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

Tiwari, Varun; Tulbure, Mirela G.; Caineta, Júlio; Gaines, Mollie D.; Perin, Vinicius; Kamal, Mustafa; Krupnik, Timothy J.; Aziz, Md Abdullah; Islam, AFM Tariqul

doi:10.1016/j.jenvman.2023.119615

Cited by 11 publications

(1 citation statement)

References 82 publications

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“…We used Sentinel-2 imagery that was collected a day after Hurricane Ida passed through the study area (September 2, 2021) with little (< 1%) cloud cover, making it an optimal data source. In Google Earth Engine (GEE), we obtained the imagery, ltered it temporally and spatially, and removed cloud and cirrus pixels using the quality assessment band (QA60) (Tiwari et al, 2024).…”

Section: Satellite Imagerymentioning

confidence: 99%

Quantifying Urban Flood Extent Using Satellite Imagery and Random Forest: A Case Study in Southeastern Pennsylvania

Composto,

Tulbure,

Tiwari

et al. 2024

Preprint

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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 Northeast United States and released unprecedented rainfall. Satellite imagery and the Random Forest algorithm are a reliable combination to map flood extents. However, this combination is not usually applied to urban areas. We used Sentinel-2 imagery (10 m), along with derived indices, elevation, and land cover data, as inputs to a Random Forest model to make a new flood extent for southeastern Pennsylvania. The model was trained and validated with a dataset created with input from PlanetScope imagery (3 m) and social media posts related to the flood event. The overall accuracy of the model is 99%, and the flood class had a user’s and producer’s accuracy each over 99%. We then compared the flood extent to the Federal Emergency Management Agency (FEMA) flood zones at the county and tract level and found that more flooding occurred in the Minimal Hazard zone than in the 500-year flood zone. Our Random Forest model relies on publicly available data and software to efficiently and accurately make a flood extent map that can be deployed to other urban areas. Flood extent maps like the one developed here can help decision-makers focus efforts on recovery and resilience.

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Section: Satellite Imagerymentioning

confidence: 99%

Quantifying Urban Flood Extent Using Satellite Imagery and Random Forest: A Case Study in Southeastern Pennsylvania

Composto,

Tulbure,

Tiwari

et al. 2024

Preprint

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Quantifying urban flood extent using satellite imagery and machine learning

Composto,

Tulbure,

Tiwari

et al. 2024

Nat Hazards

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The risk of oods from tropical storms is increasing due to climate change and human development.Maps of past ood extents can aid in planning and mitigation efforts to decrease ood risk. In 2021, Hurricane Ida slowed over the Mid-Atlantic and Northeast United States and released unprecedented rainfall. Satellite imagery and the Random Forest algorithm are a reliable combination to map ood extents. However, this combination is not usually applied to urban areas. We used Sentinel-2 imagery (10 m), along with derived indices, elevation, and land cover data, as inputs to a Random Forest model to make a new ood extent for southeastern Pennsylvania. The model was trained and validated with a dataset created with input from PlanetScope imagery (3 m) and social media posts related to the ood event. The overall accuracy of the model is 99%, and the ood class had a user's and producer's accuracy each over 99%. We then compared the ood extent to the Federal Emergency Management Agency (FEMA) ood zones at the county and tract level and found that more ooding occurred in the Minimal Hazard zone than in the 500-year ood zone. Our Random Forest model relies on publicly available data and software to e ciently and accurately make a ood extent map that can be deployed to other urban areas. Flood extent maps like the one developed here can help decision-makers focus efforts on recovery and resilience.

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Integrating digital technologies in agriculture for climate change adaptation and mitigation: State of the art and future perspectives

Parra-López,

Ben Abdallah,

Garcia-Garcia

et al. 2024

Computers and Electronics in Agriculture

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

Cited by 11 publications

References 82 publications

Quantifying Urban Flood Extent Using Satellite Imagery and Random Forest: A Case Study in Southeastern Pennsylvania

Quantifying Urban Flood Extent Using Satellite Imagery and Random Forest: A Case Study in Southeastern Pennsylvania

Quantifying urban flood extent using satellite imagery and machine learning

Integrating digital technologies in agriculture for climate change adaptation and mitigation: State of the art and future perspectives

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