land cover

Land is the basis of most human activities, production systems, and achieving sustainable development. On the other hand, with social and economic changes and unconventional resource exploitation, land instability conditions are intensifying. Measuring land use changes requires examining issues such as land use system, land cover, and monitoring patterns. Land use and land cover patterns, by providing monitoring and understanding the process of changes in the exploitation system through the use of remote sensing techniques, facilitate the possibility of reforming and changing policymaking, optimal management, and future planning of environmental resources. The aim of the present study is to monitor the changes in the LULC system in the Kuhpayeh-Segzi sub-basin using satellite images in the period 2000 to 2023 and update spatial information. The results of the evaluation of the land use and land cover maps using the Decision Tree Algorithm indicate an increase in the area of ​​more than 97% of built-up land, 173% of rangeland, 230% of irrigated areas, 72% of agricultural land, and a decrease in the area of ​​more than 14% of barren land. In this process, 913 hectares of barren land, 244 hectares of rangeland, 44 hectares of agricultural land, and 0.155 hectares of irrigated areas have been converted into built-up land. Sustainability of environmental resources, especially reducing the trend of land use and land cover change and stabilizing the exploitation system, requires the implementation of optimal resource management and the application of land protection models with the participation and empowerment of local rural communities.

In the research, the carbon storage capacity of Urmia's urban and suburb green infrastructures were analyzed. Urmia is characterized by a cold climate with a dense building structure with scattered green spaces, especially in the surrounding areas and neighborhoods of the city. The research was carried out using the carbon storage model available in the InVest software package. The results revealed that more than 57% of the study area has a carbon storage capacity of less than 2 tons, and less than 6 percent of the area has more than 30 tons per hectare, which is limited to the gardens and groves along the Shahrchai River. Soil carbon storage has the highest share with 2344.41 tons, and above biomass carbon storage followed closely with 1403.47 tons. The total amount of carbon storage in the studied area is 3900.9 tons per year. The carbon storage was highest in gardens and groves, followed by barren lands with sparse vegetation and rocky outcrops mountain’s without vegetation and soil cover had no carbon storage. Dense plants and trees have the highest storage capacity per unit area, However, the amount of storage depends on the season, so it is necessary to consider the selection and expanding the urban green spaces according to the types of plants and their compatibility with the climatic conditions and urban spaces of Urmia. As a result, it is necessary to create and develop urban green spaces at the same time as physical development, it should be the priority of Urmia's urban development plans.

Land cover maps are essential elements in geographical analysis and spatial planning. The accuracy and effectiveness of these maps rely on three factors: Satellite imagery, classification algorithms and training samples. The quality of the training dataset significantly impacts the accuracy of classification results. This study aims to generate reliable training samples using the training sample migration method to monitor land cover changes in northwestern Iran from 2002 to 2022.

The study area covers 7653 square kilometers in northwestern Iran, situated between 44°35′59′′ to 46°01′25′′ longitude and 38°38′46′′ to 38°47′48′′ latitude. Data utilized in this research include satellite images and ground truth data, specifically Landsat images. The research methodology comprises five main steps. Initially, satellite images were obtained, followed by pre-processing steps involving radiometric and geometric corrections. Subsequently, training samples were prepared using high-resolution satellite images (Google Earth images) and ground surveys. The third step involved training sample migration, where spectral similarity between training samples from reference and target years was assessed using two parameters: Euclidean distance (ED) and spectral angle distance (SAD). After determining a suitable threshold, migrated training samples were distinguished from non-migrated samples. Evaluation of the accuracy of migrated training samples was conducted using reference data derived from Google Earth. In the fourth step, classification of satellite images from different years was performed using the migrated training samples. Finally, the accuracy of the classified images was assessed through the calculation of a confusion matrix in the fifth step.

The results indicate that a threshold of 0.9 to 1.1 is optimal for distinguishing migrated training samples from non-migrated training samples across different years. It can be observed that there is an inverse relationship between the accuracy of migrated training samples and the percentage of migrated training samples, with an increase in the percentage leading to decreased accuracy. Evaluation of the accuracy of migrated training samples based on each parameter (SAD and ED) reveals that migrated training samples based on the SAD parameter exhibit higher accuracy than those based on the ED parameter. Furthermore, the use of migrated samples based on both parameters has resulted in a 10.45% increase in accuracy compared to using the ED parameter alone, and a 5% increase compared to using the SAD parameter alone. Analysis of the percentage of migrated training samples in different land cover classes demonstrates that, on average, 80.6% of water class training samples, 75.4% of bare land class samples, 71.2% of built-up class samples, 64.6% of grassland class samples, 60.2% of cropland class samples, and 54.4% of wetland class samples were migrated from the reference year (2022) to each of the target years (2002, 2007, 2012, and 2017). The accuracy assessment of migrated training samples in different land cover classes also reveals that the water, built-up, bare land, grassland, cropland, and wetland classes had the highest accuracy in the migrated training samples, in that order. Analysis of land cover changes between 2002 and 2022 indicates a decrease in the area of bare land, water, and wetland classes from 2002 to 2022, while the area of the built-up class has increased during this period. Additionally, the grassland and cropland classes did not exhibit a consistent trend of change during this period, with their trends differing in different years. However, overall, the area of these two classes increased in 2022 compared to 2002. 

Future studies should consider using other satellite images (including Sentinel-2) for migrating training samples to evaluate the impact of different spectral bands and satellite images on the migration process. Furthermore, investigating the effectiveness of the training sample migration method for migrating training samples of other land covers could be a potential research topic for future studies.The study area is located in northwestern Iran with an area of 7653 square kilometers. The study area lies between 44°35′59′′ to 46°01′25′′ longitude and 38°38′46′′ to 38°47′48′′ latitude. This study used satellite images and ground truth data.The research methodology consists of five main steps. The initial step involved obtaining satellite images and performing pre-processing steps (radiometric and geometric correction). In the second step, training samples were collected using high-resolution satellite images (Google Earth images) and ground surveys. The third step involved the migration of training samples. To do this, the spectral similarity of the training samples from the reference and target years was first calculated using two parameters: Euclidean distance (ED) and spectral angle distance (SAD). Then, adopting the specified threshold, the migrated training samples were separated from the non-migrated samples. Furthermore, the accuracy of the migrated training samples was evaluated using reference data prepared from the Google Earth. In the fourth step, using the migrated training samples, the classification of satellite images in different years was performed. Finally, using the indices obtained from the confusion matrix, the accuracy of the classified images was evaluated.The results showed that the threshold of 0.9 to 1.1 is the optimal threshold for separating migrated training samples from non-migrated training samples in different years. We also found a reverse correlation between the accuracy of the migrated training samples and the percentage of the migrated training samples, with higher percentages resulting in lower accuracy.The accuracy assessment of the migrated training samples based on each parameter (SAD and ED) showed that the migrated training samples using the SAD parameter have higher accuracy than the migrated training samples using the ED parameter. Moreover, employing the migrated samples considering both parameters has increased the accuracy by 10.45% compared to using the ED parameter to migrate the training samples, and by 5% compared to using the SAD parameter to migrate the training samples.The analysis of the percentage of migrated training samples in different land cover classes showed that, on average, 80.6% of the training samples of the water class, 75.4% of the bare land, 71.2% of the built-up, 64.6% of the grassland, 60.2% of the cropland, and 54.4% of the wetland were migrated from the reference year (2022) to each of the target years (2002, 2007, 2012, and 2017). The accuracy assessment of the migrated training samples in different land cover classes also showed that the water, built-up, bare land, grassland, cropland, and wetland classes had the highest accuracy in the migrated training samples, in that order.The classification of satellite images was performed using Landsat images between 2002 and 2022. Accordingly, satellite images were classified into six different land cover classes. The accuracy assessment results showed that the overall accuracy of the classified images in 2022, 2017, 2012, 2007, and 2002 was 94.95%, 91.93%, 90.74%, 89.45%, and 88.94%, respectively. The accuracy assessment of different land cover classes based on two parameters, producer accuracy and user accuracy, showed that the water class has the highest producer and user accuracy among different classes (98.2% and 99.34%, respectively in 2022). In contrast, the wetland class had the lowest producer and user accuracy (90.1% and 91.25%, respectively in 2022).The analysis of land cover changes between 2002 and 2022 showed that the area of bare land, water, and wetland classes decreased from 2002 to 2022, while the area of built-up class increased during this period. Furthermore, the grassland and cropland classes did not exhibit a constant trend of change during this time period, and their trends varied by year. However, the area of these two classes increased in 2022 compared to 2002. The analysis of the changes in the area of built-up class throughout this period shows a significant increase in the area of this land cover, which has increased from 20.38 square kilometers in 2002 to 123.98 square kilometers in 2022.It is suggested that in future studies, other satellite images, like Sentinel-2, be used to migrate training samples in order to evaluate the effect of different spectral bands and satellite images on the migration of training samples. In addition, investigating the effectiveness of the training sample migration method in migration training samples of other land covers can be one of the research topics in future studies.

The rapid growth of the urban population and its increasing concentration in metropolitan areas is one of the most obvious developments in the settlements of contemporary Iran. Spatio-temporal population movements have led to complex changes in land cover patterns. This article aims to investigate and analyze the spatial-temporal changes in the urban population and the resulting land cover patterns in the last few decades in the metropolitan area of Isfahan. The research method in this research is descriptive-analytical, and by using the analysis of census data and statistics of urban blocks, as well as the method of classification of Landsat satellite images, it has been done. The findings of the research show that during recent decades, the absolute increase of the urban population has reached the area of 35 km to the area of 25 km of Isfahan city. The results show that the spatial-temporal movement of the urban population in the Isfahan metropolitan area is in the stage of centralization and extensive development. According to the spatio-temporal movement of the urban population, the land cover pattern has also undergone extensive changes. The most important pattern of urban growth resulting from demographic changes in the metropolitan area of Isfahan indicates that the semi-centralized pattern of this region in the 1981s turned into a centralized pattern and was pushed in the 2011s.

Normally, images with a high resolution (temporal or spatial) are available, while there is a limitation in accessing images which are simultaneously high spatial and temporal resolution. While, in some applications, access to images with high spatial and temporal resolution is necessary. Therefore, this study was conducted to downscaling MODIS images to Sentinel-2 spatial resolution by STARFM, ESTARFM and FSDAF spatio-temporal downscaling algorithms in different land cover classes including urban, garden, pasture, agricultural and water classes. The study area was selected with a variety of land covers around the city of Mahabad, Iran. First, the corresponding visible and near-infrared bands in Sentinel-2 and MODIS were selected and necessary pre-processes such as geometric correction were done on these images. Then, Sentinel-2 images were simulated using downscaling algorithms. The results indicated the accuracy of downscaling in the urban, garden and pasture classes compared to the agricultural and water classes. So that the ESTARFM, FSDAF and STARFM algorithms averagely showed the coefficient of determination of 88.25, 87.25 and 86.5 for the urban class, the coefficient of determination of 83.75, 83.25 and 80.5 for the garden class and the coefficient of determination of 90.75, 70.5 and 87.5 for the pasture class in all bands.

Remote sensing is considered as the most important source of spatial data in the current era, which we witness its increasing development in different dimensions. The release of global products of these data in recent years with the aim of easier access and use by experts in geospatial science is one of the dimensions of this development. The land cover product is one of these products that is used more than other remote sensing products. When presenting these products, their qualitative and quantitative characteristics, including their global accuracy, are also published. Expressing the accuracy of these products globally makes it necessary and necessary to re-evaluate their accuracy regionally for the users of these products in different regions of the world.

In this research, the accuracy of the European Space Agency's Copernicus Global Land Service (CGLS), GlobeLand30 and Esri's land cover product were evaluated for regional use in the north of Iran - Mazandaran province. After calculating the area of the classes for each of the land cover products, Pearson's correlation coefficient was used to calculate the correlation between them. For quantitative evaluation, the error matrix was used as one of the most common ways to evaluate the accuracy of land cover products. This method is based on the comparison of classified data and ground reality data. Also, the categorized random sampling method was used to select 1329 evaluation samples in Mazandaran province. For visual evaluation, three areas with dimensions of 6 x 6 km were selected.

The regional accuracy evaluation of the studied products shows opposite results compared to the global accuracy of these products. Based on the global accuracy reported for the studied products, the highest accuracy is calculated for the Esri product at 86%, followed by GlobeLand30 and CGLS at 83-85 and 80%. Meanwhile, based on the regional accuracy obtained from the results of this research, the highest regional accuracy for the CGLS product has been calculated at 84% and then for GlobeLand30 and Esri products at 81 and 75%. In evaluating the regional accuracy of the classes, all three studied products (CGLS, GlobeLand30 and Esri) have acceptable accuracy (above 90%) in the classes of snow and ice (100, 100 and 100%), forest (90, 95 and 98 percent), water (96, 94 and 90 percent) and impervious surface (94, 91 and 90 percent). For the agricultural class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers.In the 3 classes of shrubland, Impervious surface and wetland, the accuracy results are less than other classes for all three land cover products and in the amount of (29, 0 and 13 percent), (65, 66 and 42 percent) and (67, 38 and 0 percent).

Conclusion:

By evaluating and comparing the regional accuracy of three CGLS products, GlobeLand30 and Esri, this research answered the question of whether the accuracy stated in global land cover products can be trusted for regional studies and planning. The results show that the regional accuracy of CGLS, GlobeLand30, and Esri are 84, 81, and 75 percent, respectively, compared to their global accuracy (80, 83, 85, and 86 percent). These results show the difference obtained for the Esri product more than the two products CGLS and GlobeLand30. Meanwhile, the remote sensing data used for the Esri product (Sentinel-2 data) and its pixel size (10 meters) are of higher quality and quantity than the other two products. In fact, these results show that only paying attention to the type of data used and the global accuracy is not enough to use products in regional scales and requires evaluations before using them.In addition, by evaluating the classes of each product and comparing them, the need for this evaluation before using these products seems necessary. The results showed that in the evaluation of the regional accuracy of the classes, all three studied products had an accuracy of over 90% in the classes of snow and ice, forest, water areas and human construction. For the agricultural land class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers. In the 3 classes of shrubland, herbaceous cover and wetland, the results show lower accuracy than other classes for all three land cover products. Significant results were also obtained in the visual evaluation, and it seems necessary to pay attention to this evaluation before the applications where it is important to pay attention to a particular class.

This research was conducted with the aim of investigating the ecological assessment of Jiroft region under the influence of land use changes. For this purpose, from the satellite image in 1990 (Landsat 5-TM) and 2020 (Landsat 8-OLI) to extract land surface temperature with the help of single-band algorithm and land use map with the support vector machine (SVM) classification method. used. Next, to investigate the role of land cover in the creation of thermal islands, the contribution index (CI) and UTFVI index were used to evaluate the ecological situation. The results showed that in 2020, barren areas had the highest land-use change since 90 and decreased by 7,300 hectares, but man-made, agricultural, and irrigated land uses increased the most. The water area has increased with the construction of water dam in the region. The reduction of barren areas and the increase of water levels, agricultural use and gardens have improved the ecological situation compared to 1990, while the temperature of the earth's surface has increased by 11 degrees in 2020, which can be attributed to the increase in global warming. It can be said that although the ecological situation of the region is in a better state than in 1990, the presence of desert lands and rocky outcrops around Jiroft has caused the formation of thermal islands in Jiroft region.

The present study was conducted to investigate the trend of land use changes and its impact on erosion and sediment, using Landsat satellite images of 1987, 2010 and 2019 and EPM model in the Kal-Aji watershed of Golestan province. Land use map was prepared using the maximum likelihood method. Training data were used to evaluate the accuracy of the results. Then, the most important methods of accuracy assessment, including total accuracy and classification kappa coefficient, were extracted. Those for 1987, 2010 and 2019, the total accuracy were 95, 98 and 95 and kappa coefficient were 0.93, 0.96 and 0.93, respectively. Erosion and sedimentation zoning maps were performed using the resulted land use maps, and such factors as slope, lithology, soil and precipitation were performed using the EPM method. The results showed that one of the most important factors in the Kal-Aji watershed is the loss of rangeland vegetation and its conversion into agricultural land. According to the hypothesis, the role of land use changes in the amount and increase of erosion and sediment was significantly determined. That is, with the change of land use, erosion and sediment have also increased. The reasons for the increase in the amount of sediment in the Kal-Aji area are the development of agricultural land uses and the decrease in rangeland between 1987 and 2019 (16.83%). These changes have reduced the important role of rangelands and vegetation of Kal-Aji watershed and have increased the amount of sediment in the outflow of sub-watersheds. Based on the obtained results, the total amount of special sediment was related to the period of 1987 (4353 cubic meters), which increased to 5164 cubic meters in 2019, which indicates the changes in land use that occurred in the watershed.

Land use classification has always been one of the most important applications of remote sensing, and for this reason, different methods have been developed. With the passage of time, more advanced methods with higher accuracy were created, which increased the accuracy and performed better in extracting classes that were spectrally closer to each other. Algorithms for detecting changes in remote sensing images are divided into two categories: pixel-based and object-oriented, based on the minimum processing unit. The purpose of this research is to evaluate and prepare a land use map of the Nirchai watershed in Ardabil province using the object-oriented method. Land use classification including segmentation of image data was done using multi-scale segmentation algorithm in eCognition software environment. Then these parts were selected and classified and evaluated using the object-oriented nearest neighbor algorithm. The results showed that the object-oriented classification has an overall accuracy of 99 and a kappa coefficient of 0.88%, which indicates the high accuracy of the object-oriented method in classification. Also, the land use map showed that the use of water areas and poor pastures occupied the least (70 hectares) and the most (8069 hectares), respectively.

The world is experiencing an unprecedented flow of urbanization. Population growth and urbanization have changed land use in urban areas. One of the consequences of these changes is the increase in the temperature of the earth's surface in urban areas and the formation of a heat island. The main purpose of this research is to monitor the surface temperature of the earth and its relationship with land cover. For this purpose, Landsat 8 and 7 satellite images were taken first. Then, using the supervised classification method - maximum likelihood algorithm, the land cover map was classified into three classes of man-made land, barren land and vegetation and finally, in order to monitor the surface temperature of the earth, the surface temperature map of Kashan city was extracted using single channel algorithm. The analysis of land cover showed that man-made lands increased by 21.05 percent during the years 1385 to 1400, and barren lands and lands with vegetation (with 11.17 percent and 9.88 percent, respectively) has had a decreasing trend. The minimum temperature of the earth's surface has reached from 34.87 degrees Celsius to 42.33 degrees Celsius in 1385 and 1400, respectively. Also, during the 15-year period, the maximum temperature has increased from 59.63 degrees Celsius to 60.70 degrees Celsius. Vegetation has the lowest surface temperature in the studied years. Due to its climatic and spatial nature, the city of Kashan, like other arid and semi-arid regions of Iran, has a lower surface temperature than its surrounding environment, this phenomenon is known as urban cool islands. Moran's spatial autocorrelation analysis at the 99% confidence level showed that the surface temperature data of Kashan city are distributed in clusters. According to the Gi* index, in all the studied years, the maximum temperature belongs to barren lands.

This study aimed at evaluating the supervised pixel-based classification of the maximum likelihood in the examination of the climactic changes of the Urmia Lake catchment area in 1-, 6-, 10-, and 14-year spans and then evaluating and modeling the changes in the gardens neighboring Lake Urmia during a 30-year span and its role in the changes at Lake Urmia water level. In this study, first the Landsat images of the years 1990, 2000, 2014, 2020, and 2021 were downloaded. Then, using the ENVI5.3 software, a classification was made based on the maximum likelihood method. Next, the IDRISSI TERRSET software and CA-MARKOV model were used to model conditions for the year 2051. This model was then analyzed in the GIS software. It was found that the classification based on the maximum likelihood method has been an appropriate one. The results of this classification showed that overall, the maximum changes from 1990 to 2021 has been related to the irrigated gardens and fields. In this period, 3495 square kilometers have been added to these lands, i.e., the number has doubled. Modeling the 2051-year conditions in the light of the transition probability matrix showed that the maximum likelihood of land use changes is in the irrigated garden and farm use. On the other hand, the modeling in this study indicated that there has been a reduction in the expanse of irrigated gardens and fields as well as salt lands in the region. Finally, the model predicted an increase in water area in 2051.

So much of the river sediment occurrs due to bank erosion. Understanding and predicting bank erosion is a vital requirement for river management. During  a  flood, river flow shear stress exceeds river bank materials shear strength and causes a meandering. To prevent a river accresion in a floodplain, it is necessary to understand the river and perform river management and organization operations, especially in erosion reaches. Bank erosion occurs commonly during a relatively long reaches of the river. Due to the high cost, stabilization practices are only possible locally. In order to reduce this phenomenon, it is necessary to create vegetation in river reaches. The Bank Stability and Toe Erosion (BSTEM) model is one of the most widely used models in the world in most river engineering projects developed by the National Sediment Laboratory in Oxford, Mississippi, USA. The BSTEM model was initiallly developed by Pallen Benkhead and Simon to investigate riverbank stability under vegetation and water conditions.Subsequently, in BESTEM model, various plant and hydraulic parameters such as slope patterns, roughness, angle, and pore water pressure are used. BSTEM is a bank erosion model that examines hydraulic processes, toe erosion and bank failure in soil homogeneous layers and is a suitable tool for determining riverbank conditions in order to protect river from streamflow erosion. A review of the research studies conducted in this regard showed that although the BSTEM model is a model with a long history and validity (based on numerous projects, articles and dissertations) and had been used in different countries and important rivers of the world to study the stability and bank erosion. In Iran, few studies have been done on the BSTEM model, which shows the need to conuct further studies for the purpose of validating this model. Therefore, this study was designed using the BSTEM model to investigate the stability of the river bank with respect to existing plant species in a section of the Bashar River in the city of Yasuj.

In this study, the Bank Stability and Toe Erosion (BSTEM) model has been used to investigate the stability of the reach of Bashar River in Kohgiluyeh and BoyerAhmad Province. This model is one of the most comprehensive models used for river engineering and management operations in the world, which besides stability, the failure rate, safety factor, erosion rate, sediment loading rate, and river accrestion rate are calculated. The appropriate reaches of the Bashar River for modeling, should have the same characteristics in terms of morphology, geology, land use and have a suitable length (300 meters). The study was condcuted by analyzing two parts: control (without cover, length = 300 meter) and experimental (with cover, length = 300 meter). Because of using the BSTEM model, it has been necessary to select several sections in the selected range. For this purpose, topographic maps and aerial photographs of the area were prepared. Then, with the help of these maps and Google Earth satellite images, a reach of the Bashar River was selected near the Darshahi region for modeling. Then, the selected reach was divided into two parts: control (without cover) and experimental (with cover) with the same length of 300 meters, and each part was divided into 3 sections of 100 meters. Shear strength of tree species was measured in cross-sections. The reserachers performed data comparison analysis with HEC-RAS software and BSTEM model.

The results of flood simulation in the HEC-RAS model in section 1 with cover reach and section 4 without cover reach showed that, in a 2-year flood, the average flow velocity in the middle of the cross section is 2.18 and 1.41 m/s, respectively (the estimated speed of the HEC-RAS model), and the flow height were 1.39 and 2.72 meters, respectively. The results of this study also showed that in terms of stability, there is a difference between sections with land cover (Fs <1) and no cover land (Fs <1). The results also pointed to the fact that the average width and volume of failure of the river bank in the no land cover section was 2.72 meters and 6280 cubic meters, respectively, and in the land cover section was 1.42 meters and 1686.33 cubic meters.

The results showed that in terms of stability, there is a significant difference between covered and no covered sections because of the safety factor, which confirmed the instability of the studied reach. Based on the results of uncovered sections, due to fact that their geometric condition are unstable, the need for river management operations intesifies. The results showed that for sections with vegetation, the safety factor is higher than one, which indicated the bank stability in the studied reach, while in sections of no cover, the river is unstable. Increasing tensile crack depth, and bank angle resulted in bank instability, and increasing river flow level, groundwater level and suction matrix can lead to bank stability. In order to prevent bank river erosion, cultivation and growth of plants that are compatible with the climate and the erosive conditions are necessary. Therefore, it is suggested that native plants and other various plant forms be used to prevent bank river erosion. In addition, comparative studies need to be performed on other species in order to determie the role of plant cover on river bank stability in other rivers.

Today, dust events in western and southwestern Iran have become one of the most important environmental hazards. The main purpose of this study is to reveal the relationship between land cover changes and dust distribution in Khuzestan province during the last two decades. In this regard, land cover data of MODIS sensor, Aerosol optical depth product of the same sensor aerosol and cross-analysis matrix technique were used to reveal the relationship among them. The results showed that firstly, the dominant coverings of Khuzestan province were generally pastures, barren lands, agricultural lands and urban areas. Analyzing the 20-year trend of these uses showed that rangelands, decreasing trend, agricultural and urban lands had an increasing trend and barren lands also had a relatively stable trend. However, the results of spatial analysis of AOD index showed that the mean and standard deviation of this index have reached from 0.35 ±09 in 2000 to 0.44 ± 0.04, in 2020 in the province during the last two decades. The important finding obtained by the cross-analysis matrix along with the analysis of periodic trends was that the use of medium to rich rangelands, which had not increased in the last two decades, the level of AOD in this use, with a slope of 6500 hectares per year, had a decreasing trend, while the agricultural land use on which the AOD index has had an increasing trend, with an annual slope of 18,700 hectares has had a significant increasing trend. Therefore, in general, it can be said that the increase in the area under cultivation of agricultural lands, and the lack of control of barren lands in the province, has led to the intensification of dust in the province over the past two decades

Land use research programs at a global scale have become central to international climate and environmental change research since the launch of land use and land cover (LULC) change project. LULC has two separate terminologies that are often used interchangeably. Land cover refers to the biophysical characteristics of earth surface, including the distribution of vegetation, water, soil, and other physical features of the land, although land use refers to the way in which land has been used by humans and their habitat, usually with an emphasis on the functional role of land for economic activities. Land use and land cover change is the conversion of different land use types which is the result of complex interactions between humans and the physical environment. LULCC is a major driver of global change and has a significant impact on ecosystem processes, biological cycles and biodiversity and also lead to some hazards such as dust storms. Today, dust events in western and southwestern Iran have become one of the most important environmental hazards. The western and southwestern regions of Iran, is faced with the phenomenon of dust and its problems significantly. In the west and southwest of Iran, there are desert areas such as the deserts of southern Iraq, Saudi Arabia and farther north in Africa, which causes frequent occurrence of dust with different intensities throughout the year in this part of the country. In addition to the mentioned deserts, the role of the local dust area is also very important in injecting dust into the air of the province. Among these, the role of extensive land use changes in Khuzestan province, including drying of wells and wetlands, degraded pastures with overgrazing, abandoned rain-feed agricultural, expansion of irrigated agricultural, are among the important changes that have coincided with the increase of dust. The main purpose of this study is to reveal the relationship between land cover changes and dust distribution in Khuzestan province of Iran during the last two decades (2000-2020).

In this study we used two types of data: the land cover data of MODIS sensor (MCD12Q1), and Aerosol optical depth product (AOD)(MOD04) during a 20-year period (2000-2020). The MCD12Q1 product of MODIS Terra+Aqua identifies 17 classes defined by the International Geosphere Biosphere Program (IGBP), including 11 natural vegetation classes, 3 human-altered classes, and 3 non-vegetated classes. The AOD data daily level-2 (MOD_04) data are produced at the spatial resolution of a 3 x 3 1-km (at nadir) pixel array. The aerosol product includes the "deep-blue" algorithm recently developed to get aerosol optical thickness over bright land areas. In this study, since the purpose was to investigate the status of spring and summer atmospheric aerosols in Khuzestan province, so MODIS sensor of satellite Terra (MOD04-3K) and Aqua (MYD04-3K) with a spatial resolution of 3 km for Khuzestan province were used out of 3650 frames of AOD product. In this study, the cross-analysis matrix technique in GIS was applied to reveal the relationship between AOD and land cover class. The temporal trend also was investigated using regression models during 2000-2020.

The results showed that firstly, the dominant coverings of Khuzestan province were generally pastures (Grassland), barren lands (Bare land), agricultural lands (Crop land) and urban areas (Built up class). Analysis of the 20-year trend of these uses showed that grasslands area has the decreasing trend, while the agricultural (cropland) and urban lands (built up area) had an increasing trend during last 2 decades. Barren lands also had a relatively stable trend, although the results of spatial analysis of Aerosols Optical Depth (AOD) index showed that the mean and standard deviation of this index in Khuzestan province during the last two decades, changed from 0.35 ±09 in 2000 to 0.44 ± 0.04, by 2020. An important result obtained by the cross-analysis matrix along with the analysis of periodic trends was that the use of medium to rich rangelands, which had not increased in the last two decades, the level of AOD in this use, with a slope of 6500 hectares per year, had a decreasing trend. However, the agricultural land use on which the AOD index has had an increasing trend, with an annual slope of 18,700 hectares has had a significant increasing trend.

In terms of foresight, it can be expected that if the situation of the two dust drivers in the region follows the same trajectory of the last 20 years, i.e. agricultural lands, especially irrigated lands, will have an increasing trend, and on the other hand, the province rangelands will trend. If they have a decrease, we should expect dust events with much more intensity and density in the province.

Up-to-date information on land use and land cover, especially in urban areas, and awareness of its changes and developments over a period of time is very important for planners and managers. The present study aims to monitor spatial changes in land use and land cover in the urban area of Babol and Amirkola in the period 1986 to 2020 and forecasting it for 2030. The present research is applied based on the purpose and descriptive-analytical based on the nature and method. Data are taken from Landsat 5 and 8 satellite images. The Fuzzy ARTMAP model was used to detect and classify images, the cross-tab and LCM models were used to analyze land use and land cover changes, and the CA-Markov model was used to predict land use and land cover in 2030 in the IDRISI TerrSet software environment. ArcGIS software was also used to draw the final maps. The results showed that the conversion of garden-use and irrigated agriculture into the built environment has a growing trend, so that these lands have increased from 11.3% in 1986 to 19.7% in 2020 and according to the forecast by 2030, built-up land will reach 22.2% of the total area. Also, the ratio of built-up to non-built land has increased from 12.8 percent in 1986 to 24.5 percent in 2020, and is projected to reach 28.6 percent in 2030, which shows the high rate of garden and agricultural lands destruction.

The purpose of this study is to monitor the effects of exploration and melting service on vegetation in areas of Varzeqan city, including Andaryan. The heap leaching method is used to produce gold in these areas, it is more likely that the chemicals used in the melting services would spread in the environment or penetrate. Vegetation is one of the phenomena that is severely affected and reduced by environmental pollution, especially cyanide leakage and infiltration. Therefore, monitoring of vegetation trends may put an end to such concerns. Therefore, the study area is divided into three parts: Zone 1 (mined area), Zone 2 (pristine and untouched area), and Zone 3 (downstream of Zone 1). For this aim, Landsat 5 and 8 satellite data in the period 1984-2019 were used as time series and temperature and precipitation data of Varzeqan station in the period 1989-2017. Using Normalized Difference Vegetation Index (NDVI), vegetation density was driven as a pixel scale (a pixel = 900 m2), the non-parametric Mann-Kendall statistical test was applied to NDVI at 95 and 99% confidence levels. Then, the areas of a significant reduction were analyzed using a sequential Mann-Kendall test to investigate the starting point of a significant decrease in all three areas. The results show that chemicals materials have not been returned into the environment and the climate controls land cove changes in the study area.

Land use and land cover change are critical motivations for environmental changes. It mainly arises from human activities, e.g., the expansion of urban areas, the changes in agricultural land areas, and the destruction of water area which rooted in population growth. The present research used a combination of cellular automata (CA) and the Markov chain to simulate the present land-uses in the Lake Urmia Basin using remote sensing data. First, the land-use map was produced by the maximum likelihood classification method using the Landsat satellite imagery for the years 1998, 2008, and 2018. After the integrated CA-Markov approach assessed the model, the land-use maps were predicted for the years 2028 and 2038. The trend of land-use change between 1998 and 2018 revealed that agricultural areas and urban/human-made areas have increased by 3.31 and 2.74 percent, respectively, but water areas and other uses have decreased by 6.87 and 0.71 percent, respectively. The kappa coefficient was estimated at 80% for the model, implying its high accuracy in predicting land-uses. Based on the simulation results for 2028 and 2038, agricultural land area and urban/man-made areas will expand by 40.12 and 476.36% versus those in 1998 whereas water areas and other uses will shrink by 26.67 and 5.80%, respectively. The results can greatly help policymakers and managers of natural resources to make management decisions on land uses in different regions.

Population growth, development of residential areas, as well as man-made land use, are causing landcover change. Shorelines are more sensitive to landcover change. Therefore, the current study aims to evaluate the trend of landcover changes in the southeast of Bushehr province from Asaloyeh city to Shirino village. The study used Landsat satellite images from the 1990, 2000, 2010 and 2019 and the LCM model to assess the trend of landcover change. The procedure is that first the landcover maps are prepared and then the process of land use change changes, changes in other landcovers to man-made areas and also the coastline change process is evaluated. The assessment of the change indicates that the use of man-made areas has had significant changes in a period from 1990 to 2019 under the influence of the development of economic activities and in parallel with the development of residential areas, refineries, as well as other facilities related to the Gas Company. Depending on the location of the study area and the type of anthropogenic, man-made use (refineries and other facilities and infrastructure related to the gas company), land-use changes mostly direct to coastline. Therefore, 11.2 km2 of the sea has been changed to an arid area during a 29-year period. Besides, the development of economic activities and job creation, residential areas in Assaluyeh and Nakhl have experienced significant growth, and this development has further changed the use of sandy lands to the construction of man-made areas.