Mitra Shirazi; Mohammad Akhavan Ghalibaf; Hamidreza Matinfar; Mansour Nakhkesh
Volume 26, Issue 4 , December 2019, , Pages 855-867
Abstract
Dust is one of the most important effective factor on solar radiation forcing and reflection on earth's atmosphere, and in this point, it has a significant impact on local climate. Detection of aerosols on desert zones, despite the sea and oceans (dark surfaces), is difficult because of reflectometric ...
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Dust is one of the most important effective factor on solar radiation forcing and reflection on earth's atmosphere, and in this point, it has a significant impact on local climate. Detection of aerosols on desert zones, despite the sea and oceans (dark surfaces), is difficult because of reflectometric interference spectroscopy of bright surfaces. Representing a simple and low costs method for detecting dusts and predicting their effects is essential. One of the most important indexes for dust and smoke detection is the AOT (Aerosol Optical thickness), which provided in large-scale (10x10 km) which is not suitable for local dust scales detection. The purpose of this study is using visible and mid-infrared spectrum of OLI sensor for detection dust of deserts. In this study, by using of mid-wave infrared (2.1 μm), red and blue wavelengths the AOT was calculated. The results indicated that ratio between the red and mid-wave infrared wavelengths is 0.95 and blue wavelengths and mid-wave infrared is 1.05 respectively. The comparison results of AOT index by radiometer showed that the correlation between computational method for data and the direct measurement for the red and blue wavelengths were 0.83 and 0.95 with root-mean-square deviation (RMSE) were 0.91 and 9.4 respectively. Therefore, it can be said that this method for estimating the Aerosol optical thickness at 0.65 μm (AOT 0.65μm) is enough accuracy and is not suitable to measure Aerosol optical thickness at 0.47 μm (AOT 0.47μm).
Mitra Shirazi; Mohammad akhavan GHalibaf; Hamidreza Matinfar; Mansour Nakhkesh
Volume 26, Issue 3 , September 2019, , Pages 570-586
Abstract
One of the problems of most airborne and space-based sensors is the lack of high spatial, radiometric and temporal resolution, due to the high technical and sensor design costs. On the other hand, the identification and monitoring of the factors in natural ecosystems, such as water, soil, and atmosphere ...
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One of the problems of most airborne and space-based sensors is the lack of high spatial, radiometric and temporal resolution, due to the high technical and sensor design costs. On the other hand, the identification and monitoring of the factors in natural ecosystems, such as water, soil, and atmosphere requires high spatial, radiometric and temporal resolution. Therefore, it is necessary using merge methods for integrating two or more spatial, radiometric and temporal resolution. Aerosols, especially dust of mines and industries, are part of the contaminate particles that are important in identifying them. Aerosol monitoring requires high spatial, radiometric and temporal resolution sensor, which is practically impossible in a sensor. For this purpose, it is possible to merge images with a high radiometric resolution like Modis and high spatial images like Landsat. One of the most popular indicators for dust detection is the NDDI index, which is obtained using SWIR (2.1μm) and blue (0.47 μm) wavelengths. In this research, we used several merging algorithms, including Bovery, Gram-Shcmidt, STARFM, ESTARFM, wavelet, PBIM, SIFM and HPF to integrate Modis and Landsat image data of 8 July 2016, and then provided NDDI index maps. The results of the evaluation showed that the best method was STARFM, ESTARFM, and PBIM with correlation coefficient (R2) of 0.88, 0.91, and 0.99, respectively with Landsat image and 0.51, 0.5, 0.57 with Modis image. The mean squared error (RMSE) for all three methods was negligible: 0.02, 0.400, and 0.055 respectively, with the original Landsat images and 0.004, 0.6 and 0.1 with the main images of Modis. Therefore, the STARFM, ESTARFM and PBIM methods could be used to merge Modis and Landsat images to extract data with high precision.
Mitra Shirazi; Gholam reza Zehtabian; Hamid reza Matinfar
Volume 17, Issue 2 , September 2010, , Pages 256-275
Abstract
Recently there is a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science. One of important methods for extracting information from satellites image is use of indices. In this study for enhancement ...
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Recently there is a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science. One of important methods for extracting information from satellites image is use of indices. In this study for enhancement of land cover in region of northwest Tehran near Hashtgerd some indices such as BI, MIRV2, GREENNESS, TVI, VNIR, MND، NIR, OSAVI, RA, NDVI, IR1, MSI IPV ,MSAVI, SAVI, TSAVI, PD322 ,BI, INT1, INT2, PVI, SI1, SI2, SI3, GEMI, WDVI Are used. Most of study area covers by density of vegetation (such as irrigation farming and vegetation cover around streams) and bare lands. The results have shown that TSAVI, DVI, IPVI, RA, NIR, IR1 Indices have the most effective efficiency for vegetation enhancement and SI2, BI, TVI, PVI, INT1, SI3, SI2 indices have the most effective efficiency for salinity surface. This study addressed that all of vegetation indices except DVI have correlation more than 0.8 and DVI has correlation around 0.4 with others. Meanwhile all of salinity indices have more than 0.9 correlations with each other. As conclusion, this study has shown that IRS satellites image have high accuracy for providing land cover map by use of vegetation indices, also use of salinity indices having high capability for salinity surface can be used for providing salinity maps, meanwhile vegetation indices with high correlation can be used instead each other for providing vegetation maps.
Hamid reza Matin far; Kazem Alavi panah; Ammar Rafiei Emam
Volume 16, Issue 4 , January 2010, , Pages 560-573
Abstract
One of the apparent characteristics of soil is color which shows high correlation with soil characteristics and spectral reflectance. Soil color is identified using visual comparison of sample and colored chips of Munsell color charts .In arid regions due to the prolonged sunny days, low soil moisture, ...
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One of the apparent characteristics of soil is color which shows high correlation with soil characteristics and spectral reflectance. Soil color is identified using visual comparison of sample and colored chips of Munsell color charts .In arid regions due to the prolonged sunny days, low soil moisture, sparse vegetation cover and close relation between land units and soils, there is an ideal condition for application of remote sensing data especially for study of relation between satellite data and color of surface features. The soil color and the most effective factors on color and spectral reflectance of soil are explained in brief. Color composite images produced from TM7, TM4 and TM2 as red, green and blue respectively used in order to choose sample sites. The 20 sample sites were chosen based on resample 3×3 pixels (90×90 m). In each site, the soil surface conditions and the munsell color of the soil surface were investigated in the field. Some physico-chemical properties of soil samples were also determined in the laboratory. The results of this study indicates that munsell notation of hue, value and Chroma are significantly correlated to the visible bands of Landsat (TM) data. From this study it may be concluded that visible reflectance of Landsat can be used to estimate soil color, if very precise result is not expected .More investigation are necessary in order to improve the obtained results.