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Poster: Comparison of Interpolation Methods for Global Scale Topographic Reconstruction of the Earth Back in Time

Franziskakis, Florian

Publisher
Zenodo
DOI
10.5281/zenodo.17395923
Repository
zenodo.org
Language
en

Abstract

Topography modelling relies on interpolating a raster using input vector points containing elevation values. However, due to high-variations of terrain in mountain areas or continental margins,the accurate modelling of these area can become challenging. Geographic information systems (GIS) such as QGIS or ArcGIS offer plenty of interpolation methods, making it a complex task to identify which method and parameters are able to render the terrain with the most fidelity. Studying the geography of the Earth past (palaeogeography), one cannot rely on spaceborne-derived digital elevation models (DEMs) of the current world, hence the need to use other input data, such as plate tectonics models. DEMs of the Earth past are very useful products that can help estimate sea-level variations and climates of the past (Marcilly et al. 2022). Generating a palaeo-DEM form a plate tectonic model therefore strongly relies on interpolating a raster from an irregular grid of nodes extracted from the model. However, no studies have been conducted to assess the performances of the available inteprolation methods for a global scale-reconstruction. We compare the performance of seven interpolation methods with varying parameters (including resolution, power and search radius) using ArcGIS and QGIS and its associated libraries (GDAL, GRASS, SAGA). We use the nodes from the PANALESIS model present-day reconstruction (Vérard et al. 2015), with elevation values resampled from ETOPO 2022 (NOAA 2022). In total,130 inteprolations are compared with the ETOPO 2022 reference raster in terms of difference of absolute values, and using the Terrain Ruggedness Index (TRI) as an indicator of how much variation in the topogrpahy is captured. Statistical analysis of the absolute and TRI difference show that best results (with minimal NRMSE for absolute differences and TRI) are obtained using GDAL. In general, open-source solutions available in QGIS tend to perform better than proprietary ones in ArcGIS.

Full text

Comparison of Interpolation Methods for Global Scale Topographic Reconstruction of the Earth Back in Time Florian Franziskakis1, Christian Vérard2, Grégory Giuliani1 1EnviroSPACE Lab, Institute for Environmental Sciences, University of Geneva, Boulevard Carl-Vogt 66, 1205 Genève 2Department of Earth Sciences, University of Geneva, Rue des Maraîchers 13, 1205 Genève [email protected] Problem Workflow Performance: Methods & Providers Performance: Maps Performance: Elevation Profiles Conclusion References A) Mid-Oceanic Ridge (South Pacific) B) Collision Zone (Himalayas) C) Rift Zone (East Africa) a) ETOPO 2022 Ice Surface (reference) b) GDAL Nearest Neighbour Interpolation c) QGIS Triangulated Irregular Network Interpolation Statistically better More realistic SRTM[1] ETOPO [2] ALOS[3] Earth observations for the present-day… …but what about the Earth millions of years ago? What is the best interpolation method to create topographic maps at the global scale using an irregular grid of nodes as input? We use today’s world reference data (ETOPO) to compare 7 methods and 5 providers using points with elevation from the PANALESIS[4] plate tectonic model. We compare absolute values and terrain rendering performances (using the Terrain Ruggedness Index (TRI) between ETOPO and the interpolated rasters. Nearest Neighbour & Triangulated Irregular Network methods perform the best. GDAL & GRASS providers perform the best. Open-source is better! Statistically, the Nearest Neighbour Method is the best method, but it creates patchy & unrealistic maps. The Triangulated Irregular Network method performs slightly less good statistically, but produces more continuous and realistic maps. Some artefacts are observed from all methods, and stem from the irregular distribution of input nodes. Nodes distribution is good enough to capture abrupt landscape changes. Too many or too few nodes in some areas leads to large errors. A more uniform distribution would lower the errors. [1] Farr, T. G. et al. (2007). The Shuttle Radar Topography Mission. Reviews of Geophysics, 45(2). https://doi.org/10.1029/2005RG000183 [2] NOAA. (2022). NOAA National Centers for Environmental Information. 2022: ETOPO 2022 15 Arc-Second Global Relief Model. [Dataset]. https://doi.org/10.25921/fd45-gt74 [3] Tadono, T. (2016). Generation Of The 30 M-mesh Global Digital Surface Model By ALOS PRISM. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , XLI-B4 , 157–162. https://doi.org/10.5194/isprsarchives-XLI-B4-157-2016 [4] Vérard, C. (2019). Panalesis: Towards global synthetic palaeogeographies using integration and coupling of manifold models. Geological Magazine , 156 (2), Article 2. https://doi.org/10.1017/S0016756817001042 Poster Reference: Franziskakis et al. (in prep). Comparison of Interpolation Methods for Global Scale Topographic Reconstruction of the Earth Back in Time. SNSF grant #213539: Long-term evolution of the Earth from the base of the mantle to the top of the atmosphere: Understanding the mechanisms leading to ‘greenhouse’ and ‘icehouse’ regimes C A B Elevation [m] Elevation [m] Elevation [m] Profile length [°]