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At least 325 records · Page 18
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 OSTI2023

Photosynthesis and rhizome carbohydrate concentrations of switchgrass grown from reserve-depleted rhizomes

A long-standing question in perennial grass breeding and physiology is whether yield improvement strategies could compromise winter survival. Perennial grasses rely on the pool of carbohydrates accumulated in storage organs from the previous growing season for winter maintenance and spring regrowth. Yield improvement strategies could reduce winter survival if they increase biomass and grain yields at the expense of carbon allocation to storage. Therefore, it is crucial to better understand the dependence of regrowth on storage reserves. We experimentally depleted switchgrass ( Panicum virgatum L.) rhizome reserves by storing rhizomes for two weeks at 5 °C (control treatment) and 25 °C (reserve-depleted treatment).

bioenergy↗
DOE OSTI2024

Biomass yield potential, feedstock quality, and nutrient removal of perennial buffer strips under continuous zero fertilizer application

Perennial-based buffer strips have been promoted as having the potential for improving ecosystem services from riparian areas while producing biomass as livestock feed or as a bioenergy feedstock. Both biomass production and nutrient removal of buffer strips are substantially influenced by the vegetation types for the multipurpose perennial buffers. In this 2016–2019 study in western Illinois, two perennial cropping systems, including forage crops composed of cool-season grass mixtures (forage system) and bioenergy crops made up of warm-season grass mixtures (bioenergy system), were used to establish buffer strips for assessing biomass production, feedstock quality, nutrient removal, and buffer longevity. Treatments for this study reflecting agronomic practices included (1) two harvests occurring in summer (at anthesis) and fall (after complete senescence) and (2) one harvest in fall for the forage system (two-cut vs. one-cut forage) and (3) one fall harvest for the bioenergy system (one-cut bioenergy). Successively harvesting without any fertilizer input resulted in a yield decline in forage biomass over 3 years by approximately 30 % (6.3 to 4.4 Mg DM ha –1 (dry matter) with a rate of 1.0 Mg ha –1 yr –1 ) in the two-cut forage and by 35 % (4.9 to 3.2 Mg DM ha –1 with a rate of 0.9 Mg ha –1 yr –1 ) in the one-cut forage systems. The feed quality also decreased over the years by showing declined rates of 12.9 (crucial protein), 0.9 (calcium), 0.7 (copper), and 1.3 g kg –1 DM yr –1 (zinc). Empirical models predicted enteric CH 4 emissions from cattle ranged from 225.7 to 242.6 g per cow per day based on the feed nutritive values. In contrast, bioenergy biomass yield increased by 27 % from 4.9 to 6.7 Mg DM ha –1 with a consistent quality (cellulose of ~ 397.9 g kg –1 ; hemicellulose of ~ 299.4 g kg –1 ), corresponding to the increased total theoretical ethanol yield from 1.8 × 10 3 to 2.4 × 10 3 L ha –1 (~ 33 % increase). Annual nutrient removals of N, P, K, Ca, and Mg were significantly higher in the forage systems (e.g., two-cut: 52.6–106.9 kg N ha –1 ; one-cut: 44.5–84.1 kg N ha –1 ) than those in the bioenergy system (e.g., 25.9–34.4 kg N ha –1 ); however, the removal rate declined rapidly over 3 years (e.g., ~ 49 % reduction) as the annual biomass yield declined in the forage systems. This on-farm field study demonstrated the potential of the perennial crop used as buffer strip options for biomass production and buffer sustainability at the edge of the field.

09 BIOMASS FUELS↗
DOE OSTI2021

Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication "Integrating New Arctic Plant Functional Types in a Land Surface Model Using Above- and Belowground Field Observations". We used field observations of biomass and leaf traits (Salmon et al., 2019b) across a gradient of plant communities on the Seward Peninsula in northwest Alaska (Breen et al., 2020; Iversen et al., 2017) to replace the original ELM configuration with nine, arctic-specific PFTs. Original arctic PFTs in the model were (1) broadleaf deciduous boreal shrub and (2) C3 arctic grass. New PFTs were (1) lichen, (2) bryophyte, (3) evergreen dwarf shrub, (4) deciduous dwarf shrub, (5) deciduous low shrub, (6) deciduous low to tall shrub, (7) alder shrub, (8) forb, and (9) graminoid. New PFTs were parameterized and evaluated using site-level trait and biomass measurements (Salmon et al., 2019b), root C:N ratios and rooting depth patterns (Salmon et al., 2019c), and root biomass (Salmon et al., 2020). This archive contains forcing data and model output from ELM simulations conducted at the Kougarok Hillslope site (Kougarok Road Mile Marker 64). Simulations were conducted for the Kougarok Hillslope site using meteorological driving data from the Scenarios Network for Alaska and Arctic Planning (SNAP) downscaled climate projection dataset using NCAR-CCSM forcing (Bieniek et al., 2020 dataset; Walsh et al., 2018). The archive includes three versions of model simulations representing a progression from the original model configuration to an Arctic-specific configuration (see Output section below). Simulation 1 used grid cell data from a global E3SM configuration, including the fractional area of E3SM PFTs assigned to the grid cell containing the Kougarok Hillslope site in global simulations. This simulation did not distinguish between different plant communities on the Kougarok Hillslope, but instead used a single point simulation to represent the entire area. Simulation 2 used default E3SM PFT definitions combined with adjusted depth to bedrock for plant communities with shallow rocky layers to represent the role of abiotic soil factors in driving site differences (based on depth measurements of Iversen et al., 2019a), and adjusted the relative areas of the broadleaf deciduous boreal shrub and C3 arctic grass PFTs to reflect the observed spatial coverage of shrub and non-shrub PFTs across Kougarok Hillslope plant communities. Simulation 3 used the new arctic PFT definitions and parameterizations based on the vegetation types present at the site, along with the adjusted soil depths used in Simulation 2. Included are *.pdf, *.nc (NetCDF), *tar.gz, *.py, and *.bash files. We would like to thank Mary's Igloo Native Corporation for allowing us to perform this research on their land and giving us the opportunity to share this research with their community. Thanks to Shawn Serbin and Alistair Rogers for helpful discussions. Holly Vander Stel, Joanne Childs, and Stan Wullschleger helped with collection of field data. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

Spectral reflectance measurements of a virus host model

A technique has been developed to detect the characteristic spectral signatures of healthy and infected St. Augustine grass. It is possible to predict the coverage of the infected area provided ground truth coverage shows positive St. Augustine grass turf. Qualitative measurements from photographs of plants in the blue and red regions with polarization show that light reflected from healthy plants is more strongly polarized than that from diseased plants. Photographs taken through the blue Wratten 47 filter in conjuction with a polarizer show an excellent differentiation. A large photographic difference also appears in the red region. Much smaller differences were noted in the 540 to 550 nm region. Although the intensity in the near-IR region is much higher than the visible region of the spectrum, differences in the healthy and diseased plants' reflectance were quite small.

Toler, R. W.↗
NASA NTRS1973

Reflectance of vegetation, soil, and water

The author has identified the following significant results. The ability to read the 24-channel MSS CCT tapes, select specified agricultural land use areas from the CCT, and perform multivariate statistical and pattern recognition analyses has been demonstrated. The 5 optimum channels chosen for classifying an agricultural scene were, in the order of their selection the far red visible, short reflective IR, visible blue, thermal infrared, and ultraviolet portions of the electromagnetic spectrum, respectively. Although chosen by a training set containing only vegetal categories, the optimum 4 channels discriminated pavement, water, bare soil, and building roofs, as well as the vegetal categories. Among the vegetal categories, sugar cane and cotton had distinctive signatures that distinguished them from grass and citrus. Acreages estimated spectrally by the computer for the test scene were acceptably close to acreages estimated from aerial photographs for cotton, sugar cane, and water. Many nonfarmable land resolution elements representing drainage ditch, field road, and highway right-of-way as well as farm headquarters area fell into the grass, bare soil plus weeds, and citrus categories and lessened the accuracy of the farmable acreage estimates in these categories. The expertise developed using the 24-channel data will be applied to the ERTS-1 data.

Wiegand, C. L.↗
NASA NTRS1973

Evaluation and comparison of ERTS measurements of major crops and soil associations for selected sites in the central United States

The author has identified the following significant results. An unsupervised classification was run for an area around Tahoka Lake, Texas. Classes from the unsupervised were correlated with the available ground observations. Unsupervised classes which corresponded to major rangeland composition groups were used as a basis for a classification of most of Lynn County. Three classes of pasture were identified: (1) clear pasture, mostly grasses; (2) mixture of grasses and mesquite; and (3) areas of thick mesquite. In addition, one class called other included lakes and agricultural areas. It was found that differences in rangeland composition could be mapped from spectral data using computer-aided processing techniques. The T Bar Ranch, which surrounds the Double Lakes west of Tahoka, Texas, was chosen as a test area for the classification of rangeland. It was found that the classification was reasonably consistant with the low altitude oblique air photos.

Baumgardner, M. F.↗
NASA NTRS1973

Mapping coastal vegetation, land use and environmental impact from ERTS-1

The author has identified the following significant results. Digital analysis of ERTS-1 imagery was used in an attempt to map and inventory the significant ecological communities of Delaware's coastal zone. Eight vegetation and land use discrimination classes were selected: (1) Phragmites communis (giant reed grass); (2) Spartina alterniflora (salt marsh cord grass); (3) Spartina patens (salt marsh hay); (4) shallow water and exposed mud; (5) deep water (greater than 2 m); (6) forest; (7) agriculture; and (8) exposed sand and concrete. Canonical analysis showed the following classification accuracies: Spartina alterniflora, exposed sand, concrete, and forested land - 94% to 100%; shallow water - mud and deep water - 88% and 93% respectively; Phragmites communis 83%; Spartina patens - 52%. Classification accuracy for agriculture was very poor (51%). Limitations of time and available class-memory space resulted in limiting the analysis of agriculture to very gross identification of a class which actually consists of many varied signature classes. Abundant ground truth was available in the form of vegetation maps compiled from color and color infrared photographs. It is believed that with further refinement of training set selection, sufficiently accurate results can be obtained for all categories.

Klemas, V.↗
NASA NTRS1974

Inventories of Delaware's coastal vegetation and land-use utilizing digital processing of ERTS-1 imagery

Digital analysis of ERTS-1 imagery was used in an attempt to map and inventory the significant ecological communities of Delaware's coastal zone. Eight vegetation and land use discrimination classes were selected: (1) phragmites communis (Giant Reed grass); (2) spartina alterniflora (Salt marsh cord grass); (3) spartina patens (Salt marsh hay); (4) shallow water and exposed mud; (5) deep water (2 meters); (6) forest; (7) agriculture; and (8) exposed sand and concrete. Canonical analysis showed that classification accuracy was quite good with spartina alterniflora, exposed sand-concrete, and forested land - all discriminated with between 94% and 100% accuracy. The shallow water-mud and deep water categories were classified with accuracies of 88% and 93% respectively. Phragmites communis showed a classification accuracy of 83% with all confusion occurring with spartina patens which may be due to use of mixed stands of these species as training sets. Discrimination of spartina patens was very poor (accuracy 52%).

Klemas, V.↗
NASA NTRS1975

Aquatic plants for removal of mevinphos from the aquatic environment

Fragrant waterlily (Nymphaea odorata, Ait.), joint-grass (Paspalum distichum L.), and rush (Juncus repens, Michx.) were used to evaluate the effectiveness of vascular aquatic plants in removing the insecticide mevinphos (dimethyl-1-carbomethoxy-1propen-2-yl phosphate) from waters contaminated with this chemical. The emersed aquatic plants fragrant waterlily and joint-grass removed 87 and 93 ppm of mevinphos from water test systems in less than 2 weeks without apparent damage to the plants; whereas rush, a submersed plant, removed less insecticide than the water-soil controls. Water-soil control still contained toxic levels of this insecticide, as demonstrated by fish bioassay studies, after 35 days.

Wolverton, B. C.↗
NASA NTRS1975

Investigation of land-use spectral signatures

A technique was developed to obtain bidirectional reflectance data from natural surfaces by using a folding mirror to transfer the reflected energy from the test surface to a spectroradiometer. The folding mirror was a first surface reflector made by stretching Mylar vacuum coated with aluminum over a light weight frame. The optically folding mirror was positioned over the test surfaces with a moveable platform for both laboratory and field tests. Field tests were conducted using a tethered balloon system to position the folding mirror. A spectroradiometer was designed and built specifically for this investigation. The spectroradiometer had an angular field of view of twenty-four minutes in one axis and ten minutes in the other axis. The radiometer was capable of detecting energies in small bandwidths throughout the electromagnetic spectrum from 0.3 microns to 3.0 microns. Bidirectional reflectance data and variations in the data with source angles were obtained for Saint Augustine grass, Bermuda grass, and a black alluvium soil from the Mississippi River delta.

Hagewood, J. F.↗
NASA NTRS1976

Urban land use monitoring from computer-implemented processing of airborne multispectral data

Machine processing techniques were applied to multispectral data obtained from airborne scanners at an elevation of 600 meters over central Indianapolis in August, 1972. Computer analysis of these spectral data indicate that roads (two types), roof tops (three types), dense grass (two types), sparse grass (two types), trees, bare soil, and water (two types) can be accurately identified. Using computers, it is possible to determine land uses from analysis of type, size, shape, and spatial associations of earth surface images identified from multispectral data. Land use data developed through machine processing techniques can be programmed to monitor land use changes, simulate land use conditions, and provide impact statistics that are required to analyze stresses placed on spatial systems.

Todd, W. J.↗
NASA NTRS1978

Feature discrimination/identification based upon SAR return variations

A study of the statistics of The look-to-look variation statistics in the returns recorded in-flight by a digital, realtime SAR system are analyzed. The determination that the variations in the look-to-look returns from different classes do carry information content unique to the classes was illustrated by a model based on four variants derived from four look in-flight SAR data under study. The model was limited to four classes of returns: mowed grass on a athletic field, rough unmowed grass and weeds on a large vacant field, young fruit trees in a large orchard, and metal mobile homes and storage buildings in a large mobile home park. The data population in excess of 1000 returns represented over 250 individual pixels from the four classes. The multivariant discriminant model operated on the set of returns for each pixel and assigned that pixel to one of the four classes, based on the target variants and the probability distribution function of the four variants for each class.

Rasco, W. A., Sr.↗
NASA NTRS1978

Nationwide forestry applications program: Procedure 1 applicability to rangeland classification

The author has identified the following significant results. An assumption that short prairie grass and salt grass could be differentiated on aircraft photographs was inaccurate for the Weld County site. However, rangeland could be differentiated using procedure 1 from LACIE. Estimates derived from either random or systematic sampling were satisfactory. Level 1 features were separated and mapped, and proportions were estimated with accompanying confidence statements.

Reeves, C. A.↗
NASA NTRS1978

Vegetation and other parameters in the Brevard County bar-built estuaries

It is shown that low-altitude aerial photography, using specific interpretive techniques, can effectively delineate sea-grass beds, oyster beds, and other underwater features. Various techniques were used on several sets of aerial imagery. Imagery was tested using several data analysis methods, ground truth, and biological testing. Approximately 45,000 acres of grass beds, 2,500 acres of oyster beds, and 4,200 acres of dredged canals were mapped. This data represents selected sites only. Areas chosen have the highest quality water in Brevard County and are among the most highly recognized biologically productive waters in Florida.

Down, C.↗
NASA NTRS1978

A demonstration of wetland vegetation mapping in Florida from computer-processed satellite and aircraft multispectral scanner data

The author has identified the following significant results. Major vegetative classes identified by the remote sensing technique were cypress swamp, pine, wetland grasses, salt grass, mixed mangrove, black mangrove, Brazilian pepper. Australian pine and melaleuca were not satisfactorily classified from LANDSAT. Aircraft scanners provided better resolution resulting in a classification of finer surface detail. An edge effect, created by the integration of diverse spectral responses within boundary elements of digital data, affected the wetlands classification. Accuracy classification for aircraft was 68% and for LANDSAT was 74%.

Butera, M. K.↗
NASA NTRS1985

Discrimination of Coastal Vegetation and Biomass Using AIS Data

The Airborne Imaging Spectrometer (AIS) was flown over a coastal wetlands region near Lewes, Delaware, adjacent to the Delaware Bay on 16 August 1984. Using the AIS data, it was possible to discriminate between four different types of wetland vegetation canopies: (1) trees; (2) broadleaf herbaceous plants (e.g., Acnida cannabina, Hisbiscus moscheutos); (3) the low marsh grass Spartina alterniflora; and (4) the high marsh grasses Distichlis spicata and Spartina patens. The single most useful region of the spectrum was that between 1.40 and 1.90 microns, where slopes of portions of the radiance curve and ratios of radiance at particular wavelengths were significantly different for the four canopy types. The ratio between the highest digital number in the 1.40 to 1.90 microns and .84 to .94 microns regions and a similar ratio between the peaks in radiance in the 1.12 to 1.40 microns and .84 to .94 microns spectral regions were also very effective at discriminating between vegetation types. Differences in radiance values at various wavelengths between samples of the same vegetation type could potentially be used to estimate biomass.

Gross, M. F.↗
NASA NTRS1987

Methane flux from the Central Amazonian Floodplain

A total of 186 methane measurements from the three primary Amazon floodplain environments of open water lakes, flood forests, and floating grass mats were made over the period 18 July through 2 September 1985. These data indicate that emissions were lowest over open water lakes. Flux from flooded forests and grass mats was significantly higher. At least three transport processes contribute to tropospheric emissions: ebullition from sediments, diffusion along the concentration gradient from sediment to overlaying water to air, and transport through the roots and stems of aquatic plants. Measurements indicate that the first two of these processes are most significant. It was estimated that on the average bubbling makes up 49% of the flux from open water, 54% of that from flooded forests, and 64% of that from floating mats. If the measurements were applied to the entire Amazonian floodplain, it is calculated that the region could supply up to 12% of the estimated global natural sources of methane.

Bartlett, Karen B.↗