Flooding

Quantifying urban flood extent using satellite imagery and machine learning

The risk of floods from tropical storms is increasing due to climate change and human development. Maps of past flood extents can aid in planning and mitigation efforts to decrease flood risk. In 2021, Hurricane Ida slowed over the Mid-Atlantic and …

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

We developed a novel framework to map boro rice at peak season using Sentinel images. These Boro rice maps in Bangladesh showed high classification accuracy (mean of 87.90%). There was no requirement of sample data collection for training the classification model. Multi-Otsu effectively maps rice in low-data areas, outperforming other ML methods and provides stakeholders rice area statistics to support food security management.

Can we detect more ephemeral floods with higher density harmonized Landsat Sentinel 2 data compared to Landsat 8 alone?

Spatiotemporal quantification of surface water and flooding is essential given that floods are among the largest natural hazards. Effective disaster response management requires near real-time information on flood extent. Satellite remote sensing is …

Towards global flood mapping with machine learning based on the Harmonized Landsat-Sentinel 2 data

Floods are the largest natural hazard in terms of life loss and economic damage, regardless of their cause. In the United States alone, floods cause billions of dollars in property damage, with an estimate exceeding $78 billion due to fluvial floods …

Can we detect more ephemeral floods with higher density harmonized Landsat 8/Sentinel 2 data compared to just one sensor?

Floods, defined as water that temporarily submerges land for over 72 hours or longer, are the largest natural hazard in terms of life loss and economic damage. Effective and immediate disaster response management can reduce the impact of floods but it requires near real-time information on flood occurrence, typically derived based on Earth Observation data.

Surface water and flooding dynamics based on seasonally continuous Landsat data (1986-2011) in a dryland river basin (monthly, seasonally, and yearly animations)

The animations provided here are part of the publication,[Tulbure, M.G. and M. Broich (2018)]. The method is described in [Tulbure et al. (2016)]. The animations are based on statistically validated surface water and flooding extent dynamics data …

Surface water and flooding dynamics data set based on seasonally continuous Landsat data (1986-2011) in a dryland river basin

The layers provided here are part of the publication, [Tulbure, M.G. and M. Broich (2018)]. The method is described in [Tulbure et al. (2016)]. Data are provided in GeoTIFF format per season per year. File naming convention is …

Quantifying Australia's dryland vegetation response to flooding and drought at sub-continental scale

Vegetation response to flooding across large dryland areas such as Australia's Murray Darling Basin (MDB) is not understood synoptically and with locally relevant detail.

Addressing spatio-temporal resolution constraints in Landsat and MODIS-based mapping of large-scale floodplain inundation dynamics

Recent studies have developed novel long-term records of surface water (SW) maps on continental and global scales but due to the spatial and temporal resolution constraints of available satellite sensors, they are either of high spatial and low temporal resolution or vice versa.

Surface water network structure, landscape resistance to movement and flooding vital for maintaining ecological connectivity across Australia’s largest river basin

Landscape-scale research quantifying ecological connectivity is required to maintain the viability of populations in dynamic environments increasingly impacted by anthropogenic modification and environmental change. To evaluate how surface water …