This dataset provides a presentation that highlights the role NASA research and researchers played in developing a wide range of significant, quantitative ecological applications of satellite data. The presentation by Dr Diane E. Wickland, former NASA Terrestrial Ecology Program Manager and Lead for NASA Carbon Cycle and Ecosystems Focus Area, provides a top-level overview from her perspective of the development and evolution of the program. Dr Wickland joined NASA in 1985 to manage a newly formed Terrestrial Ecosystems Program. Along with other NASA program managers, she was charged with reorienting the program to be less empirical and have a greater focus on first principles, and to prepare for a next generation of earth-observing satellites. As an ecologist, she thought that focusing on important ecological questions and recruiting practicing ecologists to the program would facilitate such a change in directions. The presentation emphasizes the early years of U.S. satellite remote sensing and covers a few highlights after 2005.

The main objective of this NASA-funded project was to develop a remote sensing and modeling capability to monitor and predict ecosystem changes in coastal Arctic waters, in particular, changes in primary production and organic carbon dynamics, due to changing riverine fluxes caused by recent warming trends. Research activities focused on the coastal waters around Colville, Kuparuk, and Sagavanirktok rivers â€" three of the largest rivers in the North Slope of Alaska. Changing riverine fluxes affect light and nutrient availability â€" two most critical factors affecting primary production â€" in a complex manner. Understanding how riverine materials transported into the coastal Arctic mix with ocean waters and affect primary production and phytoplankton community structure using a combination of in situ data, remote sensing measurements, and modeling was the overarching objective of this effort. Project funding was through NASA OBB Award: 80NSSC22K1043 (to CCNY, PI: Maria Tzortziou) and NASA OBB Award: 80HQTR21T0050 (to NRL, PI: Wes Moses).

- 다중대역폭(Multi Band) 위성영상(Landsat, Sentinel-2) 기반 5년 단위 탄소흡수원(산림지, 농경지, 초지, 습지, 정주지, 기타) 시계열 데이터로, 기후변화 대응을 위한 효율적 토지이용 계획수립의 기초자료로 활용됨

This data set includes in situ vegetation data collected during the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.

This data set includes in situ vegetation data collected during the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07) campaign. Sampling was designed to coincide with satellite overpasses, such as Landsat's Thematic Mapper (TM) 5 and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Terra satellite (MODIS/Terra), which can be then used to estimate vegetation water content on the regional scale.

NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent and are available at 30 m spatial resolution. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule.

This dataset provides estimates of topsoil loss and economic loss associated with decreased crop productivity resulting from topsoil loss at county- and state-levels across the Corn Belt region of the Midwestern USA. Intermediate products used to derive topsoil loss are provided and include 4 m gridded estimates of study sites elevation, curvature, slope, soil organic carbon index (SOCI), and the probability of exposed B-horizon soil. Topsoil loss at the county- and state-levels was derived from analyses of agricultural land at selected sites across the study area. From WorldView imagery, 759 fields were identified that had exposed bare soil (210 km2) and were grouped into 28 sites. Gridded estimates of the SOCI and of the probability of exposed B-horizon soil were determined for each field within the sites. Topography measures, including elevation (m), curvature (m-1), and slope (deg), were extracted over the entire study area from LiDAR-derived digital elevation models at a 4 m resolution acquired from 2003-2018. Within each of the 28 study sites, the SOCI and topographic curvature values were extracted from co-located pixels. Topsoil loss was estimated from the relationship between subsoil exposure and topography and averaged across each site.The relationship between topsoil loss and topographic curvature was used to up-scale and predict topsoil and economic losses at the county and state-levels across the entire 375,000 km2 study area. The data have been used to demonstrate a robust and scalable method for estimating the magnitude of erosion in agricultural landscapes.

Monitoring plays a central role in detecting change in coastal ecosystems. The National Estuarine Research Reserve System (NERRS) invests heavily in assessing changes in tidal wetlands through the System-wide Monitoring Program (SWMP). This monitoring is conducted in 1m2 permanent plots every 1-3 years via in situ sampling and at reserve-wide scales via airplane imagery every 5-10 years. While both approaches have strengths, important processes at intermediate spatial (i.e., marsh platform) and finer temporal (i.e., storm events) scales may be missed. Uncrewed Aerial Systems (UAS, i.e., drones) can provide high spatial resolution and coverage, with customizable sensors, at user-defined times. Based on a needs assessment and discussions with NERRS end users, we conducted a regionally coordinated effort, working in salt marshes and mangroves within six reserves in the Southeast and Caribbean to develop, assess and collaboratively refine a UAS-based tidal wetlands monitoring protocol aimed at entry-level UAS users. Using ground-based surveys for validation, we 1) assessed the efficacy of UAS-based imagery for estimating vegetation percent cover, delineating ecotones (e.g., low to high marsh), and generating digital elevation models, and 2) assessed the utility of multispectral sensors for improving products from #1 and developing vegetation indices to estimate aboveground biomass (e.g., normalized difference vegetation index, NDVI). UAS-derived elevation models and canopy height estimates were generally of insufficient accuracy to be useful when compared to field measures. Across sites, root mean squared error ranged from 0.25 to 0.59m for bare earth models, 0.15 to 1.58m for vegetation surface models, and 0.33 to 2.1m for canopy height. The accuracy of ecotones delineated from UAS imagery varied among ecotones. The average distance between image- and field-based delineations of the wetland-water ecotone was 0.18 +/- 0.01m, whereas differences of the low-high marsh ecotone were 1.25 +/- 0.11m. Overall accuracy of vegetated and unvegetated classifications among sites was 85 +/- 4%. Comparison of field- and image-based estimates of total percent vegetated cover indicated modest agreement between the two approaches, although percent cover was generally overestimated from imagery. Average differences in percent cover between approaches was ~5% at one reserve, but >25% at four reserves. Overall accuracy of species-specific classifications among reserves was 74 +/- 6% when using both orthomosaics and surface vegetation models. Comparison of field- and image-based estimates of species-specific cover indicated minimal agreement between the two approaches; the interquartile ranges of the differences were wide for all species (>40%). Aboveground biomass in monospecific Spartina alterniflora plots was highly correlated to NDVI (R2 > 0.69), although the relationship was reserve- and sensor-specific. The strength of the relationship between NDVI and biomass was weaker in mixed-species plots (R2 = 0.52). This project serves as a critical first step for improving tidal wetland monitoring conducted as part of SWMP. Furthermore, the project increased the technical capacity of end users to conduct UAS-based wetland monitoring. This research collaboration was the first of its kind in the region and has catalyzed continued collaboration to identify regional management needs and expand UAS-based monitoring to additional coastal habitats (e.g., oyster reefs).

Geospatial corner coordinates for 33 landscape blocks used as units of analyses for research described in the paper, "Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes."

NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs (https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Land Cover Mapping and Estimation (GLanCE (https://sites.bu.edu/measures/)) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids (https://measures-glance.github.io/glance-grids/) that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, and Oceania are available. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product provides seven layers: the land cover class (https://sites.bu.edu/measures/project-overview/methods/), the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule. Known Issues * Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. * Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. * The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. * Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. * Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. * Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. * Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. * Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. * High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs. * The GlanCE data product tends to modestly overpredict developed land cover in arid regions.