pp. 147-159.

Ferris, J. and R. Congalton. 1989. Satellite and GIS estimates of Colorado River Basin snowpack. Photogrammetric Engineering and Remote Sensing. Special GIS Issue. Vol. 55, No. 11, pp. 1629-1635.

Stenback, J. and R. Congalton. 1990. Using Thematic Mapper imagery to examine forest understory. Photogrammetric Engineering and Remote Sensing. Vol. 56, No. 9, pp. 1285-1290.

Lunetta, R., R. Congalton, L. Fenstermaker, J. Jensen, K. McGwire, and L. Tinney. 1991. Remote sensing and geographic information system data integration: error sources and research issues. Photogrammetric Engineering and Remote Sensing. Vol. 57, No. 6, pp. 677-687.

Congalton, R. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment. Vol. 37, pp. 35-46.

Congalton R. and K. Green. 1992. The ABCs of GIS: An introduction to geographic information systems. Journal of Forestry. Vol 90, No. 11, pp. 13-20.

Congalton, R. and D. Schallert. 1992. Exploring the effects of vector to raster and raster to vector conversion. EPA Peer Reviewed Publication Series Report EPA/600/R-92/166. Office of Research & Development, Washington, D.C. 48p.

Congalton, R. and G. Biging. 1992. A pilot study evaluating ground reference data collection efforts for use in forest inventory. Photogrammetric Engineering and Remote Sensing. Vol. 58, No. 12, pp. 1669-1671.

Congalton R. and K. Green. 1993. A practical look at the sources of confusion in error matrix generation. Photogrammetric Engineering and Remote Sensing. Vol. 59, No. 5. pp. 641-644.

Congalton, R., J. Stenback, and R. Barrett. 1993. Mapping deer habitat suitability using remote sensing and GIS. Geocarto International. Vol. 8. No. 3. pp. 23-33.

Congalton, R., K. Green., and J. Teply. 1993. Mapping old growth forests on National Forest and Park lands in the Pacific Northwest from remotely sensed data. Photogrammetric Engineering and Remote Sensing. Vol. 59, No. 4. pp. 529-535.

Schriever, J. R. and R. G. Congalton. 1995. Evaluating seasonal variability as an aid to cover-type mapping from Landsat Thematic Mapper data in the northeast. Photogrammetric Engineering and Remote Sensing. Vol. 61, No. 3. pp. 321-327

Congalton, R. G. 1996. Accuracy assessment: A critical component of land cover mapping. IN: Gap Analysis: A Landscape Approach to Biodiversity Planning. A Peer-Reviewed Proceedings of the ASPRS/GAP Symposium. Charlotte, NC. pp. 119-131.

Sperduto, M. and R. Congalton. 1996. Predicting rare orchid (small whorled pogonia) habitat using GIS. Photogrammetric Engineering and Remote Sensing. (Special Issue on GIS) Vol. 62, No. 11. pp. 1269-1279.

Congalton, R. 1997. Exploring and Evaluating the Consequences of Vector to Raster and Raster to Vector Conversion. Photogrammetric Engineering and Remote Sensing. Vol. 63, No. 4. pp.425-434.

Becker, M., R. Congalton, R. Budd, and A. Fried, 1998. A GLOBE collaboration to develop land cover data collection and analysis protocols. Journal of Science Education and Technology Vol. 7., No. 1 pp. 85-96.

Turner, M. and R. Congalton. 1998. Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African floodplain. International Journal of Remote Sensing. Vol. 19, No. 1. pp. 21-41.

Macleod, R. and R. Congalton. 1998. A quantitative comparison of change detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering and Remote Sensing. Vol. 64, No. 3. pp. 207-216.

Congalton, R., M. Balogh, C. Bell, K. Green, J. Milliken, and R. Ottman. 1998. Mapping and monitoring agricultural crops and other land cover in the Lower Colorado River Basin. Photogrammetric Engineering and Remote Sensing. Vol. 64, No. 11. pp. 1107-1113.

Martin, M., S. Newman, J. Aber, and R. Congalton. 1998. Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing of Environment. Vol. 65, No. 3. pp. 249-254.

Congalton, R. 2001. Accuracy assessment and validation of remotely sensed and other spatial information. The International Journal of Wildland Fire. Vol 10. pp. 321-328.

Pugh, S. and R. Congalton. 2001. Applying spatial autocorrelation analysis to evaluate error in New England forest cover type maps derived from Landsat Thematic Mapper Data. Photogrammetric Engineering and Remote Sensing. Vol. 67, No. 5. pp. 613-620.

Lunetta, R. J. Iiames, J. Knight, R. Congalton, and T. Mace. 2001. An assessment of reference data variability using a "virtual field reference database". Photogrammetric Engineering and Remote Sensing. Vol. 67, No. 6. pp. 707-715.

Zarin, D.J., V.F.G. Pereira, H. Raffles, M. Pinedo-Vasquez, F.G. Rabelo and R.G. Congalton. 2001. Landscape changes in tidal floodplains near the mouth of the Amazon River. Forest Ecology and Management. Vol. 154. pp. 383-393.

Pereira, V., R. Congalton, and D. Zarin. 2002. Spatial and temporal analysis of a tidal floodplain landscape – Amapa, Brazil using geographic information systems and remote sensing. Photogrammetric Engineering and Remote Sensing. Vol. 68, No. 5, pp. 463-472.

Wormstead, S., M. Becker, and R. Congalton. 2002. Tools for successful student-teacher-scientist partnerships: Lessons from GLOBE. Journal of Science Education and Technology. Vol. 11, No. 3. pp. 277-287.

Congalton, R., K. Birch, R. Jones, and J. Schriever. 2002. Evaluating remotely sensed techniques for mapping riparian vegetation. Computers and Electronics in Agriculture. Vol. 37. pp. 113-126.

Plourde, L. and R. Congalton. 2003. Sampling method and sample placement: How do they affect the accuracy of remotely sensed maps? Photogrammetric Engineering and Remote Sensing. Vol. 69, No. 3, pp. 289-297.

Thomas, N., C. Hendrix, and R. Congalton. 2003. A comparison of urban mapping methods using high-resolution digital imagery. Photogrammetric Engineering and Remote Sensing. Vol. 69, No. 9. pp. 963-972.

Bjerklie, D., S. Dingman., C. Vorosmarty, C. Bolster, and R. Congalton. 2003. Evaluating the potential for measuring river discharge from space. Journal of Hydrology. Vol. 278. pp. 17-38.

Congalton, R. 2004. Putting the map back in map accuracy assessment. A peer-reviewed chapter IN: Lunetta, R.S., and J.G. Lyon (Eds.), Remote Sensing and GIS Accuracy Assessment, CRC Press, Boca Raton, FL 304p.

Green, K and R. Congalton. 2004. An error matrix approach to fuzzy accuracy assessment: the NIMA Geocover project. A peer-reviewed chapter IN: Lunetta, R.S., and J.G. Lyon (Eds.), Remote Sensing and GIS Accuracy Assessment, CRC Press, Boca Raton, FL 304p.

Hermann, H., K. Babbitt, M. Baber, and R. Congalton. 2005. Effects of landscape characteristics on amphibian distribution in a forest-dominated landscape. Biological Conservation. Vol. 123. pp. 139-149.

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. 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.

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

Congalton, R. J. Gu, K. Yadav, P. Thenkabail, and M. Osdogan. 2014. Global land cover mapping: A review and uncertainty analysis. Remote Sensing, 6, pp. 12070-12093; doi:10.3390/rs61212070

Gu, J., R. G. Congalton, and Y. Pan. 2015. The impact of positional errors on soft classification accuracy assessment: A simulation analysis. Remote Sensing. 7, pp. 579-599; doi:10.3390/rs70100579

Campbell, M., R. Congalton, J. Hartter, and M. Ducey. 2015. Optimal land cover mapping and change analysis in northeastern Oregon using Landsat imagery. Photogrammetric Engineering and Remote Sensing. Vol. 81, No. 1, pp. 37-47. doi:10.14358/PERS.81.1.37

Iiames, J.S., R.G. Congalton, T.E. Lewis, and A. Pilant. 2015. Uncertainty analysis in the creation of a fine-resolution leaf area index (LAI) reference map for validation of moderate resolution LAI products. Remote Sensing. 7, pp. 1397-1421; doi:10.3390/rs70201397

Hartter, J., F. Stevens, L. Hamilton, R. Congalton, M. Ducey, and P. Oester. 2015. Modelling associations between public understanding, engagement and forest conditions in the Inland Northwest, USA. PLoS ONE 10(2): e0117975. doi:10.1371/journal.pone.0117975

Lee, T., A. Perkins, A. Campbell, J. Passero, N. Roe, C. Shaw, and R. Congalton. 2015. Incipient invasion of urban and forest habitats in New Hampshire USA by the non-native tree, Kalopanax septemlobus. Invasive Plant Science and Management. Vol. 8, pp. 111-121. doi:10.1614/IPSM-D-14-00047.1

MacLean, M. and R. Congalton. 2015. A review of using fragmentation programs to identify possible invasive plant species in locations in forest edge. Landscape Ecology. doi 10.1007/s10980-015-0175-7 (published online Feb. 2015)

Grybas, H, and R. Congalton. 2015. Land cover change image analysis for Assateague Island National Seashore following Hurricane Sandy. Journal of Imaging. Vol. 1. pp. 85-114. doi:10.3390/jimaging1010085.

Sivanpillai, R. and R. Congalton. 2016. Future Landsat data needs at the local and state levels: An AmericaView perspective. Photogrammetric Engineering and Remote Sensing. Vol. 82, No. 8. pp. 617-623.

Teluguntla, Pardhasaradhi, Prasad S. Thenkabail, Jun Xiong, Murali Krishna Gumma, Russell G. Congalton, Adam Oliphant, Justin Poehnelt, Kamini Yadav, Mahesh Rao and Richard Massey. 2017. Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data. International Journal of Digital Earth. DOI: 10.1080/17538947.2016.1267269

Xiong, Jun, Prasad S. Thenkabail, Murali K. Gumma, Pardhasaradhi Teluguntla, Justin Poehnelt, Russell G. Congalton, Kamini Yadav, and David Thau.^.2017. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing. 126:225-244. http://dx.doi.org/10.1016/j.isprsjprs.2017.01.019.

Grybas, Heather, Lindsay Melendy, and Russell G. Congalton. 2017. A comparison of unsupervised segmentation optimization approaches using moderate- and high-resolution imagery. GIScience and Remote Sensing. DOI: 10.1080/15481603.2017.1287238

Dowhaniuk, Nicholas, Joel Hartter, Sadie J. Ryan, Michael W. Palace, and Russell G. Congalton. 2017. The impact of industrial oil development on a protected area landscape: population pressure and struggles for land at Murchison Falls Conservation Area, Uganda. Population and Environment. 39:197-218. https://doi.org/10.1007/s11111-017-0287-x

Sun, Peijun, Jinshui Zhang, Russell G. Congalton, Yaozhong Pan, and Xiufang Zhu. 2017. A quantitative performance comparison of paddy rice acreage estimation using stratified sampling strategies with different auxiliary indicators. International Journal of Digital Earth. DOI: 10.1080/17538947.2017.1371256.

Massey, Richard, Temuulen T. Sankey, Russell G. Congalton, Kamini Yadav, Prasad S. Thenkabail, Mutlu Odzogan, and Andrew J. Sanchez Meador. 2017. MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types. Remote Sensing of Environment. Vol. 198. pp.490-503. http://dx.doi.org/10.1016/j.rse.2017.06.033.

Sun, Peijun, Russell G. Congalton, Heather Grybas, and Yaozhong Pan. 2017. The impact of crop mapping error on the performance of upscaling agricultural maps. Remote Sensing. 9. DOI:10.3390/rs9090901.

Xiong, Jun, Prasad S. Thenkabail, James C. Tilton, Murali K. Gumma, Pardhasaradhi Teluguntla, Adam Oliphant, Russell G. Congalton, Kamini Yadav, and Noel Gorelick. 2017. Nominal 30-m cropland extent of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sensing. 9, 1065; https://doi:10.3390/rs9101065.

Yadav, Kamini and Russell G. Congalton. 2018. Issues with large area thematic accuracy assessment for mapping cropland extent: a tale of three continents. Remote Sensing. 10, 53. DOI:10.3390/rs10010053.

Sun, Peijun, Russell G. Congalton, and Yaozhong Pan. 2018. Improving the upscaling of land cover maps by fusing uncertainty and spatial structure information. Photogrammetric Engineering and Remote Sensing. Vol. 84, No. 2. pp. 87 – 100. DOI: 10.14358/PERS.84.2.87.

Sun, Peijun and Russell G. Congalton. 2018. Using a similarity matrix approach to evaluate the accuracy of rescaled maps. Remote Sensing. 10, 487. DOI:10.3390/rs10030487.

Sankey, Temuulen Tsagaan, Richard Massey, Kamini Yadav, Russell G. Congalton, and James Tilton. 2018. Post-socialist cropland changes and abandonment in Mongolia. Land Degradation and Development. 1-14. https://doi.org/10.1002/ldr.2997

Fraser, Benjamin T. and Russell G. Congalton. 2018. Issues in Unmanned Aerial Systems (UAS) Data Collection of Complex Forest Environments. Remote Sensing. 10, 908. DOI:10.3390/rs10060908.

Sun, Peijun, Russell G. Congalton, and Yaozhong Pan. 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. DOI:10.1080/17538947.2018.1499827.

Teluguntla, P., P. Thenkabail, A. Oliphant, J. Xiong, M. Gumma, R. Congalton, K. Yadav, and A. Huete. 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. https://doi.org/10.1016/j.isprsjprs.2018.07.017

Crowley, Morgan A., Joel Hartter, Russell G. Congalton, Lawrence C. Hamilton, and Nils C. Christoffersen. 2018. Characterizing non-industrial private forest landowners' forest management engagement and advice sources. Society and Natural Resources. doi:10.1080/08941920.2018.1505013

Healy, Christine, Peter J. Pekins, Russell G. Congalton, Shadi Atallah, and Lee Kantar. 2018. Habitat use of moose during critical periods in the winter tick life cycle in northern New England. ALCES. Vol. 54. pp. 85-100.

Massey, Richard, Temuulen T. Sankey, Kamini Yadav, Russell G. Congalton, and James Tilton. 2018. Integrating cloud-based workflows in continental-scale cropland extent classification. Remote Sensing of Environment. Vol. 219. pp. 162-179. https://doi.org/10.1016/j.rse.2018.10.013

Yadav, Kamini and Russell G. Congalton. 2018. Accuracy assessment of Global Food Security-support Analysis Data (GFSAD) cropland extent maps produced at three different spatial resolutions. Remote Sensing. 10, 1800. DOI:10.3390/rs10111800

Fraser, Benjamin and Russell G. Congalton. 2019. Evaluating the effectiveness of Unmanned Aerial Systems (UAS) for collecting thematic map accuracy assessment reference data in New England Forests. Forests 10, 24; doi:10.3390/f10010024

Oliphant, Adam J., Prasad S. Thenkabail, Pardhasaradhi Telunguntla, Jun Xiong, Murali Krishna Gumma, Russell G. Congalton, and Kamini Yadav. 2019. Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine. International Journal of Applied Earth Observations and Geoinformation. Vol. 81. Pp. 110-124. https://doi.org/10.1016/j.jag.2018.11.014

Yadav, Kamini and Russell G. Congalton. 2019. Evaluating sampling designs for assessing the accuracy of cropland extent maps in different cropland proportion regions. Journal of Geography, Environment and Earth Science International. 20(4), 1-20. https://doi.org/10.9734/jgeesi/2019/v20i430111

Von-Thaden, J., R. Manson, R. Congalton, B. Lopez-Barrera, and J. Salcone. 2019. A regional evaluation of the effectiveness of Mexico’s payments for hydrological services. Regional Environmental Change. https://doi.org/10.1007/s10113-019-01518-3

Sun, Peijun and Russell G. Congalton. 2019. The impact of landscape characteristics on the performance of upscaled maps. Geocarto International. (In Press). https://doi.org/10.1080/10106049.2019.167868

Healy, Christine, Peter J. Pekins, Shadi Atallah, and Russell G. Congalton. 2019. Using Agent-Based Models to Inform the Dynamics of Winter Tick Parasitism of Moose. Ecological Complexity (In Review).

Gu, Jianyu and Russell G. Congalton. 2019. The positional effect in soft classification accuracy assessment. American Journal of Remote Sensing. Vol. 7, No. 2. Pp. 50-61. https://doi.org/10.11648/j.ajrs.20190702.13

Von-Thaden, J., R. Manson, R. Congalton, B. Lopez-Barrera, and K. Jones. 2020. Evaluating the environmental effectiveness of payments for hydrological services in Veracruz, Mexico: A landscape approach. Land Use Policy. https://doi.org/10.1016/j.landusepol.2020.105055

Yadav, Kamini and Russell G. Congalton. 2020. Extending crop type reference data using phenology-based approach. Frontiers in Sustainable Food Systems. 4:99. doi:10.3389/fsufs.2020.00099

Grybas, Heather, Russell G. Congalton, and Andrew F. Howard. 2020. Using geospatial analysis to map forest change in New Hampshire: 1996 – present. Journal of Forestry. 2020, 598–612: https://doi:10.1093/jofore/fvaa039

Berry, Carter Z., Kelly Jones, Leon Rodrigo Gomez Aguilar, Russell G. Congalton, Friso Holwerda, Randall Kolka, Nathaniel Looker, Robert Manson, Alex Mayer, Lyssette Muñoz-Villers, Perla Ortiz Colin, Humberto Romero-Uribe, Leonardo Saenz, Juan Von Thaden, Mariana Quetzalli, Guadalupe Williams-Linera, and Heidi Asbjornsen. 2020. Evaluating ecosystem service trade-offs along a land-use intensification gradient in central Veracruz, Mexico. Ecosystem Services Vol. 45. https://doi.org/10.1016/j.ecoser.2020.101181

Gu, Jianyu and Russell G. Congalton. 2020. Analysis of the impact of positional accuracy when using a single pixel for thematic accuracy assessment. Remote Sensing. 12, 4093; https://doi:10.3390/rs12244093

Gu, Jianyu and Russell G. Congalton. 2021. Individual Tree Crown Delineation from UAS Imagery based on Region Growing by Over-segments with a Competitive Mechanism. IEEE Transactions on GeoScience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3074289

Grybas, Heather and Russell G. Congalton. 2021. A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests. Remote Sensing. 13, 2631. https://doi.org/10.3390/rs13132631

Fraser, Benjamin and Russell G. Congalton. 2021. Estimating Primary Forest Attributes and Rare Community Characteristics Using Unmanned Aerial Systems (UAS): An Enrichment of Conventional Forest Inventories. Remote Sensing. 13, 2971. https://doi.org/10.3390/rs13152971

Grybas, Heather and Russell G. Congalton. 2021. Evaluating the capability of unmanned aerial system (UAS) imagery to detect and measure the effects of edge influence on forest canopy in New England. Forests. 12, 1252. https://doi.org/10.3390/f12091252

Fraser, Benjamin and Russell G. Congalton. 2021. A comparison of methods for determining forest composition from high-spatial resolution remotely sensed imagery. Forests. 13, 2971. https://doi.org/10.3390/f12091290

Gu, Jianyu and Russell G. Congalton. 2021. Analysis of the impact of positional accuracy when using a block of pixels for thematic accuracy assessment. Geographies. 1, 143-165; https://doi:10.3390/geographies1020009

Fraser, Benjamin and Russell G. Congalton. 2021. Monitoring fine-scale forest health using unmanned aerial systems (UAS) multispectral models. Remote Sensing. 13, 4873. https://doi.org/10.3390/rs13234873
Thenkabail, P.S., P. Teluguntla, J. Xiong, A. Oliphant, R. Congalton, M. Ozdogan, M. Gumma, J. Tilton, C. Giri, C. Milesi, A. Phalke, R. Massey, K. Yadav, T. Sankey, Y. Zhong, I. Aneece, and D. Foley. 2021. Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud: U.S. Geological Survey Professional Paper 1868, 63p., https://doi.org/10.3133/pp1868

Fraser, Benjamin, Christine Bunyon, Sarah Reny, Isabelle Lopez, and Russell G. Congalton. 2022. Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review. Geographies, 2, 303–340. https://doi.org/10.3390/geographies2020021

Gu, Jianyu and Russell G. Congalton. 2019. The positional effect in soft classification accuracy assessment. American Journal of Remote Sensing. 7, No. 2. Pp. 50-61. https://doi.org/10.11648/j.ajrs.20190702.13

Healy, Christine, Peter J. Pekins, Shadi Atallah, and Russell G. Congalton. 2020. Using Agent-Based Models to Inform the Dynamics of Winter Tick Parasitism of Moose. Ecological Complexity, Volume 41. https://doi.org/10.1016/j.ecocom.2020.100813

Gu, Jianyu, Heather Grybas, and Russell G. Congalton. 2020. A comparison of forest tree crown delineation from unmanned aerial imagery using canopy height models vs. spectral lightness. Forests. 11, 605; doi:3390/f11060605

Phalke, A., M. Ozdogan, P. Thenkabail, T. Erickson, N. Gorelick, Yadav, and R. Congalton. 2020. Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 167, pp. 104-122. https://doi.org/10.1016/j.isprsjprs.2020.06.22

Gu, Jianyu, Heather Grybas, and Russell G. Congalton. 2020. Individual tree crown delineation from UAS imagery based on region growing and growth space considerations. Remote Sensing. 12, 2363. doi:3390/rs12152363

Von-Thaden, J., R. Manson, R. Congalton, B. Lopez-Barrera, and K. Jones. 2020. Evaluating the environmental effectiveness of payments for hydrological services in Veracruz, Mexico: A landscape approach. Land Use Policy. https://doi.org/10.1016/j.landusepol.2020.105055

Yadav, Kamini and Russell G. Congalton. 2020. Extending crop type reference data using phenology-based approach. Frontiers in Sustainable Food Systems. 4:99. doi:13389/fsufs.2020.00099

Fraser, Benjamin and Russell G. Congalton. 2021. A comparison of methods for determining forest composition from high-spatial resolution remotely sensed imagery. 13, 2971. https://doi.org/10.3390/f12091290

Gu, Jianyu and Russell G. Congalton. 2021. Analysis of the impact of positional accuracy when using a block of pixels for thematic accuracy assessment. 1, 143-165; https://doi:10.3390/geographies1020009

Thenkabail, P.S., P. Teluguntla, J. Xiong, A. Oliphant, R. Congalton, M. Ozdogan, M. Gumma, J. Tilton, C. Giri, C. Milesi, A. Phalke, R. Massey, K. Yadav, T. Sankey, Y. Zhong, I. Aneece, and D. Foley. 2021. Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud: U.S. Geological Survey Professional Paper 1868, 63p., https://doi.org/10.3133/pp1868

Grybas, H and Russell G. Congalton. 2022.Evaluating the impacts of flying height and forward overlap on tree height estimates using UnmannedAerial Systems. Forests 13, 1462. https://doi.org/10.3390/f13091462

Bunyon, Christine .L., Benjamin T. Fraser, Amanda McQuaid, and Russell G. Congalton. 2023. Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes. Remote Sensing. 15, 2839. https://doi.org/10.3390/rs15112839

Chapman, Simone, Catherine M Ashcraft, Lawrence Hamilton, and Russell G. Congalton. 2023. Informed aquatic restoration decisions using environmental justice in New Hampshire. J of Environmental Policy and Planning. DOI: 1080/1523908X.2023.2229247

BOOKS, BOOK CHAPTERS, AND MONOGRAPHS

Bartlett, D., R. Congalton, M. Becker, E. Abrams, and J. Campbell. 1996. Land Cover/Biology Investigation. IN: GLOBE Program Teachers Guide. Second Edition. GLOBE Office, Washington, DC.

Congalton, R. and M. Becker. 1997. Validating Student Data for Scientific Use: An Example from the GLOBE Project. IN: Internet Links for Science Education: Student-Scientist Partnerships, Karen Cohen, (Editor). Plenum Press, New York. pp.133-156.

Biging, G., D. Colby, and R. Congalton. 1998. Sampling Systems for Change Detection Accuracy Assessment. IN: Remote Sensing Change Detection Environmental Monitoring Methods and Applications, R. Lunetta and C. Elvidge (Editors). Ann Arbor Press, Chelsea, MI. pp. 281-308.

Congalton, R. and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC/Lewis Press, Boca Raton, FL. 137 p.

Congalton, R. 1999. Multi-scale Resource Data. IN: GIS Solutions in Natural Resource Management, S. Morain, (Editor). OnWord Press, Sante Fe, NM. pp. 125-139.

Khorram, S., G. Biging, N. Chrisman, D. Colby, R. Congalton, J. Dobson, R. Ferguson, M. Goodchild, J. Jensen, and T. Mace. 1999. Accuracy Assessment of Remote Sensing-Derived Change Detection, A Monograph published by the American Society for Photogrammetry and Remote Sensing. Bethesda, MD. 64 p.

Mowrer, H. T. and R. G. Congalton. (eds.) 2000. Quantifying Spatial Uncertainty in Natural Resources: Theory and Applications for GIS and Remote Sensing. Ann Arbor Press, Chelsea, Michigan. 244p.

Congalton, R. and L. Plourde. 2002. Quality Assurance and Accuracy Assessment of Information Derived from Remotely Sensed Data. IN: Manual of Geospatial Science and Technology. John Bossler. (Editor). Taylor & Francis, London. pp. 349-361.

Bishop, J., R. Congalton, and M. Becker. 2008. Monitoring Biodiversity of Select Restoration Sites in New Zealand. IN: Biodiversity for Sustainable Development. Azmal Hussain (Editor).The Icfai University Press, Hyderabad, India. Pp.273-280.

Congalton, R. and K. Green. 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2^nd 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, 2^nd Edition. John Bossler. (Editor). Taylor & Francis, Boca Raton, FL pp. 403-421.

Dodge, R. and R. Congalton 2013. Meeting Environmental Challenges with Remote Sensing Imagery. American Geosciences Institute. Alexandria, VA. 82p.

Congalton, R. 2016. Assessing Positional and Thematic Accuracies of Maps Generated from Remotely Sensed Data. IN: Remote Sensing Handbook; Vol. I: Data Characterization, Classification, and Accuracies P. Thenkabail (Editor). CRC/Taylor & Francis, Boca Raton, FL. pp. 583-601.

Teluguntla, P., P Thenkabail, J. Xiong^,M. Krishna Gumma, C. Giri, C. Milesi, M. Ozdogan, R. Congalton, J. Tilton, T. Sankey, R. Massey, A. Phalke, and K.Yadav. 2016. Global Food Security Support Analysis Data (GFSAD) at Nominal 1-km (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities. IN: Remote Sensing Handbook; Vol. II: Land Resources Monitoring, Modeling, and Mapping with Remote Sensing. P. Thenkabail (Editor). CRC/Taylor & Francis, Boca Raton, FL. pp. 131-159.

Green, Kass, Russell G. Congalton, and Mark Tukman. 2017. Imagery and GIS: Best Practices for Extracting Information from Imagery. ESRI Press. Redlands, CA. 437p.

Grybas, Heather, and Russell G. Congalton. 2018. Land cover change image analysis for Assateague Island National Seashore following Hurricane Sandy. IN: Image Processing in Agriculture and Forestry. Gonzalo Pajares Martinsanz and Francisco Rovira-Mas (Editors). MDPI. Basel, Switzerland. pp. 172-198.

Congalton, R. and K. Green. 2019. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 3rd Edition. CRC/Taylor & Francis, Boca Raton, FL 328p.

Pischke, Erin C., Z. Carter Berry, Randall K. Kolka, Jacob Salcone, Diana Cordoba, Xoco Shinbrot, Sergio Miguel Lopez Ramirez, Kelly W. Jones, Russell G. Congalton, Robert H. Manson, Juan Jose Von Thaden Ugalde, Theresa Selfa, Sophie Avila, Heidi Asbjornsen. 2019. Lessons learned about collaborating across coupled natural-human systems research on Mexico’s Payments for Hydrological Services Program. IN: Collaboration Across Boundaries for Social-Ecological Systems Science: Experiences Around the World. Stephen Perz (Editor). Palgrave Macmillan. 437p. (ISBN-13: 978-3030138264)

Congalton, Russell G. and Benjamin Fraser. 2020. Unmanned Aerial Systems (UAS) and Thematic Map Accuracy Assessment. IN: Applications of Small Unmanned Aircraft Systems: Best Practices and Case Studies. J. B. Sharma (Editor). CRC/Taylor & Francis, Boca Raton, FL. 289p.

SYMPOSIUM PROCEEDINGS AND OTHER ARTICLES

Congalton, R. G. and R. A. Mead. 1980. State of the art of Landsat classification accuracy assessment. Proceedings of the Sixth Annual Symposium on Machine Processing of Remotely Sensed Data. Purdue University, West Lafayette, IN. p. 337.

Congalton, R. G., R. A. Mead, R. G. Oderwald, and J. Heinen. 1981. Analysis of forest classification accuracy. Remote Sensing Research Report 81-1. Agristars Report RR-U1-04066, JSC 17123. 85 pp.

Congalton, R. G. 1981. The use of discrete multivariate analysis techniques for the assessment of Landsat classification accuracy. Masters Thesis. Virginia Polytechnic Institute and State University, Blacksburg, VA. 111 pp.

Congalton, R. G., R. G. Oderwald, and R. A. Mead 1982. Accuracy of remotely sensed data: sampling and analysis procedures. Remote Sensing Research Report 82-1. Agristars Report RR-U2-04257, Coop Agreement 13-1134. 83 pp.

Congalton, R. G., J. T. Heinen, and R. G. Oderwald. 1983. Update and review of accuracy assessment techniques for remotely sensed data. Remote Sensing Research Report 83-1. Final Report for Nationwide Forestry Applications Program. Coop Agreement 13-1134. 34 pp.

Congalton, R. G. 1984. A comparison of five sampling schemes used in assessing the accuracy of land cover/land use maps derived from remotely sensed data. Ph. D. Dissertation. Virginia Polytechnic Institute and State University. Blacksburg, VA. 147 pp.

Congalton, R. and A. Rekas. 1985. COMPAR: A computerized technique for the in-depth comparison of remotely sensed data. Proceedings of the Fifty First Annual Meeting of the American Society of Photogrammetry, Washington, DC. p. 98-106.

Lunetta, R., R. Congalton, A. Rekas, and J. Stoll. 1985. Using remotely sensed data to map vegetative cover for habitat evaluation in the Saginaw River Basin. Proceedings of the Fifty First Annual Meeting of the American Society of Photogrammetry, Washington, D.C. p. 88-97.

Congalton, R. 1986. Geographic information systems specialist: a new breed. Proceedings of Joint US Forest Service and ASP workshop on Geographic Information Systems. Atlanta, Ga. p. 37-42.

Congalton, R. and L. Pierce. 1986. An assessment of evapo-transpirational water losses in a Sierran mixed conifer forest using remotely sensed data. Proceedings of the Fifty Second Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Washington, D.C. Vol. 5, p. 53-62.

Congalton, R. 1986. Procedures for the preparation of a geographic spatial data base: Description and instructions for digitizing, plotting, and gridding data to be input to a geographic spatial data base. Miscellaneous Paper EL-86. Environmental Laboratory, US Army Engineer Waterways Experiment Station,Vicksburg, MS.

Congalton, R., R. Thomas, P. Zinke, J. Helms, and G. Smoot. 1987. Development of EOS-Aided Procedures for the Determination of the Water Balance or Hydrologic Budget of a Large Watershed. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS). Ann Arbor, Michigan. pp. 949-954.

Stenback, J., C. Brown, R. Barrett, and R. Congalton. 1987. Application of remotely sensed digital data and a GIS in evaluating deer habitat suitability on the Tehama deer winter range. Proceedings of the Second International Conference, Exhibits, and Workshops on Geographic Information Systems (GIS '87), San Francisco, California. pp. 440-445.

Helms, J. and R. Congalton. 1987. Remote sensing of transpirational use of water by forest vegetation. IUFRO Conference on Management of Water and Nutrient Relations to Increase Forest Growth. Canberra, Australia. (abstract only).

Stenbeck, J. and R. Congalton. 1988. Assessing canopy-understory relationships using Thematic Mapper imagery and an unsupervised classification approach. Proceedings of the Fifty-Fourth Annual Meeting of the American Society of Photogrammetry and Remote Sensing, St. Louis, Missouri. Vol. 6, pp. 202 (abstract only).

Mann, L., R. Congalton, K. Green, and M. Cosentino. 1989. Mapping forest vegetation type and structure on the Okanogan National Forest using Landsat Thematic Mapper data. Proceedings of Image Processing 89, Reno, Nevada. May, 1989. pp. 156-165.

Congalton, R. 1989. Considerations and techniques for assessing the accuracy of remotely sensed data. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS). Vancouver, Canada. July, 1989. pp. 1847-1850.

Biging, G. and R. Congalton. 1989. Development of remote sensing technologies for use in forest inventory. Proceedings of Global Natural Resource Monitoring and Assessments: Preparing for the 21st Century. Venice, Italy. September, 1989. Volume 3. pp. 1241-1249.

Congalton, R., J. Helms, and P. Zinke. 1989. A procedure for determining the water balance of a large forested watershed from remotely sensed data. Proceedings of Global Natural Resource Monitoring and Assessments: Preparing for the 21st Century. Venice, Italy. September, 1989. Volume 1. pp.334-343.

Biging, G. and R. Congalton. 1989. Using satellite data for forest inventory. Proceedings of the National Computer Graphics Association Mapping and GIS Conference: Visual Solutions ... Right at your Fingertips. Los Angeles, CA. November, 1989. p. 36. (Abstract only).

Green, K. and R. Congalton. 1990. Mapping potential old growth forests and other resources on National Forest and Park lands in Oregon and Washington. Proceedings of GIS/LIS 90. Anaheim, CA. November 1990. p. 712-723.

Congalton, R. 1990. Beware the black box: Comments on the keynote address. Proceedings of GIS/LIS 90. Anaheim, CA. November 1990. p. 851-853.

Biging, G., R. Congalton, and E. Murphy. 1991. A comparison of photointerpretation and ground measurements of forest structure. Proceedings of the Fifty-Sixth Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Baltimore, Maryland. Vol. 3, pp. 6-15.

Congalton, R. 1991. Error analysis of remotely sensed data: Where do we go from here? Proceedings of the National Center for Geographic Information and Analysis. Initiative 12. Special Session on The Integration of Remote Sensing and GIS held at the Fifty-Sixth Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Baltimore, Maryland. pp.129-135.

Congalton, R. and G. Biging. 1992. How to validate stand maps. Proceedings of Stand Inventory Technologies 92 Conference. September 13-17, Portland, OR. Am. Society for Photo. & Remote Sensing. Bethesda, MD. pp. 74-82.

Congalton, R. 1992. Exploring multi-spectral and hyper-spectral data for forest productivity and damage assessment. Proceedings of the International Symposium on Spectral Sensing Research. November 15-20, Maui, HI. Science and Technology Corp., Hampton, VA. pp. 956-963..

Schriever, J. R. and R. G. Congalton. 1993. Mapping forest cover-types in New Hampshire using multi-temporal Landsat Thematic Mapper data. Proceedings of the Fifty-Ninth Annual Meeting of the American Society of Photogrammetry and Remote Sensing, New Orleans, Louisiana. Volume 2. pp. 333-342.

Congalton, R., R. Macleod, and F. Short. 1993. Developing accuracy assessment procedures for change detection analysis. Final Report submitted to NOAA CoastWatch Change Analysis Program, Beaufort, NC. 57 p.

Congalton, R. G. and R. D. Macleod. 1994. Change detection accuracy assessment on the NOAA Chesapeake Bay pilot study. Proceedings of the International Symposium of Spatial Accuracy of Natural Resource Data Bases, Williamsburg, VA. pp. 78-87.

Khorram, S., G. Biging, N. Chrisman, D. Colby, R. Congalton, J. Dobson, R. Ferguson, M. Goodchild, J. Jensen, and T. Mace. 1994. Accuracy assessment of land cover change detection. Report for the NOAA Coastal Change Analysis Program. Computer Graphics Center, North Carolina State University. 70p.

Brennan, M., D. Izraelevitz, and R. Congalton. 1994. Product accuracy assessment final report. TR 7477-1 prepared under contract FA7056-93-C-0029 Task 3 for the Remote Earth Sensing Program Office. The Analytical Sciences Corp. Reading, MA.

Congalton, R. G. 1994. Accuracy assessment of remotely sensed data: future needs and directions. Proceedings of the Pecora 12 Symposium: Land Information from Space-Based Systems, Sioux Falls, SD. Am. Soc. Photo. and Remote Sensing. pp. 385-388.

Macleod, R., R. Congalton, and F. Short. 1995. Using quantitative accuracy assessment techniques to compare various change detection algorithms for monitoring eelgrass distributions in Great Bay, NH generated from Landsat TM data. Proceedings of the Sixty First Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Charlotte, North Carolina. Volume 3. pp. 876-885.

Congalton, R. and S. Pugh. 1996. Evaluating error in a remotely sensed forest cover type map using spatial autocorrelation analysis. ERDAS User’s Group Meeting Proceedings. Atlanta, GA. (abstract only).

Budd, R., A. Fried, M. Becker, and R. Congalton. 1996. Validating remotely sensed environmental data: The GLOBE initiative. Eco-Informa ‘96, Lake Buena Vista, FL. published by ERIM, Ann Arbor, MI. Volume 10 pp. 497-502.

Fried, A., R. Budd, M. Becker, and R. Congalton. 1996. Monitoring global environmental resources: The GLOBE perspective. Proceedings of GIS/LIS 96. Denver, CO. November 1996. pp. 740-747.

Pugh, S. and R. Congalton. 1997. Applying spatial autocorrelation analysis to evaluate error in New England forest cover type maps derived from Thematic Mapper data. Proceedings of the Sixty Third Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Seattle, Washington. Volume 3. pp. 648-657.

Short, F., R. Congalton, D. Burdick, and R. Boumans. 1997. Modelling Eelgrass Habitat Change to Link Ecosystem Processes with Remote Sensing. Final Report submitted to NOAA CoastWatch Change Analysis Program, Beaufort, NC. Grant #NA36RG04970.

Congalton, R., M. Becker, R. Budd, and A. Fried. 1997. Using GLOBE student data to validate land cover maps derived from remotely sensed imagery: Is it good enough? IN: Building Our GLOBE Community; The Second Annual GLOBE Conference. Airlie, VA.

Congalton, R. and M. Brennan. 1998. Change detection accuracy assessment: Pitfalls and considerations. Proceedings of the Sixty Fourth Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Tampa, Florida. pp. 919-932 (CD-ROM).

Brennan, M., R. Congalton, P. Pekins, and K. Taylor. 1998. Use of remote sensing and GIS tools for common loon (Gavia Immer) management in New Hampshire. Proceedings of the Sixty Fourth Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Tampa, Florida. pp. 470-478 (CD-ROM).

Congalton, R., and M. Becker. 1998. Using GLOBE student data in land cover classification and accuracy assessment. Proceedings of the Spring Meeting of the American Geophysical Union. Boston, MA. (abstract only).

Fried, A., R. Congalton, and M. Becker. 1998. New frontiers in land cover classification and accuracy assessment. Proceedings of the First International Conference on Geospatial Information in Agriculture and Forestry. ERIM International, Inc. Ann Arbor, MI. Vol. I. pp. 290-297.

Congalton, R. and M. Becker. 1998. Evaluating the GLOBE land cover/biology investigation training methods and materials: A teacher/student pilot study. Proceedings of the Third Annual GLOBE Conference, Snowmass Village, CO.

Congalton, R. and M. Brennan. 1999. Error in remotely sensed data analysis: Evaluation and reduction. Proceedings of the Sixty Fifth Annual Meeting of the American Society of Photogrammetry and Remote Sensing. Portland, OR. pp. 729-732 (CD-ROM)

Brennan, M and R. Congalton. 1999. Analysis of spatially related data for Common Loon (Gavia Immer) management in New Hampshire. Proceedings of the Sixty Fifth Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Portland, OR. pp. 203-206. (CD-ROM)

Wormstead, S., M. Becker, J. Bourgeault, R. Congalton, and L. Ryan. 1999. The GLOBE teacher's guide: A critical link in ensuring data quality. Proceedings of the Fourth Annual GLOBE Conference, Durham, NH.

Congalton, R. 1999. Sampling issues for assessing the accuracy of remotely sensed data. Proceedings of the Fourth Annual GLOBE Conference, Durham, NH.

Congalton, R., L. Cannon, M. Golden, and J. Schriever. 1999. Use of regional forest vegetation mapping for analyzing endangered species habitat. EOM. Vol. 8, No. 5. pp. 8-9.

Plourde, L. and R. Congalton. 1999. Important factors in assessing the accuracy of remotely sensed forest vegetation maps. Proceedings of the Pecora 14/Land Satellite Information III Symposium: Demonstrating the Value of Satellite Imagery, Denver, CO. Am. Soc. Photo. and Remote Sensing. pp. 261-271 (CD-ROM).

Congalton, R., K. Birch, R. Jones, J. Powell, and J. Schreiver. 2000. Evaluating remotely sensed techniques for mapping riparian vegetation. Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry. ERIM International, Inc. Ann Arbor, MI. Vol I. pp.77-85.

Barrett, M. and R. Congalton., 2000. GIS in the design and management of a potential ecological reserve in the University of New Hampshire's College Woods. Proceedings of the Sixty Sixth Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Washington, DC. 11 p. (CD-ROM)

Rowe, R. and R. Congalton., 2000. Using GLOBE student-collected reference data to validate the accuracy of land cover maps derived from remotely sensed data. Proceedings of the Sixty Sixth Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Washington, DC. 8 p. (CD-ROM)

Bourgeault, J, R. Congalton, and M. Becker. 2000. GLOBE Muc-a-thon: A method for effective student land cover data collection. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS). Honolulu, Hawaii. July, 2000. Vol. II pp. 551 - 553. (CD-ROM)

Congalton, R. and L. Plourde. 2000. Sampling methodology, sample placement, and other important factors in assessing the accuracy of remotely sensed forest maps. Proceedings of the 4^th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Science. Delft University Press, Amsterdam. pp. 117-124.

Rowe, R., R. Congalton, and M. Becker. 2000. Using GLOBE student-collected validation data to assess the accuracy of a remotely sensed land cover map of Dutchess County, New York. Proceedings of the Fifth Annual GLOBE Conference, Annapolis, MD.

Congalton, R., R. Rowe, and M. Becker. 2001. Using GLOBE student-collected muc-a-thon data to aid in assessing the accuracy of a Landsat Thematic Mapper-derived land cover map of Dutchess County, New York. Proceedings of the Sixth Annual GLOBE Conference, Blaine, Washington. pp. 260-269.

Tardie, P. and R. Congalton. 2002. A change detection analysis: Using remotely sensed data to assess the progression of development in Essex County, MA from 1990 to 2001. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Washington, DC. 7 p. (CD-ROM)

Congalton, Russell G. 2002. The GLOBE Program: A potential source of land cover reference data. Proceedings of the 5^th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Science. Melbourne, Australia. pp. 102-109.

Congalton, R. and M. Becker. 2002. MUC-A-THONS and land cover mapping: The saga continues: Proceedings of the Seventh Annual GLOBE Conference, Chicago, IL. pp. 55-64.

West, D. and R. Congalton. 2003. Incorporating GLOBE data into a remotely-sensed change detection analysis of Androscoggin County, Maine. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Anchorage, AK. 12 p. (CD-ROM)

Lennartz, S. and R. Congalton. 2004. Classifying and mapping forest cover types using IKONOS imagery in the northeastern United States. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Denver, CO. 11 p. (CD-ROM)

Iiames, J., D. Pliant, T. Lewis, and R. Congalton. 2004. Leaf area index (LAI) change detection on loblolly pine forest stands with complete understory removal. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Denver, CO. 9 p. (CD-ROM)

Iiames, J., R. Congalton, D. Pliant, and T. Lewis. 2004. Accounting for error propagation in the development of a leaf area index (LAI) reference map to assess the MODIS MOD15A LAI product. Proceedings of the 6^th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Science. Portland, ME. 14 p. (CD-ROM)

Bishop, J., R. Congalton and M. Becker. 2004. Monitoring biodiversity of select restoration sites in New Zealand. Proceedings of the Eighth Annual GLOBE Conference, Boulder, CO pp. 68-73.

Congalton, R., J. Bourgeault, and M. Becker. 2005. Androscoggin County, Maine land cover change analysis: A successful collaboration. Proceedings of the Ninth Annual GLOBE Conference, Prague, Czech Republic. CD ROM and www.globe.gov

Bourgeault, J., M. Becker, and R. Congalton. 2005. Environmental programs unite to do “NHEET” things for teachers. Proceedings of the Ninth Annual GLOBE Conference, Prague, Czech Republic. CD ROM and www.globe.gov

Iiames, J. and R. Congalton. 2006. A comparison of inter-analyst differences in the classification of a Landsat ETM+ scene in South-Central Virginia. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Reno, NV. 9 p. (CD-ROM)

Bishop, J. and R. Congalton. 2006. An evaluation of the effect of terrain normalization on classification accuracy of Landsat ETM+ imagery. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Reno, NV. 12 p. (CD-ROM)

Jacques, K., R. Congalton, and K. Babbitt. 2007. Effects of urbanization on the spatial distribution and size of wetlands in New Hampshire. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Tampa, FL. 11 p. (CD-ROM)

Congalton, R. 2008. Thematic and positional accuracy assessment of digital remotely sensed data IN: Proceedings of the 7^thAnnual Forest Inventory and Analysis Symposium, 2005. pp. 149-154. R. Roberts, G. Reams, P. Van Deusen, and P. McWilliams, (eds.) Gen. Tech. Report WO-77, USDA Forest Service. 319 p.

Graham, M. and R. Congalton. 2009. Evaluating issues in map accuracy: A study of mapping benthic habitat on the Texas Gulf Coast. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Baltimore, MD. 11 p. (CD-ROM)

Congalton, R. 2009. Change detection accuracy assessment: pitfalls and possibilities. Proceedings of MultiTemp 2009: The Fifth International Workshop on the Analysis of Multi-temporal Remote Sensing Images. Groton, CT. pp. 276-282.

Graham, M. and R. Congalton. 2009. A comparison of the 1992 and 2001 National Land Cover Datasets in the Lamprey River Watershed, NH. Proceedings of the Fall Meeting of the American Society of Photogrammetry and Remote Sensing, San Antonio, TX. 8 p. (CD-ROM)

Rudko, A. and R. Congalton. 2010. Using GIS to model common loon (Gavia immer) habitat. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, San Diego, CA. 6 p. (CD-ROM)

Maclean, M. A. Rudko, and R. Congalton. 2010. Multi-temporal image analysis of the Coastal Watershed, NH. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, San Diego, CA. 7 p. (CD-ROM)

Maclean, M. and R. Congalton. 2010. Mapping and analysis of fragmentation in southeastern New Hampshire. Proceedings of the Fall Meeting of the American Society of Photogrammetry and Remote Sensing, Orlando, FL. 5 p. (CD-ROM)

Maclean, M. and R. Congalton. 2011. Using object-oriented classification to map forest community types. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Milwaukee, WI. 10 p. (CD-ROM)

Congalton, R. J. Jensen, and J. Shan. 2011. Writing a scientific journal paper: Preparation through publication. Feature Article in: Photogrammetric Engineering and Remote Sensing. Vol. 77. No. 5. pp. 445-450.

Maclean, M. and R. Congalton. 2012. Map accuracy assessment issues when using and object-oriented approach. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Sacramento, CA. 5 p. (CD-ROM)

Campbell, M. and R. Congalton. 2012. Landsat-based land cover change analysis in northeastern Oregon’s timber-resource-dependent communities. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Sacramento, CA. 12 p. (CD-ROM)

Hamilton, L., J. Hartter, F. Stevens, R. Congalton, M. Ducey, M. Campbell, D. Maynard, and M. Staunton. 2012. Forest views: Northeast Oregon survey looks at community and environment. Carsey Institute, University of New Hamsphire, Issue Brief No. 47. 12p.

Thenkabail, P., J. Knox, M. Ozdogan, M. Gumma, R. Congalton, A. Wu, C. Milesi, A. Finkral, M. Marshall, I. Mariotto, S. You, C. Giri, and P. Nagler. 2012. Assessing future risks to agricultural productivity, water resources and food security: how can remote sensing help: Highlight Article in: Photogrammetric Engineering and Remote Sensing. Vol. 78. No. 8. pp. 773-782.

MacLean, M. and R. Congalton. 2013. Predicting woody invasive species presence using a new fragmentation program: Polyfrag. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Baltimore, MD. 8 p. (www.asprs.org)

Kovacs, J. and R. Congalton. 2013. Forest cover type analysis of New England forests using innovative WorldView-2 imagery. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Baltimore, MD. 7 p. (www.asprs.org)

Sivanpillai, R. and R. Congalton. 2014. Panel discussion on future Landsat data needs at the local and state levels. Proceedings of the Annual Meeting of the American Society of Photogrammetry and Remote Sensing, Loiusville, KY. 3 p. (www.asprs.org)

Hartter, J. F. Stevens, L. Hamilton, P. Oester, R. Congalton, M. Ducey, and M. Crowley. 2014. Forest Management and Wildfire Risk in Inland Northwest. Carsey Institute. University of New Hampshire. National Issue Brief No. 70. 8 p.

Xiong, J., Thenkabail, P. S., James C. T., Gumma, M. K., Teluguntla, P., Congalton, R. G., Poehnelt, J., Kamini Yadav., et al., and Massey, R. 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m Africa: Cropland Extent Product (GFSAD30AFCE). NASA EOSDIS Land Processes DAAC. Retrieved from https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001

Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M., Congalton, R., Oliphant, A., Sankey, T., Poehnelt, J., Yadav, K., Phalke, A., Smith, C. 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m for Australia, New Zealand, China, and Mongolia: Cropland Extent Product (GFSAD30AUNZCNMOCE). NASA EOSDIS Land Processes DAAC. Retrieved from https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AUNZCNMOCE.001

Oliphant, A., Thenkabail, P. S., Teluguntla, P., Xiong, J. Congalton, R., Yadav, K., Massey, R., Gumma, M., Smith, C. 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m for Southeast & Northeast Asia: Cropland Extent Product (GFSAD30SEACE). NASA EOSDIS Land Processes DAAC. Retrieved from https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SEACE.001

Gumma, M.K., Thenkabail, P.S., Teluguntla, P., Oliphant, A., Xiong, J., Congalton, R., Yadav, K., Smith, C. 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m for South Asia, Afghanistan and Iran: Cropland Extent Product (GFSAD30SAAFGIRCE). NASA EOSDIS Land Processes DAAC. Retrieved from https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SAAFGIRCE.001

Phalke, A., Ozdogan, M., Thenkabail, P. S., Congalton, R., Yadav, K., Massey, R., Teluguntla, P., Poehnelt, J., and Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m for Europe, Middle-east, Russia and Central Asia: Cropland Extent Product (GFSAD30EUCEARUMECE). NASA EOSDIS Land Processes DAAC. Retrieved from https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001

Massey, R., Sankey, T.T., Yadav, K., Congalton, R.G., Tilton, J.C., Thenkabail, P.S., (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30m for North America: Cropland Extent Product (GFSAD30NACE). NASA EOSDIS Land Processes DAAC. Retrieved from https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30NACE.001

Zhong, Y., Giri, C., Thenkabail, P.S., Teluguntla, P., Congalton, R., Yadav, K., Oliphant, A., Xiong, J., Poehnelt, J., and Smith, C. 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) @ 30-m for South America: Cropland Extent Product (GFSAD30SACE). NASA EOSDIS Land Processes DAAC. Retrieved from https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30SACE.001

Congalton, R.G., Yadav, K., McDonnell, K., Poehnelt, J., Stevens, B., Gumma, M.K., Teluguntla, P., and Thenkabail, P.S., 2017. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-Support Analysis Data (GFSAD) @ 30-m: Cropland Extent Validation (GFSAD30VAL), NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center: Sioux Falls, SD, USA. https://doi:10.5067/MEaSUREs/GFSAD/GFSAD30VAL.001

Sun, Peijun, Russell G. Congalton, and Yaozhong Pan. 2018. Comparing the impact of the mapping error on aggregation models. Proceedings of Spatial Accuracy 2018. Beijing, China. May 21-24, 2018. pp. 25-34. https://docs.wixstatic.com/ugd/ab9b0d_17da0c4c0983484a86f77801faf95089.pdf

Sun, Peijun and Russell G. Congalton. 2019. Comparing the impact of the mapping error on the representation of landscape pattern on upscaled maps. Proceedings of the Eighth International Conference on Agro-Geoinformatics. Istanbul Turkey. July 16-19, 2019. pp. 319-324. https://ieeexplore.ieee.org/document/8820256.

Congalton, Russell G., Roberta Lenczowski, Lisa Wirth, and Christopher McGinty, 2022. AmericaView and its Landsat Connection. Photogrammetric Engineering and Remote Sensing, Vol. 88, No. 7. pp. 421-424. https://doi.org/10.14358/PERS.88.7.421