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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2022

Mexico Disasters: Comparing Feasibility of Flood Detection Methods Using Google Earth Engine and Open Data Cube for Flood Mitigation in Mexico

In coordination with out partner's, we identified three case studies for the application of the flood detection algorithms. The first case study analyzes the impacts of Hurricane Sally on Villahermosa, Tabasco resulting in urban flooding and impacting adjacent cropland. The second case study investigates floods in Salto de Agua, Chiapas caused by Hurricane Eta, where the Xanil River level exceeded 14 meters, pouring into the surrounding villages. Finally, we look further north to Tula de Allende, Hidalgo during Hurricane Ida, where floods collapsed the power supply citywide and caused numerous deaths.

John Willis↗
NASA NTRSDate not supplied

GEO Disaster Risk Reduction Working Group

GEO is an international partnership of more than 100 national governments, 100 Participating Organizations and multiple Associate members working towards a future where decisions and actions for the benefit of humankind are informed by coordinated, comprehensive and sustained Earth observations.

David Borges↗
NASA NTRS2022

Chile Disasters: Automating Wildfire Risk and Occurrence Mapping in Google Earth Engine to Improve Wildfire Detection and Response Time Efforts

Wildfires in Chile in the last decade were the worst on record, destroying homes and livelihoods, polluting the air, and displacing whole towns. To predict locations where wildfires were likely to start, the Corporación Nacional Forestal (CONAF) created a wildfire risk model within ArcGIS Pro and Google Earth Engine (GEE) that utilized the NOAA Global Forecast System (GFS) and the NASA Shuttle Radar Topography Mission (STRM) 90-meter datasets. The previous CONAF model was very resource-heavy and time-intensive to run. NASA DEVELOP, in partnership with CONAF, automated the previous model and transferred it fully into GEE where all Earth observation datasets could be used without downloading. The new model substantially reduced the runtime. The final model was used to create a near real-time wildfire monitoring application as well as fire severity maps. The end products will be used by CONAF for wildfire prediction and management to prevent more destruction in the future.

Maria De Los Santos↗
NASA NTRS2022

Boulder County Disasters: Mapping Forest Carbon Stocks to Understand Carbon Implications of Treatment and Wildfire

In recent years, record-breaking wildfire activities in the western US illustrate the need for fire mitigation efforts, such as forest fuels reduction treatments. Forests serve as crucial carbon sinks that combat the increasing effects of climate change while fuels reduction treatments may remove carbon from forested systems. As a result, forest managers need to find a balance between fire mitigation and carbon preservation. This project partnered with Boulder County Parks and Open Space (BCPOS) and the University of Colorado, Denver to investigate the 2020 Cal-Wood fire in Boulder County, Colorado. Using remote sensing data from Landsat 8 OLI, SRTM, Sentinel-2 MSI, and LiDAR, we mapped post-fire forest carbon pools and compared these values with values derived from measurements from plots on the ground. Results indicate that the correlations are R2=0.76 for aboveground live carbon, R2=0.44 for standing carbon, and R2=0.43 for aboveground dead carbon (R2=0.43), and R2=0.35 for total carbon (R2=0.35). Additionally, 38.5% of total carbon was stored in dead carbon and 14.4% was stored in live carbon. We next compared the post-fire pools between treated and untreated areas. Our analysis suggests fuels reduction treatments did not reduce carbon loss in the presence of wildfire enough to clearly distinguish the post-fire carbon in the treated and untreated areas. However, our final carbon maps still provide BCPOS and researchers with an opportunity to explore carbon estimation models based on remotely sensed data as well as a framework to evaluate fuels reduction treatment effectiveness and impact on forest carbon stocks for future wildfire events.

Sarah Hettema↗
NASA NTRS2022

Chile Disasters: Automating Wildfire Risk and Occurrence Mapping in Google Earth Engine to Improve Wildfire Detection and Response Time Efforts

The purpose of this document is to outline the steps the NASA DEVELOP team took to complete wildfire risk and occurrence mapping for our partners at Chile’s Corporación Nacional Forestal and the Embassy of Chile in the United States. The instructions are intended for individuals already knowledgeable in coding to recreate our methodology.

Maria De Los Santos↗