ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 29–35, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-29-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 29–35, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-29-2021

  17 Jun 2021

17 Jun 2021

EVALUATING AN AUTOMATED OBJECT-ORIENTED METHOD TO DELINEATE DRUMLINS FROM BOTH TERRESTRIAL AND SUBMARINE DIGITAL ELEVATION MODELS

K. Saha1 and K. J. J. Van Landeghem2 K. Saha and K. J. J. Van Landeghem
  • 1Department of Planning, School of planning and Architecture, Bhopal, Bhopal, 462030, India
  • 2School of Ocean Sciences, Bangor University, Menai Bridge, LL59 5AB, UK

Keywords: Object-oriented classification, Drumlins, eCognition Developer

Abstract. In the field of geomorphological mapping, the demand for automated delineation of bedforms is growing due to the increasing availability of Digital Elevation Models (DEMs) in small to medium resolutions. This automated technique is not commonly applied in submarine DEMs, where bedform morphology is often subdued due to erosion and part-burial. Here we analyse drumlins in both terrestrial and submarine environments to compare and contrast the set of rules needed for their automated delineation from 3D topographic data. An existing set of rules for automated extraction to delineate the perimeter of terrestrial drumlins was developed in 2011 using object-oriented classification tools, available through eCognition Developer (V.8.7.2). This partly supervised method is evaluated here and subsequently adjusted to be applied to extract drumlins from a submarine DEM with a higher resolution. Several adjustments were needed due to the morphologic differences between the terrestrial and the submarine drumlins. For submarine drumlins, a focus on variation in elevation in the tool is needed, as part-burial and overprinting by other bedforms is common in submarine settings. A Canny Edge Detector filter was used instead of the Sobel Edge detection filter, whilst slope gradient and direction played a larger role in the set of rules. Visual and quantitative comparison with manually delineated drumlin perimeters confirms the success of this revised automated extraction method in both terrestrial and submarine environments. The flexibility and precision of this method thus allow for the future development of object-oriented classification tools to delineate a wide range of bedforms from large-scale DEMs collected from all environments.