Partitioning of Terrain Features Based on Roughness

Jiang Wu,Yuru Li,Qinke Yang

doi:10.3390/rs10121985

Abstract

Surface roughness is a key parameter that reflects topographic characteristics and influences surface processes, and characterization of surface roughness is a fundamental problem in geoscience. In recent years, although there have been basic studies on roughness, few studies have compared the concept and quantification of roughness, and there have been few studies that have evaluated the ability of partition terrain features. Based on 1″ resolution Shuttle Radar Topography Mission (SRTM) data and previous studies, we selected the Qinba Mountain region of China and its adjacent areas as our study area, and used 13 different roughness algorithms to extract roughness in this study. Using spatial patterns and statistical distributions, the results were analyzed, and the best algorithm suited to partitioning terrain features was selected. We then evaluated the ability of the algorithm to distinguish the terrain morphology. The results showed the following: (1) The 13 algorithms were able to be classified into four types, that is, gradient (SLOPE), relief (root mean squared height, RMSH), local vector (directional cosine eigenvalue, DCE) and power-spectral (two-dimensional continuous wavelet transform, 2D CWT). (2) The SLOPE and RMSH algorithms were better able to express and distinguish terrain, as they were able to macroscopically distinguish between four types of terrain in the study areas. Based on power-spectral methods, 2D CWT had the same discrimination ability as the first two methods following a normalization transform, whereas the DCE method had a general effect and could only distinguish two types of terrain. (3) Different roughness algorithms had their own applicability for different terrain areas and application directions.

Highlights

The basic definition of roughness is the degree of deviation between a real surface and an ideal surface in the vertical direction
From the macro point of view, the 13 algorithms were able to express the spatial variability of the terrain from image surface features to some extent, but there were some differences in their spatial patterns and statistical distributions
The advantage of the algorithms based on spectral analysis was that they could quantify different terrain patterns according to different frequency bands and had an equivalent ability to partition terrain as the slope-based methods after canonical transformation

Summary

Introduction

The basic definition of roughness is the degree of deviation between a real surface and an ideal surface (geoid) in the vertical direction. The concept of roughness in the literature has been used in three ways: For local variation in surface elevation [2,3], for the influence of topography on surface fluid (flow resistance or roughness height, a property of a flow) [4,5,6], and for random variation in soil surface elevation [7]. These three aspects emphasize the structural characteristics of the topography, the relationship between the surface and airflow or water flow, and the influence of the surface microrelief on runoff at the subpixel level. (3) Tohnethaeresaurrfaactieo, i(sAoRn)e oisf tahecommomst oimnlpyorutsaendt smureftahcoedmtoorpmhoelaosguircealsiunrdfiaccaetorros.uBgyhnceaslcsu, lwathinicghthies ceallecvualtaitoendgarsadthieenrt,atthioe oEfvathnes aalcgtuorailthamrea[1t9o] twhaespardoojepctteeddtaorceaalcouflathteetshuersfalocpee[3in].tIhnisastmudoyv.ing (2) wThinedloowca,ltehleevaraetiaornatriaonogfee(aRcAh NpiGxeEl)i,swcahliccuhlaistemdeaasstuhreedreacisptrhoecahleoigfhtht edceovsiaintieovnawluiethoifnthaeceslrotapien.

Methods

Results

Discussion

Conclusion