Downscaling of coarse‐grained geomorphological data

Abstract

One of the basic limitations to the use of geomorphological maps is their coarse resolution relative to the needs of pure and applied geomorphological research. In response to this, attempts have been made to 'downscale' geomorphological information to finer spatial resolutions. However, the potential of statistical downscaling in geomorphology has been insufficiently examined. We downscaled four different periglacial features (wind deflation, palsa mire, earth hummock and sorted solifluction sheet) from a 100 ha grid to a 1 ha grid resolution utilizing two different techniques: point sampling (PSA) and direct (DA) approaches. We assessed the predictive accuracy of the models with the area under the curve (AUC) of a receiver operating characteristic plot using independent evaluation data. The PSA technique yielded encouraging results with a mean accuracy of 0·81, whereas the performance of DA was poorer. The predictive performance of the palsa mire and solifluction sheet models was excellent (AUC values from 0·89 to 0·96), whereas the AUC values of deflation and earth hummock models were lower (AUC = = = = = 0·57-0·81). The application of a point sampling approach as used here provides an efficient method to translate geomorphological information to finer resolution. However, further testing of the downscaling approaches is required before they can be applied to real-world situations.

An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province

“…Moreover, if another spatial scale or resolution is adopted, then a different pattern of model performances might be observed (Mackey and Lindenmayer, 2001;Thuiller et al, 2003). Therefore, predictive models performance in addition to the nature and algorithms of the approach used in the modeling will be affected by characteristics of the species as geographic range distribution or ecological niche and the territorial place species (Luoto and Hjort, 2005).…”

Scale in GIS: An overview

“…Luoto and Hjort (2008) downscaled data on periglacial features using simple techniques, and compared the results to ground truth. Zhu et al (2001) and Toomanian et al (2006) experimented with downscaling soil properties while Zhang et al (1999) attempted the downscaling of slope estimates.…”

High resolution, basin extent observations and implications for understanding river form and process

“…The introduction of massive, increasingly high resolution topographic data sets has driven adoption and development of these tools in geomorphology. Wavelet analysis (Pike, 2000;Jordan and Schott, 2005), artifi cial neural networks (Luoto and Hjort, 2008), multivariate statistics (Thoms et al, 2007), fuzzy classifi cation (Legleiter and Goodchild, 2005), and feature recognition approaches (Bue and Stepinski, 2007;Molloy and Stepinski, 2007) are all techniques that can help defi ne multi-scalar patterns in complex, massive, multi-dimensional data sets.…”