Background: Land use/land COVER has long been considered for natural resource planning and management and remote sensing techniques are the best tools to produce land use/COVER MAPs. There are various methods for preparing land use MAPs. Objective: The purpose of this study is to prepare a land COVER MAP of Ghoorichay watershed using processing and classification of satellite images, which is one of the most important watersheds of Ardabil province. Materials and Methods: For this purpose, Landsat 8 satellite images related to June 2015 were classified using supervised maximum likelihood and fuzzy classification methods. Results: The results showed that rangelands, bare lands, dry lands, and residential lands (village) are the major land uses in the area, respectively. According to the results, maximum likelihood method with kappa coefficient of 0. 82 and overall accuracy of %88 is more accurate than fuzzy classification method with kappa coefficient of 0. 81 and overall accuracy of %87. Discussion and Conclusion: Based on the results of this study, despite the high capability of satellite images in the preparation of land use MAP, in order to increase the accuracy of classification, peripheral parameters should be used.

Snow is a huge water resource in most parts of the world. Snow water equivalent supplies 1/3 of the water requirement for farming and irrigation throughout the world. Water content estimation of a snow-COVER or estimation of snowmelt runoff is necessary for Hydrologists. Several snowmelt-forecasting models have been suggested, most of which require continuous monitoring of snow-COVER. Today monitoring snow-COVER patches is done through satellites imagery and remote sensing methods. MODIS have smaller Spatial Resolution and more bands in comparison with Meteorology Satellite like NOAA. Therefore, in this research we used MODIS data for creating snow COVER imagery. Existence of cloud in the study area is a major problem for snow COVER monitoring. Therefore, in this research snow COVER area changes were estimated without MODIS data period, but with DEM imagery and regressions between temperature, height and aspect. For this purpose, on 10 Esfand when the image was suitable we estimated the snow COVER area. In comparison with real image, precision of the method was confirmed.

The aim of this study was to evaluate the efficiency of three support vector machine algorithms, fuzzy decision trees and neural networks for MAPping land vegetation MAP of Arakvaz watershed using OLI sensor of Landsat images (2014). Geometric correction and image pre-processing were utilized to determine the training samples of land vegetation classes for the classification operations. Sample resolution in the vegetation classes has been evaluated using a statistical divergence index. On the next stage, to evaluate the accuracy of algorithms' classification results, ground truth MAP with the dimensions of 550 m was designed using systematic approach and land vegetation types in the sampling plots were determined. Finally, the efficiency of each classification method was investigated by such criteria as overall accuracy, kappa coefficient, producer accuracy and user accuracy. Comparing the accuracy and kappa coefficient obtained for three categories with a proper band set in comparison with the ground truth MAP indicates that the Support Vector Machine (SVM) classifier with overall accuracy of 91.26% and kappa coefficient of 0.8731 has had more appropriate results than other algorithms. The results showed that the separation and classification of forest lands with high accuracy have beenperformedas compared to the other land use classes.

The preparation of land COVER MAPs provides the possibility of studying the impact of land surface changes on sustainable development and is significant for a wide range of important issues at the global level. The current research aims to facilitate the preparation of land COVER MAPs using the classification of Normalized Difference Vegetation Index (NDVI) values ​​and prepare land COVER MAPs from it. For this purpose, first, two complete consecutive Landsat-8 scenes of parts of Iran and Turkmenistan were selected for August 30, 2021. Then the images were classified using supervised classification algorithms including Neural Network Classification (NNC), maximum Likelihood Classification (MLC), Support Vector Machine (SVM), Minimum Distance (MinD) and Mahalanobis Distance (MahD). In the next step, to perform an evaluation, by using a thousand ROI for a test, the overall accuracy, kappa coefficient, user accuracy and producer accuracy of the MAP produced by each of the algorithms were calculated. Then, using the most optimal algorithm, the threshold of NDVI image values ​​was extracted in order to classify it and the obtained MAP was re-evaluated for accuracy. Among the evaluated algorithms, the MLC algorithm had the most optimal performance with a kappa coefficient of 0. 75 and overall accuracy of 80. 86%. The results of evaluating the accuracy of the NDVI Based land COVER Classification (NBC) index also indicated that this MAP has extracted the land COVER MAP with an overall accuracy of 83% and a Kappa coefficient of 0. 77. This index showed good performance in the classification of Bare Land Class (BLC), Water Area Class (WAC) and Salt Marsh Class (SMC) with user accuracy and producer accuracy above 94%. This is while the Agricultural Land Class (ALC) and Vegetation Class (VC) were classified by this index with producer accuracy of above 73% and user accuracy of 69% and 97%, respectively. The results of this research indicate the acceptable accuracy of NDVI index values ​​for the production of natural land COVER MAPs and can be used in order to prepare these MAPs for geographic monitoring and achieving sustainable environmental development.

In Iran, like many other developing countries, high population growth rate causes unfairly uses of natural resources and consequently land COVER change. Therefore, detection of land COVER (rangelands, irrigated and rainfed agricultural lands, urban areas...) changes can influence local planning and natural resource management. Present study efforts to find a rapid and exact method of recognition different land COVERs using Landsat satellite data. Methods used in this research were image enhancement, false color composite (FCC), principal components analysis (PCA) and Image classification, i.e. normalized different vegetation index (NDVI) and supervised classification. A GIS environment, ILWIS software, was used. Results showed that irrigated agriculture, rainfed agriculture, rock out crop, rangeland classes (fair, moderate, poor condition) could be separated with overall accuracy of 89%.

Introduction High-resolution satellite imagery data is widely utilized for Land Use/Land COVER (LULC) MAPping. Analyzing the patterns of LULC and the data derived from changes in land use caters to the increasing societal demands, improving convenience, and fostering a deeper comprehension of the interaction between human activities and environmental factors. Although numerous studies have focused on remote sensing for LULC‎ MAPping, there is a pressing need to improve the quality of LULC MAPs to achieve sustainable land management, especially in light of recent advancements made. This study was carried out in an area COVERing approximately 8000 hectares, characterized by diverse conditions in LULC, geomorphology and pedology. The objective was to investigate the potential for achieving maximum differentiation and accurate MAPping of land features related to LULC. Additionally, the study assessed the impact of various spectral indices on enhancing the results from the classification of Landsat 8 imagery, while also evaluating the efficacy of support vector machine (SVM) and maximum likelihood algorithms in producing MAPs with satisfactory accuracy and precision.   Materials and Methods As an initial step, LULC features were identified through fieldwork, and their geographic coordinates were recorded using GPS. These features included various types of LULC, soil surface characteristics, and landform types. Following the fieldwork, 12 types of LULC units were identified. Subsequently, the LULC pattern in the study area was classified using the RGB+NIR+SWIR1 bands of Landsat 8, employing both SVM and maximum likelihood classifiers. To assess the impact of various spectral indices on improving the accuracy of the LULC MAPs, a set of vegetation indices (NDVI, SAVI, LAI, EVI, and EVI2), bare soil indices (BSI, BSI3, MNDSI, NBLI, DBSI, and MBI), and integrated indices (TLIVI, ATLIVI, and LST), and digital elevation model of study area were successively incorporated into the classification algorithms. Finally, the outcomes from the two classification algorithms were compared, taking into account the influence of the applied indexes. The classification process continued with the selected classifier and indices until reaching the maximum overall accuracy and kappa coefficient.   Results and Discussion Field observations revealed that the study area could be categorized into 12 primary LULC units, including irrigated farms, flow farming, dry farming, traditional gardens (with no evident order observed among planted trees), modern gardens (featuring regular rows where soil reflectance is visible between tree rows), grasslands, degraded grasslands, highland pastures (COVERed by Astragalus spp., dominantly), lowland pastures (COVERed by halophyte plants), salt domes (with no or very poor vegetation), outwash areas (River channel with many waterways), and resistant areas. The results of image classification indicated that the performance of the SVM algorithm across different band combinations is superior to that of the maximum likelihood method. Using SVM resulted in an increase in overall accuracy and Kappa coefficient by 3-8% and 0.03-0.08, respectively. For the MAP generated using RGB+NIR+SWIR1 bands and employing SVM, overall accuracy and Kappa coefficient were determined to be 76.6% and 0.72, respectively. Among the vegetation indices used in the SVM algorithm, LAI had the most significant impact, increasing the classification accuracy by 2.64%. Among the soil indices, BSI and MBI indices demonstrated the best performance; with BSI increasing the classification accuracy by 1.95% and MBI by 1.64%. Among the integrated indices, LST and ALTIVI enhanced the classification accuracy by 2.75% and 2.35%, respectively. It should be noted that the inclusion of the digital elevation model did not significantly improve the classification accuracy when using the support vector machine algorithm; in fact, it led to a decrease in accuracy when applied to the maximum likelihood classification. The probable reason for this issue is the different nature of DEM data compared to the other input data, as well as the limitations of parametric statistical approaches to effectively integrating data from diverse sources. Finally, the classification process was executed using the three visible bands, NIR, and SWIR1, in conjunction with selected indices (LAI, BSI, MBI, LST, and ALTIVI). Results indicated that using these spectral indices significantly improved classification accuracy, particularly for the DF, DGL, MG, O, and IF land COVER/use classes. The calculated accuracies for these classes increased by 11.62%, 18.57%, 20.06%, 29.39%, and 33.19% respectively. Consequently, the accuracy of the classification and the Kappa coefficient (using support vector machine algorithm) increased to 85.24% and 0.82, respectively.   Conclusion In this research, we aimed to accurately MAP various land use/land COVERs by utilizing Landsat 8 imagery and incorporating three group of spectral indexes. Despite spectral interferences and overlaps among various phenomena related to LULC, the utilization of different spectral indices resulted in significant differentiation among LULC classes. Finally, considering the limitations of modelling in ENVI software, it is recommended to investigate the effectiveness of other models for classification in more specialized software, such as R.

Snow is one of the major sources of water in most parts of the world. Hydrology and climate studies determine that the snow COVER surface area to be one of the important parameters of snow. One of the tools that has lots of uses in the watershed snow COVER survey and hydrological properties is remote monitoring by satellites images. MODIS imageries compared with other images like NOAA has better spatial resolution and more bands and is better for this surveying. Therefore in our study for MAPping of snow COVER we used MODIS images and NDSI indicators. In the snow MAPping algorithm, at the first stage the NDSI index snow was isolated but for discrimination between snow and other wet lands we used the thresholds in 2,  4and 6 bands. The results showed that the NDSI index in conjunction with the thresholds has appropriate effects for this purpose. In this research, the average error of snow COVER MAPs including the error NDSI index was less than 20 percent.

Land COVER MAPs derived from satellite images play a key role in regional and national land COVER assessments. In order to compare maximum likelihood and minimum distance to mean classifiers, LISS-III images from IRS-P6 satellite were acquired in August 2008 from the western part of Isfahan. First, the LISS-III image was georeferenced. The Root Mean Square error of less than one pixel was the result of registration. After creating false color composite and calculating transformed divergence index, the images were classified using maximum likelihood and minimum distance to mean classifiers into six categories including river, bare land, agricultural land, urban area, highway and rocky outcrops. The results of classification showed that the dominant land COVER type is urban area, occupying about 6821.1 ha representing 38.86% of total area. The accuracy of maximum likelihood and minimum distance to mean classifiers was obtained using error matrix and Kappa analysis. According to the results, the maximum likelihood algorithm had an overall accuracy of 94.93% and the minimum distance to mean method was 85.25% accurate. The results illustrate that the maximum likelihood method is superior to minimum distance to mean classifier.

This research study was an attempt to explore hidden cultural components in an ELT textbook from Oxford University Press (OUP) titled 'COVER to COVER'. Two research methodologies were relied on to unveil the western ideologies in this series: Firstly, a qualitative review over its reading textbooks was undertaken for authenticating the hidden western values for Iranian contexts. At this stage, analyses over randomly chosen units of the three-volume CTC book were managed via qualitative content analysis using inductive category formation techniques. In a second stage, a focus group including English language teachers indulged in teaching this series were interviewed to enrich the data with lived experiences. Overall, the findings revealed that the hidden values in the sampled texts might transmit some counter-local perspectives against Iranian leaners' local culture. Pedagogical suggestions as to improving critical cultural awareness practices for non-native students in the light of material development practices for EFL settings were discussed at the end.