Russell Congalton
Russell Congalton
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A review of assessing the accuracy of classifications of remotely sensed data
RG Congalton
Remote sensing of environment 37 (1), 35-46, 1991
Assessing the accuracy of remotely sensed data: principles and practices
RG Congalton, K Green
CRC press, 2019
Accuracy assessment: a user’s perspective
M Story, RG Congalton
Photogrammetric Engineering and remote sensing 52 (3), 397-399, 1986
Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques
RG Congalton, RG Oderwald, RA Mead
Photogrammetric engineering and remote sensing 49 (12), 1671-1678, 1983
A quantitative method to test for consistency and correctness in photointerpretation
RG Congalton, RA Mead
Photogrammetric Engineering and Remote Sensing 49 (1), 69-74, 1983
Application of remote sensing and geographic information systems to forest fire hazard mapping
E Chuvieco, RG Congalton
Remote sensing of Environment 29 (2), 147-159, 1989
A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data
RD Macleod, RG Congalton
Photogrammetric engineering and remote sensing 64 (3), 207-216, 1998
Accuracy assessment and validation of remotely sensed and other spatial information
RG Congalton
International journal of wildland fire 10 (4), 321-328, 2001
Determining forest species composition using high spectral resolution remote sensing data
ME Martin, SD Newman, JD Aber, RG Congalton
Remote sensing of environment 65 (3), 249-254, 1998
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing
J Xiong, PS Thenkabail, MK Gumma, P Teluguntla, J Poehnelt, ...
ISPRS Journal of Photogrammetry and Remote Sensing 126, 225-244, 2017
Remote sensing and geographic information system data integration- Error sources and research issues
R Lunetta, R Congalton, L Fenstermaker, J Jensen, K Mcgwire, LR Tinney
Photogrammetric engineering and remote sensing 57 (6), 677-687, 1991
A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data.
RG Congalton
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
P Teluguntla, PS Thenkabail, A Oliphant, J Xiong, MK Gumma, ...
ISPRS journal of photogrammetry and remote sensing 144, 325-340, 2018
A comparison of urban mapping methods using high-resolution digital imagery
N Thomas, C Hendrix, RG Congalton
Photogrammetric Engineering & Remote Sensing 69 (9), 963-972, 2003
Evaluating the potential for measuring river discharge from space
DM Bjerklie, SL Dingman, CJ Vorosmarty, CH Bolster, RG Congalton
Journal of hydrology 278 (1-4), 17-38, 2003
Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine
J Xiong, PS Thenkabail, JC Tilton, MK Gumma, P Teluguntla, A Oliphant, ...
Remote Sensing 9 (10), 1065, 2017
Global land cover mapping: A review and uncertainty analysis
RG Congalton, J Gu, K Yadav, P Thenkabail, M Ozdogan
Remote Sensing 6 (12), 12070-12093, 2014
A practical look at the sources of confusion in error matrix generation.
RG Congalton, K Green
Using spatial autocorrelation analysis to explore the errors in maps generated from remotely sensed data.
RG Congalton
Effects of landscape characteristics on amphibian distribution in a forest-dominated landscape
HL Herrmann, KJ Babbitt, MJ Baber, RG Congalton
Biological Conservation 123 (2), 139-149, 2005
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