In remote sensing, linear transformation methods like the Tasselled Cap (TC) transform have the advantage of reducing the amount and redundancy of data, providing different information in derived components. The TC transform though, has never been specifically developed to perform the study of desert areas. This paper addresses this issue discussing the possible approaches and performing the calculation of a new set of TC transform parameters for SPOT4 and Landsat5 satellites for Top of Atmosphere Reflectance images of selected arid and semi-arid locations in the Middle East and the USA. Compared to previously calculated transforms, results show some differences explained by desert conditions and give the chance for a proper use of this technique in change detection for drylands.
Temporal changes and spatial patterns are often studied by analyzing land-cover changes (LCCs) using spaceborne images. LCC is an important factor, affecting runoff regime within watersheds through processes such as urbanization, agricultural activities, quarries and afforestation. The objective of this research was to estimate the effects of 20 years of LCCs on rainfall-runoff relations in an extreme rainfall event, in a sub-basin scale. A Landsat TM-derived classification map was used as an input for the Kinematic Runoff and Erosion (KINEROS2) hydrological model along with precipitation data of an extreme rainfall event. Model calibration was performed by using total runoff volume data based on hydrometric measurements taken during this rainfall event. Validation of the model performance was conducted by comparing the model results to measured data in order to receive output accuracy estimation. A similar procedure was then used with a 2009 land-cover classification map, derived from a Landsat TM image, as an input to KINEROS2 model, along with the same precipitation data and calibration parameters, in order to understand the possible outcomes of a rainfall event of such magnitude and duration after 20 years of LCCs. The results show a slight increase in runoff volume and peak discharge values between the examined time periods as a result of LCCs. In addition, a strong relationship was spotted between vegetation cover along the six sub-basins and the runoff volume. The LCCs that had the most pronounced effects on runoff volumes were related to urbanization and vegetation removal.
Due to the increasing development of image spectroscopy techniques, airborne and spaceborne hyperspectral images have in recent years become readily available for use in geological applications. One of the prominent advantages of imaging spectroscopy is its high spectral resolution, producing detailed spectral information in each pixel. The current study aims at exploring the feasibility of the Earth-Observing-1 Hyperion imaging spectrometer to map the geology arena over the Dana Geology National Park, Jordan. After overcoming the common preprocessing difficulties (e.g., smile effect), a classification scheme of two levels was applied. The first level resulted in a stratigraphic classification product of eleven classes and the second level in a lithologic classification product of six classes. The overall accuracy of the stratigraphic product was 57%, while that of the lithologic product was 79%. Mismatches in classification were mostly related to terrestrial cover of the lower topography formation by rock and sand debris. In addition, low accuracy values can be attributed to Hyperion's high sensitivity, leading to recognition of different mineral compositions as different classes within a rock formation, while the conventional geology-stratigraphic map generalizes these different classes into one formation. The methods practiced in the current research can advance the Hyperion's classification capabilities and therefore can be applied in different geological settings and additional disciplines such as penology, agriculture, ecology, forestry, urban, and other environmental studies.
Improved accuracy in the retrieval of crop biophysical variables is needed to enable a greater contribution of hyperspectral remote sensing data to site-specific crop management. One season of maize and two seasons of wheat field experiments were used to explore the potential of multivariate data analysis for retrieving crop biophysical variables from spectroscopic data. Canopy spectral data at 350-2500 nm were collected during the experiments, in which various seeding densities, fertilization, and irrigation treatments were applied to generate dry biomass, water-content and nitrogen-content variability. Partial least squares (PLS) models that considered the reflectance derivatives (1st and 2nd) and that used only significant wavelengths were evaluated. As the measurements were conducted throughout the whole season, a wide variability was observed, which was critical for obtaining a good calibration model. The use of derivatives and selection of the most significant wavelengths were found to be the best pre-processing methodologies to increase prediction accuracy. Significant predictive power was achieved for the validation dataset, especially for the wheat dry biomass (R2 ~0.75), for which similar results were obtained, even with data from a different season (R2 ~0.70). PLS-predicted wheat water content had a correlation of R2 ~0.60 with the measured values. An advantage was found in the use of PLS models, compared to common vegetation indices. Based on ground spectral measurements, this study confirms the potential of multivariate-data-analysis procedures for the interpretation of hyperspectral remote sensing data.
Dryland wheat of semi-arid areas is significantly affected by water and nitrogen availability since deficiency in these resources creates a stress status over the crop, reduce the chlorophyll content in the leaves, and damage the yield production. The objective of the current research was to characterize wheat stresses caused by the lack of nitrogen fertilization on one hand, and water on the other hand. This objective was implemented by measuring the spectral reflectance of Wheat plants in different growth conditions, in the leaf level. The reflectance was measured in the spectral range of 400-1100 nm by a Licor LI-1800 high spectral resolution spectroradiometer, equipped with an integrated sphere. We aimed to create spectral vegetation indices that would be sensitive to changes in chlorophyll, nitrogen and water contents in the leaves and hence serve as indicators to the wheat stress. The following indices were applied to the spectral data: NDVI, Green-NDVI, NDGI, and the ratios R695/R420, R695/R760, and R970/R900 (where R is the reflectance of the marked wavelength). The sensitivity of these indices was estimated by correlating the spectral data with the bio-physiological variables that were taken in parallel. We found that the green range of the electromagnetic spectrum (around 550 nm) is the most sensitive for the nitrogen wheat stress while the NIR range of the spectrum is sensitive for both nitrogen and water stresses. We improved the sensitivity to water status by using a water absorption wavelength in the ratio R970/R900, instead of the entire NIR region. Therefore, using the green range and water absorption wavelengths in different indices, enables the user to distinguish between these two types of stress.
Drought years are a very frequent phenomenon in Israel. Since 1995 Israel had suffered from three years of drought. Yatir, as a pine forest, is located on the border of the desert has suffered like other plants from the water shortage. The aim of this research was to detect and monitor the changes within the forest, during the growing season, and between the years. The use of the Normalized Vegetation Index NDVI to detect the state of a forest under stress has been applied in many studies. In the current study seven Landsat TM and ETM+ images along the past eight years were radiometricly, atmospherically and geometrically corrected. NDVI images were produced. In order to detect the changes along the years, eight change detection NDVI image differencing techniques were applied. The results indicated correlation between the photosynthetic activity along the growing cycle of the trees in one year (1994-1995) and NDVI dynamics. Also it was found that NDVI decreases between the year 1995 and 2000 due to drought events accruing along those years. The correlation between rainfall events and NDVI is a lag of one to two months. We concluded this study by stating that the Change Detection NDVI Image Differencing technique can be used not only to compare changes in cover types, but also to detect changes in the state of the same plant species.
The Dead Sea is very harsh environment even for microorganisms adapted to hypersaline environment. Not only does the Dead Sea contain the highest salt concentration of all natural lakes inhabited by living organisms, but the peculiar ionic composition of its water, with its high concentration of divalent cations magnesium and calcium, is highly inhibitory even to those microorganisms that are the most adapted to life in the sea. In this research imaging spectroscopy and microbiological studied used to investigate the spatial distribution of various Archaea populations according to the salty saturation of Mor swamp, Dead Sea Basin. Data from the DLR airborne sensor DAIS-7915 in the spectral range between 0.4 to 2.4 micrometers were acquired along with field and laboratory spectral measurements. The spatial and spectral data were completed by microbiological analysis. The spectral information helped to detect a concentric distribution of the Archaea population, which seems linked to the state of the salty substrate. In the wet muddy central zone lives an Archaea with the relatively lowest salt tolerance. From this centre to the peripheries, the tolerance to salt of the Archaea population was found to be increasing, as the substation changes from salty pools to salty muds and finally to massive salt layers.
When carrying out satellite images by imaging vertically through the atmosphere, distortions and blur arise as a result of turbulence and aerosols. Contrast is reduced by path radiance. The recently developed atmospheric Wiener filter, which corrects for turbulence blur, aerosol blur, and path radiance simultaneously, is implemented in digital restoration of Landsat imagery over seven wavelength bands of the satellite instrumentation. A required input is weather. Restoration is most impressive for high optical depth situations, which cause larger blue. Restoration improves both smallness of size of resolvable detail and contrast. Turbulence modulation transfer function (MTF) is calculated from meteorological data. Aerosol MTF is consistent with optical depth. The product of the two yields atmospheric MTF, which is implemented in the atmospheric Wiener filter. Turbulence blue, aerosol blur, and path radiance contrast loss are all corrected simultaneously, as if there were in intervening atmosphere. The primary source of atmospheric blur is seen to be aerosol forward scatter of light. Restorations are shown for various wavelength bands and are quite apparent even under clear weather conditions.
The recently developed atmospheric Wiener filter, which corrects for turbulence and aerosol blur and path radiance simultaneously, is implemented in digital restoration of AVHR imagery over the five wavelength bands of the satellite instrumentation. Restoration is most impressive for higher optical depth situations which cause more blur, with improvement in regard to both smallness of size of resolvable detail and contrast. Turbulence modulation transfer function (MTF) is calculated from meteorological data. Aerosol MTF is consistent with optical depth, measured with a sum-photometer. The product of the two yields atmospheric MTF which is implemented in the atmospheric Wiener filter. Turbulence blur, aerosol blur, and path radiance contrast loss are all corrected simultaneously, as if there were no intervening atmosphere. Image restorations with accompanying atmospheric MTF curves are presented. However, restoration results using a simple inverse atmospheric MTF filter were quite similar. This indicates the satellite images were characterized by very low noise and that turbulence jitter was very limited which, in turn, indicates that the turbulence MTFs integrated upwards over the path length wee not significant when compared to aerosol MTFs. Restorations are shown for various wavelength bands and are quite apparent even under clear weather conditions.
The recently developed atmospheric Wiener filter, which corrects for turbulence and aerosol blur and path radiance simultaneously, is implemented in digital restoration of AVHRR imagery over the five wavelength bands of the satellite instrumentation. Restoration is most impressive for higher optical depth situations, with improvement with regard to both smallness of size of resolvable detail and contrast Turbulence modulation transfer function (MTF) is calculated from meteorological data. Aerosol MTF is calculated from optical depth, measured with a sun-photometer. The product of the two yields atmospheric MiT which is implemented in the atmospheric Wiener filter. Image restorations with accompanying atmospheric MTF curves are presented. However, restoration results using a simple inverse MTF filter were quite similar. This indicates the satellite images were characterized by very low noise and that turbulence jitter was very limited which, in turn, indicates that the twbulencc MTFs integrated upwards over the path length were small compared to aerosol MTFs.
Chlorophyll distribution in Haifa Bay was estimated in a period of very high chlorophyll concentrations (up to 70 mg/m3) using remotely sensed data. Radiometric measurements with spectral resolution of 2 nm in the range from 400 to 850 nm, were taken simultaneously with samples for chlorophyll concentration, turbidity and Secchi disk transparency. Prominent features of the reflectance spectra were a wide minimum from 420 to 500 nm, a maximum at 550-570 nm, a minimum at 676 nm and a maximum reflectance showed near 700 nm. High spectral resolution data were used for the selection of the most suitable spectral bands for remote estimating of chlorophyll concentration. The magnitude and position of the peak near 700 nm were highly correlated with chlorophyll concentration. The reflectance height at 690 nm above the baseline from 670 to 850 nm and area above the baseline were used as sensitive indicators of chlorophyll concentration. The best model enabled estimation of chlorophyll concentration with an error of less than 4.3 mg/m3. For the purpose of chlorophyll mapping in Haifa Bay, the use of three relatively narrow spectral bands was sufficient. Radiometric data were also used to simulate radiances in the channels of TM Landsat and to find the algorithm for chlorophyll assessment. The ratios TM3/TM1 and (TM2-TM3)/TM1 were used and enable chlorophyll estimation with an error of less than 8 mg/m3.
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