Volume XLII-3/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W2, 37–41, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W2-37-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W2, 37–41, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W2-37-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Nov 2017

15 Nov 2017

DETERMINATION OF THE PRESENT VEGETATION STATE OF A WETLAND WITH UAV RGB IMAGERY

M. A. Boon1,2 and S. Tesfamichael3 M. A. Boon and S. Tesfamichael
  • 1Department of Zoology, University of Johannesburg, P.O. Box 524 Auckland Park, 2006, South Africa
  • 2Kite Aerial Imagery (Pty) Ltd, 1422 Topaas Street, Waverley, 0186, South Africa
  • 3Department of Geography, Environmental Management and Energy Studies, University of Johannesburg, P.O. Box 524 Auckland Park, 2006, South Africa

Keywords: Wetland vegetation, aerial photography, UAV, 3D point clouds, DSM

Abstract. The compositional and structural characteristics of wetland vegetation play a vital role in the services that a wetland supplies. Apart from being important habitats, wetland vegetation also provide services such as flood attenuation and nutrient retention. South Africa is known to be a water scarce country. The protection and continuous monitoring of wetland ecosystems is therefore important. Factors such as site transformation and disturbance may completely change the vegetation of a wetland and the use of Unmanned Aerial Vehicle (UAV) imagery can play a valuable role in high-resolution monitoring and mapping. This study assessed if the use of UAV RGB imagery can enhance the determination of the present vegetation state of a wetland. The WET-Health level two (detailed on-site evaluation) methodology was followed for the vegetation assessment, where wetland health is a measure of the deviation of a wetland’s structure and function from its natural reference condition. The mapping of the disturbances classes was then undertaken using ultra-high resolution orthophotos, point clouds and digital surface models (DSM). The WET-Health vegetation module completed with the aid of the UAV products still indicates that the vegetation of the wetland is largely modified (“D” PES Category) and that the vegetation of the wetland will further deteriorate (change score). These results are the same as determined in the baseline study. However a higher impact (activities taking place within the wetland) score were determined. The assessment of various WET-Health vegetation indicators were significantly enhanced using the UAV imagery and derived products. The UAV products provided an accurate vantage point over the wetland and surroundings, and assisted to easily refine the assessment of the disturbance classes and disturbance units.