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Creating a Map of Terrain Regions for Italy using Digital Elevation Models Craig Allison School of Geography & Earth Sciences, McMaster University, Hamilton, ON, Canada 2. Data and Methods 1. Introduction The primary purpose of this project is to divide Italy into a map of terrain regions using digital elevation models (DEMs) and a fully automated approach. Automation of traditional (manual) landform classification mapping is appealing because it is consistent, repeatable, updatable, and quantifiable. Terrain regions themselves are important because they define boundary conditions for geomorphologic, hydrologic, ecologic, and pedologic processes [1]. Further, they impact where human activities take place on the landscape. Italy has been chosen as the study area because the most current effort at delineating terrain regions [2] can be significantly improved. Specifically, these regions were derived using coarse resolution DEMs, highly correlated morphometric variables, and manual qualitative techniques that are not repeatable. This study has four objectives: 1. To create an updated and improved landform classification map of Italy. 2. To create a tool using Esri technology to automate the approach so that it can be applied to other jurisdictions. 3. To examine the impact that DEM products created from different satellite technologies have on the results. 4. To explore the impact that resolution has on the resulting classified map. 3. Results 4. Conclusions 5. References 3. Results (continued) Three DEM products – ASTER (a passive along-track scanning system with 30m resolution), SRTM (30m), and SRTM (90m) (an active scanning system, based on radar interferometry) – were retrieved using a combination of the U.S. Geological Survey’s EarthExplorer tool (earthexplorer.usgs.gov/) and the Consortium for Spatial Information (cgiar-csi.org/). Figure 1: ASTER, SRTM (30 m), and SRTM (90 m) DEMs for Italy The model for the landform classification was constructed based on the work of Hammond [3], Dikau [4], Morgan & Lesh [5], and Drescher & de Frey [6]. In the model, created in ModelBuilder, a circular window of 1.8km radius was used to calculate the percent of area occupied by gentle inclination (<8% slope gradient), the relief, and the percent of the gently sloping terrain occurring in the lower half of the local relief [7]. One step was added to the original model that solved a problem with the creation of NoData cells in the Alps. Specifically, a value of 1 was added to the denominator map in one of the divide steps. Five main types of terrain regions are created based on merging 24 terrain types described by Morgan & Lesh (Table 1). In total the model required 38 steps to produce the results. Figure 2: Italian terrain regions derived from a 90m resolution SRTM Void Filled DEM The Alps at Italy’s northern border have been classified as “high mountains,” the Po Valley as “plains,” most of the Apennine Mountains as “hills and low mountains,” and most of Sicily and Sardinia as hills and low mountains. Note that prominent features like Corno Grande (tallest peak in the Apennines; located in central Mainland Italy) and Mount Etna (tall volcano in eastern Sicily) have been successfully identified as high mountains. Features like the Po Valley in the north, Campidano plain in southwest Sardinia, and the Tavoliere in Puglia have been nicely extracted from the DEM as well. Figure 3: Model results for three different DEM products for Italy The type of DEM (i.e. ASTER vs. SRTM) appears to have a greater influence on the classification result than does resolution (i.e. 30m vs. 90m). More specifically, there is visually more of a difference between the ASTER and either SRTM map than there is between the SRTM (30m) and SRTM (90m) maps. A model for automatically dividing Italy into terrain regions has been constructed using ModelBuilder. Results from the model show that the terrain region classification depends more on the type of DEM used and less on the resolution of the DEM product. Morgan & Lesh (2005) Terrain Type Corresponding Terrain Region 11, 12, 13, 14 Plains 21, 22, 23, 24 Tablelands 31, 32, 33, 34 Plains with hills or mountains 41, 42, 43, 44, 45, 51, 52, 53, 54, 55 Hills and low mountains 46, 56 High mountains Table 1: Assigning terrain types to terrain regions % of Cells in Each Terrain Region Type DEM Product Plains Tablelands Plains w/ hills or mountains Hills and low mountains High mountains ASTER 22.7 1.2 6.1 58.4 11.7 SRTM (30m) 24.6 1.6 8.9 53.3 11.6 SRTM (90m) 24.9 2.1 10.9 51.0 11.2 Table 2: Comparison of DEMs by % of cells per terrain region type for Italy SRTM 30m and SRTM 90m DEM terrain region maps have closer percentages of cells assigned to plains, plains with hills or mountains, and hills and low mountains (i.e. 3/5 terrain region types) (Table 2). Of all five terrain region types, the percent values (Table 2) assigned to plains and high mountains are most similar between the three DEM products. Therefore, the type of DEM product used for a project matters less when you are identifying plains and high mountains. More generally, categories at the extremes of elevation are less affected by resolution or the DEM product used. Projects involving plains and high mountains should be less concerned about the specific DEM they are using. The SRTM DEMs appear to classify terrain more accurately than the ASTER DEM (Figure 4). In Piemonte, for example, the ASTER DEM classification result indicates hills and low mountains where there are none (just north of the Monferrato Hills). The same is true for the southeastern most area of Apulia. There should also be no significant highland areas in the Po Plain (Figure 4 shows the Po Plain in Lombardia), but there are in the ASTER DEM classification result. SRTM DEM results did not contain these errors. [1] Drăguţ, L., & Eisank, C. (2011). Automated classification of topography from SRTM data using object-based image analysis. Geomorphometry 2011, 7-9. [2] Guzzetti, F., & Reichenbach, P. (1994). Towards a definition of topographic divisions for Italy. Geomorphology, 11(1), 57-74. [3] Hammond, E. H. (1964). Analysis of properties in land form geography: an application to broad‐scale land form mapping. Annals of the Association of American Geographers, 54(1), 11-19. [4] Dikau, R. (1989). The application of a digital relief model to landform analysis in geomorphology. In: Raper, J. (ed.) Three dimensional applications in geographical information systems, Taylor & Francis, London, UK 51-77. [5] Morgan, J. M., & Lesh, A. M. (2005). Developing landform maps using ESRI’S Model-Builder. In ESRI International User Conference. [6] Drescher, K., & de Frey, W. (2009). Landform classification using GIS. Position IT. [7] Gallant, A. L., Brown, D. D., & Hoffer, R. M. (2005). Automated mapping of Hammond's landforms. Geoscience and Remote Sensing Letters, IEEE, 2(4), 384-388. Figure 4: Erroneous classification results when the ASTER DEM was used
