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Flood monitoring in vegetated areas using multitemporal Sentinel-1 data: Impact of time series features

Tsyganskaya, Viktoriya and Martinis, Sandro and Marzahn, Philip (2019) Flood monitoring in vegetated areas using multitemporal Sentinel-1 data: Impact of time series features. Water, 11 (1938), pp. 1-23. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/w11091938 ISSN 2073-4441

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Official URL: https://www.mdpi.com/2073-4441/11/9/1938/pdf

Abstract

Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation for local relief workers, decision-makers from crisis management authorities or insurance companies. In order to capture the full extent of the flood, open water and especially temporary flooded Vegetation (TFV) areas have to be considered. The Sentinel-1 (S-1) satellite constellation enables the continuous monitoring of the earths surface with a short revisit time. In particular, the ability of S-1 data to penetrate the vegetation provides information about water areas underneath the vegetation. Different TFV types, such as high grassland/reed and forested areas, from independent study areas were analyzed to show both the potential and limitations of a developed SAR time series classification approach using S-1 data. In particular, the time series feature that would be most suitable for the extraction of the TFV for all study areas was investigated in order to demonstrate the potential of the time series approaches for transferability and thus for operational use. It is shown that the result is strongly influenced by the TFV type and by other environmental conditions. A quantitative evaluation of the generated Inundation maps for the individual study areas is carried out by optical imagery. It shows that analyzed study areas have obtained Producer’s/User’s accuracy values for TFV between 28% and 90%/77% and 97% for pixel-based classification and between 6% and 91%/74% and 92% for object-based classification depending on the time series feature used. The analysis of the transferability for the time series approach showed that the time series feature based on VV (vertical/vertical) polarization is particularly suitable for deriving TFV types for different study areas and based on pixel elements is recommended for operational use.

Item URL in elib:https://elib.dlr.de/129474/
Document Type:Article
Title:Flood monitoring in vegetated areas using multitemporal Sentinel-1 data: Impact of time series features
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Tsyganskaya, ViktoriyaViktoriya.Tsyganskaya (at) dlr.deUNSPECIFIED
Martinis, Sandrosandro.martinis (at) dlr.dehttps://orcid.org/0000-0002-6400-361X
Marzahn, Philipp.marzahn (at) iggf.geo.uni-muenchen.deUNSPECIFIED
Date:2019
Journal or Publication Title:Water
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:No
Volume:11
DOI :10.3390/w11091938
Page Range:pp. 1-23
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2073-4441
Status:Published
Keywords:flood mapping; temporary flooded vegetation (TFV); Sentinel-1; time series data; Synthetic Aperture Radar (SAR)
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: Martinis, Sandro
Deposited On:08 Oct 2019 09:50
Last Modified:08 Oct 2019 09:50

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