Abstract
Mapping or “delimiting” landforms is one of geomorphology's primary tools. Computer-based techniques such as land-surface segmentation allow the emulation of the process of manual landform delineation. Land-surface segmentation exhaustively subdivides a digital elevation model (DEM) into morphometrically-homogeneous irregularly-shaped regions, called terrain segments. Terrain segments can be created from various land-surface parameters (LSP) at multiple scales, and may therefore potentially correspond to the spatial extents of landforms such as drumlins. However, this depends on the segmentation algorithm, the parameterization, and the LSPs. In the present study we assess the widely used multiresolution segmentation (MRS) algorithm for its potential in providing terrain segments which delimit drumlins. Supervised testing was based on five 5-m DEMs that represented a set of 173 synthetic drumlins at random but representative positions in the same landscape. Five LSPs were tested, and four variants were computed for each LSP to assess the impact of median filtering of DEMs, and logarithmic transformation of LSPs. The testing scheme (1) employs MRS to partition each LSP exhaustively into 200 coarser scales of terrain segments by increasing the scale parameter (SP), (2) identifies the spatially best matching terrain segment for each reference drumlin, and (3) computes four segmentation accuracy metrics for quantifying the overall spatial match between drumlin segments and reference drumlins. Results of 100 tests showed that MRS tends to perform best on LSPs that are regionally derived from filtered DEMs, and then log-transformed. MRS delineated 97% of the detected drumlins at SP values between 1 and 50. Drumlin delimitation rates with values up to 50% are in line with the success of manual interpretations. Synthetic DEMs are well-suited for assessing landform quantification methods such as MRS, since subjectivity in the reference data is avoided which increases the reliability, validity and applicability of results.
Highlights
In geomorphometry, land-surface segmentation is the process of exhaustively partitioning digital elevation models (DEMs) and derived land-surface parameters (LSPs; e.g. slope, curvature) into spatially discrete terrain segments (Minár and Evans, 2008)
This study presents a method for supervised testing of the performance of multiresolution segmentation (MRS), a widely-used region-growing segmentation algorithm, for the delimitation of synthetic drumlins from 5-m DEMs
This paper assessed the potential of a widely used image segmentation algorithm, multiresolution segmentation (MRS), in delimiting drumlins based on land-surface parameters (LSPs) derived from synthetic DEMs
Summary
Land-surface segmentation is the process of exhaustively partitioning digital elevation models (DEMs) and derived land-surface parameters (LSPs; e.g. slope, curvature) into spatially discrete terrain segments (Minár and Evans, 2008). Region-based methods construct terrain segments by merging adjacent grid cells of similar morphometric characteristics Detailed reviews on the topic of land-surface segmentation have been provided in recent studies (Minár and Evans, 2008; MacMillan and Shary, 2009; Drăguţ and Eisank, 2011; Romstad and Etzelmüller, 2012). MRS is a region-growing algorithm that performs iterative merging of adjacent segments (single grid cells in the first iteration) into larger segments. Landform delimitation is the process of optimizing land-surface segmentation in such a way that individual terrain segments approximate the size and shape of landforms such as drumlins. Individual terrain segments perfectly match the spatial extent of landforms
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