Engineering topics

Hunt, E. Raymond, Jr.

Publications and source records attributed to Hunt, E. Raymond, Jr..

5 records

NASA NTRS1994

FIFE data analysis: Testing BIOME-BGC predictions for grasslands

The First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) was conducted in a 15 km by 15 km research area located 8 km south of Manhattan, Kansas. The site consists primarily of native tallgrass prairie mixed with gallery oak forests and croplands. The objectives of FIFE are to better understand the role of biology in controlling the interactions between the land and the atmosphere, and to determine the value of remotely sensed data for estimating climatological parameters. The goals of FIFE are twofold: the upscale integration of models, and algorithm development for satellite remote sensing. The specific objectives of the field campaigns carried out in 1987 and 1989 were the simultaneous acquisition of satellite, atmospheric, and surface data; and the understanding of the processes controlling surface energy and mass exchange. Collected data were used to study the dynamics of various ecosystem processes (photosynthesis, evaporation and transpiration, autotrophic and heterotrophic respiration, etc.). Modelling terrestrial ecosystems at scales larger than that of a homogeneous plot led to the development of simple, generalized models of biogeochemical cycles that can be accurately applied to different biomes through the use of remotely sensed data. A model was developed called BIOME-BGC (for BioGeochemical Cycles) from a coniferous forest ecosystem model, FOREST-BGC, where a biome is considered a combination of a life forms in a specified climate. A predominately C4-photosynthetic grassland is probably the most different from a coniferous forest possible, hence the FIFE site was an excellent study area for testing BIOME-BGC. The transition from an essentially one-dimensional calculation to three-dimensional, landscape scale simulations requires the introduction of such factors as meteorology, climatology, and geomorphology. By using remotely sensed geographic information data for important model inputs, process-based ecosystem simulations at a variety of scales are possible. The second objective of this study is concerned with determining the accuracy of the estimated fluxes from BIOME-BGC, when extrapolated spatially over the entire 15-km by 15-km FIFE site. To accomplish this objective, a topographically distributed map of soil depth at the FIFE site was developed. These spatially-distributed fluxes were then tested with data from aircraft by eddy-flux correlation obtained during the FIFE experiment.

Hunt, E. Raymond, Jr.↗

NASA NTRS1991

Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data

The reflectance ratio of the middle-infrared band (MIR) to the near-infrared band (NIR) is linearly related to the log(10) equivalent water thickness (EWT) for single leaves of different morphologies, whereas the MIR/NIR radiance ratio is correlated with the leaf area index (LAI). The hypothesis that the MIR/NIR ratio measures canopy EWT was tested by reanalyzing airborne Thematic Mapper Simulator and field data obtained across a large gradient of LAI in western Oregon, U.S.A. The measured airborne MIR/NIR reflectance ratios for canopies were not significantly different from the predicted ratios using leaf data for canopy EWT, except for two desert woodland sites. The interpretation of the MIR/NIR ratio is scale-dependent, because leaf EWT is determined primarily by variations in LAI.

Hunt, E. Raymond, Jr.↗

NASA NTRS1989

Detection of changes in leaf water content using near- and middle-infrared reflectances

A method to detect plant water stress by remote sensing is proposed using indices of near-IR and mid-IR wavelengths. The ability of the Leaf Water Content Index (LWCI) to determine leaf relative water content (RWC) is tested on species with different leaf morphologies. The way in which the Misture Stress Index (MSI) varies with RWC is studied. On test with several species, it is found that LWCI is equal to RWC, although the reflectances at 1.6 microns for two different RWC must be known to accurately predict unknown RWC. A linear correlation is found between MSI and RWC with each species having a different regression equation. Also, MSI is correlated with log sub 10 Equivalent Water Thickness (EWT) with data for all species falling on the same regression line. It is found that the minimum significant change of RWC that could be detected by appying the linear regression equation of MSI to EWT is 52 percent. Because the natural RWC variation from water stress is about 20 percent for most species, it is concluded that the near-IR and mid-IR reflectances cannot be used to remotely sense water stress.

Hunt, E. Raymond, Jr.↗

NASA NTRS1987

Measurement of leaf relative water content by infrared reflectance

From basic considerations and Beer's law, a leaf water content index incorporating reflectances of wavelengths from 0.76 to 0.90 microns and from 1.55 to 1.75 microns was developed that relates leaf reflectance to leaf relative water content. For the leaf succulent, Agave deserti, the leaf water content index was not significantly different from the relative water content for either individual leaves or an entire plant. Also, the relative water contents of intact plants of Encelia farinosa and Hilaria rigida in the field were estimated by the leaf water content index; variations in the proportion of living to dead leaf area could cause large errors in the estimate of relative water content. Thus, the leaf water content index may be able to estimate average relative water content of canopies when TM4 and TM5 are measured at a known relative water content and fraction of dead leaf material.

Hunt, E. Raymond, Jr.↗

NASA NTRS1987

Relative water content of Spruce needles determined by the leaf water content index

Leaf relative water content (RWC) is defined as the volume of water in a leaf divided by the volume at full turgor. Using reflectance factors of wavelengths 0.83 micron and 1.6 microns, a Leaf Water Content Index (LWCI) was derived from the Lambert-Beer Law such that LWCI should equal RWC; LWCI was equal to RWC for Picea pungens, Picea rubens, Liquidambar styraciflua, and Quercus agrifolia. Algebraic manipulation shows that R(1.6)/R(0.83) termed the Moisture Stress Index (MSI), is near-linearly correlated to RWC and to the Equivalent Water Thickness (EWT). Five species tested so far had the same relationship between MSI and EWT, but EWT is not a measure of plant water status.

Hunt, E. Raymond, Jr.↗