Russell G. Congalton
Orcid: 0000-0003-3891-2163
According to our database1,
Russell G. Congalton
authored at least 23 papers
between 2014 and 2023.
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Bibliography
2023
Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes.
Remote. Sens., 2023
2022
Individual Tree Crown Delineation From UAS Imagery Based on Region Growing by Over-Segments With a Competitive Mechanism.
IEEE Trans. Geosci. Remote. Sens., 2022
2021
A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests.
Remote. Sens., 2021
Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models.
Remote. Sens., 2021
Estimating Primary Forest Attributes and Rare Community Characteristics Using Unmanned Aerial Systems (UAS): An Enrichment of Conventional Forest Inventories.
Remote. Sens., 2021
2020
Individual Tree Crown Delineation from UAS Imagery Based on Region Growing and Growth Space Considerations.
Remote. Sens., 2020
Analysis of the Impact of Positional Accuracy When Using a Single Pixel for Thematic Accuracy Assessment.
Remote. Sens., 2020
2019
Correction: Yadav. K. and Congalton. R. Accuracy Assessment of Global Food Security-Support Analysis Data (GFSAD) Cropland Extent Maps Produced at Three Different Spatial Resolutions. Remote Sens. 2018 10, 1800.
Remote. Sens., 2019
Using a simulation analysis to evaluate the impact of crop mapping error on crop area estimation from stratified sampling.
Int. J. Digit. Earth, 2019
Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud.
Int. J. Appl. Earth Obs. Geoinformation, 2019
Comparing the impact of mapping error on the representation of landscape pattern on upscaled agricultural maps.
Proceedings of the 8th International Conference on Agro-Geoinformatics, 2019
2018
Accuracy Assessment of Global Food Security-Support Analysis Data (GFSAD) Cropland Extent Maps Produced at Three Different Spatial Resolutions.
Remote. Sens., 2018
Issues with Large Area Thematic Accuracy Assessment for Mapping Cropland Extent: A Tale of Three Continents.
Remote. Sens., 2018
Remote. Sens., 2018
Issues in Unmanned Aerial Systems (UAS) Data Collection of Complex Forest Environments.
Remote. Sens., 2018
A quantitative performance comparison of paddy rice acreage estimation using stratified sampling strategies with different stratification indicators.
Int. J. Digit. Earth, 2018
2017
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.
Remote. Sens., 2017
Remote. Sens., 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.
Int. J. Digit. Earth, 2017
2015
Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products.
Remote. Sens., 2015
The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis.
Remote. Sens., 2015
Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy.
J. Imaging, 2015
2014