ector output of the Polygonization

Flood Mapping and Damage Assessment using S2 Data

Objective: 

The objective of this practice is to identify the extent of a flood as well as the affected infrastructure such as roads and settlements and impaired areas of interest for example agricultural regions. This information can be used by disaster management agencies and other stakeholders to undertake the rescue and relief activities in affected areas.

Disaster Cycle Phase: 

  • Recovery & Reconstruction

  • Relief & Response

Main Hazards: 

  • Flood

Test Site: 

Fitzroy River at Rockhampton, Queensland, Australia.

Context: 

The practice developed by the “Space Application Centre for Response in Emergency and Disaster” of SUPARCO (Pakistan) was initially applied to the flood event in Punjab (Pakistan) in July 2015. Thereafter, it was used annually for river monitoring during monsoon season. The extraction of the flood extent was applied to the river Jhelum upstream of Trimmu Barrage, while the map generation covered the River Indus and its tributaries in Punjab, Pakistan.

For this Recommended Practice the methodology was applied to the Fitzroy River around the city of Rockhampton in Queensland, Australia. In April 2017, the central city of Queensland was inundated by flood waters. The water rose over several days until its peak that was captured by the processed satellite imagery from 8 April 2017.

Applicability: 

Part A of this Recommended Practice can be applied to most flood events around the globe. The flood inundation is extracted from Sentinel-2 visible bands at 10 meters spatial resolution. The method can therefore only be applied for satellite scenes with little to no cloud cover.
Part B then maps and quantifies the flood affected and damaged areas and can be applied to all shapefiles that are being included in the analysis.

Abstract: 

Rapid damage assessment following a flooding event and/or inland inundation is essential for disaster management to coordinate the first responders and other activities related to response and rehabilitation of damaged infrastructure in a quick manner. The use of Earth Observation (EO) data, specifically satellite data, significantly facilitates the determination of the flood extent for large areas and does not require field work, which would be highly time and labour-intensive.
Flood mapping also benefits from the large availability of satellite data free of charge, such as Sentinel data provided by the European Space Agency (ESA). On the Copernicus Open Access Hub, the user can download Optical as well as Radar satellite dataSAR (synthetic aperture radar) measurements can also be used for flood mapping irrespective of daytime and cloud cover of the scene (see UN-SPIDER's Recommended Practice on Radar-based Flood Mapping).
However, this recommended practice uses Optical satellite data and draws on the spectral reflectances within the Green and NIR channels to calculate the NDWI (Normalized Difference Water Index). The NDWI facilitates the differentiation between water and non-water inundated areas. In the method, a threshold is derived from the NDWI to binarize the image and determine the flood extent. The recommended practice further includes a damage assessment that can also be applied to other types of natural disasters.

Requirements: 

 

Data requirements

  1. Sentinel 2A Imagery which can be downloaded from the Copernicus Open Access Hub

  2. Road Lines

  3. Local Wards or Districts

  4. Available Property boundaries such as from the City Administration or Open Street Maps (OSM)

  5. Extent of water bodies under average conditions

Software requirements

For image processing and further calculations:

  • QGIS 3.2.0 Bonn or previous versions
    • Freely available here

Skills requirements

Intermediate understanding of image processing and basic understanding of QGIS.

Hardware requirements

In terms of computing power, a potent machine is recommended for faster processing and depending on the size area of interest. The following recommendations are minimum (and tested) specifications:

  • 8GB of RAM (16GB of RAM)
  • 30GB to 1TB (500GB) of free disk space (heavily dependent on the size of the study area) the use of an external hard drive is possible!
  • Dual core processor (Intel i7-2600k, Intel i5)
  • An internet connection is required to run the script in order to download all necessary source data. The faster the connection, the better

Applications: 

The applications of flood extents include:

  • Operational estimation and detection of flooded areas in events with low cloud cover (within 6-12 h after data acquisition).
  • Damage assessment of flooded objects.
  • Calibration of hydrometeorological models.
  • Detection of water levels using high-resolution DEM.
  • Spatial extent: from villages to global scale.
  • Can be used for all stages: risk assessment, operational mapping and responserecovery.
  • Spatial resolution: from 1 m to 150 m.

 

Strengths and Limitations: 

Strengths
The use of Sentinel-2 optical data has the following advantages:

  • High revisit time of every 10 days at the equator and every 5 days at mid-latitudes.
  • 10m-60m spatial resolution
  • Smaller datasets and respectively shorter download times

Limitations

  • The presence of clouds hamper flood detection. Should the area of interest be covered with clouds throughout the whole period of the increased water level, Radar data (e.g. Sentinel-1) should be used for the assessment rather than Optical satellite data.
  • Potential false alarms from shadows
  • The NDWI is limited to rural areas, since the reflectance pattern of urban features is similar to that of water in the green band. Approaches using S2 imagery and calculating a modified NDWI (MNDWI) may be used when interested in urban flooding (Yang et al. 2017).

The difference between optical and SAR data, is that the former detects changes in the surface reflectance, while the SAR detects changes in vegetation structure and moisture.

Workflow: 

(1) Sentinel- 2 Image Acquisition

(2) NDWI Calculation

(3) Clipping of extent to Study Area

(4) Binarization

(5) Polygonize to Vector

(6) Simplification of geometry

(7) Overlay with Road Lines, Districts and Property Boundaries

(8) Damage Assessment 

Bibliography: 

Brakenridge, G R, E Andersona, S V Nghiemb, S Caquard, and T B Shabaneh. 2003. “Flood Warnings , Flood DisasterAssessments , and Flood Hazard Reduction : The Roles of Orbital Remote Sensing.” 30th International Symposium on Remote Sensing of Environmenthttp://www.dartmouth.edu/-floods/Atlas.html.

Du, Zhiqiang, Wenbo Li, Dongbo Zhou, Liqiao Tian, Feng Ling, Hailei Wang, Yuanmiao Gui, and Bingyu Sun. 2014. “Analysis of Landsat-8 OLI Imagery for Land Surface Water Mapping.” Remote Sensing Letters 5 (7): 672–81. https://doi.org/10.1080/2150704X.2014.960606.

Gao, Bo Cai. 1996. “NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” In Remote Sensing of Environment, edited by Michael R. Descour, Jonathan M. Mooney, David L. Perry, and Luanna R. Illing, 58:257–66. International Society for Optics and Photonics. https://doi.org/10.1016/S0034-4257(96)00067-3.

Groeve, Tom De, Zsofia Kugler, and G Robert Brakenridge. 2007. “Near Real Time Flood Alerting for the Global Disaster Alert and Coordination System.” Iscramhttp://floodobservatory.colorado.edu/Publications/DeGroeveKuglerBrakenri....

Islam, A.S., S.K. Bala, and M.A. Haque. 2010. “Flood Inundation Map of Bangladesh Using MODIS Time-Series Images.” Journal of Flood Risk Management 3 (3): 210–22. https://doi.org/10.1111/j.1753-318X.2010.01074.x.

McFeeters, S. K. 1996. “The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17 (7): 1425–32. https://doi.org/10.1080/01431169608948714.

Molinari, Daniela, Scira Menoni, and Francesco Ballio. 2017. Flood Damage Survey and Assessment : New Insights from Research and Practice. ISBN: 978-1-119-21792-3.

Syvitski, James P.M., Irina Overeem, G. Robert Brakenridge, and Mark Hannon. 2012. “Floods, Floodplains, Delta Plains - A Satellite Imaging Approach.” Sedimentary Geology 267–268: 1–14. https://doi.org/10.1016/j.sedgeo.2012.05.014.

Westerhoff, R. S., M. P.H. Kleuskens, H. C. Winsemius, H. J. Huizinga, G. R. Brakenridge, and C. Bishop. 2013. “Automated Global Water Mapping Based on Wide-Swath Orbital Synthetic-Aperture Radar.” Hydrology and Earth System Sciences 17 (2): 651–63. https://doi.org/10.5194/hess-17-651-2013.

Xiao, X., S. Boles, S. Frolking, W. Salas, III Moore, C. Li, L. He, and R. Zhao. 2002. “Observation of Flooding and Rice Transplanting of Paddy Rice Fields at the Site to Landscape Scales in China Using VEGETATION Sensor Data.” International Journal of Remote Sensing 23 (15): 3009–22. https://doi.org/10.1080/01431160110107734.

Yang, Xiucheng, and Li Chen. 2017. “Evaluation of Automated Urban Surface Water Extraction from Sentinel-2A Imagery Using Different Water Indices.” Journal of Applied Remote Sensing 11 (2): 026016. https://doi.org/10.1117/1.JRS.11.026016.