In this study, in order to evaluate the capabilities of Interferometric Synthetic Aperture Radar (InSAR) time series data and machine learning for land cover mapping, a time series of Sentinel-1 SAR data (including 16 SLC images with approximately 24 days time interval) from 2018 to 2020 were used for a region of Ahvaz County located in Khuzestan province. Based on different SAR pairs, 25 coherence images were obtained in different time periods using InSAR processing. Five dominant land cover classes in the region including builtup lands, agricultural lands, water bodies, bare soil, and dense natural vegetation cover were identified and selected. Through Google Earth's high-resolution imagery, a total of 4, 930 ground samples with appropriate spatial distribution for all land cover classes were obtained. The obtained multi-temporal coherence images were used as input variables to the support vector machine (SVM) classifier. The training and validation process of different SVM kernels was performed using 80% and 20% of the ground truth samples, respectively. Based on the classification results the overall accuracy in different kernels including linear, 2th-degree polynomial, 4th-degree polynomial, 6th-degree polynomial, radial base function (RBF), and sigmoid were computed 60. 7, 64. 7, 67. 7, 69. 9, 66. 3, and 59. 5%, and Kappa coefficients were reported 50. 8, 55. 87, 59. 62, 62. 38, 57. 87, and 49. 38%, respectively. Accordingly, the highest and the lowest overall accuracy and Kappa coefficient belong to the 6th-degree polynomial and sigmoid kernels, respectively. Based on the user and producer accuracy assessments in all kernels, the built-up land has the highest accuracy (93%–, up to 98. 5%), and in opposite the dense vegetation has the lowest accuracy (11%–, up to 56. 25%). Generally, the results emphasize the high potential of Sentinel-1 InSAR coherence data in land cover mapping. Meanwhile, the contribution of the classifier to the efficiency of data is also important.

The analysis of the relationship between spatial distribution of environmental factors and vegetation types is crucial for understanding mountainous ecosystems. In this research a GIS based approach was used to produce a vegetation map for Sabzkouh protected area in the Chaharmahal- Va-Bakhtiari province. To identify environmental parameters affecting the vegetation cover, 6 primary and secondary environmental parameters including hypsometric, slope steepness, slope direction, annual precipitation, temperature and sun radiation maps were derived from the study area DEM. To investigate the relationship between these factors and the spatial distribution of vegetation cover, quantitative analyses using statistical techniques like Principal Components Analysis(PCA) were undertaken. Then, the spatial distribution of vegetation types was predicted using a multi-logistic regression. Results showed that topographic variables derived from the OEM were very useful for indicating habitats of range and forest types. Although lack of information on the anthropogenic effects led to some uncertainties in the interpretation of spatial pattern of vegetation types, the topographic and climatic variables, derived from the OEM, were considerably effective in modelling the spatial distribution of vegetation types.

Land cover map show the spatial distribution of different landscapes such as agricultue, natural resources, water and manmade area. It is a valuable tool to managing and reducing risk in challenging issues such as drought and its effects, food security, flood control, and urban planning. In order to overcome the limitations of field work in the mapping of land cover, the use of satellite images due to the wide, multispectral and update data seems to be suitable. In the study area, the spatially heterogeneous landscapes also makes it difficult to classify features. Therefore, the main purpose of the study is accurate and high resolution land cover mapping using Sentinel-2A images in the Google Earth Engine platform. In this regard, three classification algorithms including RF, SVM and CART were evaluated and compared. Various indices were prepared using ratioing and transformation methods. The accuracy of the classifications was evaluated in comparison with ground reference data. Individual bands evaluation showed that the best overall accuracy (49%) was obtained using the CVI index. The best overall accuracy and kappa coefficient of 86% and 0. 82 were obtained by RF algorithm. Therefore, while pointing to the advantages of the GEE including easily accessible data and the ability to process and quickly compare of data, it can be claimed that Sentinel-2A images for land cover mapping in terms of cost, time and accuracy, have high efficiency and the map can be very useful for the management and decision making in different natural and man-made resources for the successful implementation of sustainable development.

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.

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%.

Engineering geology mapping has been central to engineering geology research since it is objectives that the project continues to develop and investigate methods for obtaining data and “mapping’ this data to be suitable for the needs of civil engineering and environmental assessments. Engineering geological maps of Chabahar area (at a scale of 1:25,000) was prepared to provide engineering geological information as an aid in land use planning. Study area consists of tertiary deposits and studied for assessment of some parameters such as foundation condition, excavation condition, waste disposal condition, engineering geological problems and environmental problems. Data collection was done through field investigations, inclusive borehole boring, systematic sampling and field and laboratory tests. Finally, results are shown in applied geological maps.