The present state of the art technologies for flood mapping are typically tested on small geographical regions due to limitation of resources, which hinders the implementation of real-time flood management activities. We proposed a unified framework (GEE4FLOOD) for rapid flood mapping in Google Earth Engine (GEE) cloud platform. With the unexpected spells of extreme rainfall in August 2018, many parts of Kerala state in India experienced a major disastrous flood. Therefore, we tested the GEE4FLOOD processing chain on August 2018 Kerala flood event. GEE4FLOOD utilizes multitemporal Sentinel-1 synthetic aperture radar images available in GEE catalog and an automatic Otsu’s thresholding algorithm for flood mapping. It also utilizes other remote sensing datasets available in GEE catalog for permanent water body mask creation and result validation. The ground truth data collected during the Kerala flood indicates promising accuracy with 82% overall accuracy and 78.5% accuracy for flood class alone. In addition, the entire process from data fetching to flood map generation at a varying geographical extent (district to state level) took ∼2 to 4 min.
Urban expansion, water bodies and climate change are inextricably linked with each other. The macro and micro level
climate changes are leading to extreme precipitation events which have severe consequences on flooding in urban areas.
Flood simulations shall be helpful in demarcation of flooded areas and effective flood planning and preparedness. The
temporal availability of satellite rainfall data at varying spatial scale of 0.10 to 0.50 is helpful in near real time flood
simulations. The present research aims at analysing stream flow and runoff to monitor flood condition using satellite
rainfall data in a hydrologic model. The satellite rainfall data used in the research was NASA’s Integrated Multi-satellite
Retrievals for Global Precipitation Measurement (IMERG), which is available at 30 minutes temporal resolution.
Landsat data was used for mapping the water bodies in the study area. Land use land cover (LULC) data was prepared
using Landsat 8 data with maximum likelihood technique that was provided as an input to the HEC-HMS hydrological
model. The research was applied to one of the urbanized cities of India, viz. Dehradun, which is the capital of
Uttarakhand State. The research helped in identifying the flood vulnerability at the basin level on the basis of the runoff
and various socio economic parameters using multi criteria analysis.