Multiscale wavelet-based analysis to detect hidden geodiversity
| Autor: | Ileana Pătru-Stupariu, Mihai-Sorin Stupariu, Magdalena Năpăruş-Aljančič |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
geography
Plateau geography.geographical_feature_category 010504 meteorology & atmospheric sciences Spline wavelet Geography Planning and Development Wavelet transform Ranging Classification of discontinuities 010502 geochemistry & geophysics 01 natural sciences Wavelet Geodiversity Earth and Planetary Sciences (miscellaneous) General Earth and Planetary Sciences Digital elevation model Geology 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Progress in Physical Geography: Earth and Environment. 41:601-619 |
| ISSN: | 1477-0296 0309-1333 |
| Popis: | Geodiversity, a relatively new concept in earth sciences, synthesizes information related to abiotic layers and indicates their spatial interactions and interrelations. Geodiversity has been used in mountainous areas to assess both their ecological and economic potential. In this paper, we link geodiversity analysis with a multi-resolution wavelet transform to parse the hidden patterns of geodiversity of a plateau area from the Swiss Jura Mountains. We decomposed a high-resolution digital terrain model using a fractional spline wavelet transform method into four coarser resolution images, ranging from 4 to 32 m, to detect image discontinuities. This generated directional high-pass coefficients, which accumulated in a bottom-up approach to determine the terrain roughness and extract obvious topographical features. In addition, we mapped and quantified total geodiversity using available geological, tectonical and topographical elements on a 32-m square grid. The geodiversity index of the area was computed by adding the terrain roughness derived from a wavelet transform to the traditional formula. The correlation among the geodiversity index, terrain roughness, elevation and slope data was tested with exploratory regression and Spatial Lag regression models. We obtained four images that represent the wavelet-detected terrain roughness at four levels of decomposition, ranging from 4 to 32 m. The geodiversity index, computed based on the wavelet-detected roughness, accurately refined the results obtained with the total geodiversity traditional formula. The distribution of the wavelet-detected topographical features was more heterogenic, with a coarser map resolution and forming areas that correspond to the mapped areas with the most obvious geodiversity patterns. Our findings provide a tool for detecting hidden geodiversity patterns within areas that lack apparent landscape variability, as well as for overcoming traditional methods for assessing geodiversity by introducing the multiscale fractional spline wavelet transform with accurate mapping of the terrain roughness. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|