Priority Index Sri Lanka Floods May 2017

For the floods in Southern Malawi of March 2019, we have combined flood extent maps (Sentinel) with HRSL settlement/population grid. This results in a calculation of # of affected buildings/people per district. The results is shared through maps and in a shapefile. ### 1. Data sources ## Sentinel 1 Imagery from 7th of March 2017 ## Sentinel 2 Imagery from 10th/12th/14th of March 2017 ## HRSL population data 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 9 March 2019. ### 2. Good to know The flood extent for Nsanje district was separately added on March 14th, to the existing flood extent for the main area from March 12th. ### 3. Methodology ## A. Flood Extent Based on SAR The following steps were used to detect flood extent(water/no water). In SNAP tool the raw data downloaded from sci-hub Copernicus was processed to calibrate image for atmospheric correction, spike filter and terrain correction(This is mainly for Sentinel 1 data). Finally defining water no water based on a threshold applied on the corrected image. Defining a threshold is always a challenge in SAR image analysis for flood detection, we collected data from the field to define this threshold. For Sentinel 2 as a first step cloud filter was calculated by applying a combined threshold on Band 2 and Band 10. The cloud mask shown in the figure below didn’t capture shadows of clouds, these were miss interpreted by the flood algorithm as water/flood. To correct this areas with more cloud cover were clipped out with a polygon. To define water no water based on sentinel data we used NDWI index, the treshold is adjusted based on data collected from the field Validation points were collected by Field team tested different values and check if the threshold identified fits with observation. The complete methodology how to detect flooding based on Sentinel 1 data and SNAP toolbox is documented in ESA website. ## B. Affected People To calculate number of affected people per each admin level, flood extent map is combined with HRSL population data. This is done in two steps: First, in step 1, we calculate a raster, which multiplies the population grid with the flood grid, such that we are left with only "population in flooded area". This is done using raster calculator where population density raster was multiplied by flood extent raster, which has a value of 0 for no flood and 1 for flood areas. Note that the flood extent grid was first resampled to match it to the population grid. This whole exercise is repeated for settlement/buildings instead of population. Step 2: We apply zonal statistics per TA to calculate total number of buildings/people affected in each admin level. For each Admin level2 estimated number of affected people and affected houses are plotted in the map. The zonal statistics data used for plotting can be found in the shape file.

**UNOSAT code: FL20241128LKA** This map illustrates cumulative satellite-detected water extent from multiple satellites in the Jaffna District, Northern Province, Sri Lanka from 3 to 10 December 2024. Within the cloud-free analysed area of approximately 760 km², an estimated 84 km² of land is observed to be affected by floodwaters, with around 43 km² of cropland Based on WorldPop spatial demographic data, approximately 2,400 people are exposed to or living near the flooded areas. Additionally, 3,400 buildings have been identified as potentially affected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20251128LKA, GDACS ID: 1001238** This application provides geospatial information from ongoing satellite-based assessments of the floods in Sri Lanka triggered by Cyclone DITWAH in November 2025. Important note: Flood analysis derived from radar imagery may underestimate the presence of standing water in built-up and densely vegetated areas due to the backscattering properties of the radar signal.

In this analysis we have combined several data sources around the floods in Bangladesh in August 2017. ## Visualization * See attached map for a map visualization of this analysis. * See http://bit.ly/2uFezkY for a more interactive visualization in Carto. ## Situation Currently, in Bangladesh many water level measuring stations measure water levels that are above danger levels. This sets in triggers in motion for the partnership of the 510 Data Intitiative and the Red Cross Climate Centre to get into action. ## Indicators and sources In the attached map, we combined several sources: * Locations of waterlevel stations and their respective excess water levels (cms above danger level) at 14/08/2017 (Source: http://www.ffwc.gov.bd/index.php/googlemap?id=20) * Population density in Bangladesh to quickly see where many people live in comaprison to these higher water-level stations. (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00018 >> the People per hectare 2015 UN-adjusted totals file is used.) * Vulnerability Index: we constructed a Vulnerability Index (0-10) based on two sources. First poverty incidence was collected from Worldpop (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00020 >> The estimated likelihood of living below $2.50/day). Second, we used a Deprivation Index which is estimated in the report Lagging District Reports 2015 (Source: http://www.plancomm.gov.bd/wp-content/uploads/2015/02/15_Lagging-Regions-Study.pdf > Appendices > Table 20), which combines many socio-economic variables into one Deprivation Index through PCA analysis. ## Detailed methodology Vulnerability * The above-mentioned poverty source file is on a raster level. This raster level poverty was transformed to admin-4 level geographic areas (source: https://data.humdata.org/dataset/bangladesh-admin-level-4-boundaries), by taking a population-weighted average. (Source population also Worldpop). * The district-level PCA components from abovementioned reports were matched to the geodata based on district names, and thus joined to the admin-4 level areas, which now contain a poverty value as well as Deprivation Index value. Note that all admin-4 areas within one district (admin-2) obviously all have the same value. The poverty rates do differ between all admin-4 areas. * Lastly, both variables were transformed to a 0-10 score (linearly), and a geomean was taken to calculate the final index of the two. A geomean (as opposed to an arithmetic mean) is often used in calculating composite risk indices, for example in the widely used INFORM-framework (www.inform-index.org).

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

# EOS-RS Flood Proxy Map: South Sulawesi, Indonesia, Floods, 17 Nov 2022, v0.5 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in Makassar, South Sulawesi. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA) before (5 Nov 2022) and during (17 Nov 2022) the event. The image covers an area indicated by the large white polygon. Each light blue pixel measures about 30 m across. Preliminary validation was done in selected areas using optical imagery. This flood proxy map should be used as a guide to identify areas that are likely flooded, and is less reliable over urban and vegetated areas. The product contains modified Copernicus Sentinel data (2022), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), using the Advanced Rapid Imaging and Analysis (ARIA) system originally developed at NASA's Jet Propulsion Laboratory, California Institute of Technology, and modified at EOS-RS. Data processing used an AWS Open Dataset of Copernicus Sentinel-1 data for the Asia region (https://registry.opendata.aws/sentinel1-slc-seasia-pds). **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Advanced Rapid Imaging and Analysis (ARIA), NASA-JPL/Caltech, Contains modified Copernicus Sentinel data (2022)