Soheila Pouyan; Mohammad Zare; Mohammad Reza Ekhtesasi
Abstract
Dust event is one of the common and destructive phenomenon in arid and desert regions. This phenomenon has negative impacts on human life and environment. Dust storms, in addition to soil loss, can cause and aggravate health problems, food production reduction, economical damages into the industrial, ...
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Dust event is one of the common and destructive phenomenon in arid and desert regions. This phenomenon has negative impacts on human life and environment. Dust storms, in addition to soil loss, can cause and aggravate health problems, food production reduction, economical damages into the industrial, agricultural and communication sections. Therefore, accurate investigative of this phenomenon is necessary. The aim of this research was regional analysis of dust storm index (DSI) in 44 meteorological stations of Iran. At first stage, the dust storm index for each station was calculated using hourly dust data. Next, monthly averages of dust storm index (DSI) were used for regional analysis using linear moments approach. Based on regional analysis, the study area is divided to six homogeneous dust storm index regions. Pearson Type III (PE3) and Generalized Logistic (GLO) distribution models were the best regional distribution models for 1, 4, 5, 6 homogeneous regions, and 2, 3 homogeneous regions, respectively. Estimation of the dust storm index and its regional analysis can be used in many environmental studies, decision making and management processes in relation to combating desertification and dust storms.
sahar sabaghzade; Mohammad Zare; Mohamad Hosein Mokhtari
Abstract
Vegetation is an important component of each global ecosystem. Determining of the biomass of plant is important to assess its impact upon climate, soil erosion, and as well for management of natural resources. The aim of this study was to estimate biomass using vegetation indices based on remote sensing. ...
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Vegetation is an important component of each global ecosystem. Determining of the biomass of plant is important to assess its impact upon climate, soil erosion, and as well for management of natural resources. The aim of this study was to estimate biomass using vegetation indices based on remote sensing. The Landsat 8 data of May 2013 and field studies coinciding with field imaging in Marac (South Khorasan province) were used. Tamarix plant biomass measured in 30 random plots of 11 vegetation indices including DVI, IPVI, NDVI, PVI, RVI, SAVI, TSAVI, WDVI, and Tasselcap were used to estimate biomass of Tamarix.Then, using cluster analysis, vegetation indices were divided into three groups among which SAVI, RVI , and IPVI were chosen. The results showed that indexes which consider soil factors are more accurate than other measures. In this study, biomass map was prepared using the SAVI index.
zahra giveiiashraf; Mohammad Ali Hakimzade; Mohammad Zare; Zohre Ebrahimi Khusfii; Kazem Dashtakian
Abstract
Desertification relates to the both the process and end state of drylands degradation. Salinization and alkalinization are two indicators of soil degradation in arid and semi-arid regions. The main objectives of this research is monitoring of soil salinity using high spectral and spatial resolution of ...
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Desertification relates to the both the process and end state of drylands degradation. Salinization and alkalinization are two indicators of soil degradation in arid and semi-arid regions. The main objectives of this research is monitoring of soil salinity using high spectral and spatial resolution of remote sensing to assess desertification in the Marvast plain, Yazd province. Two images of Terra satellite, ASTER synchronous to 2003 and 2010 are used. After preprocessing and analyzing of the images, relationship between parameters of soil salinity (i.e. SAR and EC) and spectral reflections were determined and, both two satellite images were classified using maximum likelihood method. Then, the surface area of each class and the amount of its changes were calculated. Results showed that during the period of 7 years (2003-2010), area of non-saline lands has decreased while, the area of saline land has increased, which leads to the salinization of agricultural lands, reduction of its yield and also extent of desertification in this region. Accuracy of EC map classification for 2003 and 2010 images are 87.5% and, 82.5%, respectively. Kappa coefficients for both images are 0.83 and 0.76. Accuracy of SAR map classification for 2003 and 2010images are 87.5% and 87.5%, respectively. Kappa coefficients for these two images are 0.81 and 0.77, respectively. Generally, it can be conclude that using of remote sensing data, especially ASTER images has high efficiency for change detection analysis in soil salinity and natural resources management.