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Remote sensing technique, based on hyperspectral reflectance measurements, is an alternative method with a great potential for plant monitoring and for more efficient crop management. Red Edge Position (REP), point of maximum slope on the reflectance spectrum of vegetation between red and near infrared (NIR) spectral ranges is sensitive to chlorophyll (Chl) content and vegetation stress. In this study REP was extracted from hyperspectral reflectance data of two groups of young potato plants, healthy and infected with Potato Virus Y (PVY). Leaf reflectance data were collected by a portable fiber-optics spectrometer in the visible and NIR spectral ranges with a spectral resolution of 1.5 nm. Four REP extraction techniques (maximum of first derivative, four-point linear interpolation, polynomial fitting, and inverted Gaussian modelling) were tested and compared. The results show that the wavelength and reflectance of REP for infected plants shift towards the shorter wavelengths in comparison with healthy plants that indicates the presence of a viral infection. The last three methods gave very close results for REPs (about 0.5 nm shift).Remote sensing technique, based on hyperspectral reflectance measurements, is an alternative method with a great potential for plant monitoring and for more efficient crop management. Red Edge Position (REP), point of maximum slope on the reflectance spectrum of vegetation between red and near infrared (NIR) spectral ranges is sensitive to chlorophyll (Chl) content and vegetation stress. In this study REP was extracted from hyperspectral reflectance data of two groups of young potato plants, healthy and infected with Potato Virus Y (PVY). Leaf reflectance data were collected by a portable fiber-optics spectrometer in the visible and NIR spectral ranges with a spectral resolution of 1.5 nm. Four REP extraction techniques (maximum of first derivative, four-point linear interpolation, polynomial fitting, and inverted Gaussian modelling) were tested and compared. The results show that the wavelength and reflectance of REP for infected plants shift towards the shorter wavelengths in comparison with healthy pla...

Hyperspectral remote sensing technique, based on reflectance measurements acquired in a high number of contiguous narrow spectral bands in the visible and near infrared spectral ranges, was used to detect and assess the influence of some adverse environmental conditions (viral infection) to horticultural plants. The investigations were focused on the effect of infection caused by Potato Virus Y (PVY) on the spectral reflectance of two potato cultivars, Crone and Arnova. The hyperspectral data were collected by a portable fiber-optics spectrometer in the spectral range 350-1000 nm. The changes in the data sets were assessed by means of digital processing analyses (statistical and derivative analysis, vegetation indices, etc.) in green, red, red edge, and near infrared spectral ranges. Strong relationship was found between the results from the remote sensing technique for the effect of the infection on the spectral behaviour of the plants and serological analyses of the viral concentration using enzyme immunosorbent assay DAS-ELISA.

A statistical model is proposed for analysis of the texture of land cover types for global and regional land cover classification by using texture features extracted by multiresolution image analysis techniques. It consists of four novel indices representing second-order texture, which are calculated after wavelet decomposition of an image and after texture extraction by a new approach that makes use of a four-pixel texture unit. The model was applied to four satellite images of the Black Sea region, obtained by Terra/MODIS and Aqua/MODIS at different spatial resolution. In single texture classification experiments, we used 15 subimages (50 × 50 pixels) of the selected classes of land covers that are present in the satellite images studied. These subimages were subjected to one-level and two-level decompositions by using orthonormal spline and Gabor-like spline wavelets. The texture indices were calculated and used as feature vectors in the supervised classification system with neural networks. The testing of the model was based on the use of two kinds of widely accepted statistical texture quantities: five texture features determined by the co-occurrence matrix (angular second moment, contrast, correlation, inverse difference moment, entropy), and four statistical texture features determined after the wavelet transformation (mean, standard deviation, energy, entropy). The supervised neural network classification was performed and the discrimination ability of the proposed texture indices was found comparable with that for the sets of five GLCM texture features and four wavelet-based texture features. The results obtained from the neural network classifier showed that the proposed texture model yielded an accuracy of 92.86% on average after orthonormal wavelet decomposition and 100% after Gabor-like wavelet decomposition for texture classification of the examined land cover types on satellite images.

Precision agriculture, site-specific application of inputs tailored to the needs of the crop, is one of the new ways that modern agriculture could potentially maintain or enhance crop yields and minimize environmental pollution. Knowledge about variations in vegetation species and community distribution patterns, alterations in vegetation phenological cycles, and modifications in the plant physiology and morphology provide valuable insight into the climatic, edaphic, geologic, and geophysical characteristics of Earth’s areas (Janetos & Justice, 2000). During the past decade remote sensing techniques have been widely used to monitor crops throughout their growing period to help in making decisions for good agricultural practices. Spectral remote sensing methods provide the possibility for early, efficient, objective, and non-destructive evaluation of plant responses to different stress factors of the environment (Campbell et al., 2007; Govender et al., 2009; Li et al., 2010). Field remote sensing applications addressed agriculture and forestry survey, fire detection and fire-fuel mapping, mineral mapping, and atmospheric modelling. Airborne, space-borne and hand-held technologies are commonly used to investigate the spectral responses of plants. Hyperspectral remote sensing makes possible to enhance significantly the spectral measurement capabilities over conventional remote sensing sensor systems, as well as to improve the spectral information content. This entails detailed assessment of the changes in the physiological stage of plants in response to the changes in the environment (ZarcoTejada et al., 2002; Steele et al., 2008a), detecting of early-stage vegetation stress (Krezhova et al., 2005; Ouyang et al., 2007), discriminating land cover types (Flamenco-Sandoval et al., 2007), leaf pigment concentrations (Coops et al., 2003), modelling quantitative biophysical and yield characteristics of agricultural crops (Delalieux et al., 2009a; Chatzistathisa et al., 2011). Ground-truth is essential for detecting plant stress, and two commonly used ground-based optical methods, leaf spectral reflectance and chlorophyll fluorescence, are reviewed for their usefulness and practical application. When these methods were combined with remarkable advances in Global Positioning System (GPS) receivers, geographic information systems (GIS), and enhanced crop simulation models, remote sensing technology has the potential to transform the ways that growers manage their lands and implement precision farming techniques (Upchurch, 2003; Hatfield, et al., 2008; Shuanggen & Komjathy, 2010). To obtain accurate and complementary comparative assessments for plant responses to the environmental changes, methods have been applied from different research fields remote

The influence of the environmental stress factors, salinity and enhanced UV-B radiation, on young nitrogen fixing soybean plants (Glycine max L.) was investigated by using hyperspectral reflectance data. Soybean is the leading oilseed crop produced and consumed worldwide. The soybean plants were grown in a growth clamber as water cultures on Helrigel nutrient solution. Three day's seedlings were inoculated with suspension of Bradyrhizobium japonicum strain 273. Salinity was performed at growth stage of 2nd– 4th expanded leaves by adding of NaCl in the nutrient solution in concentrations 40 mM and 80 mM. Plants were divided into six groups. The first three groups consisted of untreated (control) and treated only with two NaCl concentrations plants. The other three groups (control and salinized) on the 14th day after the treatment were illuminated for four hours with UV-B radiation at intensity 64.4 µmol m−2 s−1. Spectral reflectance was registered by a portable fiber-optic spectrometer in the visible and near infrared (NIR) spectral ranges (450–850 nm). Data were subjected to statistical analysis through the Student's t-criterion in four spectral ranges: green, red, red-edge and NIR (520–580 nm; 640–680 nm; 690–720 nm; 720–780 nm). The results from spectral reflectance and biochemical analysis (evaluated stress markers) revealed that both treatments (salinity and salinity + UV-B radiation) bring the plants to stress and to decline of the biological nitrogen fixation. The UV-B treatment decreases the salinity action and partly restores the physiological state of the plants.

Results from a remote sensing study of the leave spectral reflectance of pea plants (Pisum sativum L. cultivar Scinado) treated by the photosynthetic herbicides fluridone and acifiuorfen are presented. According to the mode of action, fluridone belongs to Fl (photobleaching) group of herbicides, and acifiuorfen -to the group E as classified by the Herbicide Resistance Action Committee. The pea plants were grown hydroponically in a growth chamber in a nutritious medium to which the herbicides were added at two low concentrations (1 muM, 0.1 muM for fluridone, and 25 muM, 2.5 muM for acifiuorfen). The high-resolution spectral data were obtained in the visible and near infrared ranges of the spectrum (450/850 nm) using a USB2000 fiber optic spectrometer at a spectral resolution (halfwidth) of 1.5 nm. After data analysis, optimal spectral intervals for evaluation of the herbicide action were specified. The changes occurring in the spectral reflectance of the pea plants were assessed in four intervals: 520/580 nm (region of maximal reflectivity of green vegetation), 640/680 nm (region of maximal leave absorption), 690/720 nm (red edge region), and 720/770 nm (near infrared region) using the t-criterion of Student and linear discriminant analysis. Statistically significant differences were found between the spectral reflectance data of leaves of control and treated with herbicides plants at a significance level p

Results from a remote sensing study of the influence of stress factors on the leaf spectral reflectance of wheat and tomato plants contaminated by viruses and pea plants treated with herbicides are presented and discussed. The changes arising in the spectral reflectance characteristics of control and treated plants are estimated through statistical methods as well as through derivative analysis to determine specific reflectance features in the red edge region.