CHARACTERISING UPLAND SWAMPS USING OBJECT-BASED CLASSIFICATION METHODS AND HYPER-SPATIAL RESOLUTION IMAGERY DERIVED FROM AN UNMANNED AERIAL VEHICLE

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.

• Publication year

  2012

• Venue

  ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

• Publication date

  2012-07-18

• Fields of study

  Environmental Science

• Identifiers
  DOI 10.5194/ISPRSANNALS-I-4-101-2012
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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