Active structures have an important role in controlling fluvial systems through longitudinal and lateral tilting. The Ghezel Ozan River in northwest of Iran has responded to ongoing tectonic deformation in the basin. The study area is located in the Western Alborz zone and includes part of the Ghezel Ozan River. This paper presents the role of active structures in making active deformations via detection and characterization of fluvial anomalies and correlation with structures. The study area is characterized by association of fluvial anomalies viz. deflection, anomalous sinuosity variations and knick point in longitudinal profile. Such fluvial anomalies have been identified on the repetitive satellite images and maps and interpreted through DEM and field observations to identify active structures in the area. Some of the structures in the study area have caused the fluvial anomalies and the most active structures are surface and sub-surface faults and folds with trend of NE-SW.

Remote sensing provides data types and useful resources for forest mapping. Today, one of the most commonly used application in forestry is the identification of single tree and tree species compassion using object-based analysis and classification of satellite or aerial images. Forest data, which is derived from remote sensing methods, mainly focuses on the mass i.e. parts of the forest that are largely homogeneous, in particular, interconnected) and plot-level data. Haft-Barm Lake is the case study which is located in Fars province, representing closed forest in which oak is the valuable species. High Resolution Satellite Imagery of WV-2 has been used in this study. In this study, A UAV equipped with a compact digital camera has been used calibrated and modified to record not only the visual but also the near infrared reflection (NIR) of possibly infested oaks. The present study evaluated the estimation of forest parameters by focusing on single tree extraction using Object-Based method of classification with a complex matrix evaluation and AUC method with the help of the 4th UAV phantom bird image in two distinct regions. The object-based classification has the highest and best accuracy in estimating single-tree parameters. Object-Based classification method is a useful method to identify Oak tree Zagros Mountains forest. This study confirms that using WV-2 data one can extract the parameters of single trees in the forest. An overall Kappa Index of Agreement (KIA) of 0.97 and 0.96 for each study site has been achieved. It is also concluded that while UAV has the potential to provide flexible and feasible solutions for forest mapping, some issues related to image quality still need to be addressed in order to improve the classification performance.

Active structures have an important role in controlling fluvial systems through longitudinal and lateral tilting. The Ghezel Ozan River in northwest of Iran has responded to ongoing tectonic deformation in the basin. The study area is located in the Western Alborz zone and includes part of the Ghezel Ozan River. This paper presents the role of active structures in making active deformations via detection and characterization of fluvial anomalies and correlation with structures. The study area is characterized by association of fluvial anomalies viz. deflection, anomalous sinuosity variations and knick point in longitudinal profile. Such fluvial anomalies have been identified on the repetitive satellite images and maps and interpreted through DEM and field observations to identify active structures in the area. Some of the structures in the study area have caused the fluvial anomalies and the most active structures are surface and sub-surface faults and folds with trend of NE-SW.

The urban thermal island phenomenon has intensified in recent years due to the changes in urban airspace along with the rise of urbanization. Spatial-temporal patterns of biophysical constituents, which include vegetation, impermeable surfaces and soil type in the city, have a significant impact on urban thermal islands. The purpose of this study is to investigate the role of effective urban parameters in the formation and clustering of Yazd urban thermal islands. In order to achieve the proposed goal, the thermal map was developed using the single-window algorithm on the thermal band of OLT sensor of Landsat ETM+ sensors for August, 2015 and 2017; Land surface temperature (LST) was calculated and using spatial correlation (LISA), hot and cold clusters of thermal islands of Yazd were extracted. In order to evaluate the surface temperature, with the intensity of LST, spatial heterogeneity of the clusters increases nonlinearly. The relationship between the thermal islands with NDVI and urban carrion layers were investigated. Cold clusters are around the places with more green space and hot clusters are in the arid areas and in areas without vegetation cover. The result of the correlation between the surface temperature and the NDVI, NDBI, and NDBaI indicated that the relationship between NDVI and LST is negative, and the relationship between NDBaI and LST is also nonlinear and negative. But the relationship between NDBI and LST is nonlinear and positive. A spatial correlation with the local index has emphasized the extent of thermal islands in the studied periods

Vegetation Indices (VIs) obtained from remote sensing (RS) based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications .In the study for modeling and estimated of density and percentage vegetation value of Artemisia Herba alba was used Green Difference Vegetation Index (GDVI), Normalized Difference Vegetation Index (NDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Soil Adjusted Vegetation Index (SAVI) by Landsat 8 ETM+ bands vegetation in the Fathabad of Darab plain, Iran in 2015 years. By software ENVI preprocessing, processing, geometric and atmospheric corrections were performed,and then vegetation index for the study area was calculated. Also ArcGIS 10.2 software for mapping of area vegetation was applied. Then relationship between Vegetation Indices and density and vegetation value of Artemisia herba alba was determined. The results show that with increasing of percentage and density of vegetation, the value of vegetation indices increase. Finally, in order to determination of suitable elevation of growing of Artemisia herba alba was determined relationship between elevation and percentage of vegetation. The results show that the best elevation for growing of Artemisia herba alba was 1767 to 1782.

Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. As such,while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to remote sensing imagery (MODIS & OLI images), and the resultant maps provided an accurate and improved representation of the land covers. Low RMSE, high accuracy. By using a Hopfield neural network, more accurate measures of land cover targets can be obtained, The Hopfield neural network used in this way represents a simple, robust, and efficient technique, and results suggest that it is a useful tool for identifying land cover targets from remotely sensed imagery at the sub-pixel scale. The present research purpose was evaluation of HNN algorithm efficiency for different land covers (Land, Water,Agriculture land and Vegetation) through Area Error Proportion, RMSE and Correlation coefficient parameters on MODIS & OLI images and related ranking,results of present super resolution algorithm has shown that according to precedence,most improvement in feature’s recognition happened for Water, Land, Agriculture land and ad last Vegetation with RMSEs 0.044, 0.072, 0.1 and 0.108.

In recent decades, rapid and incorrect changes in land use have been associated with consequences such as natural resources degradation and environmental pollution. Detecting land use changes is a suitable technique for natural resource management. The goal of this research is to study the land use change in Haraz Basin with an area of 677000 hectares in 15 years (1996 & 2011) using Landsat data. After making the necessary corrections and preparing the indices, the image classification into nine classes was by supervised classification and Maximum Likelihood algorithm. Finally, the changes were extracted by Post classification comparison. The results showed that during the 15 years there was a 27.5% change in land use of the area. These changes are related to conversion of rangelands to bare lands and dry farming ones; and also converting the dense forest to sparse forest, horticulture, farming lands, and residential areas. These changes can be due to an increase in population and human activities, which result in increasing demands for natural sources and converting them into farming lands, horticulture, residential and industrial areas. These land use changes along with climate changes are an alarm for the Haraz Basin status in the future.

Remote sensing is a useful technology as a superior to other methods thanks to features like vast and integrated view of the area, repeatability, accessibility and high precision of information, and saving in time. Vegetation index is extensively used nowadays in different continental, regional, and areal scales. Due to excessive use of natural resources, area of landscapes has been changing day to day and updating of the maps is a costly and time-consuming task. Thus, many of the well-developed countries take benefit of satellite data at different levels. The analysed factors included 1- preparation of vegetation and land use maps of North Khuzestan; 2- assessment of biological potential in agriculture development of the studied area using WLC and weighted overlay technique. Based on the acquired results and performed computations, it was demonstrated the variations in the pasture and agriculture soil during the years from 2003 to 2014 were 19 percent and a significant reduction is observed in this part of land use. The changes between the years 2014 to 2016 were equal to approximately 11 percent according to the computations. This value is remarkably high and indicates intensity of changes during the recent years.

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.

The earth surface is itself a complex system, and land cover variation is a complex process influenced by the interference of variables. In this study, the data of Sentinel 2 for 2017 and 2016 were processed and classified to study the changes in the Andika area. After discovering vegetation changes between two images over the mentioned time, vegetation increased by 661.74 hectares. Multiple regressions have been used to identify factors affecting vegetation changes. Multiple regressions can explain the relationship between vegetation changes and the factors affecting them. In order to investigate the factors affecting vegetation change, altitude data, distance from the road, distance from residential areas of the village and river were introduced into regression equation. Since this method uses three parameters such as Pseudo-R2 and Relative Operation Characteristic (ROC(, 0.23, and 0.696 values for the above parameters, which indicates that the model is in good agreement. The results of regression analysis show that linear composition of height variable as independent variables in comparison with other parameters has been able to estimate vegetation change. Subsequently, by using two classified pictures of 2017 and 2016, the amount of vegetation changes was calculated, and Markov chain method was used for 2018 forecast changes.

Soil Salinization is one of the oldest environmental problems and one of the main paths to desertification. Access to information in the shortest time and at low cost is the major factor influencing decision making. The satellite imagery provides information data on salinity and also offers large amount of data that can be analyzed and processed to understand several indices based on the type of the sensor used. In this research, the capability of different indices derived from IRS-P6 data was evaluated to identify saline soils in Mahabad County. The quality of the satellite images was first evaluated and no noticeable radiometric and geometric distortion was detected. The Ortho-rectification of the image was performed using the satellite ephemeris data, digital elevation model, and ground control points. The RMS error was less than a pixel. In this study, the correlation between the bands and used indices, including Salinity1, Salinity2, Salinity3, PCA1 (B2, B3), PCA1 (B4, B5), PCA1 (B1, B2, B3, B4, B5), Fusion (Pan and B2), Fusion (Pan and B3) and Fusion (Pan and B4) with EC were investigated. The highest correlation was related to the Fusion (Pan and B2) with a coefficient 0.76 and the lowest correlation was related to B4 with a coefficient 0.2. The results showed that the indices have a high ability for modeling, mapping and estimating the soil salinity.

Soil Salinization is one of the oldest environmental problems and one of the main paths to desertification. Access to information in the shortest time and at low cost is the major factor influencing decision making. The satellite imagery provides information data on salinity and also offers large amount of data that can be analyzed and processed to understand several indices based on the type of the sensor used. In this research, the capability of different indices derived from IRS-P6 data was evaluated to identify saline soils in Mahabad County. The quality of the satellite images was first evaluated and no noticeable radiometric and geometric distortion was detected. The Ortho-rectification of the image was performed using the satellite ephemeris data, digital elevation model, and ground control points. The RMS error was less than a pixel. In this study, the correlation between the bands and used indices, including Salinity1, Salinity2, Salinity3, PCA1 (B2, B3), PCA1 (B4, B5), PCA1 (B1, B2, B3, B4, B5), Fusion (Pan and B2), Fusion (Pan and B3) and Fusion (Pan and B4) with EC were investigated. The highest correlation was related to the Fusion (Pan and B2) with a coefficient 0.76 and the lowest correlation was related to B4 with a coefficient 0.2. The results showed that the indices have a high ability for modeling, mapping and estimating the soil salinity.