Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling

Dong Eon Kim,Jiandong Liu,Shie-Yui Liong,Philippe Gourbesville,Ludovic Andres

doi:10.3390/w12030816

Dong Eon Kim, Jiandong Liu + Show 3 more

Open Access

https://doi.org/10.3390/w12030816

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Journal: Water	Publication Date: Mar 14, 2020
Citations: 23	License type: CC BY 4.0

Affiliation: National University of Singapore, Université Côte d'Azur

Abstract

Digital elevation models (DEMs) are crucial in flood modeling as DEM data reflects the actual topographic characteristics where water can flow in the model. However, a high-quality DEM is very difficult to acquire as it is very time consuming, costly, and, often restricted. DEM data from a publicly accessible satellite, Shuttle Radar Topography Mission (SRTM), and Sentinel 2 multispectral imagery are selected and used to train the artificial neural network (ANN) to improve the quality of SRTM’s DEM. High-quality DEM is used as target data in the training of ANN. The trained ANN will then be ready to efficiently and effectively generate a high-quality DEM, at low cost, for places where ground truth DEM data is not available. In this paper, the performance of the DEM improvement scheme is evaluated over two dense urban cities, Nice (France) and Singapore; with the performance criteria using various matrices, e.g., visual clarity, scatter plots, root mean square error (RMSE) and flood maps. The DEM resulting from the improved SRTM (iSRTM) showed significantly better results than the original SRTM DEM, with about 38% RMSE reduction. Flood maps from iSRTM DEM show much more reasonable flood patterns than SRTM DEM’s flood map.

Highlights

Accurate terrain elevation information is important in many applications of land surface modeling, such as flood, volcanology, ecology, and glaciology modeling [1,2,3]
This paper presents significant improvements to the Shuttle Radar Topography Mission (SRTM) Digital elevation models (DEMs) using an artificial neural network (ANN) with remote This paper significant improvements to the using Figure an ANN
SRTM DEM and Sentinel 2 multispectral imagery as the input nodes, while high resolution and accuracy surveyed DEM was used as the target layer

Summary

Introduction

Accurate terrain elevation information is important in many applications of land surface modeling, such as flood, volcanology, ecology, and glaciology modeling [1,2,3]. Space-borne radar or air-borne laser scanning are widely applied to retrieve data on topography that is used to develop the digital elevation model (DEM) [4,5,6]. A DEM can be used to depict the terrain of the earth and is an organized array of the numbers which represent the elevations of spatial distributions above an arbitrary datum [7]. The term DEM is usually applied to land surface topography, but it is a general term that is used to depict the spatial patterns of various surfaces, e.g., surface water, ground surface, canopy, and so on. DTM is referred to as the Earth terrain, i.e., bare ground, while

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