Characterising Upland Swamps Using Object-Based Classification Methods and Hyper-Spatial Resolution Imagery Derived from an Unmanned Aerial Vehicle

Lechner, A. M., Fletcher, A., Johansen, K. and Erskine, P. (2012) Characterising Upland Swamps Using Object-Based Classification Methods and Hyper-Spatial Resolution Imagery Derived from an Unmanned Aerial Vehicle. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, I-4 . pp. 101-106. ISSN 2194-9050

Full content URL: https://doi.org/10.5194/isprsannals-I-4-101-2012

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Item Type:Article
Item Status:Live Archive

Abstract

Subsidence, resulting from underground coal mining can alter the structure of overlying rock formations changing hydrological conditions and potentially effecting ecological communities found on the surface. Of particular concern are impacts to endangered and/or protected swamp communities and swamp species sensitive to changes in hydrologic conditions. This paper describes a monitoring approach that uses UAVs with modified digital cameras and object-based image analysis methods to characterise swamp landcover on the Newnes plateau in the Blue Mountains near Sydney, Australia. The characterisation of swamp spatial distribution is key to identifying long term changes in swamp condition. In this paper we describe i) the characteristics of the UAV and the sensor, ii) the pre-processing of the remote sensing data with sub-decimeter pixel size to derive visible and near infrared multispectral imagery and a digital surface model (DSM), and iii) the application of object-based image analysis in eCognition using the multi-spectral data and DSM to map swamp extent. Finally, we conclude with a discussion of the potential application of remote sensing data derived from UAVs to conduct environmental monitoring.

Keywords:UAV, high spatial resolution, DSM, OBIA, swamps, wetlands, mining, monitoring
Subjects:F Physical Sciences > F810 Environmental Geography
Divisions:College of Science > School of Geography
ID Code:42677
Deposited On:16 Oct 2020 15:18

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