Bangladesh Floods - August 2017 - Flooding levels & Vulnerability

Here we provide poverty data created using Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to geolocated household survey data on poverty from the DHS wealth index (2011), the Progress out of Poverty Index (2014), and household income (2013). Citation: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http://rsif.royalsocietypublishing.org/content/14/127/20160690

This map illustrates satellite-detected surface water extent in the central and northern parts of Bangladesh using a Sentinel-1 satellite image acquired on the 22 August 2017. The total analysed area is about 4,284,431 ha. In this analysed area; 1,099,369 ha (39%) of lands are likely affected. These lands are mainly cropland irrigated and rainfed areas and estimated to 1,039,350 ha. The population exposure analysis using WorldPop data shows that ~10,000,000 people are potentially affected by floods in the analysed zone: ~5,400,000 are located in Dhaka Division and ~2,750,000 in Rajshahi Division. Please note that for visualization purposes the analyzed area is wider than the extent of this map.This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: FL20200713BGD** This map illustrates potentially exposed population to floods (cumulative) aggregated by district using NOAA20-VIIRS in Bangladesh between the 12th and the 21st of July 2020 and Worldpop spatial demographic data. About 34 million people were exposed or living close to flooded areas. The most exposed districts mainly located in Sylhet, Mymensingh and Rajshahi divisions.

Product ============= This priority index was derived by combining a detailed flood extent mapping with detailed human settlement geo-data. Both sources were combined to produce the location and magnitude of population living in flooded areas. This was subsequently aggregated to admin-4 areas (GND) as well as admin-3 areas (DS divisional). The flood extent mapping was derived in turn by combining two sources: Flood extent maps could be produced rather faster using satellite imageries captured by either optical sensors or Synthetic Aperture Radar (SAR) sensors. In most places flood is cause by heavy rainfall which means in most cases cloud is present, this is a limitation for optical sensors as they can’t penetrate clouds. Radar sensors are not affected by cloud, which make them more useful in presence of cloud. In This analysis we analyzed sentinel2 optical image from May 28th and Sentinel 1 SAR image from May 30th. Then we combine the two results adding up the flood extents. Main cloud covered areas and permanent water bodies are removed from the flood extent map using the Sentinel 2 cloud mask. The scale/resolution of the flood extent map is 30mts where as the permanent water body map has 250m scale resolution. This will introduce some discrepancy: part of flood extent map could be permanent water body. Scope ============= Analysis focused on 4 districts in South-West Sri Lanka based on news reports (https://www.dropbox.com/s/n0qdqe7qfgq6fyv/special_situation.pdf?dl=0). Based on the admin-3-level analysis, highest percentages of population living in flooded areas were seen in Matara district. Admin-4 level analysis concentrated only on Matara district for that reason. Caveats ============= The dataset is showing percentage flooded. The data has not yet been corrected for small populations. We believe the product is currently pointing to the high priority areas. In the shp or csv files the user of this data could easily correct for small populations, if there is a wish to target on the amount of people affected. Data used from partners ============= The human settlement data was retrieved from http://ciesin.columbia.edu/data/hrsl/. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-06-2017. The Radar imagery analysis was done by NASA JPL, whose input in this product has been crucial. Visualization =========== An example map is available here: http://bit.ly/SriLankaFloodMap Linked data ========== Admin boundaries 3 and 4 can be found here (link on OBJECT_ID): https://data.humdata.org/group/lka?q=&ext_page_size=25&sort=score+desc%2C+metadata_modified+desc&tags=administrative+boundaries#dataset-filter-start How to use ============= The ratio column in the SHPs or CSVs can be multiplied by 100 to get the percentage of flooding in the area.

This map illustrates satellite-detected surface water extent in the central part of Bangladesh using a Sentinel-1 satellite image acquired on the 12 August 2017 with a total surface of 4,280,650 ha. In this analyzed area; 1,644,983 ha (38%) of lands are likely affected. These lands are are mainly cropland irrigated and rainfed areas and estimated to 1,576,351 ha. The population exposure analysis using WorldPop data shows that ~17,000,000 people are potentially affected by floods in the analysed zone: ~8,400,000 are located in Dhaka Division and ~5,750,000 in Rajshahi Division. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

Includes a binary GeoTIFF where 0 values are non-flooded and 1 values are flooded. Included a metadata file that describes a summary and methods for the data.