High-resolution mapping of upland swamp vegetation using an unmanned aerial vehicle-hyperspectral system

Article

Banerjee, Bikram Pratap, Raval, Simit and Cullen, Patrick Joseph. 2017. "High-resolution mapping of upland swamp vegetation using an unmanned aerial vehicle-hyperspectral system." Journal of Spectral Imaging. 6 (1). https://doi.org/10.1255/jsi.2017.a6
Article Title

High-resolution mapping of upland swamp vegetation using an unmanned aerial vehicle-hyperspectral system

Article CategoryArticle
AuthorsBanerjee, Bikram Pratap, Raval, Simit and Cullen, Patrick Joseph
Journal TitleJournal of Spectral Imaging
Journal Citation6 (1)
Article Numbera6
Number of Pages8
Year2017
PublisherIM Publications
Place of PublicationUnited Kingdom
ISSN2040-4565
Digital Object Identifier (DOI)https://doi.org/10.1255/jsi.2017.a6
Web Address (URL)https://www.impopen.com/jsi-abstract/I06_a6
Abstract

Mapping of vegetation species and communities in sensitive ecosystems is essential for identification and management of anthropogenic impacts. Unmanned aerial vehicle (UAV)-hyperspectral systems are among the latest technologies in remote sensing that hold a potential for obtaining unprecedented quality of remote sensing data for vegetation mapping and health status monitoring applications. In this study, high-resolution (1–1.5 cm) spectral imaging data (15 bands) from a tunable spectrometer is used to map five species of vegetation in a complex upland swamp environment. The overall accuracy of classification was found to be 88.9% with a kappa coefficient of 0.83. Three classes (bare earth, sedgeland grass and black sheoak) have achieved higher accuracy (above 78%) and one class (bracken fern) has lower accuracy (58%). UAV-hyperspectral technology is, therefore, an effective tool to identify and map sensitive swamp vegetation. The technology can be potentially applied to determine the health status of the species.

KeywordsUAV; hyperspectral; sensitive species; upland swamps
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
410402. Environmental assessment and monitoring
Byline AffiliationsUniversity of New South Wales
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