سنجش از دور

Due to the fact that today the settlements are growing more than the population living in it (Regami et al., 2017). Therefore, we can expect that by 2030, the area of settlements will reach 3 times the population living in it (Sun et al., 2018). Naturally, this increase in the size of settlements will lead to land use change, which will lead to the destruction of ecosystems, ecological and anthropological balance, environmental pollution, destruction of agricultural lands, infrastructure changes in the structure and ecological function of the land and…will be. Therefore, this expansion needs proper management.

The present study is of developmental-applied type and its method is descriptive-analytical. In the present study, the Landsat satellite image with the specifications listed in Table (1) and Google Earth software, ENVI4.8, ArcGIS10.2 have been used. Thus, satellite images from 2011 to 2021 were referenced by removing 23 control points from the image surface with an RMS error equal to 0.41 pixels of the earth. In geometric correction, points were selected for ground control that had a good distribution on the image surface to have less error in estimating unknown coefficients in the equation used. In the present study, the method of reducing the numerical value of dark pixels in images has been used for radiometric correction. In this way, a constant value of the total value of the pixels in a given band is reduced so that radiometric corrections can be applied to each satellite image. In the next step, due to the location of Ardabil city and its surroundings in two rows (67-134 and 1367-34), the images were mosaic. Then, using field visits and GPS, educational samples were identified for each user (settlement, seal, agriculture, desert) in the study area. Given that the control points were taken in 1400 and the images used in this study are from 2011 to 2021, there was a possibility of changes in use between this time period. Based on this, the points were visually compared with the images used and some of them that were suspected of changing the user were removed. Some of the harvested points were used for training and others for classification validation. In the present study, the classification was prepared using the support vector machine algorithm. Evaluation criteria (producer accuracy, user accuracy, overall accuracy, kappa coefficient) were used to evaluate the accuracy of image classification. In the next step, a map of land use changes in the study area was prepared and the changed land uses in the study period were identified and introduced. Were analyzed. Figure (2) shows the flow chart of the research stages.

After performing the backup vector classification algorithm on the satellite images of 2011 and 2021, land use maps were prepared (Figures 4 and 5). In the next step, the accuracy of the classifications was examined based on educational samples taken from the area. After applying the training samples on the image surface, the classification error matrix, statistical characteristics of producer accuracy, user accuracy, overall accuracy and kappa coefficient were determined for each of the classes. The results are presented in Table (3). Then the area of each land use class was calculated and is presented in Figure (6). In the next step, a map of land use changes was prepared. Figure (7). After preparing the change map of each period, the area of each user category was calculated. The support vector machine method had high classification accuracy in satellite images due to its overall accuracy and high kappa coefficient.

In this study, first, satellite images (ETM-OLI) were used and the land use map of Ardabil and its surroundings was extracted by supervised classification (support vector machine). The results also show that satellite images have a unique ability to extract land uses. The results of the application of metrics used in this research show the efficiency of class area metrics, number of spots, spot density, margin density, largest spot index, total margin and percentage of appearance in land use change analysis. Based on the research findings, it can be said that the situation of Ardabil city in the current situation, due to improper use of resources and its destruction is irregular and indicates the development of destruction in this area. According to the land use change map obtained from the comparison of land use in 2011 and 2021, it can be seen that in the 11-year period, the use of the settlement has increased by 1242 hectares, while agricultural use has decreased by 859 hectares and dams by 26 hectares. Bayer has reduced 309 hectares. These changes in the present study were quantified by land use metrics. The results show that the values of metrics for each of these classes have changed over the study period. That is, the effects of destruction and conversion of land uses have also affected the shape and size of land uses. In the next step, the accuracy of the classifications was examined based on educational samples taken from the area. After applying the training samples on the image surface, the classification error matrix, statistical characteristics of producer accuracy, user accuracy, overall accuracy and kappa coefficient were determined for each of the classes. Landscape analysis in this study shows the negative effects of human activities on land landscape changes. Given that the large number of spots indicates fragmentation and instability in the appearance of the land; As a result, low NP indicates stability. In the present study, metric analysis of the number of spots indicates that residential, agricultural and barren land use are in an unstable state. Metric analysis of land landscape percentage indicates that in both time periods, the highest land landscape percentage is agricultural land, barren, residential and dam, respectively. Therefore, the analysis of land landscape in this study shows the impact of human activities on land landscape change.

Dust storms are one of the natural phenomena that have affected many arid and semi-arid regions of the world in recent decades And it has increased significantly in the past years And as a result, it has had many harmful effects on the residents of the areas, so that the living conditions are very difficult in many areas due to the large amount of dust. Unfortunately, due to climate changes, including the decrease in rainfall, which on the other hand leads to the barrenness of the land surface and soil erosion, the conditions for the transport of fine dust can be provided, and when there are storms, a lot of fine dust is carried towards residential areas, and this affects It has a negative effect on the economy, health and environment. Fine dust enters the atmosphere affected by various factors including atmospheric conditions, characteristics of the earth's surface and characteristics (temperature, rain, wind, soil). Desert and devoid of vegetation are among the natural resources. Therefore, the most important factors affecting the intensity of dust are climatic changes and land surface conditions, so that with the decrease of rainfall and the decrease of water resources, especially in wetlands and the increase of barren lands, conditions prone to dust increase. Sistan plain is one of the important regions of the country which is very negatively affected by storms. Because, the lack of drainage of the Hamon wetland has led to the desertification of many areas of the wetland, and this has caused many of the wetlands to be transported to residential areas during dust storms, causing the destruction of agricultural and residential lands, damage to infrastructure, and many heart diseases. and be respiratory. Therefore, it is very important to investigate the factors affecting the intensity of dust in Sistan region so that necessary measures can be taken to manage and plan dust control. Therefore, the purpose of this research is to investigate the frequency of days with dust in relation to climatic variables (temperature, rainfall and wind speed) and water intake of Hamon lagoon.

In this research, the degree of correlation between the frequency of dusty days in relation to climatic variables and water intake of Hamon wetland in the Sistan plain in the period (2011-2021) was investigated. For this purpose, the average annual data of temperature, rainfall, wind speed and catchment area of Hamon lagoon were used in the studied time period. The data used were obtained from the meteorological station of Zabul. In order to use the data of the catchment area of Hamon lagoon, satellite images related to Landsat 7 and Landsat 8 satellites for 11 years (2011-2021) have been used.in order to prepare a map for extracting NDVI of water resources from Landsat satellite images related to the years 2011 to 2021 were used. The water resources index was used in the studied years. Then, the water layer was extracted by reclassification from the spectral index of each year and prepared as a Boolean layer of zero and one. Pearson's correlation coefficient was used in order to investigate the correlation between dusty days with climatic variables and the catchment area of Hamon lagoon. For modeling, multivariable regression was used, multivariable regression shows the change rate of one variable for other variables, and in other words, the rate of change in the dependent variable that occurs due to a unit change in the independent variable. In this method, a multi-equation A variable is used that summarizes the relationship between the dependent variable and the independent variables in a formula using the measured values. In this model, the number of days with dust is selected as the dependent variable, and the variables, temperature, rainfall, wind speed and water intake of Hamon lagoon are selected as independent variables. The coefficients of the equation for each variable are calculated and determined based on its importance in predicting the criterion variables. The degree of correlation between predictor variables is shown by coefficients.

The purpose of this research was to investigate the intensity of correlation and model the relationship between the frequency of days with dust storms and the water intake variables of Hamon lagoon, wind speed, rainfall and temperature in Zabul station. The results showed that the highest correlation coefficient with the number of days with dust is related to the wind speed with a value of 0.808, which shows the highest correlation. The correlation coefficient of temperature with the number of days with dust shows a significant and positive relationship of 0.422. The correlation coefficient of rainfall with the number of days with dust shows a significant and inverse relationship of -0.333, in addition to this, the frequency of dusty days with Hamon lagoon drainage has an inverse correlation coefficient with a value of -0.748. Because with the lack of dewatering of the Hamon wetland and the drying of the wetland bed and the reduction of vegetation, as a result of wind erosion, the wetland bed becomes the main centers of dust. And with the wind blowing, if the wind speed is high, a significant amount of sediments are transported from the wetland bed to the residential areas. Multivariate regression modeling between dust and the studied parameters showed that Hamon lagoon drainage and wind speed have a great effect on dust. According to the correlation coefficient of the studied variables, it was found that the water intake status of Hamon wetland has a greater effect on dusty days in the region than rainfall and temperature, so that with the increase of 1000 hectares, the water intake of Hamon wetland will decrease by about 0.3 units of dusty days. became. The results of multivariate regression modeling for dusty days and studied parameters showed that Hamon lagoon water intake and wind speed have a great effect on the frequency of dusty days. According to the value of R2, 61% of the dependent variable (number of dusty days) is explained by the independent variables (wetland drainage status, wind speed, temperature and rainfall) entered into the model.

In this study, the MODIS satellite imaging sensor, which is a sensor with a visible band of 36 and an infrared band, was used to produce satellite images of water surface temperature and chlorophyll-a in the Pesrian Gulf and Oman Sea. In addition, field measurements of phytoplankton species density were performed to investigate the pattern of dispersal of red tide events during 2008–2009. Finally, the effect of the current and temperature on the dispersal and expansion of the red tide in the Strait of Hormuz and the Persian Gulf was investigated. Satellite images showed that the M. polykrikoides's red tidal expansion path in the Persian Gulf and Gulf of Oman was in line with the overall water circulation path in the Persian Gulf. In the Gulf of Oman, the structure of oceanic meso-scale eddies has also played a role in red tide dispersion, and it helps to disperse the red tide to the Strait of Hormuz and adjacent Iranian coastal waters in the Persian Gulf. Moreover, the results of this study showed that decreasing water temperature increased the intensity of the red tidal phenomenon.

Water quality is the process to determine the chemical, physical and biological characteristics of water bodies and identifying the source of any possible pollution or contamination which might cause degradation of the water quality. Due to the rapid growth of industries, the disposal of liquid and solid wastes is increasing, thereby polluting soil and water. If the waste is not disposed of properly, then it percolates into the ground and causes problems like groundwater contamination, degradation of vegetation, soil contamination and modification of soil properties, etc. Nevertheless, traditional methods of water quality monitoring are often expensive and time-consuming. This is especially important for large water bodies such as lakes, dams, and rivers where sampling does not cover the entire body of water. Publicly available RS data are collected at regional scales and temporal resolutions (i.e., repeat collection time) that are much more frequent than field sampling campaigns. The physics and chemical characteristics of water can be determined from spectral signatures. Also, extracting water quality measurements directly from satellite imagery can allow rapid identification of impaired waters, potentially leading to faster responses by water agencies. Remote sensing data is an appropriate alternative to monitoring water resources due to its time and cost-effectiveness in a wide range of temporal and spatial scales. Currently, there are various types of remote sensing data such as hyperspectral and multispectral data that can be used to monitor and evaluate water quality. Geographical information systems (GIS) and remote sensing (RS) have been used extensively to assess the water quality all over the world. The Euphrates River is one of the most important rivers in Iraq, which has hosted various civilizations in the ancient Mesopotamia region since ancient times and is still of great importance to the urban and rural communities of Iraq. The Hillah River is one of the two main branches of the Euphrates River, which flows eastward by branching off from it. This river is the most important river in the Babylon governorate in Iraq, which passes through a wide area and several small streams flow from it to supply water to agricultural lands in other governorates. The Hillah River passes through several cities and is affected by industrial, agricultural, and domestic wastewater, which has received less attention than other areas of the Euphrates River. For this purpose, in this research, a detailed assessment of the quality and pollution of the Hillah River in the Babylon governorate is carried out using different methods of remote sensing, GIS, and field and laboratory operations to determine the quality of this river.the purpose of its performance is to assessment the quality of water and soil for the area of Hillah river in Babylon governorate in Iraq. The method of collecting data and information needed to perform quantitative and qualitative analyzes in research was based on field, laboratory and library operations, and various software tools were used in data processing. In order to determine and collect water and soil quality samples, field operations have been used. For this purpose, the area of Hillah city is considered as the base point and samples have been collected parallel to the river Hillah in the north and south of the city. Accordingly, in terms of number, distribution and accuracy in field sampling, 10 points were collected from the area by using Garmin handheld GPS device, 7 points were taken from water and 3 points were taken from the soil of the area. The field work to determine the sampling locations was based on several reconnaissance trips and as a result, the locations of the main water sampling stations were identified. Then, they visited the desired places twice a month, and each time they visited, relevant samples were taken. The samples were collected in standard plastic bottles with a capacity of 1.5 liters and their lids were tightly closed. Paying attention to the change in composition, soil samples were taken with a wider spatial distribution and from places with far distances from each other in the Hillah river basin, and the volume of each soil sample varied between 1 and 1.5 kg. Two different laboratories in Babylon governorate have been referred to perform quality tests on the collected samples. The laboratory measures have been carried out in two separate stages. In the first step, the measures of preparing the samples and separating them from each other have been carried out, which includes labeling, determining the date of water and soil samples, and classifying the samples for laboratory analysis. In the second stage, laboratory equipment and operations have been used for the qualitative analysis of the samples, and various devices such as CRISON have been used to test the physical and chemical parameters on the samples. Using laboratory tools and facilities, various physical and chemical variables of water quality have been measured based on the collected samples. For this purpose, 13 parameters have been tested on the samples. Electrical conductivity and total dissolved solids were measured using an EC meter and pH using a pH meter according to the relevant methods. The capacity of calcium and magnesium ions in water samples has been measured using the weighing method. Soluble sulfate, phosphate and nitrate were measured by a spectrophotometer. Sodium concentration in water was measured by flame photometer. Chloride in water was estimated from the scaling method using silver nitrate standard solution and using potassium chromate solution as the relevant guide and the results were expressed in ppm. Total hardness was measured as calcium and magnesium in water as milligrams per liter or ppm. Turbidimeteror is used to measure water turbidity. Finally, the iodometric method has been used to measure dissolved oxygen in water. In soil quality measurement, in addition to the parameters of electrical conductivity, pH, calcium, magnesium and sodium, which are also evaluated in the measurement of water samples, other parameters were also measured. including sodium absorption ratio (SAR) and calcium carbonate (CaCo). For the measurement of the mentioned two elements, special laboratory tools have been used like other elements. WQI index has been used to evaluate the water quality at the region of the Hillah river. For this purpose, the data related to the stations sampled from the water level were entered into the calculations of the WQI index, and based on this index, the water quality was evaluated on a monthly basis, and water quality maps were prepared for the region. The WQI index equation creates a range between 1 and 100, where 1 means the poorest and 100 the best water quality, and within this range, five classes are set to classify the water quality as very poor or inadequate, poor, moderately good, good and excellent. For satellite images processing, Landsat satellite imagery data provided by the United States Geological Survey (USGS) database archive has been used. In this regard, the image of Landsat 8 satellite OLI sensor for the date 2021/06/27 of the area has been selected as the main satellite data for processing.The research results can be presented in several sections. In the analysis of water quality in terms of quality parameters, it has been determined that, except for several cases in different months, in most cases, the concentration of chemical parameters of water did not exceed the permissible limit, and the physical parameters were appropriate. However, the results of the drinking water quality index have shown that the water of the Hillah River is at a poor and very poor level in terms of quality according to the location of the samples, and the spatial quality map of the Hillah river has also shown that the central to northern areas are of a more suitable quality than The southern regions have it, the main reason of which is the concentration of agricultural lands and the entry of waste and various effluents into the water in those areas. The results of evaluating the physical and chemical quality of soil in the studied area have also shown that soils with sandy texture are richer than mixed clay soils in terms of parameters such as electrical conductivity, sodium, calcium, magnesium, and SAR, and on the other hand, pH and calcium carbonate of Clay soils were more than sandy soils. The evaluation of the correlation of the parameters between the values of the water and soil samples has been done and the coefficient of the orrelation between them has been obtained, and in some cases, there has been a high correlation between the parameters. Finally, by evaluating the correlation between the quality parameters and the Landsat image bands in terms of combinations and band ratios, it has been determined that there was a direct correlation in a few cases, and on the other hand, the linear relationship also indicated the absence of a relationship between the WQI index and the spectral bands.

As a dynamic system, the river always changes its location and morphological characteristics according to time, geomorphic, geological, hydrological factors and sometimes due to human intervention. Masuleh Rudkhan is one of the most important rivers in Iran that the formation of bed geometry in different periods is very different from each other. This river has a dynamic morphological behavior under the influence of various factors such as the geology of the region, the characteristics of the alluvial structure, the hydrological characteristics of its upstream basin, the structures in it and the hydraulic conditions of the flow. The purpose of this paper was to identify and extract the changes of Masouleh Rudkhan in Guilan province in the period 2000 to 2020 using satellite image processing. The images used were the images of Landsat 5 satellite on 06/06/2000 and Landsat 8 on 13/06/2020. SVM classification method and NDWI, MNDWI, AWEI and WRI indices were used for image processing. Changes in the SVM classification method showed that the river area has decreased by 314.26 hectares from 2000 to 2020. These changes mean an increase in construction on the riverbed and a decrease in the amount of water in the river. Comparing kappa coefficient and overall image processing accuracy, it was observed that AWEI index with kappa coefficient and overall accuracy of 0.93 and 0.95 in 2000 and kappa coefficient and overall accuracy of 0.94 and 0.96 in 2020 had the highest accuracy and Masouleh Rudkhan route in this index to Google earth moved. By examining the river route in a period of 20 years, it was observed that at 5 km from the beginning of the basin, the river route is 100 meters to the south, at 7.5 km, the river route is 50 meters to the south, at 29.3 km, it is 45 meters to On the south side, at km 48, it is 38 meters to the north and at km 50 to 56, it has changed continuously and the reason for this change is the high erosion in this part of Masouleh River.

The sun is known as the source of energy, the beginning of life and the source of all other energies. The global radiation of the sun is considered one of the fundamental structures of every climate. Therefore, knowing the characteristics and predicting these basic structures has a great impact on energy-related planning. The use of satellite images and remote sensing models as a suitable and low-cost tool for estimating solar radiation has been in recent years. In order to carry out this research, the images of 2020 Landsat 8 satellite, OLI sensor, TIRS sensor and Sabal algorithm were used. ENVI software was used for geometrical, atmospheric and radiometric corrections of satellite images, as well as the execution of calculations related to the Sabal model, and ArcGIS software was used for creating a database, spatial analysis, cartographic operations and finally implementing the model. The results show that the average of the highest incoming shortwave radiation was 904 watts per square meter on 09/08/2020 and the lowest value was 500 watts per square meter on 10/9/2020. Meanwhile, the highest amount of net radiation on 09/08/2020 was calculated as 320 km and the lowest amount on 10/09/2020 as 39 km. The difference in the amount of net radiation reaching the ground in the studied area, It is caused by the difference in the angle of the sun and the number of sunny hours in different months of the year. Finally, it can be concluded that the solar radiation in the region has the necessary potential for the implementation of solar photovoltaic projects in the year under review.

The increasing application of remote sensing for mangrove mapping and monitoring is practical for sustainable management of the biological resources. The emergence of several vegetation indices (VIs) has certainly given significant impacts on mangrove and other forest mappings. In this study, four different vegetation indices including Normalized Different Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI) and Triangular Vegetation Index (TVI) were compared to discover a suitable vegetation index for identifying mangrove area in Nayband bay, Boushehr, Iran and using landsat imagery with 30-m from 2012. Maximum Likelihood Classifier (MLC) was used to classify Mangrove and NonMangrove area. The results demonstrated that the best accuracy (96.85%) was from combination between 7 landsats spectral bands and some vegetation indices including NDVI and SAVI.

The sun is known as the source of energy, the beginning of life and the source of all other energies. The global radiation of the sun is considered one of the fundamental structures of every climate. Therefore, knowing the characteristics and predicting these basic structures has a great impact on energy-related planning. The use of satellite images and remote sensing models as a suitable and low-cost tool for estimating solar radiation has been in recent years. In order to carry out this research, the images of 2020 Landsat 8 satellite, OLI sensor, TIRS sensor and Sabal algorithm were used. ENVI software was used for geometric, atmospheric and radiometric corrections of satellite images, as well as the execution of calculations related to the Sabal model, and ArcGIS software was used for creating a database, spatial analysis, cartographic operations and finally implementing the model. The results show that the average maximum incoming shortwave radiation was 918 watts per square meter on 08/09/2020 and the lowest value was 370 watts per square meter on 10/28/2020. Meanwhile, the highest amount of net radiation on 09/08/2020 was calculated as 232 km and the lowest amount was calculated as 13 km on 28/10/2020. The difference in the amount of net radiation reaching the ground in the studied area, It is caused by the difference in the angle of the sun and the number of sunny hours in different months of the year. Finally, it can be concluded that the solar radiation in the region has the necessary potential for the implementation of solar photovoltaic projects in the year under review.

land use/cover Change, especially the destruction of forest lands, is one of the most important factors in increasing atmospheric carbon emissions, resulting in global warming and climate change. On the other hand, forest ecosystems play an important role in absorbing and maintaining the global carbon balance. Because forest biomass constitutes about 90% of the biomass of the earth's vegetation and is considered an important parameter for evaluating the amount of carbon absorbed by the forest. Therefore, in the current situation where climate change and global warming have caused many environmental crises, evaluating the functioning of the forest ecosystem requires an accurate estimation of biomass and its changes, which should be done with minimum cost and time and without destruction. The total biomass includes Aboveground (trees, shrubs, etc.) and underground (living roots, etc.) Biomass research is concentrated in the Aboveground biomass sector. Various methods have been provided to estimate it. The most accurate method is based on ground measurements, but the destruction and economic costs of ground measurements are very high and it is not suitable for large-scale measurements. Remote sensing-based measurement methods, in addition to having the mentioned advantages, also make suitable estimates of forest Aboveground biomass possible. Because, the methods based on remote sensing, only require a small number of samples in the forest for ground measurement and include the use of various sensors, images, data, and different processing algorithms, the results have acceptable. Biomass prediction models based on satellite images can be obtained from radar data, multi-spectral bands, and vegetation indices (for example, Normalized Difference Vegetation Index (NDVI) and variables Vegetation biophysics such as leaf area index). These models can be developed with or without secondary thematic data (e.g. height). Therefore, this study aims to estimate the aboveground biomass of part of Ardabil Fandoghlo forests using vegetation indices and regression models and to evaluate the efficiency of Sentinel-2 images in estimating the aboveground biomass of the forest.

First, by conducting a forest tour, the characteristics of the mass in terms of area, topography, homogeneity and heterogeneity, and density were investigated. Then, the dimensions of the statistical network were determined to be 150 x 100 meters and the dimensions of the sample pieces were 20 x 20 meters. Taking into consideration the above-mentioned matters and to calculate the amount of biomass, 14 square samples with dimensions of 20 x 20 meters were established in a random-systematic way using a GPS device. Then, the information about the trees, including the species, diameter using a caliper and diameter measuring tape, and height using Suunto, were collected and entered into statistical forms and then into Excel. In the next step, due to the branching of the trees, the diameter weight of each group was obtained as the square root of the sum of the square of the diameter at the height of half a meter of all the trees of each group. Finally, the amount of aboveground biomass (AGB) was calculated in terms of tons per hectare. In order to carry out this research, the cloud-free image of the Sentinel-2 satellite was prepared on June 2019. QGIS 3.10, Arc GIS 10.3, SPSS, and Google Earth software were used to conduct this research. Then, to ensure the quality of the data and bands, the image used in this research was corrected for atmospheric errors using the Dark Object Subtraction (DOS) method in the QGIS 3.10 software environment, and then AVI, NDVI, DVI, SI, RVI, IPVI, SAVI, and BI indicators were calculated. A linear regression model was used to investigate the relationship between the calculated amount of biomass and the value of each sample plot in the above indices. So that the biomass measured in each plot with ground data as the dependent variable (Y) and the numbers extracted from the calculated indices as the independent variable (X) were entered into the model by means of the linear regression model. The amount of biomass corresponding to each of the indicators was obtained. In the following, in order to evaluate the accuracy between the measured and estimated biomass values, the coefficient of determination (R2) and the root-mean-square-error (RMSE) was used. It should be noted that with 80% of the data, modeling and Accuracy assessment were done with 20%. Finally, after evaluating the accuracy, the biomass map of the region was prepared using the SAVI index.

The results showed that the SAVI index with a coefficient of determination of 0.78 and a root-mean-square-error of 2.45 compared to other indices calculated in this study is more accurate in estimating aboveground biomass. For this reason, the biomass map of the study area was prepared using the SAVI index. Also, the amount of biomass in the area was estimated at 132.443 tons per hectare. The results also showed that the linear regression model is one of the common models for estimating biomass using ground data and the value of vegetation indices extracted from satellite images. Because it uses more limited data and less time than other models to provide more accurate results. It is still an important tool for estimating forest aboveground biomass. In addition, the high potential of remote sensing methods in estimating forest biomass with less time and cost was confirmed in this study. However, only a limited number of these methods are useful due to their high correlation with biomass content. It can be said that the role of bands and spectral textures in modeling aboveground biomass depends on the complexity of the forest structure. Forest aboveground biomass is one the essential data to evaluate the role of carbon storage in studies related to climate change and global warming. Therefore, measuring and estimating forest aboveground biomass using different methods based on remote sensing is possible with the least cost and time and is also without degradation. Finally, based on the results of this study, it can be said that Sentinel-2 images have acceptable accuracy in estimating aboveground biomass of forest ecosystems and also vegetation indices such as the SAVI index, which consider soil coefficients, are more accurate than Indicators that do not take these coefficients into account.

Global land use has significantly changed in recent decades. However, in order to monitor and survey of the ecological consequences of human interventions, quantification of land cover change is necessary. Wetlands are the most sensitive ecosystems to changes in land use and unique ecosystems with high diversity of flora and fauna. Ecological conditions and water quality of the wetlands are related to the characteristics of the land, including the type and ratio of land cover in the all watershed. Therefore, monitoring of land use and land cover changes is necessary in order to manage, control and take timely measures to reduce the threats and damage caused by the changes in the landscape. Whereas the land cover changes in Golpayegan Shoor Wetland have not been studied, so the main purpose of this study is to monitor land cover changes in this wetland in central of Iran. In fact, In the present study, the land cover of Golpayegan wetland for 1972, 1978, 1988, 1998, 2008 and 2018 was prepared using Landsat satellite images and the change was revealed during this period (46 years).

Golpayegan Shoor Wetland, located in the northern plain of Golpayegan, is a part of the catchment area of Namak Lake and the only wetland ecosystem of Golpayegan city. Six land use classes were identified in the region: 1- Residential lands (urban, industrial, road, etc.), 2- Agricultural lands, 3- Water resources, 4- Dense rangeland, 5- Medium density rangeland 6- Low density rangeland - barren. Then the educational samples were digitized on the screen by digitizing the points. In the second stage, the resolution of educational samples was done. The third step is to classify satellite images using the maximum likelihood classification method. Six time periods of Landsat satellite imagery belonging to the mentioned years (1972, 1978, 1988, 1998, 2008 and 2018) were selected to analyze land cover changes. In order to geometrically correct the satellite images of the study area, topographic maps were used to perform the first-order polynomial equation with a scale of 1: 25000 and 20 ground control points. Atmospheric correction was performed using dark-object subtraction method. In order to detect and analyze changes during the studied periods, land change modeler was used in TerrSet software.

Kappa coefficient in 1972, 1978, 1988, 1998, 2008 and 2018 were 8.08%, 84.45%, 85.79%, 90.12%, 92.67% and 93.85%, respectively. In the study period, dense rangelands decreased during the years 1978-1972 with an annual rate of -1.86% percent (9795 hectares). Medium density rangelands and residential areas also showed an increase of 13,460 and 1,080 hectares with annual rates of 2.63 and 11.84 percent, respectively. The annual growth rate is 6.33 percent in irrigated areas, and the rate of increase is 142.2 hectares in this land use. The rate of reduction is 4050 hectares in low density pastures with an annual reduction rate of 16.3%. Between 1972 and 1978, there were 499, 394, and 194 hectares of net conversion of agricultural land use, dense pastures, and low-density-barren pastures to residential areas, respectively. The rate of net change is 70, 75 and 9 hectares from agricultural lands, medium density pastures and low density-barren pastures to irrigated areas, respectively. The highest net change is 13333 hectares from dense rangelands to medium density rangelands. Residential areas, agricultural lands and low-density-barren pastures also showed an increase of 1420, 14540 and 2860 hectares with annual rates of 5.1, 9.2 and 7.7 percent, respectively, during the studied years. Medium-density rangelands also decreased from 92,000 hectares in 1978 to 83100 hectares. Dense water areas and pastures have also decreased during this period, with annual rates of -7.87% and -1.12%. Dense pastures of 1545, 11246 and 3752 hectares were converted into residential areas, agricultural lands and low-density-barren pastures, respectively. The rate of net change is 119, 2018, 11246 and 751 hectares from irrigated areas, medium density pastures, dense pastures and low density-barren pastures to agriculture, respectively. 91, 1545 and 182 hectares of medium-density rangelands, dense rangelands and low-density-barren rangelands were converted to pure residential areas. During the years, the rate of net change is from medium, dense and low-density pastures to irrigated areas as 107, 10 and 1 hectare, respectively.During this period, residential areas increased at an annual rate of 2.37 percent. Agricultural lands, irrigated areas and dense pastures also showed an increase of 4066, 90 and 13562 hectares with annual rates of 1.55, 3.65 and 1.68 percent, respectively. The annual reduction rate is in medium density rangelands and low-density rangelands - Bayer - 2.08 and -13.77 percent respectively. Between 1978 and 1988, there were 632, 6, 32, 266 and 29 hectares of net conversion of agricultural uses, irrigated areas, medium-density rangelands, dense rangelands and low-density-barren pastures to residential areas, respectively. During these years, 3279 hectares of net change was observed from dense rangelands to agricultural lands and the rate of net change is from low density densities to medium density rangelands 2698 hectares. Agricultural lands increased from 28,200 hectares in 1998 to 29,600 hectares in 2008 with an annual growth rate of 0.51 percent, with 90 hectares of medium-density rangelands and 932 hectares of dense rangelands converted. Rangelands decreased with average density of 2235 hectares and annual rate of -0.33%. The rate of net change was 1687 hectares from medium-density to low-density rangelands. The rate of increasing in low-density-barren pastures is 2565 hectares and the rate of net change is from dense rangelands to this land use 693 hectares. Water areas also decreased by 67 hectares from 1998 to 2008. An increasing of 743 hectares was observed in residential areas with an annual growth rate of 1.64 percent, which was the main net change from dense pastures to this use of 718 hectares. A large increasing was observed in medium density rangelands from 65,259 hectares in 2008 to 77,706 hectares in 2018 with an annual growth rate of 1.74%. The rate of increasing was in the water zones with 637 hectares, that it showed a net change from agricultural lands, medium density pastures and dense pastures to water zones 331, 217 and 120 hectares, respectively. In this period, the annual growth rate is 0.33% in agricultural lands. During the study period, residential areas, agricultural lands and water areas increased by 3857, 20261 and 558 hectares with annual rates of 3.35%, 2.34% and 2.24%, respectively. Dense rangelands decreased with medium and low density with annual rates of -0.5%, -0.02% and -3.3%. Agricultural lands, dense rangelands, with medium and low density at 771, 2335, 273 and 481 hectares were converted into pure residential areas. 224, 70 and 259 hectares of agricultural lands, dense and medium density pastures were converted to pure water areas, respectively. 2931 and 36 hectares were converted from dense pastures to medium and low-density pastures. The results of the spatial trend of changes from all land cover classes to residential areas revealed that the densely populated urban areas and human activities.

 Noctiluca scintillans is a dinoflagellate without armor that has extensive winter blooms in the northern Indian Ocean. Green layers from dense algal masses of N. scintillans were observed in all the studied stations in the winter, PGE 1801 cruise of the Institute of Oceanography and Atmospheric Sciences in the Strait of Hormuz. The aim of this study was to an assessment of the distribution of the bloom in the winter of 2017 in the Strait of Hormuz using field data and remote sensing.

The density of N. scintillans species in the upper layer of water was calculated using closing net samples. The density of other phytoplankton was obtained by direct sampling of water by Niskin sampler. In all stations, the profiles of salinity, water temperature, and chlorophyll A parameters were measured from surface to layer close to the substrate using CTD. MODIS satellite data were used to investigate the distribution and extent of the bloom.

The results of correlation analysis (Pearson) showed a significant relationship between chlorophyll A (values ​​obtained from remote sensing) and the number of N. scintillans cells per liter (R = 0.74, P <0.05).

It seems that the intensity of N. scintillans bloom is higher in the western part of the Strait of Hormuz and the waters near the coast. Also, satellite remote sensing data show that there is a strong correlation between the distribution of N. scintillans bloom and mid-scale eddies in the Strait of Hormuz.

The aim of this study was to evaluate the remote sensing data and evaluate the capability of artificial neural network in estimating soil organic carbon in Abidar and Toos Nozar forest parks in Sanandaj. Soil samples were prepared from 120 points at a depth of 0-30 cm and soil organic carbon was determined by walk-block method. Remote sensing data set was performed based on two statistically significant methods of correlation coefficient and stepwise linear regression. The artificial neural network MLP was used to estimate soil organic carbon. The results indicate that using the full potential of the electromagnetic spectrum can be effective in improving the accuracy of estimating soil organic carbon. lowest error rate in the training phase (0.001) was related to the stepwise linear regression method and the highest error rate (0.036) was related to the fixed number of input parameters. the artificial neural network MLP showed that it has a high ability to extend the experimental data to other areas.

Sustainable land management depends on preparing a multi-time land cover map, it is necessary to determine the process of destruction or improvement of the natural covers for these areas by identifying the type of land cover / land use at different times. One of the most important and serious issues that endanger the sustainable development is land degradation. Man-made destructive activities in nature, including deforestation, overgrazing and pasture conversion, unprincipled construction, and increased agricultural cultivation, which may also have important socio-political consequences, are increasingly destructive. Land degradation and destruction of natural resources in developing countries is much more than other countries, so that natural resources and the environment are the most vulnerable parts of these countries, and usually the first part of poor and developing countries that are destroyed and occupied in line with economic growth and development, is their natural resources. Population and urbanization growth is one of the most important factors in the destruction of natural resources in developing countries. In fact, the most important ecological impact of urbanization and distribution urban areas is the destruction of natural resources and encroachment on the natural environment. Iran's natural resources have also been severely disrupted over the past five decades, and in many cases, irreparable effects have been inflicted on it. So that, according to the reports published by the Forests, Rangelands and Watershed Management Organization (FRWO), the level of the country's rangelands since the nationalization of forests and rangelands has increased from about 100 million hectares to less than 85 million hectares. On the other hand, Sustainable land management depends on the preparation of land use maps at different times. In fact, land classification results are the basis of many environmental and socio-economic programs. The study of changes in natural areas provides valuable information for better management of natural resources in order to protect, rehabilitate, develop and even utility them. Therefore, it is necessary to identify and monitor the process of destruction or improvement of the natural cover of these areas by classifying the type of land cover / land use of the target areas during different times and determining the amount of possible changes. This study aims to determine the tenure and conversion of natural areas using coverage / land use maps of the city of Sisakht and its surroundings and also to analyze the extent of its changes during the years 1988 to 2020 with the use of remote sensing.

Detection of change, the process of determining and / or describing changes in land cover / land use characteristics are performed on the basis of rearranging multi-time remote sensing data and satellite data. On the other hand, the appropriate spatial resolution of satellite data is determined by the user needs and the scale of the study area. Therefore, Landsat satellite data with moderate spatial resolution and appropriate time coverage is the most widely used data on a regional scale that is widely used in different parts of the world to classify land use to determine land use. Is placed. In order to prepare the land use map in this research, appropriate quality data in terms of cloudiness and dust of Landsat 5 and 8 satellites related to July 1988, 2000, 2011 and 2020 were used. For this purpose, the necessary radiometric, atmospheric and geometric corrections were applied to the satellite data. Then, to increase the accuracy of classification and increase the accuracy of land use map, a data set layer was created by combining different spectral and spatial bands. In order to increase the accuracy of detecting land cover types with a wide range of spectral characteristics, the thermal band of satellite data was also included in the data set. Finally, in order to classify as accurately as possible and prepare a map corresponding to the reality of land cover / land use in the study area, two powerful monitored base pixel methods including maximum likelihood and support vector machine were used to compare their accuracy.All corrections, data preparation, data collection, classification and analysis, and extraction of maps were performed using ENVI® 5.3, ArcGIS® 10.7, Google Earth Pro 9, Excel 2016 software.

The results of this study showed that the level of natural areas of forests and ranges in the study area has decreased by at least 24% during the 32-year period. In contrast, the gardens, construction and human intervention, and occupation has increased. So that the natural covers of the region, including the forest and range, has decreased from 4678 hectares in 1988 to 3327 hectares in 2020. While, the area of orchards has increased from 583 hectares in 1988 to 1331 hectares in 2020. In general, the destruction of natural resources or their conversion to other land uses is affected by economic and social issues. The results also show that while the effect of increasing the population of Sisakht city on the destruction and tenure of natural areas, the factor of turning forests and ranges into gardens and orchards plays a more prominent role in the destruction and tenure of natural resources. In other words, according to demographic statistics published by the Management and Planning Organization of Kohgiluyeh and Boyer-Ahmad Province, although the city of Sisakht, unlike many other cities in the country, is not considered a major city in accepting immigrants and its population growth relative to many cities have not been many (7856 people in 2016 compared to 6814 people in 2006), the level of destruction of natural resources is significant. The main factor can be considered in the attractiveness of gardening and increasing the garden areas by utilizers. Undoubtedly, the process of destruction and tenure of forests and ranges, and natural resources will not stop as long as profiteers and opportunists hope for the transfer of natural areas. Considering the economic costs and environmental considerations, the change of national land use and natural areas will have serious consequences on the path of sustainable development. Therefore, any humanizing change in the environment of the natural areas of each region and its conversion to other land uses should be based on the land use plan of that region.

The use of satellite imagery and remote sensing models has been a convenient and inexpensive tool for estimating solar radiation in recent years.images related to the two years 2019 and 2020 of Landsat 8 satellites, OLI sensors, TIRS sensors and Sabal algorithm were used. The results show that the average of the highest input shortwave radiation was 914 watts per square meter in June and the lowest in November was 548 watts per square meter. The highest amount of net radiation in June was 505 watts per square meter and the lowest in February was 467 watts per square meter. Finally, the highest amount of net radiation reached the earth's surface in June at an average of 505 watts per square meter, the lowest average amount was in November at 296 watts per square meter. The difference in the amount of net radiation reaching the earth in the study area is due to the difference in the angle of the sun and the number of hours of sunshine in different months of the year. Finally, it can be concluded that solar radiation in the region, in the two years under study has the necessary potential to implement solar photovoltaic projects.

The main aim of the present study was to investigate the effects of drought on vegetation cover. An available climatic data series (2001-2017) was analyzed to detect wet and dry years by SPI. Also, a long data series of MODIS data was analyzed by remote sensing data, and the NDVI maps have been produced. Results of the present study show that there is a direct, significant correlation (R2 = 0.364) between SPI and the NDVI. In addition, during the study period, the years 2008 and 2016 were selected as dry and wet years, respectively, based on SPI values. The values of the NDVI in the wet year (2016) were significantly higher than the values in the dry year (2008) at a 99% confidence level. The results further show that elevation classes in dry conditions do not play an important role on the value of the NDVI; however, in wet years the results were different, and by increasing the range of elevation, the value of the NDVI is also increased. Generally, the results of the present study show that MODIS data in a mountainous area can be a key tool for detecting the effects of intensive droughts on natural vegetation cover.

Eshkevarat hunting prohibited region with 30347/46 ha is located in the middle of Roodsar city in Gilan. Due to habitat diversity and valuable species, this area has high conservation value. Land use/cover (LULC) changes resulted in human activities is one of the most important factors influencing the change of plant diversity in the region. The aim of this study is to analyze the impact of LULC changes on the vegetation type and diversity in the period of (2009-2019) in the Eshkevarat hunting prohibited region‬. In order to analyze the LULC changes, the vegetation of the area was also investigated and a plant type map with 13 types or plant units was prepared. In this way, we can observe the effect of land changes based on each plant type. The results of Change detection show that the area of agriculture and semi-dense rangelands increased during 10 years while rainforest and dense rangelands decreased. The most damage and change were observed in two types, X and XI, including species such as Fagus, Acer, Quercus, Carpinus, Fraxinus, Carpinus, and Quercus as well as types V and VI, which include species such as Berberis, Crataegus, Pyrus, Lonicera, Rosa, Juniperus, Crataegus, Cotoneaster, Berberis, and Astragalus.

Due to the conservation value of the Sorkhabad Protected Area, the uses within and around the area have direct and indirect effects and consequences. Appropriate management plans should be developed for the area by reviewing changes to these applications. Land sat satellites from the 1987 and 1997 time periods were prepared and used to investigate quantitative and qualitative changes in the region's ecosystem and environmental management. In this regard, after applying geometric and spatial corrections and performing image optimization, the maximum likelihood of similarity changes was evaluated and compared using unobserved classification and supervised classification methods , The accuracy of the produced maps was calculated and calculated with 90.69 and 97.47 overall accuracy test and Kappa index 0.88 and 0.96 respectively. The results showed that for 10 years agricultural and forestry use has grown well and rangelands, orchards and bare soil have declined. According to satellite imagery, vegetation status is probably better than before.

Land use mapping is the basic tools for administrators and land planners. Different methods have been proposed for land-use mapping. The latest and most important methods is using remotey sensed data for Land-use mapping. The aim of the present study was performance evaluation of classification decision tree and maximum probability methods using Landsat 8 image of 2013 for land-use mapping of Yazad- ardacan plant. Different land use classes were difined using training samples comperison of classification. results of four different methods of, Gini decision tree, entropy, Cta and maximum probability respectively thus, Show that Kappa coefficient of 85.78, 88.95, 76.78 and 91.15 the maximum probability than decision tree methods has a higher accuracy. Map area defined by the different methods of classification, are similar in sandy lands and rocky lands. The greatest differences were observed in area of medium sand dunes and minimum differences were related to the rocky lands. Therefore, the present study proves the efficiency and feasibility of thed maximum probability method in the better classification of remote sensing images.

This article aimed to examine and assess the environmental parameters such as salinity, turbidity, temperature and chlorophyll a, using MODIS satellite images, in the coastal waters of the Strait of Hormuz. To measure and estimate the parameters, global algorithms and MODIS data were used. Matrix containing data extracted from satellite images using coding and simulation software MATLAB, were used. Finally, by applying filters in Arc GIS software, these parameters were calculated in the latitudes and longitudes consistent with other marine data. According to the results, the highest coefficient of determination R2= 0.89 was obtained for salinity parameters and RMSE between field data and parameter extraction surface salinity data 0.68, temperature 1.2, chlorophyll a 2.3 and turbidity 1.78 were obtained. Chlorophyll distribution results showed that the coastal areas of the Strait of Hormuz were more susceptible to Chlorophyll blooms than the western and eastern parts.