Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Applications of Open-Access Remotely Sensed Data for Flood Modelling and Mapping in Developing Regions

Domain:

geospatialclimate

Record type:

paper
Creator:
IguGeo
Publisher:
MDP
Host:
Flood modelling and mapping typically entail flood frequency estimation, hydrodynamic modelling and inundation mapping, which require specific datasets that are often unavailable in developing regions due to financial, logistical, technical and organizational challenges. This review discusses fluvial (river) flood modelling and mapping processes and outlines the data requirements of these techniques. This paper explores how open-access remotely sensed and other geospatial datasets can supplement ground-based data and high-resolution commercial satellite imagery in data sparse regions of developing countries. The merits, demerits and uncertainties associated with the application of these datasets, including radar altimetry, digital elevation models, optical and radar images, are discussed. Nigeria, located within the Niger river basin of West Africa is a typical data-sparse country, and it is used as a case study in this review to evaluate the significance of open-access datasets for local and transboundary flood analysis. Hence, this review highlights the vital contribution that open access remotely sensed data can make to flood modelling and mapping and to support flood management strategies in developing regions.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

Similar

Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial TechnologyApplication of open-access and 3rd party geospatial technology for integrated flood risk management in data sparse regions of developing countriesBig data; sensor networks and remotely-sensed data for mapping; feature extraction from lidarPredicting Poverty in African Cities Using Open Source Remotely Sensed DataDeveloping a Remotely Sensed Drought Monitoring Indicator for MoroccoEffectiveness of Remotely Sensed Built Areas for Constraining and Modelling Gridded Population Estimates - Script Samples and Data

Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial Technology

Consistent data is seldom available for whole-catchment flood modelling in many developing regions,

Application of open-access and 3rd party geospatial technology for integrated flood risk management in data sparse regions of developing countries

Floods are one of the most devastating disasters known to man, caused by both natural and anth

Big data; sensor networks and remotely-sensed data for mapping; feature extraction from lidar

Abstract. Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combini

Predicting Poverty in African Cities Using Open Source Remotely Sensed Data

International audience

Developing a Remotely Sensed Drought Monitoring Indicator for Morocco

Drought is one of the most serious climatic and natural disasters inflicting serious impacts on the

Effectiveness of Remotely Sensed Built Areas for Constraining and Modelling Gridded Population Estimates - Script Samples and Data

These data present the effectiveness of three different high-resolution built area datasets for prod