Extracting Urban Morphology for Atmospheric Modeling from Multispectral and SAR Satellite Imagery

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http://hdl.handle.net/10138/232068

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Wittke , S , Karila , K , Puttonen , E , Hellsten , A , Auvinen , M J S & Karjalainen , M 2017 , Extracting Urban Morphology for Atmospheric Modeling from Multispectral and SAR Satellite Imagery . in C Heipke , K Jacobsen , U Stilla , F Rottensteiner , A Yilmaz , M Ying Yang , J Skaloud & I Colomina (eds) , ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17 : 6–9 June 2017, Hannover, Germany . The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. XLII-1/W1 , ISPRS , pp. 425-431 , ISPR Hannover Workshop , Hannover , Germany , 06/06/2017 . https://doi.org/10.5194/isprs-archives-XLII-1-W1-425-2017

Title: Extracting Urban Morphology for Atmospheric Modeling from Multispectral and SAR Satellite Imagery
Author: Wittke, Samantha; Karila, Kirsi; Puttonen, Eetu; Hellsten, Antti; Auvinen, Mikko Jussi Santeri; Karjalainen, Mika
Editor: Heipke, C.; Jacobsen, K.; Stilla, U.; Rottensteiner, F.; Yilmaz, A.; Ying Yang, M.; Skaloud, J.; Colomina, I.
Contributor: University of Helsinki, Institute for Atmospheric and Earth System Research (INAR)
Publisher: ISPRS
Date: 2017-05-31
Language: eng
Number of pages: 7
Belongs to series: ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17 6–9 June 2017, Hannover, Germany
Belongs to series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
URI: http://hdl.handle.net/10138/232068
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).
Subject: 1171 Geosciences
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