Title:

Characterizing Forest Stands Using Unmanned Aerial Systems (UAS): Evaluating Forest Attributes and Ecological Health

Poster

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Abstract

Forests, both in our local environment and around the world, are facing a complex mixture of demands and disturbances. The systematic monitoring of these natural systems is long established, but often still does not meet the spatial, temporal, or informational needs of resource or ecological management. For these reasons, new technologies such as remote sensing and Geographic Information Systems (GIS) have been adopted to enhance the collection and analysis potential of a variety of end users. One prominent culmination of these and other closely related technologies are Unmanned Aerial Systems (i.e., UAS, UAV or drone). UAS, in their relatively short period of commercial and research explorations, have been successfully applied to fields such as wildlife biology and conservation, forestry, rangeland monitoring, wetlands mapping, and urban ecology. Building upon previous studies at the University of New Hampshire, this research looks to further define the potential of UAS for characterizing the complex forests of New Hampshire and assisting traditional methodologies. Specifically, we will investigate three perspectives for assessing the individual forest communities which make up the broader landscape. First, we will quantify the ability of UAS to collect reference data on the species composition of forest stands with a comparison to ground-based data and traditional remote sensing platforms. Second, we will evaluate the accuracy and precision of UAS stand inventory estimates and the ability to detect large trees. Finally, we will determine the proficiency of UAS for measuring forest health of the major species groups.

Authors

First Name Last Name
Dr. Russell G. Congalton
Benjamin T. Fraser

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Submission Details

Conference GRC
Event Graduate Research Conference
Department Natural Resources and Earth Systems Science (GRC)
Group Poster Presentation
Added April 15, 2020, 8:27 p.m.
Updated April 18, 2020, 9:29 a.m.
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