parviz zeaiean firooz abadi

Taftan is a semi-active volcano located in southeast Iran with many craters. The main objective of this study is to investigate whether subsidence or uplift in Taftan Peak. 58 Sentinel 1-A images acquired from January 2015 to December 2020 in the ascending orbit mode, and 102 Sentinel 1-A Sentinel 1-B images acquired from October 2014 to June 2020 in the descending orbit mode were preprocessed for this purpose. The interferograms with the permanent scatterer interferometry (PSI) method were created using SARPROZ and StaMPS softwares, in which atmospheric corrections were made automatically, and then the surface displacement of Taftan volcano estimated.The Line of Sight (LOS) displacement corresponding to the uplift was 0.5 mm to 1 mm yr-1 for the ascending orbit and 1 mm yr-1for the descending orbit. Because no GPS station was close to Taftan volcano, the GPS measurements of one station located in the study area (Saravan station) was used to check the accuracy of PSI method. The GPS station of SARAVAN has situated inside the town ,and it is appropriate to use and analyze PSI technique in this station. As a result, the researchers found that the PSI method is in accordance with the GPS data.

Various satellite remote sensing data, images and products have proven their place in drought, drought and agriculture studies since the production of this type of information resource. Visible, near-infrared and thermal bands are among the most widely used in the production of products such as vegetation and surface temperature. In this study, from MODIS sensor data to investigate and find the coefficients of spatial relationship between vegetation-surface temperature index (NDVI-TS) and NDVI-ΔTS to extract the time of agricultural drought from June to October 2007 to 2010 in the catchment Siminehrood has been extracted from the Temperature-Vegetation Condition Index (VTCI) and the Water Lack Index (WDI), which are able to detect drought stress on a regional scale. The results of this study showed that in both indicators, the drought stress situation was higher in 2007 and 2008. Also based on the NDVI-TS space relationship in all the years 2007 to 2010 the high slope of the triangular space for the hot edge is negative. This means that with increasing NDVI, the LST level decreases while for the cold edge the slope is positive. In addition, the slope obtained from the NDVI-ΔTS space relationship is negative for the dry line, ie the dry line or the minimum transpiration-sweat line (ETR) shows a negative correlation with NDVI. While for the wet line, especially in 2010, the slope is positive and in other years, no significant change is seen. The present study showed that the VTCI threshold for drought stress was severe in 2007 and 2008.

In optical remote sensing, hyper-spectral (HS) image which contains color information is produced byhundreds of spectral bands. Because of the trade-off imposed by the physical constraint between spatialand spectral resolutions, the HS image has poor spatial resolution. On the contrary, the panchromatic(PAN) images have high spatial resolution but no color information. Image fusion can combine thegeometric detail of PAN image and the color information of the HS image to produce a high-resolutionHS image. The aim of this study is the fusion of Hyperion and OrbView-3 PAN images based on DiscreteWavelet Transform (DWT). Firstly, the preprocessing methods were applied on Hyperion and OrbView-3images and registration method based on nearest neighbor method were applied on two dataset. In order tofit images pixel size, the resampling operation was applied. PAN image was decomposed by DWT andthen fused by hyper spectral image with GST algorithm. DIV, CC, Q and RMSE accuracy assessmentmethods were used on final fused image to evaluate the results accuracy. The results showed that usingDWT based decomposition PAN image, preserve the spatial information during fusion rule. Also thistechnique gains high accuracy in term of spectral information of hyper spectral image.

The aim of this study is to evaluate Pixel Purity Index(PPI) and Sequential Maximum Angle Convex Cone(SMACC) algorithms In order to extract the spectrum of Ghezel Dash copper by using the hyperspectral Image of Hyperion Sensor level 1R. For this purpose, a window of the Hyperion image, Ghezel Dash district of Khoy was selected. Then, in order to extract the pure spectra of the material to the pre-processing of the image, including radiometric correction, Contain the removal of bad bands, The strip lines containing noises, spectral effects removal, and atmospheric correction were used to convert radiation values to reflection. Next, the MNF transform was applied to reduce image dimension and noises. In the next step, the purest pixels were obtained using the PPI and SMACC algorithms. And the results was classified eith SAM classifier. In the final step, by using the ground control points we measured the accuracy and precision of the algorithms in mining minerals spectra. The overall accuracy and Kappa coefficients for the PPI algorithm are respectively 74.12% and 0.61 and for the SMACC algorithm it was 69.23% and 0.57, respectively. Also, the number of minerals pure pixels in the PPI algorithm was greater than that the SMACC.

Fusions of panchromatic and multispectral images create new permission to gain spatial and spectral information together. This paper focused on hybrid image fusion method FFT-PCA, to fuse OLI bands to apply Dimensionality Reduction (DR) methods (PCA, ICA and MNF) on this fused image to evaluate the effect of these methods on final classification accuracy. A window of OLI images from Ardabil County was selected to this purpose and preprocessing method like atmospheric and radiometric correction was applied on this image. Then panchromatic (band8) and multispectral bands of OLI were fused with FFT-PCA method. Three dimensionality reduction algorithms were applied on this fused image and the training data for classification were selected from DRs Output. A total of eight classes include bare land, rich range land, water bodies, settlement, snow, agricultural land, fallow and poor range land were selected and classified with support vector machine algorithm. The results showed that classification based on dimensionality reduction algorithms was quite good on OLI data classification. Overall accuracy and kappa coefficient of classification images showed that ICA, PCA and MNF methods 86.9%, 89%, 96.8% and 0.84, 0.91, 0.96 respectively. The MNF based image classification has higher classification accuracy between two others. PCA and ICA have lower accuracy than MNF respectively.