Abstract
Abstract. This paper presents an approach designed to derive an urban morphology map from satellite data while aiming to minimize the cost of data and user interference. The approach will help to provide updates to the current morphological databases around the world. The proposed urban morphology maps consist of two layers: 1) Digital Elevation Model (DEM) and 2) land cover map. Sentinel-2 data was used to create a land cover map, which was realized through image classification using optical range indices calculated from image data. For the purpose of atmospheric modeling, the most important classes are water and vegetation areas. The rest of the area includes bare soil and built-up areas among others, and they were merged into one class in the end. The classification result was validated with ground truth data collected both from field measurements and aerial imagery. The overall classification accuracy for the three classes is 91 %. TanDEM-X data was processed into two DEMs with different grid sizes using interferometric SAR processing. The resulting DEM has a RMSE of 3.2 meters compared to a high resolution DEM, which was estimated through 20 control points in flat areas. Comparing the derived DEM with the ground truth DEM from airborne LIDAR data, it can be seen that the street canyons, that are of high importance for urban atmospheric modeling are not detectable in the TanDEM-X DEM. However, the derived DEM is suitable for a class of urban atmospheric models. Based on the numerical modeling needs for regional atmospheric pollutant dispersion studies, the generated files enable the extraction of relevant parametrizations, such as Urban Canopy Parameters (UCP).
Highlights
According to the World Health Organization (WHO, 2016) 98% of low-income and 56% of high income countries do not meet the WHO air quality guideline in urban areas
This paper investigates the possibility to derive a minimum of urban morphology represented as a Digital Elevation Model (DEM) and a land cover classification from satellite remote sensing data and a minimal user interference in the processing of those
This work presents the design and derivation of urban morphology map based on free or low-cost remote sensing data, with the aim to optimize the automation of the urban morphology derivation process
Summary
According to the World Health Organization (WHO, 2016) 98% of low-income and 56% of high income countries do not meet the WHO air quality guideline in urban areas. Urban morphology derivation by different means has been researched for a while, as the resulting models can be used for a variety of purposes such as for example environmental impact studies, general city planning and atmospheric modeling. Urban morphological models such as BDTopo (Long et al, 2003), NUDAPT (Ching et al, 2009) and WUDAPT (Stewart et al, 2012) all represent the urban morphology in some way derived by different means. This paper investigates the possibility to derive a minimum of urban morphology represented as a Digital Elevation Model (DEM) and a land cover classification from satellite remote sensing data and a minimal user interference in the processing of those. The DEM derived in this work is a Digital Surface Model (DSM) representing the elevation on top of reflective surfaces, meaning manmade structures and vegetation is included. (Maune et al, 2001)
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