E. Raymond Hunt

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

Abstract Detection of plant water stress by remote sensing has been proposed using indices of Near-Infrared (NIR, 0.7–1.3 μm) and Middle-Infrared (MIR, 1.3–2.5 μm) wavelengths. The first objective of this study was to test the ability of the Leaf Water Content Index (LWCI) to determine leaf Relative Water content (RWC) of different species with different leaf morphologies. The second objective was to determine how the Moisture Stress Index (MSI; MIR / NIR) varies with RWC and the Equivalent Water Thickness (EWT). Reflectance factors at 0.82 μm and 1.6 μm were measured on leaves of Quercus agrifolia (sclerophyllous leaves), Liquidambar styraciflua (hardwood deciduous tree leaves), Picea rubens and Picea pungens (conifer needles), and Glycine max (herbaceous dicot leaves) as they dried on a laboratory bench. RWC and EWT were measured concurrently with the reflectance measurements. The results showed that LWCI was equal to RWC for the species tested. However, the results of a sensitivity analysis indicated the reflectances at 1.6 μm for two different RWC must be known for accurate prediction of unknown RWC; thus the LWCI is impractical for field applications. MSI was linearly correlated to RWCwith each species having a different regression equation and to log10 EWT with data of all species falling on the same regression line. Because EWT is correlated with leaf area index, MSI should also be correlated with leaf area index. Assuming that the linear regression equation of MSI to EWT can be applied to canopies, then the minimum significant change of RWC that can be detected is 52%. For most plants, the natural variation in RWC from water stress is only about 20%, so that we conclude that indices derived from NIR and MIR reflectances cannot be used to remotely-sense water stress.

Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring

Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-megapixel camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the camera-UAV system may provide important information for site-specific agriculture.

Estimating near-infrared leaf reflectance from leaf structural characteristics

The relationship between near-infrared reflectance at 800 nm (NIRR) from leaves and characteristics of leaf structure known to affect photosynthesis was investigated in 48 species of alpine angiosperms. This wavelength was selected to discriminate the effects of leaf structure vs. chemical or water content on leaf reflectance. A quantitative model was first constructed correlating NIRR with leaf structural characteristics for six species, and then validated using all 48 species. Among the structural characteristics tested in the reflectance model were leaf trichome density, the presence or absence of both leaf bicoloration and a thick leaf cuticle (>1 μm), leaf thickness, the ratio of palisade mesophyll to spongy mesophyll thickness (PM/SM), the proportion of the mesophyll occupied by intercellular air spaces (%IAS), and the ratio of mesophyll cell surface area exposed to IAS (A(mes)) per unit leaf surface area (A), or A(mes)/A. Multiple regression analysis showed that measured NIRR was highly correlated with A(mes)/A, leaf bicoloration, and the presence of a thick leaf cuticle (r = 0.93). In contrast, correlations between NIRR and leaf trichome density, leaf thickness, the PM/SM ratio, or %IAS were relatively weak (r < 0.25). A model incorporating A(mes)/A, leaf bicoloration, and cuticle thickness predicted NIRR accurately for 48 species (r = 0.43; P < 0.01) and may be useful for linking remotely sensed data to plant structure and function.

Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering

Abstract Leafy spurge, Euphorbia esula L. is an adventive, perennial weed that infests approximately 1.2 million ha of land in North America. It often forms dense stands that displace native vegetation and useful forage plants on rangelands and in riparian habitats. Leafy spurge is a good candidate for detection via remote sensing because the distinctive yellow-green color of its bracts is spectrally unique when compared to co-occurring green vegetation. During 1999, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) imagery was acquired in northeastern Wyoming and ground cover data were collected. Mixture tuned matched filtering (MTMF), a specialized type of spectral mixture analysis, was used to estimate leafy spurge canopy cover and map leafy spurge distribution. Overall performance of MTMF for estimating percent cover of leafy spurge for all sites was good (r2=0.69) with better performance in prairie areas (r2=0.79) and poorer performance occurring on wooded sites (r2=0.57). However, results demonstrated that in open canopies with leafy spurge in the understory, the spectral signature is sufficiently distinct to be detectable. The techniques presented here could be used for constructing leafy spurge distribution and abundance maps with satellite hyperspectral data for larger regional areas.

Global net carbon exchange and intra-annual atmospheric CO2 concentrations predicted by an ecosystem process model and three-dimensional atmospheric transport model

A generalized terrestrial ecosystem process model, BIOME-BGC (for BIOME BioGeoChemical Cycles), was used to simulate the global fluxes of CO 2 resulting from photosynthesis, autotrophic respiration, and heterotrophic respiration. Daily meteorological data for the year 1987, gridded to 1° by 1°, were used to drive the model simulations. From the maximum value of the normalized difference vegetation index (NDVI) for 1987, the leaf area index for each grid cell was computed. Global NPP was estimated to be 52 Pg C, and global R h was estimated to be 66 Pg C. Model predictions of the stable carbon isotopic ratio 13 C/ 12 C for C3 and C 4 vegetation were in accord with values published in the literature, suggesting that our computations of total net photosynthesis, and thus NPP, are more reliable than R h . For each grid cell, daily R h was adjusted so that the annual total was equal to annual NPP, and the resulting net carbon fluxes were used as inputs to a three-dimensional atmospheric transport model (TM2) using wind data from 1987. We compared the spatial and seasonal patterns of NPP with a diagnostic NDVI model, where NPP was derived from biweekly NDVI data and Rh was tuned to fit atmospheric CO 2 observations from three northern stations. To an encouraging degree, predictions from the BIOME-BGC model agreed in phase and amplitude with observed atmospheric CO 2 concentrations for 20° to 55°N, the zone in which the most complete data on ecosystem processes and meteorological input data are available. However, in the tropics and high northern latitudes, disagreements between simulated and measured CO 2 concentrations indicated areas where the model could be improved. We present here a methodology by which terrestrial ecosystem models can be tested globally, not by comparisons to homogeneous-plot data, but by seasonal and spatial consistency with a diagnostic NDVI model and atmospheric CO 2 observations.

Applications and Research Using Remote Sensing for Rangeland Management

Rangelands are grasslands, shrublands, and savannas used by wildlife for habitat and livestock in order to produce food and fiber. Assessment and monitoring of rangelands are currently based on comparing the plant species present in relation to an expected successional end-state defined by the ecological site. In the future, assessment and monitoring may be based on indicators of ecosystem health, including sustainability of soil, sustainability of plant production, and presence of invasive weed species. USDA Agricultural Research Service (ARS) scientists are actively engaged in developing quantitative, repeatable, and low-cost methods to measure indicators of ecosystem health using remote sensing. Noxious weed infestations can be determined by careful selection of the spatial resolution, spectral bands, and timing of image acquisition. Rangeland productivity can be estimated with either Landsat or Advanced Very High Resolution Radiometer data using models of gross primary production based on radiation use efficiency. Lidar measurements are useful for canopy structure and soil roughness, indicating susceptibility to erosion. The value of remote sensing for rangeland management depends in part on combining the imagery with other spatial data within geographic information systems. Finally, ARS scientists are developing the knowledge on which future range-land assessment and monitoring tools will be developed.

Imaging radar for ecosystem studies

Recently a number of satellites have been launched with radar sensors, thus expanding opportunities for global assessment. In this article we focus on the applications of imaging radar, which is a type of sensor that actively generates pulses of microwaves and, in the interval between sending pulses, records the returning signals reflected back to an antenna.

A globally applicable model of daily solar irradiance estimated from air temperature and precipitation data

Although not measured at many ground stations, the total daily solar irradiance (Rs) received at the earth’s surface is a critical component of ecosystem carbon, water and energy processes. Methods of estimating Rs from other meteorological data, particularly daily temperatures, have not worked as well in tropical and maritime areas. At Luquillo, Puerto Rico, the daily atmospheric transmittance for solar radiation was approximately equal to one minus the daily-average relative humidity (1 −rhave). From these observations, we developed a model (VP-RAD) for estimation of Rs with inputs of daily maximum and minimum air temperature, daily total precipitation, mean annual temperature, mean annual temperature range, site latitude, and site elevation. VP-RAD performed well over large areas; it showed a good agreement with the site data used for model development and for seven other warm, humid locations in the southeastern United States. Comparisons with a similar model revealed that predictions using VP-RAD had lower average errors and improved day-to-day correlation to measured solar irradiance. In a global comparison for the year 1987, VP-RAD-estimated and satellite-derived photosynthetically active radiation converged to within 1.0 MJ m −2 day − 1 at 72% of the 13072 1° latitude by 1° longitude vegetated grid cells. Although these comparisons revealed areas where VP-RAD may need improvement, VP-RAD should be a useful tool for applications globally. In addition, VP-RAD’s similarity in form to the Bristow–Campbell equation provides a convenient method to calculate the site-specific coefficients for this model that is widely used when solar irradiance data are not available.

Relationship between woody biomass and PAR conversion efficiency for estimating net primary production from NDVI

Abstract Terrestrial net primary production (NPP) may be determined from remotely-sensed vegetation indices by estimating the amount of photosynthetically active radiation (PAR) absorbed by vegetation. Studies from the literature were used to determine the upper limit of the PAR conversion efficiency (e) as a function of woody biomass for forest vegetation. Without climatic or other limitations, the upper limit was about 3·5gMJ-1 of absorbed PAR at very low values of stem biomass. Because of increased maintenance respiration with increasing woody biomass, the upper limit decreased to 2·0g MJ-1. Therefore, global estimates of NPP based on vegetation indices should include a classification among established forest, young forest and non-forest ecosystems to account for differences in e.

The influence of seasonal water availability on global C3 versus C4 grassland biomass and its implications for climate change research

Abstract Climate-change induced alterations in the global distribution of cool season (C3) and warm season (C4) grasses would impact the global carbon cycle and have differing, local effects on range and agricultural production. We hypothesize that a major influence on C3/C4 distribution may be the seasonal timing of water availability with respect to the different C3 and C4 growing seasons. An algorithm expressing this hypothesis (the SAW hypothesis for Seasonal Availability of Water), estimates C3 versus C4 grass biomass from climate data. Sensitivity analysis indicated that temperatures used to delineate the start and end of the C3 and C4 grass growing seasons were more important than photosynthetic responses to temperature. To evaluate the SAW hypothesis, this algorithm was applied globally on a 1°×1° latitude–longitude grid. When compared with vegetation survey data at 141 locations in North America, Argentina, Australia, and South Africa, SAW algorithm predictions yielded an R2 of 0.71. Error resulted primarily from comparing large grid cells to plot data, interannual variability of climate, and from gridding measured climate to data-sparse locations with a single lapse rate of air temperature with elevation. Application of the SAW algorithm to a climate change scenario suggested that changes in temperature and precipitation patterns could offset C3 photosynthetic advantages offered by elevated atmospheric CO2 concentrations. These results underscored the importance of accurately representing the timing and spatial distribution as well as the magnitude of temperature and precipitation in scenarios of future climate.