Volume XLII-4/W8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W8, 155–162, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W8-155-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W8, 155–162, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W8-155-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  11 Jul 2018

11 Jul 2018

DEVELOPING A LAND COVER CLASSIFICATION OF SALT MARSHES USING UAS TIME-SERIES IMAGERY AND AN OPEN SOURCE WORKFLOW

D. J. Myers1, C. M. Schweik2, R. Wicks3, F. Bowlick4, and M. Carullo5 D. J. Myers et al.
  • 1UMassAir, Department of Geography, University of Massachusetts, Amherst, USA
  • 2Department of Environmental Conservation and School of Public Policy, University of Massachusetts, Amherst, USA
  • 3UMassAir, University of Massachusetts, Amherst, USA
  • 4Department of Environmental Conservation and Department of Geosciences, University of Massachusetts, Amherst, USA
  • 5Massachusetts Office of Coastal Zone Management, USA

Keywords: UAS, QGIS, OpenDroneMap, Salt Marsh, RGB, Supervised Classification Plug-in

Abstract. Salt marsh ecology classification is difficult using traditional coarse resolution remote sensing techniques. Salt marshes exhibit a spatial pattern of vegetation zonation that are visually identifiable using imagery that has an improved 0.04 meter per pixel resolution. This project applies high resolution unmanned aerial system (UAS) imagery to aid in multi-temporal classification of our study area (Horseneck Beach) in Westport, Massachusetts, USA. We flew a DJI Phantom Pro 3 at low- and high-tide to capture effects the changing tide has on vegetation in an effort to predict effects of the rising sea level on saline plant species. We implement an open source software workflow using OpenDroneMap and the Semi-Automatic Classification Plugin for QGIS to create the necessary orthomosaics and to conduct vegetation classification required of this project. We compare land cover classifications using one-time-point RGB imagery to a multi-time-point (low tide, high tide) RGB image stack to investigate whether the multi-time point stack improves land cover classification accuracy. We find it does. More generally, this paper provides a model for others wishing to use low-cost UAS equipment carrying a simple low-cost RGB camera, and free and open source for geospatial (FOSS4G) tools, to develop multi-band image stacks to improve land cover classification accuracy. Further, we provide some reflections and technical notes on our experience. The approach we present here could be extended to include other image layers that UAS can provide when equipped with other sensors, such as multi-spectral (e.g., NIR, thermal), or by adding another band with photogrammetry-produced digital elevation data.