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On the capability of optical remote sensing imagery with various spatial resolutions on impervious surface estimation

Leichtle, Tobias and Martin, Klaus and Taubenböck, Hannes (2018) On the capability of optical remote sensing imagery with various spatial resolutions on impervious surface estimation. EARSeL 5th Joint Workshop “Urban Remote Sensing – Challenges & Solutions”, 24.-26.09.2018, Bochum, Deutschland.

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Abstract

Urbanization and population growth led to an increasing demand of land resources worldwide. This global process of land transformation is not only persistent in highly dynamic urban areas in developing countries, but also in cities of the Western world. The exponential increase of impervious surfaces for establishment of settlements and transport infrastructure yields negative consequences in many regards, e.g., increasing surface runoff and flood risk, decreasing groundwater recharge, or intensification of the urban heat island effect. Thus, exact and area-wide estimation of impervious surfaces is of high value and must be repeated regularly in order to provide up-to-date information for policy makers. For example in Germany, surveys on imperviousness are often conducted at irregular intervals with patchy spatial coverage and distribution only. In this regard, suitable remote sensing data offers a cost-effective solution for area-wide surveying and monitoring of impervious surfaces. This study investigates the capability of optical satellite remote sensing imagery with various spatial resolutions for impervious surface estimation. The utilized input data sets range from WorldView-2 imagery with very-high resolution (VHR) of 0.5 m to high resolution (HR) imagery acquired by Sentinel-2 and Landsat-8 with only 30 m spatial resolution. These HR sensors possess the advantage of cost-free imagery and high temporal resolution, which enables effective capabilities for monitoring imperviousness. VHR data is analyzed by means of object-based image analysis (OBIA), while Support Vector Regression (SVR) is employed for estimation of impervious surfaces in HR imagery since individual urban objects cannot be resolved at lower spatial scales. In addition, different input features are evaluated systematically in order to assess the potential of data compression by means of Principal Component Analysis (PCA) as well as spectral indices like the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation index (SAVI), or Normalized Difference Built-up Index (NDBI). Our results illustrate that the OBIA classification of the WorldView-2 imagery with 0.5 m spatial resolution for the city of Munich achieved estimations of imperviousness with a Root Mean Square Error (RMSE) of about 10.0 % compared to official data of imperviousness provided by the city municipality. Naturally, the classification based on HR data obtained lower accuracies. However, RMSE values in the order of 20.0 % - 25.0 % dependent on the utilized features and spatial resolutions reveal that these remote sensing data sets still allow a general assessment with fair to high accuracy at no cost. In general, the accuracy of impervious surface mapping is highly dependent on the spatial resolution of the input data, i.e. the accuracy increases with spatial detail of the imagery. In addition, the combination of spectral indices or data compression techniques like PCA yielded competitive results compared to the original input values of the multispectral bands.

Item URL in elib:https://elib.dlr.de/122298/
Document Type:Conference or Workshop Item (Speech)
Title:On the capability of optical remote sensing imagery with various spatial resolutions on impervious surface estimation
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Leichtle, Tobiastobias.leichtle (at) slu-web.deUNSPECIFIED
Martin, Klausklaus.martin (at) slu-web.deUNSPECIFIED
Taubenböck, Hanneshannes.taubenboeck (at) dlr.deUNSPECIFIED
Date:24 October 2018
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:optical remote sensing, object-based image analysis, support vector regression, impervious surface
Event Title:EARSeL 5th Joint Workshop “Urban Remote Sensing – Challenges & Solutions”
Event Location:Bochum, Deutschland
Event Type:international Conference
Event Dates:24.-26.09.2018
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Leichtle, Tobias
Deposited On:23 Oct 2018 12:24
Last Modified:23 Oct 2018 12:24

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