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NASA NTRSDate not supplied

Maipo River Valley Agriculture: Determining Crop Coefficients Using Remote Sensing for the Maipo River Valley Basin in Chile

Agriculture is the primary use of water in the Maipo River basin of Central Chile, accounting for ~ 75% of the total demand. Assessment of irrigation needs for agricultural production has commonly relied on reference crop coefficients (Kc) derived from geographic and climatic conditions that differ from those of Chile. In partnership with the Centro de Información de Recursos Naturales (CIREN), this work focused on calculating site-specific crop coefficients tailored to crop production in the water-stressed Maipo River Valley. Two distinct approaches were implemented, each relying on remotely-sensed Earth observation datasets from NASA over consecutive growing seasons from 2019 to 2022. The first method estimated Kc values based on their linear relationship with the Normalized Difference Vegetation Index (NDVI) obtained from either Terra Moderate Resolution Imaging Spectroradiometer (MODIS) or Landsat 8 Operational Land Imager (OLI) surface reflectance. The second technique leveraged information from the ISS Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) by computing the ratio between actual crop evapotranspiration (ET) and potential evapotranspiration (PET). Both procedures showed promising results that can build on one another. The former approach best captured vegetation signals of annual crops while the latter appeared suited for perennials. Overall, this study provides a strong basis and novel way to accurately estimate Kc using remote sensing, with the potential for improved irrigation management and reduction in water consumption.

Benjamin Goffin↗
NASA NTRSDate not supplied

Grand Valley Ecological Forecasting II: Forecasting Trends in Pinyon-Juniper and Sagebrush Habitat Relative to Wildfire, Drought, Beetle Disturbance, and Treatment Impact for Management Planning

Disturbances and landcover change in pinyon-juniper and sagebrush ecosystems are exacerbated by environmental conditions such as variability in climate characteristics. Our DEVELOP team partnered with the National Park Service (NPS) in Colorado National Monument and the Bureau of Land Management (BLM) in McInnis Canyons and Dominguez-Escalante National Conservation Areas to investigate disturbances to land cover. NPS partners were interested in identifying areas at risk of pinyon-juniper die-off or encroachment by invasive species. The BLM partners prioritized identifying areas suitable for fire reduction and prevention treatment. To address these concerns, we forecasted landcover change in the Grand Valley region of Colorado using NASA Earth observation data from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua. We collected and analyzed these data in conjunction with Term I of this project. We found that the primary drivers of forecasted landcover change in the study area were aspect and elevation. Our forecasted land cover maps, created using the Idrisi TerrSet Land Change Modeler, indicated that by 2040, ecosystems within partner management areas will likely see tree encroachment on shrublands. These maps addressed the needs of our partners by showing potential habitat suitability trends, which will inform management planning.

Bill Curtiss↗
NASA NTRS2022

Delaware Basin Ecological Forecasting: Identifying Vegetation Trends and Atmospheric Stressors in the Guadalupe Mountains and Carlsbad Caverns National Parks

The Guadalupe Mountains and Carlsbad Caverns National Parks, located in the Delaware Basin in the southwestern United States, observed both a decrease in precipitation and an increase in temperature over the last decade. Furthermore, activity from local oil fields generated nitrogen dioxide (NO2) plumes that spread over the parks and augmented the effects of the drought. NO2 is a precursor for tropospheric ozone (O3) which is known to have adverse effects on vegetation and ecosystems at large. These new climate dynamics prompted the National Park Service (NPS) to collaborate with NASA DEVELOP to assess the impact on vegetation within the parks. We used NASA Earth observations including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI), and Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) to assess vegetation health, water stress, and precipitation in the affected parks. After creating a homogeneous reference area in the Sierra Diablo Mountains, the team visualized vegetation health through a Normalized Difference Vegetation Index (NDVI) time series map from 2010-2021. This did not show strong evidence that the NO2 plume is causing vegetation decline. Following this, we created a water stress map with a Normalized Difference Moisture Index (NDMI) time series map from 2010-2021, which revealed a pattern of increasing water stress. We also confirmed that precipitation in the region decreased over the span of 2010-2021. These observations and findings will allow the NPS Intermountain Region to more effectively plan for the preservation and maintenance of vegetation health within the parks.

Jack Mezger↗
NASA NTRS2022

Wichita Climate: Using Satellite Data to Identify Neighborhoods Vulnerable to Extreme Heat for Equitable Climate Mitigation and Planning

Wichita, Kansas is facing a host of climate threats, one being extreme heat that is manifested through the urban heat island (UHI) effect. The uneven distribution of heat risk in Wichita across socioeconomic status is an environmental justice issue. We worked with the City of Wichita to map heat exposure, tree canopy, and heat risk in order to support the City's climate resilience initiatives. To visualize heat exposure, we quantified and mapped average summer heat from 2013–2021 using Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Land Surface Temperature (LST) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) night-time LST. To understand tree canopy cover gaps, we created a tree canopy map using 2021 PlanetScope imagery, which identified 20% more trees than the US Geological Survey’s (USGS) National Land Cover Database (NLCD) tree canopy coverage estimates for Wichita. To characterize high risk areas, we used socioeconomic census data and existing social vulnerability indices, highlighting populations that were exposed and vulnerable to extreme heat. The spatial analyses demonstrated that heat exposure is concentrated in the city center and southwest Wichita, areas that are also low in tree canopy coverage. The three census block groups and 17 census tracts with the highest heat risk primarily circle the city center, in areas home to more socially vulnerable populations and near enough to the dense urban center to feel significant urban heat island effects.

Brooke Laird↗
NASA NTRS2022

Grand Valley Ecological Forecasting II: Forecasting Trends in Pinyon-Juniper and Sagebrush Habitat Relative to Wildfire, Drought, Beetle Disturbance, and Treatment Impact for Management Planning

Disturbances and landcover change in pinyon-juniper and sagebrush ecosystems are exacerbated by environmental conditions such as variability in climate characteristics. Our DEVELOP team partnered with the National Park Service (NPS) in Colorado National Monument and the Bureau of Land Management (BLM)in McInnis Canyons and Dominguez-Escalante National Conservation Areas to investigate disturbances to land cover. NPS partners were interested in identifying areas at risk of pinyon-juniper die-off or encroachment by invasive species. The BLM partners prioritized identifying areas suitable for fire reduction/prevention treatment. To address these concerns, we forecasted landcover change in the Grand Valley region of Colorado using NASA Earth observation data from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua. We collected and analyzed these data in conjunction with Term I of this project. We found that the primary drivers of forecasted landcover change in the study area were aspect and elevation. Our forecasted landcover change maps, created using the Idrisi TerrSet Land Change Modeler, addressed the needs of our partners by showing potential habitat suitability trends, which will inform management planning. Forecasted landcover maps indicated that by 2040, ecosystems within partner management areas will likely see tree encroachment on shrublands.

William Curtiss↗
NASA NTRS2022

Maine Ecological Forecasting III: Utilizing Earth Observations to Monitor Federally Endangered Atlantic Salmon (Salmo salar) Habitat in Maine: An Interactive Workshop

Shifting patterns in land use and land cover (LULC), temperature, and precipitation have exacerbated a rapid decline in Federally Endangered wild Atlantic salmon (Salmo salar) populations. The team at NASA DEVELOP partnered with the Maine Department of Marine Resources (DMR) and the Downeast Salmon Federation (DSF) to create a comprehensive workshop designed to demonstrate the applicability of Earth observations in examining these threats using the Penobscot, Union, and Machias Rivers as case studies. This entailed curating tutorials for acquiring and analyzing satellite data using Google Earth Engine, EarthExplorer, and Earthdata. The team demonstrated how to classify LULC in ArcGIS Pro from 1985 until 2021 using Landsat 5 Thematic Mapper (TM), Landsat 8 Operation Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and datasets from the United Stated Geological Survey (USGS) National Land Cover Database (NLCD), showing an overall transition from coniferous forests to other LULC classes. The team also demonstrated how to use historical data from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) to generate 2021 land surface temperature (LST) and precipitation maps, respectively, showing that Maine was abnormally dry during the summer in an increasingly warm region. These workshop materials will aid the partners in integrating NASA Earth observations into their future salmon habitat restoration initiatives.

Jonathan Falciani↗
NASA NTRS2022

Black Hills Wildfires: Mapping Post-Fire Conifer Regeneration using Snow-On Imagery

The 2000 Jasper Fire in the Black Hills of South Dakota was the largest wildfire to date in the region, burning over 83,000 acres of ponderosa pine forest. In collaboration with partners from the United States Forest Service (USFS) Black Hills Experimental Forest, USFS Rocky Mountain Research Station, and United States Geological Survey Geosciences and Environmental Change Science Center, we characterized post-fire forest regeneration within high-severity burn patches. We accomplished this by implementing novel conifer detection techniques using a snow index mask to create a winter, snow-on image composite from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data. We utilized 2015 USFS stem maps of field-observed regeneration plots and ocularly sampled additional reforestation sites planted in 2001–2013. In Google Earth Engine (GEE), the field data and imagery were used to train a Random Forest (RF) model. The RF model classified 2021 conifer regeneration density as low, medium, or high across the high-severity burn area with an overall accuracy of 81.3%. Approximately 45.9% of the high-severity burn had low or no regeneration (0-40 trees per acre) 20 years post-fire. Given our partners' desire to find easily accessible low conifer regeneration zones, we identified 4,079 acres of priority planting sites that were within 1,500 feet of roads, had not been planted previously, and were larger than 50 acres. This method supports the use of snow-on imagery as a successful technique to identify conifer regeneration.

Casey Menick↗
NASA NTRSDate not supplied

Feasibility Study to Interactive Workshop: Building End-user Capacity to Integrate Earth Observation Data into Federally Endangered Atlantic Salmon (Salmo salar) Habitat Monitoring in Main

Changes in temperature and precipitation patterns, along with alterations in land cover threaten ongoing conservation efforts for Federally Endangered Atlantic salmon (Salmo salar) in Maine. Earth observation data offers a unique perspective for habitat monitoring that can complement habitat restoration and conservation activity on the ground. As a dual capacity building program, the NASA DEVELOP National Program strives to build the capacity of program participants by leveraging Earth observation data to address environmental concerns across the globe, while also building capacity in partner organizations to integrate Earth observation data into their decision making practices. Between September 2021 and August 2022, three NASA DEVELOP teams demonstrated the feasibility of utilizing NASA Earth observations including Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Terra MODIS, Landsat 5 Thematic Mapper (TM), Landsat 8 Operation Land Imager (OLI), and Shuttle Radar Topography Mission (SRTM) in conjunction with Sentinel-2 MultiSpectral Instrument (MSI) to assess temperature, precipitation, and land use land cover (LULC) over time throughout salmon habitat in Maine. While the first two teams completed projects that were categorized as NASA DEVELOP’s traditional feasibility projects, the third and final project team generated resources and planned an interactive workshop to transfer project methods to end-user organizations. Ultimately, the goal of this work was to not only inform the partner’s ongoing salmon population recovery and habitat restoration initiatives but provide tools that allow partner organizations to continue integrating Earth observation data into their work beyond their partnership with the program. This project serves as a case study within the NASA DEVELOP Program and provides lessons learned for moving beyond traditional feasibility studies to more interactive partner engagement and knowledge transfer practices.

Nicole Ramberg-Pihl↗
NASA NTRSDate not supplied

Determining Crop Coefficients Using Remote Sensing for the Maipo River Valley Basin in Chile

Agriculture is the primary use of water in the Maipo River Valley of Central Chile, accounting for ~ 75% of the total demand. Assessment of irrigation needs for agricultural production has commonly relied on reference crop coefficients (Kc) derived from geographic and climatic conditions that differ from those of Chile. This work focused on calculating site-specific Kc values tailored to crop production in the water-stressed Maipo River Valley over consecutive growing seasons from 2019 to 2022. Two distinct approaches were implemented, each relying on remotely-sensed NASA Earth observation datasets. The first method estimated Kc values based on the linear relationship with the Normalized Difference Vegetation Index (NDVI) obtained from either Terra Moderate Resolution Imaging Spectroradiometer (MODIS) or Landsat 8 Operational Land Imager (OLI) surface reflectance. The second technique leveraged information from the ISS Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) by computing the ratio between actual crop evapotranspiration and potential evapotranspiration. The former approach best captured vegetation signals of annual crops while the latter appeared best suited for perennials. Overall, this study provides a strong basis and novel way to accurately estimate Kc using remote sensing, with the potential for improved irrigation management and reduction in water consumption.

Rishudh Thadur↗
NASA NTRSDate not supplied

Mapping Post-fire Conifer Regeneration using Snow-on Imagery

The 2000 Jasper Fire in the Black Hills of South Dakota was the largest wildfire to date in the region, burning over 83,000 acres of ponderosa pine forest. In collaboration with partners from the United States Forest Service (USFS) Black Hills Experimental Forest, USFS Rocky Mountain Research Station, and United States Geological Survey Geosciences and Environmental Change Science Center, we characterized post-fire forest regeneration within high severity burn patches. We accomplished this by implementing novel conifer detection techniques using a snow index mask to create a winter, snow-on image composite from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data. We utilized 2015 USFS stem maps of field-observed regeneration plots and ocularly sampled reforestation sites planted from 2001–2013. The field data and imagery were used to train a Random Forest (RF) model in Google Earth Engine. The RF model classified conifer regeneration density as low, medium, or high across the high-severity burn area with an overall accuracy of 81.3% for 2021. Approximately 45.9% of the high-severity burn area had low or no regeneration (0-40 trees per acre) 20 years post-fire. Given our partners' desire to find easily accessible low conifer regeneration zones, we identified 4,079 acres of priority planting sites that were within 1,500 feet of roads, had not been planted previously, and were larger than 50 acres. This method supports the use of snow-on imagery as a successful technique to identify conifer regeneration in a post-wildfire landscape.

Yeshey Seldon↗
NASA NTRSDate not supplied

Employing NASA Earth Observations and Socioeconomic Data to Conduct Site Suitability Analyses on Residential Tree Planting Initiatives in Phoenix, Arizona

Phoenix, Arizona is the hottest large city in the United States with an average summer daytime temperature of 106°F. Temperatures in Phoenix continue to climb due to increasing global greenhouse gas concentrations and regional urbanization. The impacts of high temperatures, including heat-related illnesses and deaths, are disproportionately concentrated in low-income neighborhoods often characterized by little tree canopy, lack of green space, and insufficient access to shade. The City of Phoenix’s Office of Heat Response and Mitigation and Arizona State University’s Urban Climate Research Center, partnered with NASA DEVELOP to identify residential neighborhoods and parcels within qualified census tracts (QCTs) to be prioritized for tree planting initiatives using funding from the American Rescue Plan Act (ARPA). This project conducted analyses using NASA Earth observations, socioeconomic data from the 2019 American Community Survey, and local tree canopy and mobility data. For Earth observations, daytime land surface temperature, vegetation, and land cover were obtained from the Landsat 8 Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI). The project team incorporated these data into a heat vulnerability index (HVI) with an emphasis on tree canopy and social vulnerability to rank block groups within QCTs and focused on the resulting top 25 block group HVI scores. These top 25 most vulnerable block groups were then processed through a parcel analysis to determine the feasibility of residential tree planting based on building footprints on each parcel. Within the top 25 block groups, 2,411 parcels were analyzed, and 3,133 existing trees were identified averaging 1.3 trees per parcel with 90% of parcels having 3 trees or less. Homes with 2 trees or fewer were considered high priority for future planting efforts. Based on the City’s goals for increased tree canopy the project team determined that <10,000 additional trees would need to be planted within the most vulnerable 25 block groups. The project findings have helped initiate community engagement efforts and have contributed to the approval of tree planting funds in Phoenix.

Ryan Hammock↗
NASA NTRS2022

San Diego Water Resources: Monitoring Pollution Plumes due to Storm and Wastewater Runoff in the San Diego Bay and Tijuana River Estuary to Inform Water Quality Management

Stormwater and wastewater runoff are a large source of pollutant discharge along the southern California coast and are a major concern to the health of local communities and ecosystems. In partnership with the Tijuana River National Estuarine Research Reserve and the California Department of Environmental Quality, NASA DEVELOP utilized satellite imagery to visualize and analyze the water quality of the Tijuana Estuary and southern California coast after major storm and wastewater events. Using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI), we estimated the extent and severity of plumes released from the Tijuana River Estuary. We used remotely sensed turbidity to map the extent of plumes, and used remotely sensed turbidity, Chlorophyll-a (chl-a), and colored dissolved organic matter (CDOM) to quantify and visualize stormwater, wastewater, and mixed plumes from 2013 to 2022. Furthermore, remotely sensed CDOM, turbidity, and chl-a were validated with in-situ data from NOAA and the San Diego Public Utilities in the San Diego coastal area to evaluate the accuracy of water quality data derived from satellite imagery. End products of this project include maps of stormwater, wastewater, and mixed plumes, tables illustrating the average area, CDOM, turbidity, and chl-a of each plume type, and validation graphs between satellite and in-situ data sources. These end products informed the environmental management of the Tijuana River National Estuarine Research Reserve and the public beaches in San Diego.

Ethan Gates↗
NASA NTRS2022

Maldives Climate: Monitoring Shoreline Changes and Island Loss in Response to Climate Change

Global sea level rise as a result of climate change continues to pose a critical threat to coastal ecosystems and populations. The archipelagic country of the Maldives is of critical concern due to being one of the lowest lying areas in the world. The development of reclaimed land in the Maldives by sand dredging has been a frequent response to both increasing sea levels and population increase. Such disturbance can lead to increased sedimentation off the coast and negatively impact coastal environments. Remote sensing tools such as satellite imagery have proved to be an effective tool in observing coastal changes in response to climate change and development. NASA DEVELOP created a methodology to analyze both water quality and shoreline erosion in the Maldives utilizing satellite imagery. Methods relied on open-source software such as QGIS and Google Earth Engine (GEE) and Satellite Imagery from PlanetScope, Landsat 8 Operational Land Instrument (OLI), Sentinel-2 Multi Spectral Instrument (MSI), and Aqua & Terra Moderate Resolution Imaging Radiospectrometer (MODIS) to analyze the changes in shorelines and assess water quality of select atolls within the Maldives. Findings show less shoreline change in developed parts of the island and more shoreline change in natural parts of the island. Additionally, water quality varies throughout the year and our data did not indicate seasonal trends. The methodology will be replicated to continue to monitor island erosion and water quality with the Maldives and will be applicable to other island and coastal systems.

remote sensing↗
NASA NTRS2022

Bryce Canyon Water Resources: Monitoring Vegetation Health and Water Availability in Bryce Canyon National Park for Drought Stress Mitigation Planning

Bryce Canyon National Park is home to groundwater-dependent ecosystems (GDEs) that are threatened by a multidecadal drought and increased groundwater extraction due to a spike in tourism. These ecosystems contain unique species that are only found in areas where near-surface groundwater is present, such as aspen groves and fens. These species contribute to the high biodiversity found in Bryce Canyon, which boosts an ecosystem’s productivity and the services it provides to the park. Unfortunately, many of these GDEs are too small to identify with traditional Earth observation platforms and are difficult to physically reach for monitoring purposes. This project partnered with the National Park Service to identify springs and seeps as a proxy for GDEs within Bryce Canyon from 2013–2022. Furthermore, this project tested the feasibility of various methods to detect and monitor springs and seeps and therefore facilitate the partner’s efforts to conserve these ecologically valuable GDEs in Bryce Canyon. The team mapped groundwater discharge with high resolution National Agriculture Imagery Program (NAIP) and assessed park vegetation trends with Landsat 8 Operational Land Imager (OLI) and PlanetScope imagery. In-situ precipitation data and the Western Land Data Assimilation System (WLDAS) were used to produce time series of climatic variables. Seeps and spring locations were predicted using random forest classification and maximum entropy machine learning models.

Groundwater dependent ecosystems↗
NASA NTRS2022

Western Tennessee Water Resources: Leveraging High Resolution Remotely Sensed Data to Assess Water Availability and Vulnerability in the Memphis Aquifer Area in West Tennessee

The Memphis Aquifer (MA) is located in the Mississippi Embayment that extends 250,000 square kilometers across eight states. Fayette and Haywood counties in West Tennessee are situated within the recharge zone of the MA and include the forthcoming Ford “mega campus” named Blue Oval City (BOC), which will consist of a vehicle-production facility and battery assembly division. Increased water demand and land cover change resulting from urban development, such as BOC in the MA’s narrow recharge zone, threaten the aquifer’s groundwater storage and recharge rate. Groundwater recharge factors that influence the narrow recharge zone of the MA include precipitation, evapotranspiration, runoff, and land cover type. In partnership with Protect Our Aquifer (POA) and the Center for Applied Earth Science and Engineering Research (CAESAR) at the University of Memphis, the team used data from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM IMERG), and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The team also used ancillary data from the National Land Cover Database (NLCD) and the North American Land Data Assimilation System (NLDAS) Noah Land Surface Model. These results identified “thriving” recharge locations, which are areas most conducive to aquifer recharge in Fayette County. The partners may use the results to prioritize specific areas in need of protection before they become susceptible to the effects of urbanization and industrialization.

precipitation↗
NASA NTRSDate not supplied

Monitoring Vegetation Health and Water Availability in Bryce Canyon National Park for Drought Stress Mitigation Planning

Bryce Canyon National Park is home to groundwater-dependent ecosystems (GDEs) that are threatened by a multidecadal drought and increased groundwater extraction due to a spike in tourism. These ecosystems contain unique species that are only found in areas where near-surface groundwater is present, such as aspen groves and willows. GDEs contribute to the high biodiversity found in Bryce Canyon, which boosts an ecosystem’s productivity and increases the overall ecological health of the park. Unfortunately, many GDEs in this area are too small to identify with traditional Earth observation platforms and are difficult to physically reach for identification and monitoring. NASA DEVELOP partnered with the National Park Service to establish a framework that both identified and assessed the health of GDEs within Bryce Canyon from 2013–2022. The team used high-resolution PlanetScope and Landsat 8 Operational Land Imager (OLI) to map springs, seeps, and GDEs. Modeled data from the Western Land Data Assimilation System (WLDAS) and in situ precipitation data were used to produce time series of climatic variables for the National Park Service to gain an understanding of how the GDEs are changing with the ongoing drought. This study is essential to the goal of the National Park Service to conserve the health of these GDEs.

Aaron Carr↗
NASA NTRSDate not supplied

Assessing Change in Aspen Extent in Northern Yellowstone National Park

The trophic cascade among wolves, elk, and aspen has influenced the landscape of Yellowstone National Park. Aspen promote greater biodiversity and have been indirectly affected by the 1926 removal and 1995 reintroduction of wolves in the park. Partnered with Yellowstone National Park, Utah State University, and the University of Wisconsin–Stevens Point, NASA DEVELOP analyzed the change in aspen stand extent from 1954 to 2021 over the elk wintering range that occurs in the northern part of Yellowstone and southern Montana. Focusing on 113 stands corresponding with belt transects monitored annually since 1999, the team georeferenced and digitized 1954 historical aerial imagery to determine aspen stand extent. From 1986 to 2019, the team processed Landsat 5 Thematic Mapper (TM) and Sentinel-2 Multispectral Instrument (MSI) imagery for the entire elk wintering range using a random forest model to classify landcover types. Outputs were refined using a phenological approach that distinguishes between deciduous and evergreen landcover by differencing summer and fall vegetation indices. Finally, the team conducted a similar analysis for 2021, utilizing both Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MSI imagery. The team again focused on the stands associated with belt transects for this analysis to compare the beginning and end of the study period. Preliminary results indicate a slight decline over time. These results expand the understanding of the role of wolves on aspen in the Yellowstone ecosystem and inform future rewilding decisions within the park and beyond.

Vanessa Bailey↗
NASA NTRSDate not supplied

Marin County Wildland Fires: Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wild fire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry, a Marin-based fire service work force development program, to develop new models to assist with fire management. Using data from Sentinel-2A, Planet Scope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), the team developed several input data layers to three models evaluating wild fire severity. One model performed a suitability analysis with weights based on scientific literature, another utilized machine learning based on past fires in Marin and neighboring Sonoma County to predict the difference normalized burn ratio, and the third inputted data layers into the Flam Map tool, which outputs risk categories. The team compared model outputs and, using the best-fit model, performed fuzzy logic analysis to identify specific locations where a fire break could be constructed to interrupt the progress of an active fire. These tools were proven useful and will assist partners in preparing for and managing an active wildfire event.

Suhani Dalal↗