Sri Lanka environment data

**ADAM ID: **FL-20251201-LKA-00 A flood occurred on Dec 01 2025 in Sri Lanka. It affected an estimated 258648 people and 57122 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

# Sri Lanka Cyclone Ditwah: Damage and Flood Proxy Maps, Dec 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage and flood proxy maps for Sri Lanka Cyclone Ditwah (Dec 2025) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Sri Lanka, Cyclone Ditwah, 02 Dec 2025, v0.9** - **EOS-RS Flood Proxy Map: Sri Lanka, Cyclone Ditwah, 02 Dec 2025, v0.9** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2025)

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

Disaster loss and damage dataset for Sri Lanka

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.

**UNOSAT code: FL20240603LKA, GDACS ID: 1102660** This application provides geospatial information regarding the floods in Sri Lanka in June 2024. Important note: The boundaries and names shown, and the designations used on this map do not imply official endorsement or acceptance by the United Nations. The United Nations Satellite Centre. UNOSAT is not responsible for the misuse or misrepresentation of the map.

**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.