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.
Highlights
Upland swamps are extensive areas of treeless heaths and sedgelands and are listed as highly sensitive ecosystems in New South Wales, Australia.[1]
We present preliminary results of the species differentiation and vegetation complexes over this spectrally diverse swamp environment
The high overall classification accuracy obtained in this study indicates benefits of high-resolution near-surface spectroscopy through Unmanned aerial vehicle (UAV) for a heterogeneous ecosystem
Summary
Upland swamps are extensive areas of treeless heaths and sedgelands and are listed as highly sensitive ecosystems in New South Wales, Australia.[1]. Precise and frequent mapping of the vegetation communities is essential for the sustainability of the environment. Traditional aerial and satellite-based methods are limited to the delineation of the swamp boundaries.[2] Identification of individual species and vegetation group is critical to characterise the vegetation communities in the swamps and is the first step towards monitoring of the changing health of the characteristic species under natural or anthropogenic stress.
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