Search published articles
Showing 3 results for Rahimzadegan
M. R. Mobasheri, M. Rahimzadegan,
Volume 14, Issue 1 (1-2012)
Abstract
The reflectance spectrum of green leaves is considerably affected by their biochemical and biophysical properties. It is possible to extract biochemical information from a continuous vegetation spectrum produced using hyperspectral sensors. The numerous absorption features present in the vegetation spectrum carry a considerable amount of information related to the content and the structure of the leaves and stems. In the present study, we tried to introduce a method for relative quantification of vegetation leaves protein contents using EO-1 Hyperion datasets through an innovative index named PALI (Protein Absorption Lines Index). The results of applying PALI to AVIRIS data also showed its robustness. However, applying PALI index for Hyperion images can only show the vegetation leaves protein contents of a pixel relative to its neighboring pixels and not absolute values. Nonetheless, it is assumed that absolute measurements will be possible if one can calibrate this index with field data.
Volume 19, Issue 7 (July 2019)
Abstract
Carbon nanotubes have special importance due to unique properties as an amplifier phase. In this paper, the effect of multiwall carbon nanotubes on water absorption and fatigue life of poly methyl methacrylate is investigated. To this end, nanocomposites based on polymethyl methacrylate, containing 0-1.5 weight percentage of multiwall carbon nanotubes are produced with screw and injection molding process. The morphology was studied, using scanning electron microscopy. Microscopic images examination showed that carbon nanotubes have been well released in the field of polymer. The fatigue testing of each of the prototypes was carried out under identical conditions. Based on the results of fatigue test, nanocomposite fatigue strength containing 0.5% carbon nanotubes increased than base polymer. Also, based on the results of water absorption test, the existence of multiwall carbon nanotubes in polymer field decreased absorption water of the samples.
Volume 27, Issue 4 (Winter 2023)
Abstract
Snow depth plays a critical role as a key input parameter in various agricultural, hydrological, and climatological models. Nevertheless, the process of estimating snow depth through optical remote sensing tools is subject to uncertainties stemming from constraints within the imaging technique. Consequently, the primary objective of this study is to employ active microwave remote sensing technology for the purpose of snow depth estimation in regions characterized by mountainous terrain. The radar interferometric technique employing active microwave imagery was utilized for the specific objective of examining the microwave signal's interaction with snow accumulation. Utilizing Sentinel 1 satellite images of the Zagros mountains in Iran during the months of February 2017, March 2019, and 2020, relevant data was acquired. Furthermore, field measurements of snow depth were conducted to validate the proposed algorithm. In order to enhance the accuracy of snow depth estimations, the data from both VV and VH channels was integrated by applying a weighting factor determined based on the local radiation angle. The comparison between the outcomes of the suggested approach and the field data revealed a correlation coefficient of 0.86. Furthermore, the calculated values for RMSE and P-Value were 14.37 cm and 0.009, correspondingly. Based on the statistical metrics derived from the validation process of the proposed technique, it demonstrated a satisfactory performance in the estimation of snow depth.
