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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Light interception and leaf area estimates from measurements of grass canopy reflectance

Grassland is a major component of the earth's available land. The vast area and remoteness of this ecosystem makes it difficult to assess its condition and monitor productivity by traditional methods. Remote sensing potentially offers a rapid nondestructive approach for monitoring such ecosystems. A study was carried out in a tallgrass prairie site near Manhattan, Kansas, during the 1983 and 1984 seasons to investigate the feasibility of estimating light interception and green leaf area index (LAI) from measurements of canopy multispectral reflectance. Greenness (Gn) index was found to be strongly correlated with intercepted photosynthetically active radiation (PAR). Two methods, a direct regression (RGR) and an indirect approach (IND), were used to estimate LAI from Gn index. The LAI values estimated by RGR method were consistently lower than the measured ones; however, good agreement was obtained between the LAI values estimated by IND method and the measured LAI. This suggests that Gn transformation of canopy spectral reflectance is more closely related to the fraction of intercepted PAR by green foliage than the quantity of green LAI.

Asrar, G.↗
NASA NTRS1989

Use of vegetation indices to estimate intercepted solar radiation and net carbon dioxide exchange of a grass canopy

Results are presented from field experiments relating spectral reflectance to intercepted photosynthetically active radiation (PAR) and net CO2 exchange in a natural canopy composed of the marsh cordgrass (Spartina alterniflora). Reflectance measurements made by a hand-held radiometer with Landsat TM spectral wavebands are used to compute remote sensing indices such as the normalized difference vegetation index. Consideration is given to the impact of standing dead canopy material on the relationship between intercepted PAR and spectral vegetation indices and the impact of changes in photosynthetic efficiency on the relationship between vegetation indices and CO2 exchange rates. The results suggest that quantitative remote assessment of photosynthesis and net gas exchange in natural vegetation is feasible, especially if the analysis incorporates information on biological responses to environmental variables.

Bartlett, David S.↗
NASA NTRS2004

Towards a High Temporal Frequency Grass Canopy Thermal IR Model for Background Signatures

In this paper, we present our first results towards understanding high temporal frequency thermal infrared response from a dense plant canopy and compare the application of our model, driven both by slowly varying, time-averaged meteorological conditions and by high frequency measurements of local and within canopy profiles of relative humidity and wind speed, to high frequency thermal infrared observations. Previously, we have employed three-dimensional ray tracing to compute the intercepted and scattered radiation fluxes and for final scene rendering. For the turbulent fluxes, we employed simple resistance models for latent and sensible heat with one-dimensional profiles of relative humidity and wind speed. Our modeling approach has proven successful in capturing the directional and diurnal variation in background thermal infrared signatures. We hypothesize that at these scales, where the model is typically driven by time-averaged, local meteorological conditions, the primary source of thermal variance arises from the spatial distribution of sunlit and shaded foliage elements within the canopy and the associated radiative interactions. In recent experiments, we have begun to focus on the high temporal frequency response of plant canopies in the thermal infrared at 1 second to 5 minute intervals. At these scales, we hypothesize turbulent mixing plays a more dominant role. Our results indicate that in the high frequency domain, the vertical profile of temperature change is tightly coupled to the within canopy wind speed In the results reported here, the canopy cools from the top down with increased wind velocities and heats from the bottom up at low wind velocities. .

Ballard, Jerrell R., Jr.↗
NASA NTRS2023

Kankakee Water Resources: Monitoring Temperature and Vegetation to Detect River Flow Impediments at Energy Intake Structures

In recent years, unpredictable grassing events have occurred at the Dresden Generating Station, located on the Kankakee River in northern Illinois. Grassing events are characterized by large mats of aquatic vegetation that accumulate downstream, resulting in the clogging of water intake structures and leading to major disruptions in power generation. Currently, employees at the Dresden Generating Station are responsible for reactively responding to each grassing event individually. This project, in partnership with Constellation Nuclear and the United States Geological Survey (USGS), assessed the feasibility of using Earth observations (Landsat 9 OLI-2, Landsat 8 OLI, Sentinel-2 MSI, DOVE PlanetScope, WorldView-3, and GPM IMERG) to detect floating aquatic vegetation within the Kankakee River and identify predictive factors that trigger grassing events, as doing so will provide the Dresden Generating Station the ability to anticipate future grassing events and enhance general hydrologic modeling efforts held by the USGS. The results of this study illustrated that, while aquatic vegetation can be detected by satellites with up to moderate spatial resolution (30 m), temporal resolution is a major limiting factor for tracking movements in floating aquatic vegetation and identifying predictive measures for these events. In addition, correlation results suggest a possible negative relationship between grassing events and river discharge (-0.875 correlation coefficient). In the future, pairing these results with ground control surveys and sensors with higher temporal capabilities would allow our project partners to predict and proactively address future grassing events, ensuring the reliable operation of the Dresden Generating Station.

Marisa Smedsrud↗
NASA NTRS1976

Remote sensing of St. Augustine Decline (SAD) disease

Laboratory and field spectral reflectance measurements of healthy and infected St. Augustine grass were made using several different instruments. Spectral differences between healthy and infected grass occured in the visible and near infrared regions. Multiband and color infrared photographs were taken of healthy and diseased turf from ground-based platforms and low altitude aircraft. Qualitative (density slicing) and quantitative (transmission densitometry) analyses revealed distinct tonal differences between healthy and St. Augustine disease (SAD) infected grass. Similar experiments are described for determining if healthy and diseased grass can be distinguished from waterstressed grass and grass deficient in either nitrogen or iron.

Odle, W. C.↗
NASA NTRS2025

Nevada Wildland Fires: Mapping Historical Burned Areas and Identifying Drivers of Fuel Load Growth to Inform Desert Tortoise Habitat Management in Southern Nevada

Wildfire frequency and intensity in the Mojave desert are increasing, driven by invasive grasses that outcompete native vegetation not well-adapted to wildfire. These grasses, primarily cheatgrass (Bromus tectorum) and red brome (Bromus rubens), and the wildfires that occur when the grasses ignite, are a serious threat to the critically endangered Mojave desert tortoise (Gopherus agassizii). Declining Mojave desert tortoise populations are of great concern to project partners, the Bureau of Land Management Southern Nevada District Office, the US Forest Service’s Rocky Mountain Research Station, and the US Department of Agriculture Agricultural Research Service’s Arid Lands Ecology Lab, who currently lack the resources for fire recovery studies essential for informed fire and invasive grass management. To address this gap, the team utilized NASA Earth observations including Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Terra MODIS, Landsat 5 Thematic Mapper (TM), and Landsat 8 Operational Land Imager (OLI), to analyze trends in burned areas over the past 20 years through historic burn maps and to examine fuel load growth drivers, with a focus on invasive vegetation. These historical burn maps revealed a pattern of precipitation anomalies that strongly correlate with higher Normalized Difference Vegetation Index (NDVI) values. Years with significant precipitation and NDVI spikes led to greater fuel load in the dry season and larger burns. Additionally, El Niño years correlated significantly with precipitation anomalies which may provide our partners with insights into burn trends and enable better allocation of fuel load treatment practices. However, while identifying drivers of fuel load growth proved feasible, a lack of in situ data hindered attempts to analyze pre-fire growth of invasive grasses compared with native grasses. Despite this limitation, the project demonstrated the feasibility of using Earth observations for wildfire and habitat management, ultimately benefiting conservation efforts for the Mojave desert tortoise.

fuel load↗
NASA NTRS2016

Combining Livestock Production Information in a Process-Based Vegetation Model to Reconstruct the History of Grassland Management

Grassland management type (grazed or mown) and intensity (intensive or extensive) play a crucial role in the greenhouse gas balance and surface energy budget of this biome, both at field scale and at large spatial scale. However, global gridded historical information on grassland management intensity is not available. Combining modelled grass-biomass productivity with statistics of the grass-biomass demand by livestock, we reconstruct gridded maps of grassland management intensity from 1901 to 2012. These maps include the minimum area of managed vs. maximum area of unmanaged grasslands and the fraction of mown vs. grazed area at a resolution of 0.5deg by 0.5deg. The grass-biomass demand is derived from a livestock dataset for 2000, extended to cover the period 19012012. The grass-biomass supply (i.e. forage grass from mown grassland and biomass grazed) is simulated by the process-based model ORCHIDEE-GM driven by historical climate change, risingCO2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6.1 x 10(exp 6) km(exp 2) in 1901 to 12.3 x 10(exp 6) kmI(exp 2) in 2000, although the expansion pathway varies between different regions. ORCHIDEE-GM also simulated augmentation in global mean productivity and herbage-use efficiency over managed grassland during the 20th century, indicating a general intensification of grassland management at global scale but with regional differences. The gridded grassland management intensity maps are model dependent because they depend on modelled productivity. Thus specific attention was given to the evaluation of modelled productivity against a series of observations from site-level net primary productivity (NPP) measurements to two global satellite products of gross primary productivity (GPP) (MODIS-GPP and SIF data). Generally, ORCHIDEE-GM captures the spatial pattern, seasonal cycle, and inter-annual variability of grassland productivity at global scale well and thus is appropriate for global applications presented here.

Livestock dataset↗
NASA NTRS1972

Application of ecological, geological and oceanographic ERTS-1 imagery to Delaware's coastal resources planning

The author has identified the following significant events. Coastal vegetation species appearing in the ERTS-1 image taken of the Southern Coast of Delaware, during orbit 333 on August 16, 1972, have been correlated with ground truth vegetation maps, and imagery obtained from high altitude RB-57 and U-2 overflights. The vegetation maps of the entire Delaware Coast were prepared using data collected on foot, in small boats, and from low altitude aircraft. Multispectral analysis of high altitude RB-57 and U-2 photographs indicated that five vegetation species could be clearly discriminated from 60,000 feet altitude, including: (1) salt marsh cord grass (Spartina alterniflora); (2) salt marsh hay and spike grass (Spartina patens and Distichlis spicata); (3) reed grass (Phragmites communis); (4) high tide bush and sea myrtle (Iva species and Baccharus halimifolia); and (5) a group of fresh water species found in impounded areas built to attract water fowl. Major Spartina alterniflora and Spartina patens communities within the tidal marshes can be identified in the ERTS-1 imagery. Phragmites, and other species however, occur in smaller, more dispersed groupings and are difficult to discriminate within the resolution capability of the ERTS-1 scanner. Similarly, major impounded areas, built to attract water fowl can be detected; however, mosquito drainage ditches, covering many of Delaware's marshes, are too narrow and not long enough to be resolved by ERTS-1 sensors. High-marsh and dune communities dominated by high tide bush (Iva frutescens) and sea myrtle (Baccharus halimifolia) can be distinguished from adjacent maritime forest and beach grass communities.

Klemas, V.↗
NASA NTRS1982

Reflectance of litter accumulation levels at five wavelengths within the 0.5- to 2.5 micron waveband

Reflectance was measured for 1-m2 range grass plots with two canopy treatments (standing and clipped) and four levels of litter accumulation and for grain sorghum with two canopy treatments. Reflectance was significantly higher at the 0.65- to 1.65-, and 2.20-micrometer. Wavelengths for both grass and grain sorghum canopies when the canopies were clipped and the resulting litter was removed. The natural accumulation of litter under the grass canopy did not significantly affect reflectance. The 1.65- and 2.20-micrometer wavelength reflectances of the live grass and the intact litter were 21.8% and 16.2%, respectively, and those of grain sorghum were 21.8% and 16.5%, respectively.

Gerbermann, A. H.↗
NASA NTRS2001

Characteristics of AVIRIS Band Measurements in Desert Agroecosystems in the Area of Blythe, California: Studies of Cotton Spectra - 1

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data from Blythe, California, were acquired in June 1997 to study agricultural spectra from different crops and to identify crops in other areas with similar environmental factors and similar spectral properties. The main objectives of this study are: (1) to compare the spectral and radiometric characteristics of AVIRIS data from agriculture crops with ground spectra measured by a FieldSpec ASD spectrometer; (2) to explore the use of AVIRIS spectral images for identifying agricultural crops; (3) to study the spectral expression of environmental factors on selected crops; and (4) to build a spectral library for the crops that were studied. A long-term goal is to extend the spectral library for different vegetation or crops in different stages of growth. To support our study, on July 18 and 19, 2000, we collected spectra using the FieldSpec spectrometer from selected fields with different crops in the Blythe area of California (longitude 114 deg 33.28 W and latitude 33 deg 25.42 N to longitude 1140 44.53 W and latitude 33 deg 39.77 N). These crops were cotton in different stages of growth, varieties of grass pure or mixed, Sudan grass, Bermuda grass, Teff grass, and alfalfa. Some of the fields were treated with different types of irrigation (i.e., wet to dry conditions). Additional parameters were studied such as the soil water content (WC), pH, and organic matter (OM). The results of this study showed that for crops known to be similar, there is a significant correlation between the spectra that were collected by AVIRIS in 1997 and spectra measured by the FieldSpec (registered) spectrometer in 2000. This correlation allowed development of a spectral library to be used in ENVI-IDL analysis software. This library was used successfully to identify different crops. Furthermore, using IDL algorithms of Spectral Angle Mapper classification (SAM), spectral feature fitting (SFF) and spectral binary encoding (SPE) showed that there is excellent agreement between the predicted and the actual crop type (i.e., the correlation is between 85-90% match). Further use of the AVIRIS images can be of a value to crop identification or crop yield for commercial use.

Hanna, Safwat H. Shakir↗