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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover

The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m 2 , we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m 2 , we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A meta-analysis of global cropland soil carbon changes due to cover cropping

Including cover crops within agricultural rotations may increase soil organic carbon (SOC). However, contradictory findings generated by on-site experiments make it necessary to perform a comprehensive assessment of interactions between cover crops, environmental and management factors, and changes in SOC. In this study, we collected data from studies that compared agricultural production with and without cover crops, and then analyzed those data using meta-analysis and regression. Our results showed that including cover crops into rotations significantly increased SOC, with an overall mean change of 15.5% (95% confidence interval of 13.8% to 17.3%). Whereas medium-textured soils had highest SOC stocks with (mean = 39 Mg ha-1) and without (mean = 37 Mg ha-1) cover crops, fine-textured soils showed the greatest increase in SOC after the inclusion of cover crops (mean change of 39.5%). Coarse-textured (11.4%) and medium-textured soils (10.3%) had comparatively smaller changes in SOC. Cover crop mixtures resulted in greater increases in SOC compared to mono-species cover crops, and using legumes resulted in greater SOC increases than grasses and broadleaf species. Cover cropping was associated with significant SOC increases in shallow soils (= 30 cm), but not in subsurface soils (> 30 cm). The regression analysis revealed that SOC changes from cover cropping were correlated with improvements in soil quality, specifically decreased runoff and erosion and increased mineralizable carbon, mineralizable nitrogen, and soil nitrogen. Soil carbon change was also affected by annual temperature, number of years after start of cover crop usage, latitude, and initial SOC concentrations. Finally, the mean rate of carbon sequestration from cover cropping across all studies was 0.56 Mg ha-1 yr-1. If 15% of current global cropland were to adopt cover crops, this value would translate to 0.17 Pg of carbon sequestered per year or ~1-2% of current fossil fuels emissions. Altogether, these results indicated that inclusion of cover crops into agricultural rotations can enhance soil carbon concentrations, improve many soil quality parameters, and serve as a potential sink for atmosphere CO2.

Cover crop, soil organic carbon, soil quality, soi↗
NASA NTRS2005

Multidecadal Changes in Near-Global Cloud Cover and Estimated Cloud Cover Radiative Forcing

The first paper was Multidecadal changes in near-global cloud cover and estimated cloud cover radiative forcing, by J. R. Norris (2005, J. Geophys. Res. - Atmos., 110, D08206, doi: lO.l029/2004JD005600). This study examined variability in zonal mean surface-observed upper-level (combined midlevel and high-level) and low-level cloud cover over land during 1971-1 996 and over ocean during 1952-1997. These data were averaged from individual synoptic reports in the Extended Edited Cloud Report Archive (EECRA). Although substantial interdecadal variability is present in the time series, long-term decreases in upper-level cloud cover occur over land and ocean at low and middle latitudes in both hemispheres. Near-global upper-level cloud cover declined by 1.5%-sky-cover over land between 1971 and 1996 and by 1.3%-sky-cover over ocean between 1952 and 1997. Consistency between EECRA upper-level cloud cover anomalies and those from the International Satellite Cloud Climatology Project (ISCCP) during 1984-1 997 suggests the surface-observed trends are real. The reduction in surface-observed upper-level cloud cover between the 1980s and 1990s is also consistent with the decadal increase in all-sky outgoing longwave radiation reported by the Earth Radiation Budget Satellite (EMS). Discrepancies occur between time series of EECRA and ISCCP low-level cloud cover due to identified and probable artifacts in satellite and surface cloud data. Radiative effects of surface-observed cloud cover anomalies, called "cloud cover radiative forcing (CCRF) anomalies," are estimated based on a linear relationship to climatological cloud radiative forcing per unit cloud cover. Zonal mean estimated longwave CCRF has decreased over most of the globe. Estimated shortwave CCRF has become slightly stronger over northern midlatitude oceans and slightly weaker over northern midlatitude land areas. A long-term decline in the magnitude of estimated shortwave CCRF occurs over low-latitude land and ocean, but comparison with EMS all-sky reflected shortwave radiation during 1985-1997 suggests this decrease may be underestimated.

Norris, Joel↗
NASA NTRS2009

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008.

Riggs, George A.↗
DOE OSTI2026

Data for Immediate Impacts of Soybean Cover Crop on Bacterial Community Composition and Diversity in Soil Under Long-Term Saccharum Monoculture

Saccharum yield decline results from long-term monoculture practices. Changes in cropping management can improve soil health and productivity. Below-ground bacterial community diversity and composition across soybean (Glycine max (L.) Merr) cover crop, Saccharum monoculture (30+ year) and fallowed soil were determined. Near full length (~1,400 base pairs) of 16S rRNA gene sequences were extracted from the rhizospheres of sugarcane and soybean and fallowed soil were compared. Higher soil bacterial diversity was observed in the soybean cover crop than sugarcane monoculture across all measured indices (observed operationational taxonomic units, Chao1, Shannon, reciprocal Simpson and Jackknife). Acidocateria, Proteobacteria, Bacteroidetes and Planctomycetes were the most abundant bacterial phyla across the treatments. Indicator species analysis identified nine indicator phyla. Planctomycetes, Armatimonadetes and candidate phylum FBP were associated with soybean; Proteobacteria and Firmicutes were linked with sugarcane and Gemmatimonadetes, Nitrospirae, Rokubacteria and unclassified bacteria were associated with fallowed soil. Non-metric multidimensional scaling analysis showed distinct groupings of bacterial operational taxonomic units (97% identity) according to management system (soybean, sugarcane or fallow) indicating compositional differences among treatments. This is confirmed by the results of the multi-response permutation procedures (A = 0.541, p = 0.00045716). No correlation between soil parameters and bacterial community structure was observed according to Mantel test (r = 211865, p = 0.14). Use of soybean cover-crop fostered bacterial diversity and altered community structure. This indicates cover crops could have a restorative effect and potentially promote sustainability in long-term Saccharum production systems.

Field Data↗
NASA NTRS1993

Shuttle landing facility cloud cover study: Climatological analysis and two tenths cloud cover rule evaluation

The two-tenths cloud cover rule in effect for all End Of Mission (EOM) STS landings at the Kennedy Space Center (KSC) states: 'for scattered cloud layers below 10,000 feet, cloud cover must be observed to be less than or equal to 0.2 at the de-orbit burn go/no-go decision time (approximately 90 minutes before landing time)'. This rule was designed to protect against a ceiling (below 10,000 feet) developing unexpectedly within the next 90 minutes (i.e., after the de-orbit burn decision and before landing). The Applied Meteorological Unit (AMU) developed and analyzed a database of cloud cover amounts and weather conditions at the Shuttle Landing Facility for a five-year (1986-1990) period. The data indicate the best time to land the shuttle at KSC is during the summer while the worst time is during the winter. The analysis also shows the highest frequency of landing opportunities occurs for the 0100-0600 UTC and 1300-1600 UTC time periods. The worst time of the day to land a shuttle is near sunrise and during the afternoon. An evaluation of the two-tenths cloud cover rule for most data categorizations has shown that there is a significant difference in the proportions of weather violations one and two hours subsequent to initial conditions of 0.2 and 0.3 cloud cover. However, for May, Oct., 700 mb northerly wind category, 1500 UTC category, and 1600 UTC category there is some evidence that the 0.2 cloud cover rule may be overly conservative. This possibility requires further investigation. As a result of these analyses, the AMU developed nomograms to help the Spaceflight Meteorological Group (SMG) and the Cape Canaveral Forecast Facility (CCFF) forecast cloud cover for EOM and Return to Launch Site (RTLS) at KSC. Future work will include updating the two tenths database, further analysis of the data for several categorizations, and developing a proof of concept artificial neural network to provide forecast guidance of weather constraint violations for shuttle landings.

Atchison, Michael K.↗
NASA NTRS2004

Merging the MODIS and NESDIS Monthly Snow-Cover Records to Study Decade-Scale Changes in Northern Hemisphere Snow Cover

A decade-scale record of Northern Hemisphere snow cover has been available from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite Data and Information Service (NESDIS) and has been reconstructed and validated by Rutgers University following adjustments for inconsistencies that were discovered in the early years of the data set. This record provides weekly, monthly (and, in recent years, daily) snow cover from 1966 to the present for the Northern Hemisphere. With the December 1999 launch of NASA's Earth observing System (EOS) Terra satellite, snow maps are being produced globally, using automated algorithms, on a daily, weekly and monthly basis from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. The resolution of the MODIS monthly snow maps (0.05deg or about 5 km) is an improvement over that of the NESDIS-derived monthly snow maps (>approx.10 km) the maps, it is necessary to study the datasets carefully to determine if it is possible to merge the datasets into a continuous record. The months in which data are available for both the NESDIS and MODIS maps (March 2000 to the present) will be compared quantitatively to analyze differences in North American and Eurasian snow cover. Results from the NESDIS monthly maps show that for North America (including all 12 months), there is a trend toward slightly less snow cover in each succeeding decade. Interannual snow-cover extent has varied significantly since 2000 as seen in both the NESDIS and MODIS maps. As the length of the satellite record increases through the MODIS era, and into the National Polar-orbiting Environmental Satellite System (NPOESS) era, it should become easier to identify trends in areal extent of snow cover, if present, that may have climatic significance. Thus it is necessary to analyze the validity of merging the NESDIS and MODIS, and, in the future, the NPOESS datasets for determination of long-term continuity in measurement of Northern Hemisphere snow cover.

Hall, Dorothy K.↗
DOE OSTI2026

Multi-resolution Arctic Shrub Cover Dataset Derived from UAS and Airborne SfM and LiDAR (2013-2025)

We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

canopy height model↗
NASA NTRS2002

Improving Land Cover Product-Based Estimates of the Extent of Fragmented Cover Types

The effects of changing land use/land cover on regional and global climate ecosystems depends on accurate estimates of the extent of critical land cover types such as Arctic wetlands and fire scars in boreal forests. To address this information requirement, land cover products at coarse spatial resolution such as Advanced Very High Resolution Radiometer (AVHRR) -based maps and the MODIS Land Cover Product are being produced. The accuracy of the extent of highly fragmented cover types such as fire scars and ponds is in doubt because much (the numerous scars and ponds smaller than the pixel size) is missed. A promising method for improving areal estimates involves modeling the observed distribution of the fragment sizes as a type of truncated distribution, then estimating the sum of unobserved sizes in the lower, truncated tail and adding it to the sum of observed fragment sizes. The method has been tested with both simulated and actual cover products.

Hlavka, Christine A.↗
NASA NTRS1981

LANDSAT applications by the Adirondack Park Agency for land cover analyses and forest cover change

The New York State Adirondack Park Agency is using LANDSAT imagery to provide current, consistent parkwide data on forest cover, forest change and other land cover characteristics for the Adirondack Park, an area of 9,375 sq. miles (24,280 sq km). Boundaries of the study area were digitized and the data were enhanced and geographically rectified. A classification scheme was devised which emphasized the basic land cover types of the Park: hardwoods, spruce-fir, pine, wet conifer, brushland, grassland, agricultural areas, exposed earth, urban areas, and water bodies. Cover type classifications for disturbed forest land were also chosen: cut hardwoods, regenerating hardwoods, and cut spruce fir. Field verification of 1978 classification revealed an accurate differentiation of forest types within types and between nonforested/forested areas. The classification accurately detects forest land disturbances; however, it is not always descriptive of the level of disturbance.

Banta, J. S.↗
NASA NTRS1997

Mapping Surface Cover Parameters Using Aggregation Rules and Remotely Sensed Cover Classes

A coupled model, which combines the Biosphere-Atmosphere Transfer Scheme (BATS) with an advanced atmospheric boundary-layer model, was used to validate hypothetical aggregation rules for BATS-specific surface cover parameters. The model was initialized and tested with observations from the Anglo-Brazilian Amazonian Climate Observational Study and used to simulate surface fluxes for rain forest and pasture mixes at a site near Manaus in Brazil. The aggregation rules are shown to estimate parameters which give area-average surface fluxes similar to those calculated with explicit representation of forest and pasture patches for a range of meteorological and surface conditions relevant to this site, but the agreement deteriorates somewhat when there are large patch-to-patch differences in soil moisture. The aggregation rules, validated as above, were then applied to remotely sensed 1 km land cover data set to obtain grid-average values of BATS vegetation parameters for 2.8 deg x 2.8 deg and 1 deg x 1 deg grids within the conterminous United States. There are significant differences in key vegetation parameters (aerodynamic roughness length, albedo, leaf area index, and stomatal resistance) when aggregate parameters are compared to parameters for the single, dominant cover within the grid. However, the surface energy fluxes calculated by stand-alone BATS with the 2-year forcing, data from the International Satellite Land Surface Climatology Project (ISLSCP) CDROM were reasonably similar using aggregate-vegetation parameters and dominant-cover parameters, but there were some significant differences, particularly in the western USA.

Arain, Altaf M.↗
NASA NTRS1982

Cloud cover estimation: Use of GOES imagery in development of cloud cover data base for insolation assessment

The potential of using digital satellite data to establish a cloud cover data base for the United States, one that would provide detailed information on the temporal and spatial variability of cloud development are studied. Key elements include: (1) interfacing GOES data from the University of Wisconsin Meteorological Data Facility with the Jet Propulsion Laboratory's VICAR image processing system and IBIS geographic information system; (2) creation of a registered multitemporal GOES data base; (3) development of a simple normalization model to compensate for sun angle; (4) creation of a variable size georeference grid that provides detailed cloud information in selected areas and summarized information in other areas; and (5) development of a cloud/shadow model which details the percentage of each grid cell that is cloud and shadow covered, and the percentage of cloud or shadow opacity. In addition, comparison of model calculations of insolation with measured values at selected test sites was accomplished, as well as development of preliminary requirements for a large scale data base of cloud cover statistics.

Huning, J. R.↗
NASA NTRS1981

Computer implemented land cover classification using LANDSAT MSS digital data: A cooperative research project between the National Park Service and NASA. 3: Vegetation and other land cover analysis of Shenandoah National Park

Four LANDSAT frames, each corresponding to one of the four seasons were spectrally classified and processed using NASA-developed computer programs. One data set was selected or two or more data sets were marged to improve surface cover classifications. Selected areas representing each spectral class were chosen and transferred to USGS 1:62,500 topographic maps for field use. Ground truth data were gathered to verify the accuracy of the classifications. Acreages were computed for each of the land cover types. The application of elevational data to seasonal LANDSAT frames resulted in the separation of high elevation meadows (both with and without recently emergent perennial vegetation) as well as areas in oak forests which have an evergreen understory as opposed to other areas which do not.

Cibula, W. G.↗