سنجش از دور

Analyzing the morphological patterns of rivers is crucial for better comprehension of the rivers’ current status and their potential for prospective alterations, thereby enabling effective river management. Meandering is one of the most important river issues that influences human activities along riverbanks. Therefore, this study sought to prepare the map of meander formation-prone areas using Kernel Logistic Regression (KLR), Bagging Kernel Logistic Regression (BKLR), and Support Vector Machine (SVM) models in the northern part of Khorramabad River, Lorestan Province.

The data used in this study included the land use data extracted from Sentinel-2 satellite images, digital elevation model, slope and aspect, geology, and lithology, distance from the fault, distance from the road, topographic wetness index, and some geometric attributes such as topographic position index, profile curvature index, plain curvature index, and terrain ruggedness index, together with the data obtained from field surveys. To this end, a Sentinel-2 satellite image of the study area taken on 15/05/2021 with less than 10% cloud cover and no geometric errors was extracted from the European Space Agency website. Moreover, pre-processing (geometric and radiometric) of the image was performed in ENVI5.3 software and SNAP software, which are specifically designed for processing Sentinel satellite images. Then, atmospheric correction was applied using the Sen2Cor plugin in SNAP software. Subsequently, land use was estimated using a supervised maximum likelihood classification method based on pixel-level training samples. To study meandering in the northern part of the Khorramabad River, a 200-meter buffer zone was considered based on river topography, agricultural lands, access network, and other relevant factors. All input layers in Arc GIS and SAGA-GIS were clipped based on the 200-meter buffer map and entered into the model. On the other hand, 70% of the total data collected for meandering analysis was used for training the model and the remaining 30% was used for testing the model. It should be noted that the training samples used in this study consisted of samples of meandered cases along the studied waterway. Finally, to compare the performance of different models and select the best one for generating meandering potential maps, RMSE, MAPE, NS, and CORR error metrics were used.

This study investigated the potential of the Khorramabad River for meandering and meander formation. To analyze different parts of the river, three main segments were designated, including the northern part consisting of kilometers 0 to 4, the middle part consisting the kilometers 4 to 8, and the southern part consisting the kilometers 8 to 14. The meandering potential was generally assessed in all three parts mentioned above. Accordingly, the highest level of meandering was observed in the middle and southern parts of the river. On the other hand, most of the meanders found in the northern part were well-developed and stable, as they are located in mountainous areas, thus being relatively tough and erosion-resistant. In the coastal zone, due to the soft and erodible nature of the sediments, meandering may expand, and meanders can develop into horseshoe shapes, an example of which can be observed in the middle part of the river, i.e., at kilometer 8.5. As the topographic position index along the river displays a concave pattern, this factor tends to promote meandering both in the longitudinal and lateral directions of the river. However, in areas with a convex pattern, the factor hinders meandering.The high numerical value of the topographic wetness index along the banks of the Khorramabad River which indicates high moisture and wetness conditions makes the banks susceptible to landslides and erosion, which in turn contributes to meandering.Moreover, it can be argued that the overall condition of convex curvature in Khorramabad River and its floodplain has contributed to a decrease in meandering, as the flow velocity is higher in convex bends compared to the concave ones, thereby acting as an inhibiting factor for meandering. While the curvature of the profile varies at nearly every pixel level, its values tend towards negative in parts where the river is located near ridges, indicating increased erosion. Conversely, positive values suggest increased sedimentation due to a decrease in flow velocity.Characterized by simpler conditions, the Kernel Logistic Regression Method examines areas with the potential for meandering, thus selecting a smaller and narrower extent for prediction. In contrast, the Support Vector Machine Method acts more strictlyand selects a broader range. However, the method is considered to be more accurate, as RMSE and MAPE evaluation statistics bear the minimum values of 0.15 and 0.20, respectively, while the NS and CORR statistics enjoy the maximum values of 0.83 and 0.85 compared to other methods, respectively.

Meandering in the river located north of Khorramabad is a process influenced by geomorphological and hydrological features, leading to agricultural land degradation, reduced production, and increased suspended sediment load. From a geomorphological perspective, this study can play a significant role in river engineering. Therefore, activities such as channel modification, shoreline protection, dredging, and sand mining cannot be carried out without knowledge of river engineering.

Drought is an environmental hazard that occurs in nearly all regions, particularly in arid and semi-arid climates, and it can be effective at both regional and global levels (15). Given the negative and catastrophic effects of this phenomenon on human life and all living beings, it is crucial to offer constructive solutions to handle, control, and manage the drought crisis (39,36, 46). Various multi-spectral indices based on satellite images have been created by many researchers for drought monitoring purposes due to this reason (31, 32, 51). The results of past research showed that univariate indices are highly useful for specific locations and purposes. However, they are incapable of providing a comprehensive evaluation of drought characteristics as a climate phenomenon that is multivariable. Therefore, combining these indices can provide a superior outcome and cover all aspects of drought (4) Hence Multivariable integrated indices like the Vegetation Health Index (VHI) (27), Vegetation Supply Water Index (VSWI) )5(, Temperature Vegetation Dryness Index (TVDI) (44), Aggregate Drought Index (ADI) (24), Multivariate Drought Index (MDI) (40), Multivariate Integrated Drought Index (MIDI) (6), Comprehensive Meteorological Drought Index (CI) (45), Microwave Integrated Drought Index (MIDI) (47), Composite Vegetation Drought Indices (CVDI) and Meteorological Drought (CMDI) (49), Modified Composite Drought Index (MCDI) (7), Factual Drought Index (FDI) (13), Composite Drought Index (CDI) (22), Standard Integrated Drought Index (SIDI) (8) and so on, were presented by researchers. A comprehensive index can be obtained for drought analysis and more acceptable results can be obtained by adding more parameters that affect drought occurrence. Satellite indices can be used to monitor drought, but their results are dependent on the environmental and climatic conditions in the region, according to research findings. To monitor this phenomenon, it is necessary to evaluate the relationship between each satellite index and the occurrence of drought in the studied region, and the appropriate index must be selected in each region according to its vegetation and climatic conditions (2, 25). Most drought studies in Iran have not paid much attention to how plants respond to drought, which is linked to crop water stress, and so far, there have been fewer studies that have examined and compared drought trends with multivariate indices. Therefore, the purpose of this study is to monitor drought using satellite indices and evaluate the efficiency of multivariate indices for investigating droughts that have occurred during the 2001–2022-period in the Karkheh basin. Three drought indices, including the Vegetation Health Index (VHI), the Normalized Land Surface Water Index (NLSWI), and the Normalized Vegetation Supply Water Index (NVSWI), were chosen for this study.

The Karkheh basin is located in the middle and southwestern parts of the Zagros Mountains in the west of Iran. The basin covers a total area of 50,768 km2, with 55% of it located in mountainous areas, and the rest in plains and foothills. The average annual temperature in the basin is between less than 5 °C in the higher parts of the basin and 25 °C in the southern regions (fig. 1). According to satellite images and research, the growth period of green vegetation in the studied region is from March to July, and depending on the climatic conditions in some parts of the basin, it will last until the end of August (11, 3, 12). The peak of vegetation occurs at the end of May (23). To monitor condition of vegetation and thermal stresses, MOD13A3 and MOD11A1 products were received for calculating the VCI, TCI, VHI, NLSWI, and NVSWI remote sensing indices from the USGS for May during the 2001–2022 period (Tables 2, 3 and 4). The performance of remote sensing indices was investigated using ground observation data from 11 rain gauge and hydrometric stations (Table 1) and SPI and SDI drought indices in this research. The accuracy and precision of remote sensing indices were evaluated by examining their correlation with SPI and SDI indices. The calculation of SPI and SDI indices was done after sorting the data and reconstructing the missing data. Pearson's correlation coefficient was employed to examine the correlation relationship between remote sensing indices with SPI1, SPI3, and SPI6 and SDI in May of 2001–2022 (fig. 2).

According to the VHI, NLSWI, and NVSWI indices, the most severe droughts took place between 2001 and 2004, 2006 to 2010, and 2012 to 2015. According to the results of the VHI and NVSWI indices, the drought was more severe and widespread in 2008 and covered almost the entire basin (fig. 3 and fig. 4, and Tables 5 and 6). The NLSWI index data revealed that the most severe drought took place in 2012 (fig. 5 and Table7). Comparing of the average indices of VHI, NVSWI and NLSWI with SPI and SDI showed that there is a significant relationship between precipitation patterns, surface flows and dry and wet conditions. The SPI and SDI indices have a good agreement on the results of all three indices (fig. 6 and Table 8). Thus, it can be concluded that remote sensing indices are powerful enough to monitor droughts. This research's validation results revealed that NVSWI and VHI multivariate indices are more effective than NLSWI univariate index. NVSWI and VHI multivariable indices can be a useful tool for investigating drought conditions because they take into account factors such as land surface temperature and vegetation condition simultaneously.

Considering the special weather conditions in Iran, particularly the climatic conditions of arid and semi-arid regions, the use of the same indicators to monitor drought in different regions cannot lead to the same results. Therefore, the relationship between each remote sensing index and drought in each region should be studied separately depending on the specific climatic conditions and vegetation of that region, and selecting the appropriate index is necessary for drought monitoring.

Soil erosion is one of the threats and environmental hazards of semi-arid and arid regions, which causes the loss of quality or the loss of a large amount of fertile soils all over the world every year, and it affects environmental, agricultural, and food security issues. The intensity of erosion is influenced by the growth and development of human activities along with land use change and resource destruction. In recent years, soil erosion in Iran has intensified due to the destruction of natural resources, and land use change has played a significant role in this process; Therefore, it is necessary to identify the areas prone to erosion and sediment yield in the internal watershed of Iran, which leads to the loss of thousands of tons of fertile soil every year, and to determine the control activities that can be adapted in these conditions. Therefore, the purpose of this study is to describe the spatial distribution of the risk of soil erosion in the Qharnaveh Watershed of Golestan Province using the CORINE model along with the geographic information system and remote sensing. The evaluation results will help prioritize critical areas adopt appropriate measures to prevent soil erosion.

The CORINE model is used to estimate and evaluate soil erosion risk. The actual soil erosion is calculated using the erodibility, erosivity, slope, vegetation cover, or land use factors in this model. The erosion of the study area was calculated using the Fournier index and dryness index for this purpose. Also, soil erodibility was estimated based on soil texture, stoniness and soil depth. Then, the erosion and erodibility maps were combined with the erosion capacity map obtained from the slope of the area and vegetation density. Finally, the real soil erosion risk map was obtained in the research area.

The results of this study indicated that 3.82% and 96.17% of the watershed were under low and moderate erosion risk, respectively, and the areas with low erosion risk were mainly located in the east of the watershed. The research findings indicated that this watershed had moderate soil loss and soil erosion potential, based on the CORINE model factors.  

Therefore, according to the importance of erosion and also the location of the watershed in supplying the water of the downstream dams, it is suggested to seriously consider evaluating and planning models, make management decisions, promote, educate, and implement beneficial projects related to reducing the rate of soil movement and surface soil loss, to be a small step in reducing the rate of soil erosion and soil loss.

The rapid growth of cities and the process of industrialization have created numerous environmental problems across many parts of the world. It is essential for planners and managers to be aware of changes in land cover and land use over extended periods to evaluate and predict the impacts caused by these changes. Remote sensing is an effective tool for monitoring land use changes in urban areas and their surroundings. Tehran has expanded significantly over the last few decades due to population growth and migration, leaving substantial effects on the surrounding environment. Consequently, this study presents a model based on the decision tree algorithm to classify and monitor land use changes using images from TM and MSS sensors in the western region of Tehran between 1975 and 2011.

In this study, one MSS sensor image and three TM sensor images from the Landsat satellite, all taken in June, were used along with ancillary data, specifically a digital elevation model extracted from the 1:25000 topographic map of the Mapping Organization. After pre-processing, land cover indices, including vegetation index, DT method, and its combination with the maximum likelihood classification method, were used to extract land use classes. The accuracy of the classified images obtained from the DT was evaluated using the kappa coefficient and overall accuracy, and finally, the changes in different land use classes over time were calculated using the image comparison method.

According to this study's findings, the overall classification accuracy for 2011 is 82%. The results of change monitoring indicate a positive and increasing trend in the density of built-up land over the 36-year period, while other land types have decreased. The density of the built-up land class in 1975, with an area of 2166 hectares (equivalent to 8%), increased to 8125 hectares (29%) by 2011. In total, the percentage of relative change is 21%, equivalent to 5959 hectares. By examining the land use changes in the west of Tehran from 1975 to 2011, shown in the maps, it is evident that urban development and increased demand for various services, coupled with a lack of adequate space, have led to the destruction of green spaces in the western part of Tehran, replaced by other land uses.

This research aimed to monitor land use/cover in the west of Tehran with high classification accuracy using a model based on the DT algorithm combined with the maximum likelihood classification method. Multi-temporal satellite images from the Landsat satellite’s TM and MSS sensors, along with ancillary data, were used to conduct the research. After preparing a land use map for each period, a map depicting land cover and land use changes was extracted. The results of this research indicate that remote sensing data combined with classification techniques have a high capability to extract various types of land use maps and evaluate land use changes. Moreover, Landsat’s MSS and TM sensor data prove to be suitable and cost-effective tools for depicting and analyzing land use/cover changes over time. Additionally, the findings highlight that using a branching or multi-stage method for classifying satellite images offers advantages such as reduced processing time, improved accuracy in small classes, and the ability to use different data sources, feature sets, and algorithms at each decision-making stage.

Soil erosion and sedimentation are among the major problems and environmental challenges in watersheds. The sediment load in rivers causes numerous issues, such as sedimentation in dam reservoirs, changes in river courses due to sedimentation in their beds, reduced water carrying capacity in waterways and water transfer facilities, and changes in water quality for drinking and agriculture. These problems are of great concern to researchers and water resource managers as they can investigate sedimentation, erosion, and potential effects on biological processes. Therefore, it is essential to investigate and evaluate suspended sediment concentrations to determine water quality and hydrological functions. In this regard, the use of accurate, extensive, and cost-effective techniques, such as remote sensing, is invaluable for improving sedimentation estimation and, consequently, water quality assessment. Understanding the spatial relationships between upstream vegetation and sedimentation is crucial for the effective control and optimal management of water resources and soil. The present study aimed to determine the relationship between vegetation cover and suspended sediment concentrations in the Doab Mereg and Gamasiab watersheds.

In this research, the Sentinel-2 satellite data were investigated in the first step. If there were cloudy conditions, dust particles, or other radiometric problems, they were removed from the calculations. Then, factors such as band type (visible and infrared bands), river width (higher than the image pixel - 40 meters at the Doab station and 80 meters at the Polchehr station), separation power, and sensor location (10 and 20 meters) were considered to select the appropriate image pixel for obtaining the spectral reflectance of water. The pixel corresponding to the hydrometric station and its surroundings was selected to extract the spectral reflectance. Then, the suspended sediment concentration statistics of the Doab Mereg station located in the Qarasu River and the Polchehr station located in the Gamasiab River in the five-year period (2016 to 2020) were used simultaneously to investigate the correlation between the spectral reflectance of the Sentinel-2 image bands and sediment concentration. Next, the Normalized Difference Vegetation Index (NDVI) for the two May and June seasons was extracted using Sentinel-2 images. The relationship between vegetation cover and suspended sediment concentration recorded at the station and extracted from the images was estimated separately.

The results of the correlation analysis of suspended sediment concentration showed that the best result for the Doab and Polchehr stations belonged to band 4 (R2 = 0.86) and band 5 (R2 = 0.83), respectively. The suspended sediment concentrations varied from 0.17 to 76.45 and 0.44 to 118.86 mg/liter in the Doab Mereg and Polchehr stations, respectively. In the Doab station, the depth of the power state had the highest correlation coefficient between the observational data (recorded in the station) and the data extracted from the images. In the Polchehr station, it had a high correlation coefficient in both polynomial and exponential modes. The best values of the coefficient of determination (R2) of the normalized vegetation cover difference index for the Doab and Polchehr stations were 0.98 and 0.64, respectively. This means that the amount of sediment decreases with an increase in vegetation cover. The average values of the vegetation index for the Mereg watershed (0.35) and the Gamasiab watershed (0.28) show the relatively sparse vegetation in the area. The lowest average values of vegetation cover in the studied season (spring) were respectively equal to 0.11 and 0.21 in the Mereg watershed in June and the Gamasiab watershed at the end of May. The results of the regression test showed a strong and significant relationship between the density of vegetation and the amount of suspended sediment concentration recorded in the hydrometric stations of the two basins.

The results showed that six models were extracted for the studied area, which had acceptable and suitable R2 and error values. Among the obtained models, better results were obtained in the single-band model than in the case of using the band ratio. The highest correlations belonged to bands B2, B3, B4, and B5 in the Doab Mereg station, and to B4 and B5 in the Pol Cheher station. The highest R2 values obtained for the two stations were 0.86 and 0.83, respectively, in the exponential model. The results of this study show that Sentinel-2 can be used as a suitable tool to estimate suspended sediment concentrations with acceptable accuracy in small-scale basins and flood conditions, which has been confirmed in a number of similar studies. Sediment rate refers to the positive effect of vegetation on soil protection and the reduction of sediment production and transport within watersheds. In general, the results demonstrate that vegetation has been effective in the quantity and quality of the spatial changes in the sedimentation rate of the basins. In fact, the NDVI vegetation index, as a representative of the vegetation, can be successfully used to create a statistical model of the changes in the sedimentation rate. The revitalization of vegetation should be included in development plans.

One of the most important approaches to preserve and restore wetlands, is identifying environmental changes from past to present and developing an integrated management plan to control these changes and decision-making to provide solutions for improving the condition of these valuable ecosystems. Ovan Lake, as one of the beautiful and touristic landscapes in the forbidden hunting area of Eastern Qazvin, has distinct mountain habitats and various species of wildlife. By employing remote sensing techniques for a 30-year period, the process of changes and land use in the hydrological unit leading to Ovan Lake were identified and the trend of their changes was obtained quantitatively in this research. Then, the effect of the related hydromorphological indicators on the area and volume of the lake was investigated. The results showed that, according to the Modified Normalized Difference Water Index (MNDWI), the average area of the lake water zone was 8.15 hectares over the past eight years and based on univariate regressions, its hydrological regime is mainly related to two important factors of precipitation and evaporation. According to the univariate regressions demonstrate a significant relationship between the lake's hydrological regime and precipitation/evaporation rates. The evaporation parameter also showed a logical trend during the statistical years, so that the area and volume of the water zone of the lake has decreased by the increase of evaporation from the free surface of the water. Also, the results of multivariate regression between lake water volume and rainfall and evaporation components showed that the lake volume is more correlated with rainfall. But in contrast, evaporation changes with a greater slope or rate.

Determining the organic carbon content of rangeland vegetation is essential for monitoring rangeland condition and facilitating reclamation efforts. Satellite data provides a valuable tool for conducting extensive vegetation studies. This research aimed to estimate the organic carbon content of vegetation using field assessments and remote sensing indices in the rangelands of Lashgardar protected area, Malayer. Leveraging Landsat time series images, this study utilized Landsat 8 data from the Operational Land Imager (OLI) sensor.

Field sampling was conducted in the rangelands of Lashgardar protected area on May 28, 2016. The dominant growth form in these rangelands is herbaceous-shrub, with Asteraceae family species being the most abundant. Forty points were randomly selected as the centers of sampling plots for plant biomass. To account for GPS accuracy error, a factor of twice the pixel size was applied, resulting in the selection of 40 plots measuring 30×30 m2 for field sampling. Subplots measuring 1×1 m2 were utilized to collect composite aboveground biomass samples from the central point and the four corners of each main plot. Samples were processed in the Rangeland Science Laboratory at Malayer University following coding for laboratory procedures. The organic carbon content of vegetation was determined using the loss on ignition (LOI) method after air-drying. Vegetation indices were extracted from Landsat 8 satellite images captured by OLI sensors, including digital bands 1 to 7 with a spatial resolution of 30 meters. Various vegetation indices such as Greenness, RVI, NDVI, IPVI, DVI, WDVI, ARVI, SAVI, TSAVI, BI, OSAVI, GEMI, EVI, LAI, and GARI were derived from Landsat images.

Comparative analysis of estimated organic carbon data with measured organic carbon content revealed that only the Green Atmospherically Resistant Vegetation Index (GARI) could effectively estimate the organic carbon content of vegetation. The best model was achieved using the GARI index for organic carbon estimation, represented as OC = 5.4 + 1.38 GARI, with an explanatory coefficient (R2) of 0.13 and Root Mean Square Error (RMSE) of 0.7. These findings suggest that remote sensing indices can serve as complementary methods in vegetation studies.

The GARI index demonstrated promising results for estimating organic carbon content in vegetation within the study area and is recommended as a suitable indicator for similar areas. However, the efficacy of each index may vary depending on specific area characteristics and vegetation types. It is advisable to conduct time-series studies with larger sample sizes tailored to the unique conditions of the study area to identify the most appropriate indices.

Haur-Al-Azim, an important ecosystem in the southwest of Iran, suffered numerous wildfires in 2018. This study aims to model the dispersal of smoke resulting from those wildfires. In order to achieve this, the emission rate of particulate matter was calculated utilizing MODIS products. The study made use of data from seven climate stations in Iran and Iraq, together with local ecological conditions and emission rates, to simulate 24-hour means of PM10 through the use of the CALMET/CALPUFF package model. The study found that on September 9, 2018, the highest emission rate was measure at 0.0024 g/m2/s. This resulted in more than 42700 individuals being exposed to PM10 concentrations that exceeded the standard. Integrating remote sensing data into an air pollution modeling system can be used as an identification method to asses air quality from a spatial and temporal perspective.

Population growth and the excessive use of natural resources have caused significant changes in natural ecosystems, including a decrease in rainfall and an increase in temperature. The potential exists for them to decrease vegetation and increase barren areas. Serious economic, social, and environmental damage can occur in natural ecosystems due to the destruction of land cover and other damages, such as dust storms. Therefore, ecosystem changes are taking place worldwide, both at the temporal and spatial scale, due to human activities and natural factors. So, investigating the amount of land use/cover changes, their effect on dust storms, and predicting these changes for the coming years can be an important step in reducing and controlling unprincipled changes, planning, and optimizing resource. Climate change and human activities, such as drought, human activities, and non-compliance with water rights, have a significant impact on the Hamon wetland area, so that the dry bed of the wetland has become the main sources of dust. This research is focused on investigating the impact of land use changes on dust storms and forecasting land use changes in the Sistan region for the next 20 years.

The impact of land use changes on dust storms in the Sistan region was examined using Markov chain forecasting methods. For this purpose, first of all, the land use maps of 2002, 2011 and 2022 were prepared using satellite images. An anomalous method was used to investigate climatic parameters, including temperature, rainfall, and the number of days with dust, in the next step. To evaluate climatic changes, it is necessary to use a method that shows long-term changes. The anomaly method was employed for this purpose. The values of this index can be either positiveor negative. In order to predict land use changes for the next 20 years, the combination of the maps of 2002 and 2022 for severe drought conditions were used by using Markov chain and Cell models. The Markov model was predicted to generate multiple images. The transfer probability matrix allows for the expression of the probability that any type of land cover will be found in any location in the future. Despite the accuracy of transmission probabilities for each user is unknown, due to the lack of information on the spatial distribution of users, the Markov model does not have any spatial dependence information.  In contrast, to the automatic network, it is an agent that has the ability to change its state based on the application of the law that shows the new state in accordance with the previous state and the state of its neighbors.

This study examined the impact of land use change on dust in the Sistan region. At first, climatic changes of temperature, rainfall and number of dusty days were investigated and the results showed that the temperature has increased and rainfall has decreased in the Sistan region during the last two decades. The land use maps also showed that in the years when the Hamon wetland has been drained, pastures and dense vegetation have increased and barren lands and salt marshes have decreased. But due to the recent droughts like the year 2022, when a drought has occurred in the region, the use of vegetation and pasture has decreased and barren and salt marshes have increased. These conditions cause an increase in the level of dust in the region. The land use map for severe drought conditions in the next 20 years was predicted using the Markov model.  It showed that in the future, pastures and dense vegetation will decrease, but barren lands and salt marsh areas will increase dramatically. As desertification and wind erosion increase, dust storms will also increase as a result of these conditions. The economic, social, environmental, and health conditions of residents in the region are adversely affected by dust storms. Therefore, proper planning and management can reduce the damages caused by dust storms in the Sistan region.

 The conditions and performance of the ecosystem are affected by the changes made in some phenomena and conditions of the earth's surface (such as vegetation condition) over time due to various factors, including natural or human ones, making it necessary to identify, predict, and pay attention to such changes.
Considering the extreme vulnerability of arid and semi-arid regions to climate change worldwide, it is crucially important to investigate and evaluate climate change-induced alterations in vegetation in such regions. Therefore, this study sought to investigate the trend of vegetation changes in the three pastures of Farash Sardo, Kal-Bido, and Shoroiye located in Jiroft city with a dry and semi-arid climate, trying to examine the relationship between such changes and climatic factors. 

 The study area comprises three pastures, namely the Farash Sardo, Shoroiye, and Kal Bido, that were selected out of 375 pastures existing in Jiroft city, taking into account their significance for vegetation. To conduct the study, the monthly data of the Normalized Difference Vegetation Index (NDVI) and climatic data, including evaporation, average air pressure, average precipitation, relative humidity, sunny hours, dew point temperature, average maximum and minimum temperature rates, average temperature rate, and wind speed were used.
To this end, first, the long-term temporal changes in vegetation and climatic variables were evaluated using Mann-Kendall statistical test. Then, the most important climatic parameters affecting such changes were identified through multivariate regression, followed by the application of the root mean square error (RMSE) and the coefficient of determination (R2) to compare and assess the performance of the obtained models. Finally, the most important climatic factors involved in the changes made in the vegetation conditions of the study area were identified based on the selected model.

 The study’s findings revealed that vegetation changes had an increasing trend in Farash Sardo pasture at annual, winter, and spring time scales throughout the study period. Moreover, significant increasing trends were observed in the pasture of the Cal-Bido at annual, autumn, winter, and spring scales. However, no significant trend was found in the Shoroiye pasture.
On the other hand, the investigation of annual climatic factors indicated a significant increase and decrease in the wind speed and the dew point temperature in the Farash Sardo pasture, respectively. Also, while a significant increasing trend was found in evapotranspiration and average minimum temperature in the Cal-Bido pasture, the trend of changes was decreasing trend in the pasture in terms of air pressure, relative humidity, dew point temperature, average maximum temperature, and average temperature. As for the Shuroiye pasture, the results suggested a significant increasing trend in evapotranspiration and average minimum temperature, and a significant decreasing trend in the values of dew point temperature, relative humidity, air pressure, and average temperature.
Moreover, the modeling results showed that the most important climatic factors involved in NDVI changes in the Farash Sardo pasture were temperature, air pressure, spring evapotranspiration, and changes in winter air pressure and annual precipitation. On the other hand, the most important climatic factors affecting NDVI changes in the Cal-Bido pasture were identified as spring dew point temperature, air pressure, autumn air pressure, winter air pressure, maximum temperature, and annual air pressure. As for the Shoroiye pasture, the most important climatic factors involved in NDVI changes were found to be wind speed, precipitation, minimum and maximum temperature rate, average spring temperature, summer air pressure, autumn sunny hours, and winter sunny hours.
In general, the investigation of the relationship between drought and vegetation status of the pastures via land sampling and remote sensing data is suggested to be used for improving the results and increasing awareness concerning climate hazards for vegetation. Such a study together with the examination of the contribution of human interventions on vegetation changes can also be used for managing the environment during critical periods and setting appropriate plans.

In the mountainous regions of Iran, a significant part of the precipitation is in the form of snow, which is considered an important source of river flow. Accurate knowledge of the quantity of these resource is necessary in terms of the ever-increasing value of fresh water and also in terms of the optimal use of water resources. From a global point of view, snow monitoring and accurate information on the spatial distribution of snow cover, are necessary for weather forecasting and hydrological and meteorological modeling. An important feature of mountainous regions is the snow cover, which has a high reflectivity, has a great influence on the local weather, reduces the net radiation at the surface and as a result, transfers energy. In addition to being an important factor for ecosystem development, snow cover is very important for human activities. Accurate estimation of the coverage level is considered as one of the central and fundamental operations in the field of water resources management, especially in areas where snowfall is a major part of precipitation. Revealing and determining different characteristics of snow and ice using remote sensing data, which is widely used in hydrology, has created a new method to obtain the required parameters of hydrology.

The Alborz Mountain range which is under study of the current research, separates the coastal plains of Mazandaran Province from the interior of Iran. The eastern half of Western Alborz and all of Central Alborz and a part of Eastern Alborz are within Mazandaran Province. In this way, along with other natural factors, certain geographical conditions have emerged. In this region, snow plays a key role in the hydrological cycle and hydroclimate, and a significant part of the total annual runoff in this region is the result of snowmelt. So that global warming affects the management of watersheds and the downstream water requirements of its sub-basins. First, MODIS sensor data was obtained daily with a spatial resolution of 500×500 meters from NASA's National Snow and Ice Database (NSIDC). The received images are related to the period of 2000-2018. To process the images, first pre-processing wacovered ars applied in the ENVI 5.3 software environment. The NDSI index was used to monitor the snowed area. Mann-Kendall test, Sen’s slope estimator, and Pettitt's homogeneity test were used to investigate the snow cover variation trend. Also, the seasonal and annual anomalies of snow cover, temperature and precipitation in the study area were investigated based on standard Z score.

The results of the Mann-Kendall test and the Sen’s slope estimator method in the northern slope of Central Alborz, show that the largest reduction of the snow covered area occurred in January and winter season, respectively, equal to 220.39 and 50.41 km2 each year. The results of Petit's homogeneity test, using the Change Point Analysis (CPA) method, in January 2010 for the snow-covered area and May 2014 and June 2010 for the monthly mean temperature, showed a climatic jump at 0.05 significant level. Also, the change point in the snow-covered area time series of January has been descending, but the change point in the mean temperature time series of May and June has been ascending. Comparing the snow cover conditions with the mean temperature and total precipitation conditions, shows that in most cases the negative anomalies of snow cover are consistent with the positive anomaly of temperature and the negative of precipitation. The obtained results are a warning about the climate change in this region, which is known as the phenomenon of global warming and meteorological drought. Surely, these changes have a direct effect on the reduction of water resources for the agricultural and drinking sectors.

In general, the analysis of the snow-covered area variations in January during the studied 19 years, shows that for an increase in the average temperature of 0.13°c, the snow-covered area in this month decreased by 220.39 km2  every year. Also, according to the results of Pettitt's homogeneity test in 2010 and 2014, it can be concluded that global warming and meteorological drought caused a sudden change in the snow-covered area and temperature in these years and months. The comparison of precipitation and temperature conditions with the snow cover condition showed that in most years, the negative anomaly of snow cover was simultaneous with the positive anomaly of temperature and the negative anomaly of precipitation. The greatest effect of temperature increase has been observed in spring. Therefore, with the increase in temperature and the change in climatic conditions, the winter precipitation that will turn into snow accumulation has decreased and can affect the runoff caused by these precipitations in the spring season. Since this region has the ability to receive snow from mid-autumn to early spring, information about the snow covered area in this region is essential for many hydrological, meteorological, and climatological applications, as well as hydroelectric power generation and flood forecasting.

Remote sensing, as a powerful tool in meteorology, is able to cover the gaps in ground measurements and provide a uniform platform for spatial analysis. However, due to the different results obtained from the accuracy and performance of sensors in different regions, it is necessary to evaluate and validate their products in each region independently. This study, therefore, aims to evaluate the performance of the satellite precipitation products (SPPs) of PERSIANN, GPM, ERA5, and TRMM in a daily rainfall scale over the Razavi Khorasan Province, against the ground observations collected from the 19 rain gauges. In this regard, 113,880 images were called to extract daily rainfall data from the above four databases. To evaluate the SPPs, statistical indices including correlation coefficient (CC), root mean square of the errors (RMSE), and percentage of bias (PBias), Kline Gupta Efficiency (KGE), and Agreement Index (d) were used. Moreover, POD, FAR, and CSI classification indices were used to evaluate the accuracy of the data presented in the indication of days with rainfall events. The climate effect was also investigated by incorporating the change in latitude. Our results revealed better performance for the ERA5 dataset with 0.2≤ CC ≤ 0.68, 2≤ RMSE ≤ 3.7, and its performance indicators have KGE ≥ 0.26 and d ≥ 0.68. Regarding classification indices, ERA5 has POD and CSI higher than 0.68 and 0.3, respectively, and TRMM has relative superiority only in the FAR index. At the level of the studied area, the index analysis shows the better performance of the ERA5 database compared to other studied data. Based on the results of this research, the increase in elevation has improved some statistical indicators in some products, whereas, in some other cases, changes in elevation have a negative effect on the accuracy. In general, the ERA5 database at the point and regional scale are evaluated to have a more appropriate performance in estimating daily precipitation data, and its data can be used in meteorological and hydrological analysis.

The aim of this study is to detect and quantify the cultivated area of potato fields in Hamadan Province using remote sensing methods and a time series of satellite photos. As a result, Awifs time-series imaging was used to determine the potato cropping area. For this purpose, pictures were taken at three different times when the potato plant turned green and yellow. Processing such as preparation, atmospheric and geometric correction, vegetation index, and unsupervised classification were performed on the images using appropriate training sites for supervised classification. Following the integration of these two layers, the studied area under the cropping map was prepared using the phase classification method. Additionally, by using the vegetation indices NDVI and SAVI, the area under cropping for the three main crop yields is determined first using the threshold level technique and in three temporal intervals. The kapa coefficient for potato under cropping area determined by phase classification, NDVI, and SAVI was 90, 87, and 85%, respectively. In 1998, the potato cropping area was determined to be 38740, 36728, and 36614 acres, respectively. This study clearly shows that the phase classification method and Awif data time series can be used to recognize and estimate potato under cropping area with acceptable precision and that vegetation indices distinguish potato under cropping area faster.

The dust storm is recognized as a global problem which has a large-scale negative impact on the world. Dust strong winds transport them long distances. The dust storm caused a lot of damage to the economy, health and the environment. Therefore, an adequate understanding of the origin and time of dust storms can be effective in reducing dust damage. The purpose of the ongoing research is to identify the source of the dust storm events. For this purpose, the dust events in a period of 11 years were analyzed using the synoptic meteorological data of Kermanshah. The number of 646 dust storms were identified, and their detection operations and identifying of the areas affected by dust storm and areas of origin were performed using MODIS and Deep Blue. The HYSPLIT was used to route the dust storm and the entry routes of dust storm into the Kermanshah city. Based on the dust codes, the highest number of dust storms per year was registered in 2008 and 2009, respectively.  Most dust storms were observed in summer and during May, June and January. The results of DRS, HYSPLIT have shown that the western directions have the largest amount of dust input in Kermanshah, and the MODIS and Deep Blue images also confirm this fact. Overall, the findings showed that most of the routes come from the north and centre of Iraq and the Syrian Desert.  Dust storms originate at the border and arid lands of northern and central Iraq and Syria.

The estimation of mangrove carbon stocks is crucial for providing vital information for the development of climate change adaptation programs and blue carbon strategies in coastal ecosystems. Therefore, the aim of this study was to estimate the carbon storage of mangroves in the Hara protected area of Hormozgan province. For this purpose, after field surveys and recording the diameter at the mangroves' collar, the above-ground and below-ground biomass was estimated using allometric equations. Then, a regression was fitted between the above-ground and below-ground biomass and the normalized vegetation index (NDVI) extracted from the satellite images to develop a map of the above-ground and below-ground biomass of mangroves in two coastal and island zones and tall and dwarf mangroves structures. The results showed that the average above-ground biomass in the coastal and island zones of the Hara protected area was 61.2 and 56.1 t/ha, respectively, and the average underground biomass was 15.6 and 12.5 t/ha, respectively. There was a significant difference between the values of these two biomasses in the two zones. The extent of tall mangroves in the coastal zone (59%) was greater than dwarf mangroves (41%), and in the island zone, the extent of tall mangroves (44%) was less than dwarf mangroves (56%). The amount of total biomass in tall mangroves in both zones was about 7.5 and 8 times greater than the value of this variable in dwarf mangroves, respectively. The results of this study can be used to prepare climate change adaptation plans for mangrove habitats.

The preparation of geological maps based on field data and the application of aerial photographs have always been an error because of the structural diversity of the earth and the difficulty of accessing certain regions. But in recent decades, the use of satellite imagery has but in recent decades, the use of satellite imagery has gone a long way to increasing the accuracy and timeliness of geological mapping. The purpose of the current study is to investigate the applicability of Landsat 8 satellite images and object-oriented pixel classification methods in mapping the geological formations of some part of the Shirkuh Mountain range in Yazd province. This area is part of the scattered mountain range of Central Iran with a dry climate and minimal vegetation. Initially, enhanced operations were performed to identify geological information’s using MNF, PCA and FCC processing. Then, the images were classified using object-oriented algorithms (BAYES, SVM, KNN, Decision Tree and Random Forest), neural network (ARTMAP, RBF, MLP and SOM), and base pixels (Maximum Likelihood, Minimum Distance, Mahalanobis and SAM). Next, the error rate of each method was calculated using Boolean logic and kappa coefficient. The results showed that the maximum probability classification with kappa coefficient of 75% in the base pixel category, Fuzzy ARTMAP classification in neural network method with kappa coefficient of 72% and Bayesian classification in object-oriented method with kappa coefficient of 82% have the best results among other methods. These results show that the methods mentioned in the identification and separation of geological formations are effective. The SAM of pixel-based methods, SOM of neural network methods and RF of object-oriented methods with 49%, 64% and 61%, respectively, showed the lowest accuracy in each category.

Nowadays remote sensing is known as practical method for studying Land Use (LU)/Land Cover (LC) changes. Due to the vast area of agricultural lands, Barandouzchay Basin is one of the important watersheds among all of watersheds in Lake Urmia River Basin. In this study, in order to evaluate LU/LC change, Landsat-5 TM and Sentinel-2A satellite images were used from 2005 to 2016. The maximum likelihood classification method was used to prepare LU/LC maps. The results of overall accuracy and Kappa coefficient showed high accuracy of maximum likelihood classification method. In order to extract the change detection maps, image difference method was used. Results showed that orchard and nonproductive trees have been increased during 2010-2016 years in Barandouzchay Basin. In the years before 2010, trees were relocated by young trees in Barandouzchay Basin. Drylands and bare lands are classified in the 2005-2010 years which has been increased. The most land use change was related to urban and lowest change was related to rainfed area from 2005 to 2010 and the most land use change is related to bare lands and lowest rate is related to nonproductive tree area from 2010 to 2016.

The purpose of this study was to investigate the oak forest dieback with respect to drought occurrence, soil moisture changes and dust occurrences factors in Ilam, Kermanshah, Lorestan and Chaharmahal and Bakhtiari provinces of Iran. The data used were field surveys collected through GPS, MODIS satellite imagery, GLDAS Soil Moisture, dust and precipitation data of the meteorological stations of the provinces during an 18-years period (2000-2017). The results of the study of greenness values of the forests in the study area showed that the first decline occurred in 2005 and repeated more severely with much wider spatial extent in 2008. Investigation of the relationship between drought events and its spatial and temporal variations with the changes in forests greenness of the study area showed that the reduction in precipitation amount is one of the main reasons for forest greenness reduction in the study area. The increased frequency of periods of rainfall shortage and drought duration, especially at 9 and 12-month time scales, showed a significant relationship between drought occurrences and forests greenness in the study area. The results indicated that by decreasing precipitation drought periods increased, soil moisture decreased, and dust storm occurrences increased. As a result, in most of the years, with decreasing soil moisture and increasing dust storms, the forests greenness of the study area has decreased and vis versa. Therefore, there is a direct relationship between soil moisture and forest greenness while an inverse relationship exists between dust and forest greenness.

Determining the exact amount of forage production can be of great help to rangeland managers and relevant specialists in determining proper stocking rate. With implementing proper sampling design, remote sensing data could be used to accurately estimate forage production due to the extent of rangelands areas, cost, time spent and other problems in data gathering from field. The objectives of this study was to select the best model and the best remote sensing index in order to map forage production using field data and vegetation indices of NDVI, SAVI, MSAVI2, DVI and GCI extracted from satellite images of Sentinel 2.

A sampling network with a total of 58 plots (1×1 meters) were established in the studied area and cut and weight method was used to measure forage production. Then, vegetation indices of NDVI, SAVI, MSAVI2, DVI and GCI were created with SNAP software. The values of the mentioned indices were extracted from the location of the plots, using the ArcGIS 10.4 software. The normality of the data was checked by the Kolmogorov Smirnov test. Then their relationships were analyzed with regression in SPSS 16 software. Also, multiple linear regression was used to investigate the relationship between plant indicators and forage production. The train model was created by 70% of the total plots and 30% of the data were used to test the model. Coefficient of determination (R2) and root mean square error (RMSE) were used to select the best model and index or indices. Finally, the selected model was used to create the map of forage production (Kg/hec), using ArcGIS 10.4. The values of final map as estimated data and a total of 58 plots as observed data were evaluated by independent t-test.

The results related to the relationship between forage production and plant indices with univariate linear regression showed that all the used indices had a significant relationship with forage production. The univariate linear regression model with MSAVI2 index had the highest coefficient of determination and the lowest RMSE (Y= 649.3-8523.7×MSAVI2; R2= 0.68 and RMSE= 16). The results also showed that the accuracy of the DVI index (R2= 0.66; RMSE= 19) was higher than the NDVI index (R2= 0.58; RMSE= 22) for estimating forage production in studied area. By applying the assumptions of multivariate linear regression model, only two indices of GCI and MSAVI2 were included in the model, and the amount of R2 and RMSE were the same as univariate linear model with MSAVI2 index. The results of independent t-test indicated that there were not significant differences between observed data and the ones estimated by selected model (p<0.05). The minimum, mean and maximum of forage production in the final map were 10, 220 and 475 kg/hec, respectively.

According to the equality of the root mean square error and the coefficient of determination of the multiple and linear regression models and also the results of independent t-test that indicted no differences between observed and estimated forage production, we suggest using the MSAVI2 index to estimate forage production in warm semi-arid rangelands.

Although land degradation is a worldwide challenge and a destructive phenomenon, little studies have been done on the application of new numerical methods (data mining and statistically), for spatial simulation of this phenomenon and identification of areas sensitive to land degradation. The aim of this study is to spatially simulate land degradation in the Qazvin plain using the frequency ratio model to identify areas prone to land degradation. For this purpose, using the trend of changes in net primary production during the years 2001 to 2020, the points of occurrence of land degradation in the Qazvin plain were determined. Approximately 70% and 30% of the points were used to prepare the land degradation vulnerability map and validate the model's efficiency, respectively. For this research, 15 parameters affecting land degradation (directly and indirectly) including temperature, rainfall, slope, aspect, elevation, EC and SAR of ground water, ground water level, annual ground water decline, land use, normalized difference vegetation index, normalize difference salinity index, vegetation soil salinity index, normalized difference moisture index, and visible and shortwave infrared drought index, were introduced into the model as predictors factors or independent parameters. Finally, using the area under the ROC curve, the effectiveness of the frequency ratio model for spatial simulation of land degradation was assessed. The map of land degradation susceptibility shows that the areas prone to degradation are located in the northeast, north, northwest, west, southwest, and south of the Qazvin plain, which mainly includes good, moderate and poor rangelands. For the land use parameter, the highest frequency ratio was associated with the sum of good, moderate, and poor rangeland (5.66). The value of AUC = 0.7 indicates the good performance of the frequency ratio model in spatial simulation of land degradation.