Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models

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.

Detection of Gully-Affected Areas by Applying Object-Based Image Analysis (OBIA) in the Region of Taroudannt, Morocco

“…Several publications treat the aspect of assessing the accuracy of landform mapping approaches with more than one method in order to derive a more comprehensive estimation on the overall quality of the classification results [14,17,29,39].…”

“…Schmidt and Andrew [6] presented an approach applied at various scales of curvature. Eisank et al [17] recently presented an approach to assess the outcomes of multiresolution segmentation for delimiting drumlins from digital elevation models. However, landform mapping may also be carried out only on satellite images or other 2D information.…”

“…The borders of such elements vary due to natural influences, i.e., mostly erosion, and are also still not clearly defined. Eisank et al [17] also treats this topic in detail in a recent study.…”

Manual mapping of drumlins in synthetic landscapes to assess operator effectiveness

“…, on the other hand, are designed terrains within which key components are known a priori, and so they have facilitated some progress on these and related questions. Specifically, synthetic DEMs were used to determine an optimal semi-automated method for drumlin extraction and to assess multi-resolution segmentation algorithms for delimiting drumlins (Eisank, Smith, & Hillier, 2014). In addition, a pilot study on manual mapping tentatively indicated that drumlin amplitude may be the key dimension governing drumlin detectability (Figure 1(c)) (Arumgam et al, 2012).…”

Object-based integrated landscape change analysis: synergy of multi-temporal LiDAR and very high resolution orthophotos

“…Step A. Following successful applications of multiresolution segmentation (MRS) in mountainous environments (AguirreGutierrez et al, 2012;Anders et al, 2013;Eisank et al, 2014) an iterative algorithm was developed producing 125 parameter combinations, similarly to the work of Clinton et al (2010). The parameter settings used for scale parameter are 10, 20, 30, 40 and 50, for the shape parameter 0.1, 0.3, 0.5, 0.7, 0.9 and for compactness 0.1, 0.3, 0.5, 0.7 and 0.9.…”