TWI computation: a comparison of different open source GISs

The opportunities of retrieving geospatial datasets as open data and the reliability of Free and Open Source Software (FOSS) GIS increased the possibilities of performing a large number of geospatial analyses. In particular, the worldwide availability of Digital Elevation Model (DEM) permits to compute several topographic indexes able to characterize the land morphology.In this paper, we evaluate the performances of different open source GIS in the calculation of the Topographic Wetness Index (TWI), a widespread index in hydrological analysis that describes the tendency of an area to accumulate water. Nowadays, there is a large number of available open source desktop GIS, maintained as FOSS projects, each of them focusing on developing specific goals. Therefore, from user point of view, the choice of the best software in solving a particular task is influenced by the GIS specific features.The test was performed computing the TWI for the Rio Sinigo basin, in northern Italy. The DEM of the test area has been processed with GRASS GIS, Whitebox GAT and SAGA GIS. In order to identify equal workflows, all the combinations of available algorithms and parameters have been studied for each considered GIS. The final TWI maps produced as output were compared and discussed.

[1]  Robert M. Haralick,et al.  Ridges and valleys on digital images , 1983, Comput. Vis. Graph. Image Process..

[2]  Elias Symeonakis,et al.  High-resolution wetness index mapping: A useful tool for regional scale wetland management , 2018, Ecol. Informatics.

[3]  T. G. Freeman,et al.  Calculating catchment area with divergent flow based on a regular grid , 1991 .

[4]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[5]  M. Costa-Cabral,et al.  Digital Elevation Model Networks (DEMON): A model of flow over hillslopes for computation of contributing and dispersal areas , 1994 .

[6]  Justin M Cohen,et al.  Local topographic wetness indices predict household malaria risk better than land-use and land-cover in the western Kenya highlands , 2010, Malaria Journal.

[7]  Helena Mitasova,et al.  Free and open source desktop and Web GIS solutions , 2012 .

[8]  D. Tarboton.  A new method for the determination of flow directions and upslope areas in grid digital elevation models , 1997 .

[9]  T. Green,et al.  Comparison of grid‐based algorithms for computing upslope contributing area , 2006 .

[10]  J. Böhner,et al.  Spatial Prediction of Soil Attributes Using Terrain Analysis and Climate Regionalisation , 2006 .

[11]  S. Gruber,et al.  Chapter 7 Land-Surface Parameters and Objects in Hydrology , 2009 .

[12]  Colin Arrowsmith,et al.  Topography Wetness Index Application in Flood-Risk-Based Land Use Planning , 2016 .

[13]  Andreas Persson,et al.  Comparison of DEM Data Capture and Topographic Wetness Indices , 2003, Precision Agriculture.

[14]  John B. Lindsay,et al.  Whitebox GAT: A case study in geomorphometric analysis , 2016, Comput. Geosci..

[15]  Helena Mitasova,et al.  Chapter Ten Free and Open Source Geospatial Tools for Environmental Modelling and Management , 2008 .

[16]  K. Beven,et al.  THE PREDICTION OF HILLSLOPE FLOW PATHS FOR DISTRIBUTED HYDROLOGICAL MODELLING USING DIGITAL TERRAIN MODELS , 1991 .

[17]  Erwan Bocher,et al.  An overview on current free and open source desktop GIS developments , 2009, Int. J. Geogr. Inf. Sci..

[18]  Gary A. Peterson,et al.  Soil Attribute Prediction Using Terrain Analysis , 1993 .

[19]  P. Arp,et al.  Evaluating digital terrain indices for soil wetness mapping – a Swedish case study , 2014 .

[20]  I. Evans.  Statistical Characterization of Altitude Matrices by Computer. Report 6. An Integrated System of Terrain Analysis and Slope Mapping. , 1979 .

[21]  L. Wang,et al.  An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling , 2006, Int. J. Geogr. Inf. Sci..

[22]  J. Fairfield,et al.  Drainage networks from grid digital elevation models , 1991 .

[23]  C. Thorne,et al.  Quantitative analysis of land surface topography , 1987 .

[24]  Günter Blöschl,et al.  Observed spatial organization of soil moisture and its relation to terrain indices , 1999 .

[25]  S. Gruber,et al.  Land-Surface parameters and objects in hydrology , 2008 .

[26]  Wayne D. Iverson,et al.  VIEWIT: computation of seen areas, slope, and aspect for land-use planning , 1975 .

[27]  Nicholas J. Lea,et al.  An Aspect-Driven Kinematic Routing Algorithm , 1992 .

[28]  Markus Metz,et al.  GRASS GIS: A multi-purpose open source GIS , 2012, Environ. Model. Softw..

[29]  Stefan Steiniger,et al.  The 2012 free and open source GIS software map - A guide to facilitate research, development, and adoption , 2013, Comput. Environ. Urban Syst..

[30]  Bai Tian.  GIS Technology Applications in Environmental and Earth Sciences , 2016 .

[31]  Martin Kopecký,et al.  Using topographic wetness index in vegetation ecology: does the algorithm matter? , 2010 .

[32]  J. Seibert,et al.  A new triangular multiple flow direction algorithm for computing upslope areas from gridded digital elevation models , 2007 .

[33]  R. Heerdegen,et al.  Quantifying source areas through land surface curvature and shape , 1982 .

[34]  John F. O'Callaghan,et al.  The extraction of drainage networks from digital elevation data , 1984, Comput. Vis. Graph. Image Process..

[35]  Markus Neteler,et al.  Open Source GIS: A GRASS GIS Approach , 2007 .

[36]  Michael Bock,et al.  System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 , 2015 .

[37]  Frédéric Darboux,et al.  A fast, simple and versatile algorithm to fill the depressions of digital elevation models , 2002 .

[38]  Tao Pei,et al.  An approach to computing topographic wetness index based on maximum downslope gradient , 2011, Precision Agriculture.

[39]  Berthold K. P. Horn,et al.  Hill shading and the reflectance map , 1981, Proceedings of the IEEE.