The main goal of the present study is to use satellite data to extract bathymetry maps of coastlines and especially the shores of the Caspian Sea. For this purpose, the area between the Neka power plant and Amirabad port in Mazandaran province was selected as a pilot. Landsat-OLI satellite image was used to extract the bathymetry map of the study area. Simultaneously with the path of the satellite, about 2700 points from the depths of 2 to 11 meters of the Caspian Sea, was measurement, of which 500 points were used as control points and the rest as training samples. The polynomial linear regression method was used to extract the bathymetry map. Also, a stepwise regression method was used to identify the best regression model and select the best independent variables to estimate the depth. Comparison between the water depth map extracted from the Landsat-OLI satellite image with the control points showed that the RMSE value of this sensor in estimating the coastal water depth was about 0.4 m with an average standard error of 7.6%. By considering the turbidity and roughness of the seawater of Caspian Sea, the obtained result is an acceptable accuracy.

Makassar is one of the metropolitan cities located in Indonesia which recently experiences massive an increased construction because of population growth. Mapping the spatial distribution and development of the built-up region is the best method that can use as an indicator to set the urban planning policy. The purpose of this study is to identify changes in land use and density in Makassar City that occurred in 2013 and 2017 primarily for built areas, including settlements using optical data, especially Landsat data. The data analyzed by using multi-temporal Landsat OLI 8 data taken from 2013 to 2017. Normalized Difference Built-Up Index (NDBI), Urban Index (UI) and Normalized Difference Vegetation Index (NDVI) are the spectral indices produced from Landsat OLI band covering Short Wave Infrared (SWIR) wavelength, visible Red (R) and Near Infrared (NIR) areas that can be revealed by examining changes in land use and area cover. The result shows that both spectral indices namely NDBI and UI indicate an increased built-up area approximately 18 and 6%, respectively over four years. Also, based on NDBI reveals that most an increased built-up area distributes in the north of Makassar (Biringkanaya sub-district), meanwhile UI shows that Biringkanaya and Manggala sub-districts experience an increased built-up area. The development of the city will also never be separated from the history of city growth, current conditions, and the growth of the town to come. The phenomenon of the development of the town will include the development of city elements in detail, aspects of the shape of the town and the development of city regulations.

Soil salinization is a world-wide land degradation process in arid and semi-arid regions that leads to sever economic and social consequences.
We analyzed soil salinity by two statistical linear (multiple linear regression) and non-linear (artificial neural network) models using Landsat OLI data in Agh-Ghala plain located in north east of Iran. In situ soil electrical conductivity (EC) of 156 topsoil samples (depth of 0-15cm) was also determined. A Pearson correlation between 26 spectral indices derived from Landsat OLI data and in situ measured ECs was used to apply efficient indices in assessing soil salinity. The best correlated indices such as blue, green and red bands, intensity indices (Int1, Int2), soil salinity indices (Si1, Si2, Si3, Si11, Aster-Si), vegetation Indices (NDVI, DVI, RVI, SAVI), greenness and wetness indices were used to develop two models.
Comparison between two estimation models showed that the performance of ANN model (R2=0.964 and RMSE=2.237) was more reliable than that of MLR model (R2=0.506 and RMSE=9.674) in monitoring and predicting soil salinity. Out of the total area, 66% and 55.8% was identified as non-saline, slightly and very slightly saline for ANN and MLR models, respectively.
This shows that remote sensing data can be effectively used to model and map spatial variations of soil salinity.

The crisis of decline or drying of oak trees in the Zagros forests is one of the problems we have faced in recent years. The first step in managing this crisis is to map the affected areas and classify the severity of deforestation. The present study aimed to evaluate the Landsat OLI capability to map oak stands dieback in the Koohdasht city of Lorestan province. In addition to the main bands and fused bands with the 15-meter panchromatic band of the OLI sensor, suitable vegetation indices and the first components from PCA were also applied in the claasification. 150 square sample plots with dimensions of 30 × 30 meters were recorded to produce ground truth map. Data classification was done using minimum distance to mean, maximum likelihood and artificial neural networks classifiers in 5 classes of dieback and accuracy assessment was done using ground truth map. Because of the low separability of some classes, these classes were merged. Finally, classification with three and two classes (healthy, dieback) was performed. The highest overall accuracy of 53%, 75% and 91% and Kappa coefficient of 0.43, 0.64 and 0.71 was obtained using fused bands and artificial neural networks classifier for five, three and two dieback classes, respectively. The results demonstrated high performance of Landsat 8-OLI for mapping of healthy and oak dieback areas, but low to moderate functionality for identification of the intensity of oak decline in the study area.

Over the past few decades, the structure and cover pattern of land in the Khuzestan coastal zone has also not been immune to such changes. Therefore, monitoring the spatial combination and arrangement of Land cover/Land use and its changes in order to identify the current threats and opportunities is considered essential. The aim of this research is to monitor or the trend of changes in landscape mosaic in the coastal regions of Khuzestan Plain in response to the growth and development of human activities. In this research, Landsat images were used across two time 2000 (Landsat 7 ETM+) and 2015 (Landsat OLI) for extracting Land cover/Land use map. In order to monitor the trend of changes, four metrics of land appearance MNND, MPS, PLAND, and NP have been used. The obtained results indicated that the spatial combination and arrangement of the landscape in the studied region have found greater heterogeneity and complexity with fragmentation, attrition, and patches. Further, with the addition of two types of use including sugar cane cultivation and industry as well as fish farming basins along with the development of industrial construction units have caused dramatic transformations in the land structure in terms of combination, continuity, and extension of valuable ecological patterns. Riparian forests with increase in the number of patches and 50% reduction in the PLAND metrics and wetlands with increase of 58% of NP metrics and 71% in MMND metrics in these regions with the maximum level of fragmentation are the most vulnerable environmental patterns.

The aim of this stydy is to proced the trend process of land cover changes as a result of land use changes into dry land using satellite images, remote sensing and GIS softwares and to find out the type and magnitude of changes by the image sensor Landsat ETM in 2000 and sensor of Landsat OLI in 2014 using ENVI®5 software and unsupervised classification methods in Dehsard and kohsefeid, Kerman province. Finally, land use classification map including rangelands, agricultural lands and gardens was specified. In order to produce the training points, field visits, GPS and Google Earth software were utilized. Supervised classification was used to assess the accuracy of classification images. Afterwards, KAPA coefficie was applied to calculate the precision of produced maps. Precision matrix was created for each map. For the detection and assessment of land use changes as compared to the others, Crosstab was used. The results of land use changes in two regions showed that in this regard, the rangelands had the most changes estimated as 77% and 73% for Dehsard and Kohsefid watersheds, respectively. The precision of classification maps was given as 98% for both watersheds. The results of this study showed that the expansion of agricultural activities concerning the rangeland ecosystems could change the rangelands into the lands with low efficiency and in two mentioned regions, 9% and 20% of changes occurred in the low efficiency lands in Dehsard nd Kohsefid watersheds, respectively.

Dust is one of the most important effective factor on solar radiation forcing and reflection on earth's atmosphere, and in this point, it has a significant impact on local climate. Detection of aerosols on desert zones, despite the sea and oceans (dark surfaces), is difficult because of reflectometric interference spectroscopy of bright surfaces. Representing a simple and low costs method for detecting dusts and predicting their effects is essential. One of the most important indexes for dust and smoke detection is the AOT (Aerosol Optical thickness), which provided in large-scale (10x10 km) which is not suitable for local dust scales detection. The purpose of this study is using visible and mid-infrared spectrum of OLI sensor for detection dust of deserts. In this study, by using of mid-wave infrared (2.1 μm), red and blue wavelengths the AOT was calculated. The results indicated that ratio between the red and mid-wave infrared wavelengths is 0.95 and blue wavelengths and mid-wave infrared is 1.05 respectively. The comparison results of AOT index by radiometer showed that the correlation between computational method for data and the direct measurement for the red and blue wavelengths were 0.83 and 0.95 with root-mean-square deviation (RMSE) were 0.91 and 9.4 respectively. Therefore, it can be said that this method for estimating the Aerosol optical thickness at 0.65 μm (AOT 0.65μm) is enough accuracy and is not suitable to measure Aerosol optical thickness at 0.47 μm (AOT 0.47μm).

One of the most important planning tools for timely supply of crops, especially the strategic wheat product, is to predict the performance of this product before harvest, which can be very important in planning for itself. Combining the results of observations and ground measurements with remote sensing techniques can be widely applied in all agricultural sectors and facilitate the access to precision farming. Agricultural products have always been associated with the risk of fluctuations in the climate and changes in international markets, although this risk is never completely eliminated, but it can be understood by identifying the various parameters affecting plant growth and estimating the amount of the product Before harvesting, they minimize them. The forecast of rainfed wheat yields as a strategic product, with the Earth's population reaching 7 billion now.

Field data includes biomass and net weight of wheat produced per farm in kilograms. These data are obtained by direct field surveys during harvesting. The GPS was used to determine the total area of the land, and considering the time zone of the crop, the Landsat-8 satellite time series was used from mid-February to late May in the studied years. After performing the necessary pre-processing on the images, the images were classified using a multi-timed classification. Initially, both NDVI and LAI indexes were obtained for all images in each ENVI environment every 5 years. Finally, the phenolic curves of both indices for each plot of land were fitted for each year from the studied years, which cultivated the wheat field, and the time of each phonological step was obtained for the studied area.

In order to evaluate the overall accuracy of the classification, the Kappa coefficient and overall accuracy for the classes defined separately were calculated using the classification error matrix. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. In this study, both indicators were affected by the multivariable regression of the product estimation, and the highest coefficient of determination was obtained for each of the indices alone. From the 5 phonological stages, the inflorescence stage with R2=0.65 has the highest correlation coefficient.

From the obtained coefficients, we conclude that GLAI or green leaf area index (absorption) has a higher coefficient than NDVI. GLAI, which represents the main part of the photosynthesis of the plant (leaf), which is the main factor in the production process in the plant, certainly has a greater impact on the plant's production process. This leads to the preference of this index for the NDVI for estimation, but since the main goal of the paper is to obtain a multivariate regression relationship, we can do this in addition to the effect of both the desired index, the coefficient of determination and continuity For each of the indicators, we increase individually and make estimates in the region more accurately. With the involvement of both indicators in our relationship, we obtained a significant coefficient, especially for biomass with R2=0.865, which ensures that our prediction values are close to real values and that the program On the basis of this estimate, the probability of success will be high. From the study of phonological stages with wheat yield, we also conclude that, firstly, the entire phonological stages have less regurgitation coefficients than the phonological graphs of the two vegetation indexes, which means the whole diagram of wheat growth stages relative to the phenological periods on these graphs Have more ability to estimate the yield of wheat. Two of the five phonological stages studied, the inflorescence formation stage with R2=0.65 the highest correlation coefficient. This step in time is about the peak of the graph or the maximum value of the phonological graph.

The study of the fracture systems and their spatial extensions can help in developing oilfields, reducting the costs of exploration and improving overall understanding of the hydrocarbon reservoir systems. In this study, using different tools such as Landsat OLI-8 satellite imagery, remote sensing techniques, by Geomatica and Rockwork softwares, the structural lineaments map of the Bibi Hakimeh oil field (Surface fractures of the field in Aghajari Formation), is prepare and compared by the data obtained from FMI logs (In Asmari Formation). According to the circular diagram, in Bibi Hakimeh oil field the shear fractures (SO1) and surface axial parallel fractures (SX) have the highest percentages. According to the density map, the highest lineation densities have been seen in the middle belt of the fold from the east to the west, adjacent to the axis of the fold, that are related to the tensile joints in the axis. According to studies on the density map, extraction oil wells in the Bibi Hakimeh oilfield, mainly found in the areas with the highest fracture density. Comparison between the results of the fracture process of FMI 4 logs related to Bibi Hakimeh oilfield with the results of structural studies in this study shows that the trend of fractures in FMI data in the East part are completely compatible with the results of remote sensing studies,Therefore, studies are proposed to develop extraction wells and location for the exploration of new exploratory wells in the east and in sections with high fracture density obtained in this study.

Dust storms is one of the most important environmental hazards, especially in arid and semi-arid climates, which causes irreparable damages to the various dimensions of the environment. The aim of this study was to identify dust sources in south-east Ahwaz, using a remote sensing technique and fuzzy logic. For this purpose, the satellite images of dust events of Ahwaz City were investigated and the wind characteristic of the study area were studied by plotting the storm rose of Ahvaz Synoptic Station. In the next step, the criteria were categorized into two categories: soil moisture, land surface temperature and vegetation constitute the main layers and land use, spatial data of soil, climate, slope and sedimentology were used as constraint layers. The main layers were extracted from Landsat OLI-TIRS satellite images. The Boolean method were used to standardize the constraint layers, and the fuzzy membership functions were used to standardize the main layers. Also, main layers weighting was done using fuzzy analytic hierarchy process. Finally, the dust sources map was obtained by overlapping the layer. To validate the results, 44 ground control point were selected from the study area that showed high accuracy (81.8%) for identified areas. Based on investigating of satellite image and storm rose of Ahvaz Synoptic Station, the south-east of Ahwaz was recognized as dust-generating source and 145162.01 ha of lands are dust generating sources from which 69343/01 ha (47.77%) belongs to destroyed rangelands and 335.99 ha (0.23%) belongs to Canebrake, contains the highest and lowest level of sources of dust production in the study area, respectively.