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Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis.

ABSTRACT: The optical signature of leaves is an important monitoring and predictive parameter for a variety of biotic and abiotic stresses, including drought. Such signatures derived from spectroscopic measurements provide vegetation indices - a quantitative method for assessing plant health. However, the commonly used metrics suffer from low sensitivity. Relatively small changes in water content in moderately stressed plants demand high-contrast imaging to distinguish affected plants. We present a new approach in deriving sensitive indices using hyperspectral imaging in a short-wave infrared range from 800?nm to 1600?nm. Our method, based on high spectral resolution (1.56?nm) instrumentation and image processing algorithms (quantitative histogram analysis), enables us to distinguish a moderate water stress equivalent of 20% relative water content (RWC). The identified image-derived indices 15XX?nm/14XX nm (i.e. 1529?nm/1416?nm) were superior to common vegetation indices, such as WBI, MSI, and NDWI, with significantly better sensitivity, enabling early diagnostics of plant health.

SUBMITTER: Kim DM

PROVIDER: S-EPMC4632122 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis.

Kim David M DM Zhang Hairong H Zhou Haiying H Du Tommy T Wu Qian Q Mockler Todd C TC Berezin Mikhail Y MY

Scientific reports 20151104

The optical signature of leaves is an important monitoring and predictive parameter for a variety of biotic and abiotic stresses, including drought. Such signatures derived from spectroscopic measurements provide vegetation indices - a quantitative method for assessing plant health. However, the commonly used metrics suffer from low sensitivity. Relatively small changes in water content in moderately stressed plants demand high-contrast imaging to distinguish affected plants. We present a new ap ...[more]

PMID: 26531782

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Project description:Abstract Hydrology is a major environmental factor determining plant fitness, and hydrological niche segregation (HNS) has been widely used to explain species coexistence. Nevertheless, the distribution of plant species along hydrological gradients does not only depend on their hydrological niches but also depend on their seed dispersal, with dispersal either weakening or reinforcing the effects of HNS on coexistence. However, it is poorly understood how seed dispersal responds to hydrological conditions. To close this gap, we conducted a common‐garden experiment exposing five wind‐dispersed plant species (Bellis perennis, Chenopodium album, Crepis sancta, Hypochaeris glabra, and Hypochaeris radicata) to different hydrological conditions. We quantified the effects of hydrological conditions on seed production and dispersal traits, and simulated seed dispersal distances with a mechanistic dispersal model. We found species‐specific responses of seed production, seed dispersal traits, and predicted dispersal distances to hydrological conditions. Despite these species‐specific responses, there was a general positive relationship between seed production and dispersal distance: Plants growing in favorable hydrological conditions not only produce more seeds but also disperse them over longer distances. This arises mostly because plants growing in favorable environments grow taller and thus disperse their seeds over longer distances. We postulate that the positive relationship between seed production and dispersal may reduce the concentration of each species to the environments favorable for it, thus counteracting species coexistence. Moreover, the resulting asymmetrical gene flow from favorable to stressful habitats may slow down the microevolution of hydrological niches, causing evolutionary niche conservatism. Accounting for context‐dependent seed dispersal should thus improve ecological and evolutionary models for the spatial dynamics of plant populations and communities. To test how seed dispersal by wind responds to abiotic contexts, we estimate fecundity and seed dispersal of five plant species under various hydrological conditions by combining common‐garden experiments and mechanistic modeling, and quantified the relationship between seed production and seed dispersal. There was a general positive relationship between seed production and dispersal distance. The positive relationship between seed production and dispersal may reduce the concentration of each species to the environments favorable for it, thus counteracting species coexistence.

Project description:Manipulating plant densities under different irrigation rates can have a significant impact on grain yield and water use efficiency by exerting positive or negative effects on ET. Whereas traditional spectral reflectance indices (SRIs) have been used to assess biophysical parameters and yield, the potential of multivariate models has little been investigated to estimate these parameters under multiple agronomic practices. Therefore, both simple indices and multivariate models (partial least square regression (PLSR) and support vector machines (SVR)) obtained from hyperspectral reflectance data were compared for their applicability for assessing the biophysical parameters in a field experiment involving different combinations of three irrigation rates (1.00, 0.75, and 0.50 ET) and five plant densities (D1: 150, D2: 250, D3: 350, D4: 450, and D5: 550 seeds m-2) in order to improve productivity and water use efficiency of wheat. Results show that the highest values for green leaf area, aboveground biomass, and grain yield were obtained from the combination of D3 or D4 with 1.00 ET, while the combination of 0.75 ET and D3 was the best treatment for achieving the highest values for water use efficiency. Wheat yield response factor (ky) was acceptable when the 0.75 ET was combined with D2, D3, or D4 or when the 0.50 ET was combined with D2 or D3, as the ky values of these combinations were less than or around one. The production function indicated that about 75% grain yield variation could be attributed to the variation in seasonal ET. Results also show that the performance of the SRIs fluctuated when regressions were analyzed for each irrigation rate or plant density specifically, or when the data of all irrigation rates or plant densities were combined. Most of the SRIs failed to assess biophysical parameters under specific irrigation rates and some specific plant densities, but performance improved substantially for combined data of irrigation rates and some specific plant densities. PLSR and SVR produced more accurate estimations of biophysical parameters than SRIs under specific irrigation rates and plant densities. In conclusion, hyperspectral data are useful for predicting and monitoring yield and water productivity of spring wheat across multiple agronomic practices.

Dataset Information

Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis.

Publications

Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis.

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