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At least 271 records · Page 15

Colorado Front Range Disasters: Understanding the Impact of Forest Management on the Cameron Peak and CalWood Fire

Along the Colorado Front Range, forest management has gained significant attention due to uncharacteristically large fires that burned late in 2020. The Cameron Peak Fire (largest in Colorado recorded history) and the CalWood Fire collectively burned an estimated 219,019 acres from August through December of 2020. Project partners at the Coalition for the Poudre River Watershed, Colorado State Forest Service, Ben Delatour Scout Ranch, The Nature Conservancy, Colorado Forest Restoration Institute, and Colorado State University were interested in understanding the effectiveness of previous forest treatments in reducing burn severity within the Cameron Peak Fire and the CalWood Fire. We first collated a forest treatment dataset from pre-existing datasets by reclassifying over 29,000 treatments, which occurred across the Northern Colorado Front Range between 1970-2020. Secondly, we mapped three burn severity indices using Landsat 8 OLI and Sentinel-2 MSI Earth observations and compared them to soil burn severity field data. Thirdly, a total of 35 topographic, disturbance, forest structure, and treatment predictor variables were generated across the fires. Finally, we assessed relationships between these predictor variables and burn severity using the random forest algorithm. Model results indicate that the primary drivers of burn severity were elevation and distance to treatment edge for the Cameron Peak Fire and fire area and forest canopy cover for the CalWood Fire. Further analysis of these variables paired with field data is necessary to understand the relationship between burn severity and treatments to guide future restoration efforts, improve forest resiliency, and mitigate fire risks.

Neal Swayze↗

Illinois Disasters: Utilizing NASA Earth Observations to Enhance Drought Monitoring in Illinois

Drought and flooding in Illinois have severe impacts on the communities and ecosystems of the state. Soil moisture is a valuable indicator of drought and flood vulnerability but can be difficult to measure since in situ monitoring is limited to discrete stations throughout the state. The team created a framework to compare in situ, modeled, and NASA satellite soil moisture measurements to increase the spatial coverage of soil moisture monitoring. The team partnered with the Illinois State Weather Survey, USDA Midwest Climate Hub, NOAA Regional Climate Services of the Central Region, NOAA National Integrated Drought Information System’s Midwest Drought Early Warning System, and the NOAA North Central River ForecastCenter. The team standardized and compared soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission, modeled soil moisture outputs from NASA’s SPoRT Land Information System (SPoRT-LIS), and in situ measurements from the Illinois State Weather Survey’s Water and Atmospheric Resources Monitoring (WARM) program. Compared to the WARM data, the satellite and modeled data showed seasonally variable differences and bias. The difference was highest in the winter months and lowest in the late summer and early fall months for the SMAP and SPoRT-LIS data products. SPoRT-LIS produced lower seasonal variability and SMAP demonstrated higher correlation values and lower differences. These analyses suggest that both SMAP and SPoRT-LIS products offer unique strengths and limitations when used for soil moisture monitoring

Joshua Green↗

St. Joseph Peninsula Disasters: Using NASA Earth Observations to Investigate Land Cover, Shoreline Change, and Sediment Transport in St. Joseph Peninsula after Hurricane Michael

T.H. Stone Memorial St. Joseph Peninsula State Park experienced significant damages from Hurricane Michael in 2018, the first Category 5 hurricane to hit the contiguous United States since 1992. These damages included a 300-meter-wide and 10-meter-deep breach in the peninsula, habitat disruption, and a forced closure of over half of the total park area. These damages, coupled with restricted visitor access, resulted in a significant loss of revenue for the park. NASA DEVELOP partnered with the Florida Department of Environmental Protection (DEP) to determine the overall impact of Hurricane Michael on land cover and shoreline change by using NASA Earth observations including Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Space Agency’s Sentinel-2 Multispectral Instrument (MSI) to analyze sediment transport and climatology to further understand the lasting impacts of hurricanes on the ecosystems of the park. The DEVELOP team’s analyses showed that chlorophyll-a concentrations, sea surface temperature, and precipitation are increasing over time. The sediment transport analysis showed dynamic movement across the peninsula, with the greatest erosion occurring within the bay and along the length of the peninsula. These results are supported by evidence of declining seagrass abundances and seasonal turbidity patterns within those areas. Providing these analyses for the partner allows for a greater understanding of how best to proceed with restoration efforts, which may include rebuilding camping services, expanding fishing recreation, and conserving habitats for endangered species.

Erica Kriner↗

Hawai‘i Island Disasters: Using NASA Earth Observations to Assess Coastal Flood Risk with Measures of Land Cover Change, Flood Extent, and Vulnerability for Adaptation and Mitigation Planning on Hawai‘i Island

As the County of Hawai‘i faces an increased risk of extreme flooding events, sea-level rise, and other hazards associated with climate change, the need for building geospatial capacity to make better-informed decisions is critical. The County of Hawai‘i and Arizona State University partnered with NASA DEVELOP to complete a macro-scale risk analysis for the island of Hawai‘i analyzing flooding, land cover, vulnerability, and exposure factors using Earth observations and socio-economic data. The team assessed the variation in urban coastal vulnerability around the entire island of Hawai‘i, using satellite imagery of coastal land cover typology from satellite products such as Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-1 Synthetic Aperture Radar (SAR). The team made a sharable geodatabase containing datasets modeling vulnerability to coastal flooding as well as the Hawai‘i Flood Risk Toolbox (HiFloRT) which contains multiple tools for the County to map land cover, extreme rainfall and flood extent across the Island. The end products will allow the County of Hawai‘i to establish a protocol and standard framework for the utilization of Earth observations in future planning.

Garren Kalter↗

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↗

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↗

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↗