Russell Congalton

Professor

University of New Hampshire

United States

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Professor Russell Congalton is a faculty member at the University of New Hampshire, USA. His research focuses on remote sensing and the application of unmanned aerial systems (UAS) in environmental monitoring. Recent articles include studies on UAS for forest health assessment, accuracy of individual tree detection methods, and the impact of mixed pixel training data on classification models.

Articles (14)

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2025

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2025

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2024

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2021

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2021

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2021

Collaborators (2)

Benjamin Fraser

Graduate Teaching Assistant and Researcher

University of New Hampshire

UNITED STATES

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Jeff Garnas

Associate Professor

University of New Hampshire

UNITED STATES

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Russell Congalton

Research Interests

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Articles (14)

Assessing the Impact of Mixed Pixel Proportion Training Data on SVM-Based Remote Sensing Classification: A Simulated Study

Quantifying the Accuracy of UAS-Lidar Individual Tree Detection Methods Across Height and Diameter at Breast Height Sizes in Complex Temperate Forests

Early Detection of Southern Pine Beetle Attack by UAV-Collected Multispectral Imagery

A Comparison of Unpiloted Aerial System Hardware and Software for Surveying Fine-Scale Oak Health in Oak–Pine Forests

Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes

Evaluating the Impacts of Flying Height and Forward Overlap on Tree Height Estimates Using Unmanned Aerial Systems

Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review

Individual Tree Crown Delineation From UAS Imagery Based on Region Growing by Over-Segments With a Competitive Mechanism

Monitoring Fine-Scale Forest Health Using Unmanned Aerial Systems (UAS) Multispectral Models

The impact of landscape characteristics on the performance of upscaled maps

A Comparison of Methods for Determining Forest Composition from High-Spatial-Resolution Remotely Sensed Imagery

Analysis of the Impact of Positional Accuracy When Using a Block of Pixels for Thematic Accuracy Assessment

Evaluating the Capability of Unmanned Aerial System (UAS) Imagery to Detect and Measure the Effects of Edge Influence on Forest Canopy Cover in New England

Mapping and Monitoring Forest Cover

Collaborators (2)

Benjamin Fraser

Jeff Garnas

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