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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Assessment of Vegetation Stress Using Reflectance or Fluorescence Measurements

Current methods for large-scale vegetation monitoring rely on multispectral remote sensing, which has serious limitation for the detection of vegetation stress. To contribute to the establishment of a generalized spectral approach for vegetation stress detection, this study compares the ability of high-spectral resolution reflectance (R) and fluorescence (F) foliar measurements to detect vegetation changes associated with common environmental factors affecting plant growth and productivity. To obtain a spectral dataset from a broad range of species and stress conditions, plant material from three experiments was examined, including (i) corn, nitrogen (N) deficiency/excess; (ii) soybean, elevated carbon dioxide, and ozone levels; and (iii) red maple, augmented ultraviolet irradiation. Fluorescence and R spectra (400-800 nm) were measured on the same foliar samples in conjunction with photosynthetic pigments, carbon, and N content For separation of a wide range of treatment levels, hyperspectral (5-10 nm) R indices were superior compared with F or broadband R indices, with the derivative parameters optimal results. For the detection of changes in vegetation physiology, hyperspectral indices can provide a significant improvement over broadband indices. The relationship of treatment levels to R was linear, whereas that to F was curvilinear. Using reflectance measurements, it was not possible to identify the unstressed vegetation condition, which was accomplished in all three experiments using F indices. Large-scale monitoring of vegetation condition and the detection of vegetation stress could be improved by using hyperspectral R and F information, a possible strategy for future remote sensing missions.

Campbell, P. K. E.↗
NASA NTRSDate not supplied

Role of Antecedent Soil Moisture and Vegetation Stress in Lightning-Initiated Wildfires

Lightning-caused wildfires are a small percentage of all wildfire events within the Conterminous U.S. (CONUS), but they account for over 56% of the acreage burned. The atmospheric conditions favoring wildfire and rapid growth are well understood: large dewpoint depressions, unstable planetary boundary layer, strong winds, etc. However, antecedent land surface conditions affecting dead and live fuel moisture is more difficult to quantify. This study examines over 20 years of antecedent land surface, vegetation stress, and wildfire characteristic data associated with nearly 77,000 lightning-initiated wildfires from the U.S. Forest Service Wildfire Database. We will invoke two in-house databases generated by the NASA Short-term Prediction Research and Transition (SPoRT) Center: an observations-driven, climatological run of the Noah land surface model within the NASA Land Information System (i.e., SPoRT-LIS) to depict soil moisture deficits / anomalies, and a satellite-constrained Evaporative Stress Index (ESI) product to denote areas of stressed vegetation. We will mine these datasets associated with lightning-caused (and null) events to determine important relationships, distributions, and delineators that correspond to elevated threat areas for lightning-initiated wildfires.

Wildfire↗
NASA NTRS1985

Remote sensing of stressed vegetation in the Carolina slate belt

Remote sensing techniques have been used to detect stress in vegetation, but they have not been very successful for identifying which environmental factors cause the vegetation to appear stressed. Controlled comparisons of spectral characteristics of plant communities experiencing known combinations of stresses were designed to examine this problem. Landsat TM, NS-001 TMS, CIR, and AIS imagery was acquired for six stressed areas in the Carolina slate belt. Preliminary results indicated that the areas of stressed vegetation were identifiable, and that the stressed communities appeared to be undergoing early fall leaf senescence. The AIS data seemed to have the greatest potential for identifying differences among plant community spectra, but calibration will be necessary before these differences can be evaluated.

Wickland, D. E.↗
NASA NTRS2000

A Passive Method for Detecting Vegetation Stress from Orbit: Chlorophyll Fluorescence Spectra from Fraunhofer Lines

Solar-stimulated chlorophyll fluorescence measured with the Fraunhofer line depth method has correlated well with vegetation stress in previous studies. However, the instruments used in those studies were limited to a single solar absorption line (e.g. 656.3 nm), obviating the red/far-red ratio (R/FR) method. Optics and detector technology have reached the level whereby multiple, very narrow Fraunhofer lines are resolvable. Thirteen such lines span the visible spectrum in the red to far-red region where chlorophyll fluorescence occurs. Fluorescence intensities at the 13 Fraunhofer line wavelengths were used to model emission spectra. The source data were collected for summer and fall bean crops (Phaseolus vulgaris L.) subjected to various levels of nitrogen fertilization. The intensities were adjusted to account for Fraunhofer line depth and atmospheric transmittance. Multiple R/FR fluorescence ratios, calculated from the modeled fluorescence spectra, correlated strongly with leaf chlorophyll concentration and well with applied nitrogen. The ratio yielding the best correlation with chlorophyll utilized red fluorescence at the 694.5 nm Fraunhofer line and farred fluorescence at the 755.6 nm Fraunhofer line. Twenty R/FR ratios, each evaluated for the maximum differential between low and high (optimal) nitrogen treatments, ranked higher in some cases and lower in others, possibly related to the time of year the crops were grown and the stage of growth of the crops. Ratios with 728.9 nm and 738.9 nm in the denominator consistently ranked in the lowest and next lowest quartile, respectively. Ratios of the 656.3 nm Fraunhofer line and the 755.6 nm line consistently ranked highest for the summer crop. Ratios with 755.6 nm in the denominator ranked in the upper quartile for 10 out of 12 measurement dates. Differences in ratio ranking indicate that physiological conditions may be estimated using selected ratios of Fraunhofer lines within the context of R/FR analysis. A passive instrument designed to monitor R/FR chlorophyll fluorescence (i.e. vegetation stress) from orbit could be built today.

Theisen, Arnold F.↗
NASA NTRS2001

Band-Moment Compression of AVIRIS Hyperspectral Data and its Use in the Detection of Vegetation Stress

A remote sensing campaign was conducted over a U.S. Department of Agriculture test farm at Shelton, Nebraska. An experimental field was set off in plots that were differentially treated with anhydrous ammonia. Four replicates of 0-kg/ha to 200-kg/ha plots, in 50-kg/ha increments, were set out in a random block design. Low-altitude (GSD of 3 m) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data were collected over the site in 224 bands. Simultaneously, ground data were collected to support the airborne imagery. In an effort to reduce data load while maintaining or enhancing algorithm performance for vegetation stress detection, band-moment compression and analysis was applied to the AVIRIS image cube. The results indicated that band-moment techniques compress the AVIRIS dataset significantly while retaining the capability of detecting environmentally induced vegetation stress.

Estep, L.↗
NASA NTRS1994

Vegetation stress as a feedback mechanism in midlatitude drought

An atmospheric general circulation model with land surface properties represented by the Simplified Simple Biosphere Model is used to investigate the effect of soil moisture and vegetation stress on drought in the mid-latitudes. An idealized land-sea distribution with simple topography is used to remove as many external sources of climate variation as possible. The land consists of a single, flat, rectangular continent covered with prairie vegetation and centered on 44 deg N of an aqua planet. A control integration of 4 years is performed, and several sets of seasonal anomaly integrations are made to test the sensitivity of seasonal climate to low initial (1 April) soil moisture and dormant vegetation like what would occur during a severe drought. It is found that the inclusion of dormant vegetation during the spring and early summer greatly reduces evapotranspiration by eliminating transpiration. This affects local climate more strongly as summer progresses. Low initial soil moisture, combined with dormant vegetation, leads to a severe drought. The reduction in precipitation is much greater in magnitude than that due to low soil moisture alone, and greater than the sum of the effects computed separately. Although the short-term drought is more severe, the dormancy of the vegetation prevents further depletion of moisture in the root zone of the soil, so soil moisture begins to rebound toward the middle of summer.

Dirmeyer, Paul A.↗
NASA NTRS1983

Remote sensing and vegetation stress detection - Problems and progress

Although considerable progress has been made in applying remote sensing technology to vegetation monitoring, considerable problems still exist in the improvement of techniques for crop type discrimination, stress detection on a large scale, and stress quantification. In this paper, some of the problems remaining in the operational use of remote sensing technology for vegetation stress detection are discussed, and directions in which some of these problems might be solved are proposed.

Duggin, M. J.↗
NASA NTRSDate not supplied

NASA SPoRT-Land Information System and Vegetation Stress Real-Time Products for Drought, Wildfire, and Pluvial Analysis

The NASA SPoRT Center has been producing a near real-time instance of the NASA Land Information System over a CONUS domain (hereafter “SPoRT-LIS”) since ~2015. The SPoRT-LIS runs the legacy Noah LSM in an observations-constrained manner, with outputs of soil moisture and temperature at layered depths along with surface energy fluxes. The unique configuration of SPoRT-LIS enables decision-making on operational timescales since it incorporates near real-time observations such as VIIRS Green Vegetation Fraction and MRMS QPE. An additional in-house Alaska-LIS is produced in real time to help inform end-users on spring snow melt and summer soil moisture trends during the wildfire season. Use of the SPoRT-LIS has gradually expanded in recent years as a component of drought analysis, feedback to the USDM, and is utilized by State Climate Offices. Operational feedback has led to increased applicability for analyses by other end-users in the drought community. This presentation will provide SPoRT-LIS applications for drought, pluvial, and fire weather case-studies. We will also present preliminary results of 2-week SPoRT-LIS forecast percentiles that were recently implemented using GFS model forecast fields. We will additionally discuss the pathway toward extending these forecasts into the future by incorporating ensemble forecasts for probabilistic guidance on soil moisture trends for potential flash drought and subseasonal outlooks.

Soil Moisture↗
NASA NTRS1971

Detection and Characterization of Stress Symptoms in Forest Vegetation

Techniques used at the Pacific Southwest Forest and Range Experiment Station to detect advanced and previsual symptoms of vegetative stress are discussed. Stresses caused by bark beetles in coniferous stands of timber are emphasized because beetles induce stress more rapidly than most other destructive agents. Bark beetles are also the most damaging forest insects in the United States. In the work on stress symptoms, there are two primary objectives: (1) to learn the best combination of films, scales, and filters to detect and locate injured trees from aircraft and spacecraft, and (2) to learn if stressed trees can be detected before visual symptoms of decline occur. Equipment and techniques used in a study of the epidemic of the Black Hills bark beetle are described.

Heller, R. C.↗
DOE OSTI2022

Surface temperatures reveal the patterns of vegetation water stress and their environmental drivers across the tropical Americas

Vegetation is a key component in the global carbon cycle as it stores ~450 GtC as biomass, and removes about a third of anthropogenic CO2 emissions. However, in some regions, the rate of plant carbon uptake is beginning to slow, largely because of water stress. Here, we develop a new observation-based methodology to diagnose vegetation water stress and link it to environmental drivers. We used the ratio of remotely sensed land surface to near surface atmospheric temperatures (LST/Tair) to represent vegetation water stress, and built regression tree models (random forests) to assess the relationship between LST/Tair and the main environmental drivers of surface energy fluxes in the tropical Americas. We further determined ecosystem traits associated with water stress and surface energy partitioning, pinpointed critical thresholds for water stress, and quantified changes in ecosystem carbon uptake associated with crossing these critical thresholds. Additionally, we found that the top drivers of LST/Tair, explaining over a quarter of its local variability in the study region, are (1) radiation, in 58% of the study region; (2) water supply from precipitation, in 30% of the study region; and (3) atmospheric water demand from vapor pressure deficits (VPD), in 22% of the study region. Regions in which LST/Tair variation is driven by radiation are located in regions of high aboveground biomass or at high elevations, while regions in which LST/Tair is driven by water supply from precipitation or atmospheric demand tend to have low species richness. Carbon uptake by photosynthesis can be reduced by up to 80% in water-limited regions when critical thresholds for precipitation and air dryness are exceeded simultaneously, that is, as compound events. Our results demonstrate that vegetation structure and diversity can be important for regulating surface energy and carbon fluxes over tropical regions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1978

Large Area Crop Inventory Experiment (LACIE). Detecting and monitoring agricultural vegetative water stress over large areas using LANDSAT digital data

The author has identified the following significant results. The Green Number Index technique which uses LANDSAT digital data from 5X6 nautical mile sampling frames was expanded to evaluate its usefulness in detecting and monitoring vegetative water stress over the Great Plains. At known growth stages for wheat, segments were classified as drought or non drought. Good agreement was found between the 18 day remotely sensed data and a weekly ground-based crop moisture index. Operational monitoring of the 1977 U.S.S.R. and Australian wheat crops indicated drought conditions. Drought isoline maps produced by the Green Number Index technique were in good agreement with conventional sources.

Thompson, D. R.↗
NASA NTRS1983

Approaches to detection of geochemical stress in vegetation

Work has been carried out to elucidate fundamental relationships between spectral properties of plants and geochemical stress, and a programme of field and laboratory work is in progress. The most significant results and conclusions at this stage are described and attention is focused on the new concepts for stress detection which have been generated. The applications of the research are relevant to the understanding of current remotely sensed data as well as relating to ideas for new instrumentation.

Horler, D. N. H.↗
NASA NTRS1987

Obtaining spectral reflectance factor measurements of stressed forest vegetation

Techniques and instruments used to collect spectral reflectance data on Vermont spruce and fir stands subjected to stress (due to acid rain and ozone) are described and illustrated. The healthy and stressed sites selected are characterized; the data-quality requirements are indicated; and particular attention is given to (1) a helicopter-mounted instrument package (radiometer, spectrometer, cameras, and video camera) for canopy-level remote sensing of reflectance and (2) a portable hemispherical light source for use in taking laboratory reflectance measurements from branches or needles. Typical data are presented in graphs and briefly discussed.

Case, David W.↗
NASA NTRS1996

Surveying Dead Trees and CO2-Induced Stressed Trees Using AVIRIS in the Long Valley Caldera

Since 1980 the Long Valley Caldera in the eastern Sierra Nevada (California) has shown signs of renewed volcanic activity. Frequent earthquakes, a re-inflation of the caldera, hydrothermal activity and gas emissions are the outer symptoms of this renewed activity. In 1990 and 1991 several areas of dying trees were found around Mammoth Mountain. The cause of the die off of the trees was first sought in the persistent drought in the preceding years. However, the trees died regardless of age and species. Farrar et al. (1995) started a soil-gas survey in 1994 in the dead-tree areas and found carbon dioxide concentrations ranging from 30 to 96% at soil depths between 30 and 60 cm. CO2 concentrations in the atmosphere are usually around 0.03% and in the soil profile CO2 levels do commonly not exceed 4 to 5%. Although not much is known about the effect of high levels of carbon dioxide in the soil profile on roots, it is most likely that the trees are dying due to oxygen deprivation: the CO2 drives the oxygen out of the soil. So far, four sites of dead trees have been mapped around Mammoth Mountain. The two largest dying trees sites are located near Horseshoe Lake and near Mammoth Mountain Main Lodge covering approximately an area of 10 and 8 ha respectively. Analysis of the gas composition regarding the He-3/He-4 ratio and the percentage biogenic carbon reveals the source of the gas: the magma body beneath the Long Valley Caldera. Until recently it was not known that volcanoes release abundant carbon dioxide from their flanks as diffuse soil emanations. As a result of the magma gas emission around Mammoth Mountain there is an excellent sequence of dead trees, stressed trees, healthy trees and bare soil surfaces. This research site provides excellent opportunities to: (1) Study the capabilities of imaging spectrometry to map stressed (and dead) pine and fir species; (2) Study methods to separate the vivid vegetation, stressed vegetation and dead vegetation from the soil background of glacial deposits and crystalline rocks. The dead tree areas are located on the flanks of Mammoth Mountain (N:37 deg 37' 45" and W:119 deg 02' 05") at an elevation between 2600 and 3000 meters. The area is covered by an open type of Montane Forest. The dominant tree species are Lodgepole Pine (Pinus contorta), the Red Fir (Abies magnifica) and the Jeffrey Pine (Pinus jeffreyi). The soil surface near Horseshoe Lake is generally fairly bright. The surface is covered by glacial deposits (till) consisting mainly of weathered granitic rocks.

deJong, Steven M.↗
NASA NTRS1988

Assessment of AVIRIS data from vegetated sites in the Owens Valley, California

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were acquired from the Bishop, CA area, located at the northern end of the Owens Valley, on July 30, 1987. Radiometrically-corrected AVIRIS data were flat-field corrected, and spectral curves produced and analyzed for pixels taken from both native and cultivated vegetation sites, using the JPS SPAM software program and PC-based spreadsheet programs. Analyses focussed on the chlorophyll well and red edge portions of the spectral curves. Results include the following: AVIRIS spectral data are acquired at sufficient spectral resolution to allow detection of blue shifts of both the chlorophyll well and red edge in moisture-stressed vegetation when compared with non-stressed vegetation; a normalization of selected parameters (chlorophyll well and near infrared shoulder) may be used to emphasize the shift in red edge position; and the presence of the red edge in AVIRIS spectral curves may be useful in detecting small amounts (20 to 30 pct cover) of semi-arid and arid vegetation ground cover. A discussion of possible causes of AVIRIS red edge shifts in respsonse to stress is presented.

Rock, B. N.↗
NASA NTRS1987

Remote detection of air pollution stress to vegetation - Laboratory-level studies

An experimental investigation of the role of leaf chemistry, anatomy, moisture content, and canopy density on spectral reflectance in healthy and pollution stressed western conifer needles and broad-leafed species of California coastal sage scrub is presented. Acid mist at a level of pH 2.0 is found to more severely effect chlorophyll loss and leaf death than ozone at a level of 0.2 ppm for a four-week period. Both pollutants cause water loss, affecting Bands 4 and 5 in nonlinear ways. The infrared bands initially rise as free water is lost, and subsequently, scattering and reflectance decline. The net effect is shown to be a reduction in TM 4/3 and a rise in TM 5/4 with pollution stress. Under more severe pollution stresses, the decline of leaf area indices due to accelerated leaf drop accentuates the expected TM 4/3 and TM 5/4 changes.

Westman, Walter E.↗
DOE OSTI2020

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. Finally, the relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in situ field survey-based data further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.

evapotranspiration↗