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At least 469 records · Page 26

Detecting Volcanic Co2 Emissions Hidden in Tropical Volcanic Forests Using Fixed-Wing sUAS

CO2 emissions are among the earliest indicators of subsurface state change and reactivation of volcanic systems and these deep signals reflect in faint diffuse emissions variations on volcanic flanks. Monitoring vast areas of rainforests covering tropical volcanoes is challenging, and space-borne sensors like OCO-2 are not sensitive enough. Fortunately, trees exposed to mild enhancements of CO2 may experience fertilization and build excess CO2 into excess biomass (Cawse-Nicholson, et al. 2018, Biogeosciences). They also process excess CO2 through photosynthesis, measurable by fluorescence (Bogue et al., 2019, Biogeosciences). Furthermore, spaceborne remote sensing instruments have demonstrated increasing NDVI months to years before eruptions, unexplained by other factors (e.g., Houlie et al. 2006, EPSL; Seiler et al. 2017, PLoS One). By providing a means to map large areas of above-canopy CO2 variations over tropical rainforest on the flanks of actively degassing volcanoes, we enable to interpret the signals plants provide in response to excess CO2 with airborne and spaceborne hyperspectral remote sensing methods, including a possible future Surface Biology and Geology (SBG) NASA satellite mission.As a critical step to mature and test this concept, we deployed small unmanned aerial systems (sUAS) using a fixed-wing sUAS designed for autonomous operations and long endurance in extreme environments, without causing significant disturbance of the sampled air. We integrated an in-situ sensor to record the CO2 enhancement field above the forest canopy. Test flight results on the flanks of Turrialba volcano in Costa Rica above forest canopies covering known moderate gas seeps demonstrate the sUAS-borne detection and mapping capabilities of above-canopy elevated CO2 gradients. The strong detection capabilities and high detector signal stability resulted from key system design elements including RF shielding, mechanical stabilization, and calibration procedures. This highly robust system is readily applied for diffuse volcanic CO2 emission studies on active volcanoes covered by dense vegetation.

Volcanic↗

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 homogenous reference area in the Sierra Diablo Mountains (SDM), 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↗

Using Earth Observations to Map Bull Kelp in the Puget Sound, Washington, to Support Conservation and Restoration

Bull kelp (Nereocystis luetkeana) is a critical component of nearshore ecosystems in the Puget Sound region of the Salish Sea. The Port of Seattle and Washington State Department of Natural Resources (DNR) have identified possible declines in bull kelp extent and changes in its distribution throughout the Central Puget Sound near Seattle, Washington. Bull kelp losses threaten critical ecological services and marine habitat, as well as important cultural resources. However, these changes are not well tracked or understood due to the expensive and time-intensive nature of traditional kelp canopy monitoring methods. The Port of Seattle and Washington DNR partnered with the NASA DEVELOP team to explore the feasibility of using Earth observations between 2016 and 2021 obtained from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) as a potential tool to monitor and map bull kelp. Our team identified a variety of challenges that need to be addressed before this approach can be utilized as an effective means for identifying or mapping nearshore urban kelp beds. We found that neither Sentinel-2 nor Landsat 8 significantly differentiates between kelp and no-known kelp using the Normalized Difference Vegetation Index (NDVI) or Normalized Difference Red-Edge Blue (NDREB). While the tidal and current filtering methods discussed here may be beneficial for identifying promising single image dates for kelp classification, the filters we used reduced the number of images each year to the point that modeling or mapping yearly kelp extent or creating time series of kelp did not appear to be feasible.

Mike Hitchner​↗

Florida Water Resources: Assessing Coastal Resiliency Across Florida's Aquatic Preserves Response To Hurricane Forces

Intensifying weather events, sea level rise, and extensive coastal development in Southwestern Florida are escalating the need for Florida’s mangrove conservation. These mangroves are imperative for coastline stabilization, habitat provision for native species, and water quality management. Our partner, the Florida Department of Environmental Protection (FDEP), Office of Resilience and Coastal Protection is tasked with monitoring and conserving the Charlotte Harbor, Estero Bay, Rookery Bay, and Pinellas County Aquatic Preserves. We developed the Growth, Resilience, and Optical Vegetation Evaluator (GROVE) Google Earth Engine toolset for partners to determine mangrove forest extent through time, analyze mangrove forest health, and collect several water quality parameters within the preserves from January 2002–August 2022. The toolset provides easily accessible data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Landsat 9 Operational Land Imager 2 (OLI-2), and the Shuttle Radar Topography Mission (SRTM). Using training datasets of known mangrove forest locations, we also established a machine learning approach to create mangrove extent maps. Maps from all four preserves indicated migration of mangrove forests inland as the greatest areas of change were transitional zones. Additionally, normalized difference vegetation index (NDVI), normalized difference turbidity index (NDTI), and chlorophyll-a maps were generated for the partners. This project provides decision makers with a useful tool for understanding temporal changes in Florida’s aquatic preserves, identifying areas of ecological stress, and providing actionable data to make informed plans for mangrove preservation.

Samuel Perrello↗

Yellowstone Ecological Forecasting: Assessing Change in Aspen Extent in Northern Yellowstone National Park

The removal and reintroduction of the gray wolf (Canis lupus) in Yellowstone National Park have played an important role in shaping the ecological composition of this distinct landscape, and it is a textbook example of multi-trophic dynamics. With particular importance to conservation science, the inter-trophic cascades between wolves and species such as the elk (Cervus canadensis) and the quaking aspen (Populus tremuloides) have been extensively studied. In conjunction with the National Park Service, Yellowstone National Park, Utah State University, and the University of Wisconsin–Stevens Point, this project utilized satellite remote sensing to investigate the long-term trends in aspen extent. Through random forest modeling and phenological approaches, Sentinel-2 Multispectral Instrument (MSI; years 2017–2019) and Landsat 5 Thematic Mapper (TM; years 1987–2011) datasets were used to derive an Enhanced Vegetation Index (EVI), a Normalized Difference Vegetation Index (NDVI), Tasseled Cap Indices (Brightness, Greenness, Wetness), and RGB true color composites. The International Space System Global Ecosystem Dynamics Investigation (ISS GEDI) was used to analyze canopy height. Results were consolidated into maps and time-series that provide an in-depth and intricate depiction of aspen stand extent. The end products will assist the National Park Service in its management practices and inform wildlife restoration and rewilding decisions within and beyond the contexts of Yellowstone National Park.

Kyle Steen↗

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↗

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↗

Florida Water Resources: Assessing Coastal Resiliency Across Florida's Aquatic Preserves in Response to Hurricane Forces

Intensifying weather events, sea level rise, and extensive coastal development in Southwestern Florida are escalating the need for Florida’s mangrove conservation. These mangroves are imperative for coastline stabilization, habitat provision for native species, and water quality management. Our partner, the Florida Department of Environmental Protection (FDEP), Office of Resilience and Coastal Protection is tasked with monitoring and conserving the Charlotte Harbor, Estero Bay, Rookery Bay, and Pinellas County Aquatic Preserves. We developed the Growth, Resilience, and Optical Vegetation Evaluator (GROVE) Google Earth Engine toolset for partners to determine mangrove forest extent through time, analyze mangrove forest health, and collect several water quality parameters within the preserves from January 2002–August 2022. The toolset provides easily accessible data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Landsat 9 Operational Land Imager 2 (OLI-2), and the Shuttle Radar Topography Mission (SRTM). Using training datasets of known mangrove forest locations, we also established a machine learning approach to create mangrove extent maps. Maps from all four preserves indicated migration of mangrove forests inland as the greatest areas of change were transitional zones. Additionally, normalized difference vegetation index (NDVI), normalized difference turbidity index (NDTI), and chlorophyll-a maps were generated for the partners. This project provides decision makers with a useful tool for understanding temporal changes in Florida’s aquatic preserves, identifying areas of ecological stress, and providing actionable data to make informed plans for mangrove preservation.

Remote sensing↗

Desert Locust Cropland Damage Differentiated from Drought, with Multi-Source Remote Sensing in Ethiopia

In 2020, Ethiopia had the worst desert locust outbreak in 25 years, leading to food insecurity. Locust research has typically focused on predicting the paths and breeding grounds based on ground surveys and remote sensing of outbreak factors. In this study, we hypothesized that it is possible to detect desert locust cropland damage through the analysis of fine-scale (5–10 m) resolution satellite remote sensing datasets. We performed our analysis on 121 swarm point locations on croplands derived from the Food and Agriculture Organization (FAO) of the United Nations, and 94 ‘non-affected’ random cropland sample points generated for this study that are distributed within 20–25 km from the ‘center’ of swarm affected sample locations. Integrated Drought Condition Indices (IDCIs) and Vegetation Health Indices (VHIs) calculated for the affected sample locations for 2000–2020 were strongly correlated (R(exp 2) > 0.90) with that of the corresponding non-affected group of sample sites. Drought indices were strongly correlated with the evaluation Standardized Precipitation Evapotranspiration Indices (SPEIs) and showed that 2020 was the wettest year since 2000. In 2020, the NDVI and backscatter coefficient of cropland phenologies from the affected versus non-affected cropland sample sites showed a slightly wider, but significant gap in March (short growing season) and August-October (long growing season). Thus, slightly wider gaps in cropland phenologies between the affected and non-affected sites were likely induced from the locust damage, not drought, with fine scale data representing a larger gap.

Desert locust↗

NASA GSFC Flood Mapping System (In beta testing)

The authors are developing the NASA GSFC Flood Mapping System, designed to provide near real time, on-demand satellite-based products to disaster management practitioners for requests provided through a website interface. The initial version of the system, which only provides products based on Planetscope data provided by Planet Labs to NASA through the NASA CSDA Program, is currently being beta tested in partnership with the disaster management community and interested research partners. Products include images, NDWI maps, NDVI maps, and water extent maps for flood scenes of interest, the same four products for a specified reference scene, and water extent change maps based on the flood scenes and reference scenes. The team’s vision for the system includes adding products based on all appropriate sources of data that are available at no cost to the project (Landsat, Sentinel-1, Sentinel-2), and obtaining funding for commercial SAR data to provide products in the case where none of the “no-cost” data meets end-user needs. We provide an overview of the system, and discuss the products offered, lessons learned from initial testing, and plans to evolve the system in accordance with our overall vision and the lessons learned.

F. Policelli↗

Yellowstone Ecological Forecasting: Assessing Change in Aspen Extent in Northern Yellowstone National Park

The removal and reintroduction of the gray wolf (Canis lupus) in Yellowstone National Park have shaped the ecological composition of this distinct landscape, representing a textbook example of trophic dynamics. With particular importance to conservation science, researchers have studied the trophic cascades between wolves and species such as elk (Cervus canadensis) and quaking aspen (Populus tremuloides). In conjunction with the National Park Service, Yellowstone National Park, Utah State University, and the University of Wisconsin–Stevens Point, this project utilized satellite remote sensing to investigate the long-term trends in aspen extent. Through random forest modeling and phenological approaches, Landsat 5 Thematic Mapper (TM; years 1986–2011) and Sentinel-2 Multispectral Instrument (MSI; years 2017–2019) datasets were used to derive color composites, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Tasseled Cap Indices (Brightness, Greenness, Wetness). The International Space System (ISS) Global Ecosystem Dynamics Investigation (GEDI) provided canopy height data. The team consolidated results into maps and time-series which provide an in-depth depiction of aspen stand extent. The National Park Service will use these end products to assist in its management practices and inform wildlife restoration decisions within and beyond Yellowstone National Park.

Kyle Steen↗

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↗

Characterizing Wildfires in Western US.: A Cloud-based Case Study for Interdisciplinary Research using NASA Resources

This presentation will demonstrate a case study of interdisciplinary research done in the Amazon Web Services (AWS) cloud platform, in addition to in the local machine. We conduct data analysis next to data by leveraging various cloud-based data in NASA Earthdata Cloud, which are distributed by different missions/NASA Distributed Active Archive Centers (DAACs), and cloud computing resources at NASA. For instance, we directly access multiple datasets stored in the AWS Simple Storage Service (S3) buckets using a Python Jupyter notebook through a JupyterHub interface hosted in AWS (without having to download data), and conduct data analysis next to data in the cloud. We will also show how to share the research results following Open Source policy. This case study characterizes the change in wildfire events in the western United States during the past 20 years. In particular, we focus on the wildfires in California in 2021, one of the most severe wildfire years occurring in the most recent 20 years in California. We will analyze the possible causes of wildfires, such as drought conditions and climate variability, and examine the impacts of wildfires on air quality and atmospheric composition, and on land cover. We will examine the data distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), including aerosols and meteorological data from the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), precipitation from the Global Precipitation Measurement (GPM) and Global Precipitation Climate Project (GPCP), and aerosol index from Ozone Monitoring Instrument (OMI). We also utilize the data distributed by the Physical Oceanography (PO) DAAC, such as Sea Surface Temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST), and the data distributed by Land Processes (LP) DAAC, such as Normalized Difference Vegetation Index (NDVI).

Xiaohua Pan↗

Puget Sound Water Resources: Using Earth Observations to Map Bull Kelp in the Puget Sound, Washington, to Support Conservation and Restoration

Bull kelp (Nereocystis luetkeana) is a critical component of nearshore ecosystems in the Puget Sound region of the Salish Sea. The Port of Seattle and Washington State Department of Natural Resources (DNR) have identified possible declines in bull kelp extent and changes in its distribution throughout the Central Puget Sound near Seattle, Washington. Bull kelp losses threaten critical ecological services and marine habitat, as well as important cultural resources. However, these changes are not well tracked or understood due to the expensive and time-intensive nature of traditional kelp canopy monitoring methods. The Port of Seattle and Washington DNR partnered with the NASA DEVELOP team to explore the feasibility of using Earth observations between 2016 and 2021 obtained from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) as a potential tool to monitor and map bull kelp. Our team identified a variety of challenges that need to be addressed before this approach can be utilized as an effective means for identifying or mapping nearshore urban kelp beds. We found that neither Sentinel-2 nor Landsat 8 significantly differentiates between kelp and no-known kelp using the Normalized Difference Vegetation Index (NDVI) or Normalized Difference Red-Edge Blue (NDREB). While the tidal and current filtering methods discussed here may be beneficial for identifying promising single image dates for kelp classification, the filters we used reduced the number of images each year to the point that modeling or mapping yearly kelp extent or creating time series of kelp did not appear to be feasible.

Mike Hitchner↗

Los Angeles Urban Development: Utilizing NASA Earth Observations to Evaluate the Impact of Tree Coverage on Urban Heat Mitigation

Over the last several decades the city of Los Angeles, California, has been experiencing increased temperatures resulting from the urban heat island effect. This is largely due to the expansion of developed areas which allow for the trapping of heat, posing dangerous health risks. As a solution, many organizations have turned to urban greening and tree planting initiatives to help cool vulnerable communities. NASA DEVELOP has partnered with City Plants and the City of Los Angeles, Office of Forest Management to study the role of trees in urban environments and their relation to the mitigation of local urban heat islands. This team used NASA Earth observation data spanning from 2016 to 2022, including land surface temperature and Normalized Difference Vegetation Index (NDVI) data collected from Landsat 8 Thermal Infrared Sensor (TIRS) and International Space Station (ISS) ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), respectively. Data from the National Agriculture Imagery Program (NAIP) were also used to obtain a supervised classification of tree canopy cover. Our analysis reveals a spatial and temporal connection between temperature and vegetation, suggesting that areas with more vegetation are less likely to suffer high summertime temperatures. Results also highlight the impacts of tree planting programs, such as the Vermont Corridor planting project, which increased tree canopy cover by up to 5% in the community between 2016 and 2022. These findings support the implementation of urban greening practices and inform residents and officials about how investing in trees will help mitigate increasing heat within Los Angeles.

Urban heat island↗

Coronado Ecological Conservation: Assessing Vegetation Change Due to Border Wall Construction and Shifting Social Trails

Species monitoring is essential for mitigating the impacts of plant invasion, such as radical changes in an area’s ecosystem, degraded soil health, increased wildfire severity, landslides, and increased flooding. For this project, NASA DEVELOP partnered with the National Park Service (NPS) to investigate invasive species in disturbed lands: specifically, areas affected by off-trail travel and U.S.-Mexico border construction activities. The team assessed how construction has impacted the distribution of Lehmann’s lovegrass and Russian thistle invasives throughout Coronado National Memorial, AZ from 1986-2022. Using data from Landsat 5 and 8, Sentinel-2, NAIP, and PlanetScope, the team computed NDVI, NDMI, MSAVI2, EVI, and Tasseled Cap Wetness, Brightness, and Greenness transformations as vegetation health indicators to input into various machine learning algorithms. To minimize noise, the team conducted Principal Component Analysis on vegetation indices and spectral bands before running k-means clustering and random forest classification algorithms. Between all datasets, the team found that the median area fully overtaken by invasive plants was 5.37% of the park’s total area in 2022. The NPS will use end products to help increase restoration efforts in disturbed areas with high concentrations of invasive plants, and this project can serve as a jumping off point for future invasive species monitoring. The NPS’s collection of ground data for 2022-2023, in conjunction with future data collection, will notably improve the accuracy of classification models, leading to more precise monitoring of invasive species spread over time.

Coronado National Memorial↗

Front Range Wildland Fires: Evaluating the Efficacy of Remote Sensing Imagery in Monitoring Forest Fuels Treatment Methods

Over the last several decades, wildfire frequency and severity in forested areas along Colorado’s Front Range have increased due to a buildup of fuels. This has led to an increase in forest treatments, as well as an increased need to evaluate the success of these treatments. Remote sensing products offer an efficient and cost-effective way to monitor forest treatments; however, not all remote sensing products and analysis techniques have been explored by Coloradan land managers. Specifically, project partners at the Colorado State Forest Service (CSFS) and the Colorado Forest Restoration Institute (CFRI) were interested in using an effective and streamlined method of mapping canopy cover to better monitor forest treatment success. To support their needs, the NASA DEVELOP Front Range Wildland Fires team explored National Agricultural Imagery Program (NAIP) imagery at different spatial resolutions and numbers of training points with NASA’s Shuttle Radar Topography Mission (SRTM) Data Elevation Model (DEM) as a predictor in addition to NAIP imagery spectral predictors. From this analysis, we created classified canopy cover rasters, and compared accuracy metrics across model iterations. We also determined that the best performing model, with an overall accuracy of 0.900 uses 2021 NAIP imagery at 2-meter resolution, 800 training points, 200 testing points, does not use topographic predictors, and reclassifies shadow pixels via a pre-selected NDVI threshold.

Remote Sensing↗

Surface Biology and Geology (SBG) Thermal Infrared (TIR) Free -Flyer Concept

Scientists and engineers from the United States and Italy are developing a thermal infrared (TIR) free-flyer concept that forms an essential element of the NASA Surface Biology & Geology (SBG) designated observable and the greater NASA Earth System Observatory (ESO), which also includes observations of aerosols, clouds, convection, and precipitation; mass change; and surface deformation & change.In response to the recommendations from the 2017 National Academies decadal survey for Earth science, NASA initiated the SBG designated observable with five key research and applications focus areas: ecosystems and natural resources, hydrology, weather, climate, and solid Earth. SBG includes spaceborne measurements of hyperspectral imagery in the visible to shortwave infrared (0.4-2.5 um) and multispectral imagery in the mid and thermal infrared (3-12 um) that provide the remote sensing data needed to inform each research and applications area. High-level TIR data products include Earth surface temperature & emissivity, evapotranspiration, substrate composition, volcanic plumes, and high-temperature features. While the science, applications, and technology build on prior mission studies conducted for more than a decade, it has only been recently that a joint team of scientists and engineers from the NASA Jet Propulsion Laboratory (JPL), Agenzia Spaziale Italiana (ASI), Istituto Nazionale Geofisica e Volcanologia (INGV), and the Istituto Nazionale Astrofisica (INAF) have developed a compelling concept for a TIR free-flyer. In this concept, the TIR instrument is an eight-band radiometer. Seven of the bands are between 3 and 12 um with an additional band at 1.65 um. The instrument’s 68.8 deg total viewing angle and the free-flyer’s 665 km operational altitude results in a ground sampling distance (GSD) of <60 m at nadir, produces a swath width of 935 km, and allows for a 2-3-day revisit time. A two-band visible and near infrared (VNIR) camera, with <30 m GSD at nadir and 935 km swath width, will complement the TIR instrument. VNIR high-level data products include top & bottom of atmosphere reflectance and normalized difference vegetation indices (NDVI). The TIR instrument and VNIR camera will be mounted on a PLATiNO+ spacecraft, a multi-application, reconfigurable, and scalable platform. The launcher selected for the TIR free-flyer is the VEGA-C. To maximize the science and applications benefits the SBG-TIR free-flyer team are collaborating with personnel from the ESA Land Surface Temperature Monitoring mission (LSTM) as well individuals from the Thermal Infrared Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), a joint mission by CNES and ISRO. Pre-Phase A concept studies for the SBG-TIR free-flyer commenced in the spring 2021. This allows for the concept to be refined; for technical risks to be identified and mitigated, as necessary; and strategies for reducing the development schedule to be defined and assessed before conducting a NASA-ASI Mission Concept Review (MCR) in the spring of 2022 and a NASA Key Decision Point-A (KDP-A) as early as the summer of 2022. These are key milestones in being able to launch and begin operating the SBG-TIR free-flyer in the second half of the decade, so that many of the questions posed by the National Academies in the 2017 decadal survey can be answered.

Fabrizia-Buongiorno, Maria↗