detection

In parallel with the ever-increasing urban population, the amount of construction in the city space has been developed. The development of construction in the horizontal space and regardless of the existing restrictions has led to environmental, economic and legal problems for the citizens. Achieving the amount, intensity and direction of development Construction from the past until now and forecasting the construction situation in the future is the first step towards the scientific and practical management of the physical development of urban construction, and planning and providing suitable solutions in order to create a balance between the spatial allocation of construction and all kinds of legal, economic and environmental considerations. The purpose of this research is to model and predict urban growth using satellite images and CA-Markov model.

In this research, firstly, using multi-time Landsat images related to the years 1976, 2001 and 2021, land use changes were investigated, and then the spatial expansion of Gorgan city in  (2021 AD) and  (2050 AD) was predicted using the CA-Markov model. Based on the results of this research, the changes in land use and the level of land use in the area have been calculated and compared.  The geographical area of ​​this research is the city of Gorgan.

The results of this research show that the largest increase in land use is related to urban land use (built land use), which increased from 6005.79 hectares in 2021 to 7141.66 hectares in 2050. Based on the results of this research, the growth of the city of Gorgan in the coming years will go towards the agricultural lands around the north, northwest and northeast.

Vegetation has always been affected by various environmental and human factors that have directly or indirectly affected the conditions and performance of the environment over time. Consequently, monitoring and investigating the vegetation cover in the northern regions of Iran is also highly considered important. Research suggests that the destruction and change of vegetation cover and forests are among the most important factors influencing natural hazards such as floods, erosion, and earthquakes. In addition to processing and presenting well-known spatial data, remote sensing can also be used to improve human understanding of annual changes in vegetation cover, from a local to a global scale. In this regard, the anomaly evaluation criterion with high differentiation can separate and display anomalous areas in order to recognize the change process and reveal the areas with anomalies over time. Thus, medium-resolution images, vegetation indices, and anomaly criteria can be used to evaluate long-term vegetation changes. Therefore, a positive step in reducing the environmental effects of a region can be made by locating the urban areas that have experienced changes over time and making decisions related to future planning.

This study utilized a time series of Landsat 5, 7, and 8 images downloaded from the Google Earth engine. To get the best representation of the vegetation in this study, spring and summer were chosen because vegetation at this time is at its greenest. The main focus of this study was on the evaluation of vegetation changes over time quantitatively and qualitatively, using remote sensing data from Google Earth Engine to prepare a map of vegetation changes over time. The general process of implementing this research can be summarized in 7 phases. The first phase involves taking Landsat images and preparing statistical meteorological data. In the second phase, the time series images were collected according to the specific period and in the third phase, the obtained images were corrected and pre-processed. As a next step, the EVI index is extracted from all Landsat images, and then to determine the anomaly of changes, a series of statistical analyses, including the mean and standard deviation, are applied. The next step involves generating the map of anomalous time series changes and extracting the map of vegetation changes to improve understanding. The end of the process also includes evaluating the results obtained from this research.

Since vegetation and drought changes are non-uniform depending on location and distance from the sea and humid areas, and vegetation is destroyed to build villas, residential areas, commercial areas, and towns, several study areas were divided into smaller pieces. Then each area was analyzed and evaluated separately for its changes. It has been observed in the first and third study areas that vegetation has generally been on the rise in the past 36 years, although sometimes there have been anomalies and fluctuations in EVI value. It was significant to see the reduced vegetation in 2008 in both regions. For example, 262.5 mm of precipitation in the first region fell this year, indicating a rain shortage. The results obtained from the second region, considered one of the coastal regions, indicate that the anomaly graph in the region during the period had a downward slope in the direction of decreasing vegetation, and EVI values reached 0.14 in 2005 and 0.09 in 2013. The 4th and 5th regions have shown a lot of fluctuations in anomalous changes and EVI values, although the trend has generally been downward. Results obtained in the 4th region show that vegetation cover peaked in 2004 and 2011. Rainfall in the 5th region, a highland region, in 2008 was deficient, with 259.8 mm reported by the meteorological station. The anomaly value in this year was -1.96. According to the Department of Meteorology in Mazandaran province, most droughts that have affected the underground water in the province have taken place in coastal and plain areas in the province's east and center, and in western cities, they have mostly affected mountainous areas.

Thirty-six years of EVI time series images obtained from Landsat images were utilized in this study to investigate the changes and identify anomalies. In order to conduct a more detailed investigation, the study area was divided into several different regions, and each region was evaluated separately. The results obtained with existing meteorological statistical data were analyzed because vegetation can be affected by climatic and environmental conditions such as weather conditions. According to the results from study area )4(, vegetation cover has consistently decreased over the last three decades due to various factors like tree cutting, landslides, or land use changes. As shown in the map showing the obtained changes, there appears to be an increase in the value of the vegetation index in some northern areas of Chalus city until around 2002, indicating an improvement in greenness. While In some areas close to the Caspian Sea and the coastline, because of the construction of villas and commercial areas, there has been a loss of vegetation, such as in area (2) based on the changed map, a major part of the vegetation in that area has been destroyed because of the establishment of a settlement and construction of a road. As a result of comparing the evaluation of two anomaly approaches, it has also been concluded that both modes show almost the same trend of changes, but the graphs in "Anomaly compared to the overall average" mode compared to "Anomaly compared to the average of each set" display the change process better.

Flood is one of the most important natural disasters in Iran. Floods carry adverse effects such as life and financial risk in the years to come, as floods are more likely to occur and also because population growth is likely to result in more people settling in flood-vulnerable areas. Monitoring and revealing flooded areas has a lot of function in managing the crisis and reducing the damages of the areas in case of the possibility of future floods. Based on this, the purpose of this study is to monitor and detect the flood event of 1398 in Lorestan province using satellite data in Google Earth Engine system. This system can be used to process and analyze flood maps without the need to download data or use high computing hardware. In this article, the time series data of TRMM and GPM satellites were extracted for the period of one year and one month of Farvardin 1398 and showed that the peak of heavy rainfall was on the 5th and 12th of Farvardin. Images related to the detection of flooded areas were also produced and analyzed using the data of Sentinel 1 and Landsat 8 satellites. The spatial survey of the flood areas in the relevant images shows that the cities of Noorabad, Al-Shatar, Borujerd, Durood, Azna and Khorramabad were the most and most prone to flood areas in Lorestan province and were more affected by floods than other cities. Also, the surface area of flooded areas for April 2018 in Lorestan province was estimated as 673.82 square kilometers. Finally, the results of land cover studies showed that the advance of flood was more in areas with grasslands, agricultural lands and urban and built areas.

Extreme weather and climate events have received increased attention in the last few years, due to the often-large loss of human life and exponentially increasing costs associated with them. These events have profound impacts on economies and livelihoods of many regions of the world. In this regard, diagnosis of extreme events in terms of intensity, duration, and frequency is crucial in order to frame mitigation and adaptation strategies to counter the impacts due to climate. The purpose of this research is to analyze the trend of changes in precipitation extreme indices to reveal the occurrence of climate change in Najaf Abad using 20-year daily statistics of Najaf Abad synoptic station from 2003 to 2022. For this purpose, the extreme indices of precipitation and the non-parametric Mann-Kendall test were used to investigate the changes. The results showed that the precipitation extreme indices present different patterns of intensity, duration and frequency of precipitation. PRCPTOT, CDD, CWD, R10mm, R20mm, R95p, Rx5day and SDII indices are decreasing and Rnnmm, R99p and Rx1day indices are increasing. But in general, due to the high dispersion and low amount of rainfall, the regional rainfall pattern cannot be clearly identified. The results of the analysis of the threshold indices of precipitation showed that the precipitation does not follow a regular trend and is irregular. The results of this research can provide solutions to workers and managers in line with correct management and use of other capacities to deal with possible situations. In addition, the combination of the results with long-term meteorological data and the perception of local communities affected by climate change can be examined and become a model for managers to make more appropriate and relevant decisions and plans.

Identification of landforms is one of the basic studies in the Knowledge of geomorphology. The importance of identifying landforms are due to their application in various rural and urban planning, landscaping and tourism planning.Traditional methods of identifying landforms like to Visual interpretation, are both time-consuming, boring and costly, particularly on a large scale and are not sufficiently accurate in identifying the boundaries of macro-landforms. Subjectivity and the application of personal tastes is one of the most important issues in only traditional methods. Because it does not have enough accuracy in determining the boundaries of landforms and sometimes the type of landforms. The solution of this problem can be investigated in digital images context analysis. In this research the study area is Yazd-Ardakan basin.It is located almost in the center of Iran. This basin is geomorphologically limited to Ardakan Playa from the north and from the south direction is limited to Shirkuh heights, and from the east to Khoranagh sub-basin and finally from the west to Nodooshan sub-basin is connected. The purpose of this study is Detection of the macro geomorphological landforms in Yazd Ardakan basin using optimum composition index.

In this research, the ETM+ sensor data from 7th generation Landsat satellite has been used to identify the large landforms such as Playa, pediments and, etc. The data of this sensor have many applications in the field of geomorphology such as shoreline displacement, sedimentary and erosion basin identification, mountain front identification, plains, beaches, separation of lithological structures, water networking and, etc. which in this study are geomorphological Yazd-Ardakan has been discussed. In this study, were used 9 bands of ETM + data related to 162 pass and 8th row and dated 19 August 2010, which were almost without cloud cover or cloud free. Exact time of imaging according to the information in the metadata file was nine hours, eleven minutes and thirty-nine seconds. Also, the general geomorphology map extracted from the geomorphology map of Iran related to the Institute of Geography of the University of Tehran and also Google Earth software have been used to investigate the accuracy and compatibility of the identified landforms resulting from digital processing of satellite images with terrestrial reality.In this study Geo-statistical methods were used.The Optimum Index Factor provides the best and most suitable band composition among the possible types of spectral band compositions based on the amount of total variance and correlation between band compositions.In this method, the selection of the optimal combination between the bands is done by quantitative evaluation of the effects in the image.This method avoids wasting time due to the large number of possible RGB compounds in the visual analysis process. OIF values are used to determine the most optimum bands composition, and bands are ranked according to their statistical information, which includes total variance and correlation between different bands.The best band combination of all three possible band combinations has the highest amount of information and the lowest repetition rate. In this study, the "law of composition" in statistics was used to select "r" objects (here a combination of 3 bands) from "n" objects (here 30-meters Landsat bands). This law is in the following relation:C(n,r)=n!/r!(n-r)!Where: C(n,r) is a combination of r object from n object and sign! It is called factorial and the factorial of any natural number (here 6 spectral bands of ETM + sensor) means the product of that number multiplied by all natural and integer numbers before itself In general, the product of n × (n-1) × (n-2) ×… 2 × 1Therefore, 20 bands compositions can be written for ETM+ sensor reflective bands, of which only one combination is the most optimal and desirable band composition for displaying geomorphological features of the earth surface due to having the most non-repetitive information, and it is the compound that have with the highest OIF statistical value.TIn order to calculate the OIF that shows better the surface landforms on a large scale, the correlation coefficients between the different bands must be calculated. Here, to show the degree of correlation between different ETM+ bands, a correlation matrix is used, which have always number one in his main diagonal.

First, the basic statistics (including mean, minimum, maximum, standard deviation and eigenvalues) were calculated for 30-meter bands of ETM+ sensor. The mentioned parameters are used in calculating OIF. Then the correlation matrix was calculated between ETM+ spectral bands in the ENVI software. Finally, the OIF index table was obtained for all possible three-band combinations by the basic statistics table and the correlation matrix table. The results of the recent table showed that the best and most optimal band compositions that lead to the maximum Detection of landforms are related to rows 12 and 20 of this table, because the mentioned rows had maximum OIF values. Other results of this research were the produce of a raster map and vector map of identified large landforms obtained by OIF.

This study showed that the higher the standard deviation between a three bands combination and the correlation coefficient between them be less, the OIF index have the higher value. This means that the three bands color combination is one of the most non-repetitive information items. In this study, two bands combinations of 124 and 234 of the ETM+ sensor had the highest values, and both combinations had approximately 54 OIF values. The advantage of calculating this index are 1. Finding the best three bands color combination to highlight and Detection the image, 2. It is fast and repeatable due to digital image processing so that if we want to visually determine the best three bands combination, it is very boring and time-consuming. The Results of the OIF index has shown that the best bands of twenty different combinations, is two-three-four spectral combination. Because the OIF index was greater than the other band combinations (OIF=54.01). The combination of recent spectral bands (two-three-four) led to identify the mountainous and flood spreading pediments too Sebkha landform.

In recent decades, built-up urban areas have expanded rapidly as a result of population growth and economic development. In developing countries, this trend is faster. It is essential to Know the trend of rapid land-use changes for urban managers to plan for the future growth of the city while providing appropriate urban services. Satellite imagery is a reliable source in built-up areas extraction. Among the various types of satellite imagery, radar imagery is effective in urban areas extraction because they captured images in all weather conditions and ascending and descending orbits. In this study, the performance of the time series of ascending and descending images of Sentinel 1 in VV and VH bands were evaluated in the extraction of built-up areas. The areas with high slopes were masked using a digital elevation model to reduce the effects of geometric distortions. The threshold of the built-up areas was extracted from the image histogram using the Otsu automatic threshold algorithm. The results were further evaluated by a high-resolution Google Earth image. In both polarimetric bands, the image in descending orbits has higher overall accuracies in comparison to ascending orbits. The overall accuracies in VV and VH were 90% and 87% in the descending orbit and 88% and 84% in ascending orbit, respectively. The findings of this study show that the VV image has higher accuracies in both orbits in comparison to the VH image. The descending image in VV has 90% overall accuracy in urban area extraction in Isfahan city.

One of the most important challenges for the human communities is Global Warming. This vital problem affected by Climate Change and corresponding effects. Thus this article attempted to assess the trend of real climate variables from synoptic stations. Daily precipitation, Daily Maximum Temperature and Daily Minimum Temperature have been selected for the Hillsides of Southern Central Alborz Mountains and have been tried to prove climate change and attribute the related forcing such as Greenhouse Gases. The Capital of Iran located in this region and this region has a special occasion, because at least a quarter of Iranian population live in these provinces (Tehran and Alborz) and four big dams located in this region. The Intergovernmental Panel on Climate Change’s defines ‘‘detection’’ of climate change as ‘‘the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change,’’ while ‘‘attribution’’ is defined as the process of evaluating the relative contribution of multiple causal factors to a change or event with an assignment of statistical confidence. Regional D&A studies provide an insight to local changes in natural systems and may help in planning and developing robust adaptation strategies. Previously, formal detection and attribution have been used to investigate the nature of changes in various climatological variables such as air temperature, surface specific humidity, ocean heat, sea level pressure, continental river runoff, global land precipitation and precipitation extremes. However, almost all of these studies deal with climatological or meteorological variables at the global or continental scale. Studies which have attempted to formally detect and attribute regional hydrometeorological changes to anthropogenic effects are rare. Regional-scale D&A analysis is more difficult because the detection of anthropogenic ‘‘signal’’ in natural internal climate variability ‘‘noise’’ is determined by the signal-to-noise ratio which is proportional to the spatial scale of analysis, especially for real observation data. For overcoming this issue interpolation method (IDW) has been applied to transfer point data to area (gridded) data. The point data gathered from 3 synoptic stations (Mehrabad, Karaj and Abali). Then transferred data have been Standard and Averaged for 3 years. Standard values of annual and seasonal amounts have been computed for individual stations as the average of the standard values of annual and seasonal amounts available 3 years anomaly values. Estimates of annual or seasonal variables anomalies were obtained by averaging the annual or seasonal by 12 or 3 respectively. For detecting and attributing 3 simulation signals (ALL, GHG and NAT) selected from Canadian General Circulation Model (CanESM2.0) of CMIP5 archive subcategories. Space–time series of observations and model simulated variables responses to external forcings (the “signals”) first have been compared qualitatively by computing correlation coefficients between observations and simulations. This simple method does not optimize the signal-to-noise ratio nor provide a quantitative measure of the magnitude of model simulated response relative to that in the observations. Nevertheless, it provides an easy-to-understand view of the similarity between observed and model-simulated changes. Optimal detection and attribution analysis very often requires a reduction of dimensionality. This is typically done by projecting both observations and simulations onto leading empirical orthogonal functions (EOFs) of internal variability and using the residual consistency check to determine the number of EOFs to be retained in the analysis. To produce internal variability for residual test and consistency, Pi-Ctrl Runs have been used. The Preindustrial simulations have high volume, this subject complicates calculation therefore Experimental Orthogonal Functions (EOFs) have been used to reduce the Pi-Ctrl simulations volume and provide situations for Optimal Fingerprint. Optimal Fingerprint method is the best method for Detection and Attribution. Results have been obtained by this manner indicated Global Warming affected the study region by affecting on mean cumulative winter precipitation (0.88), mean spring minimum temperature (0.78) and mean summer maximum temperature (0.76). These numbers are the beta coefficient that named scaling factor. Although the scaling factor for the mean spring minimum temperature affected from GHG signal obtained (0.73), but the GHG forcing alone didn’t have a significant effect on the precipitation and maximum temperature. Also, NAT signal didn’t have significant effect on the region alone, too. The obtained results of this study indicate the earlier studies, such as Wan et al, 2014.

This project is an updated estimate of the climate change trend in Iran. The time series of monthly and annually mean, maximum and minimum temperature (°C), relative humidity (%) at 2 m height, wind speed (m.sat 10 m height, sun hours (h), precipitation (mm), radiation (Mj.m-2day-1) and cloudiness data at 27 weather stations over Iran are collected from IRIMO for the period 1958-2017. Quality control, detection and modification of non-climate heterogeneity of data was performed. Due to missing data, the Sun hours trend was calculated for the period 1992-2017.

Methodology and data:

Among all stations of Iran, 27 stations have 60-yearly data in the period of 1958-2017. In this research the changes in temperature, precipitation, humidity, radiation, cloudiness and wind during the period of 2017-1958 (for sunshine the period was 1992-2017) were investigated. At first, the data were checked for quality controlling. Then their non-climatic heterogeneities were fixed. The slope of the trend was determined using the least squares method and the slope estimator and their significance was assessed using nonparametric Man-Kendal test and regression.

Temperature: The results showed that all stations in the country face a significant increase in the annual minimum temperature. The minimum temperature increasing rate per decade calculated to be between 0.2 and 0.4  degree of Celsius in Bandar-Abbas and Tehran, alternatively. In general, minimum temperatures rise were detected at all stations and in all seasons, especially in autumn and winter. The annual maximum temperature trend is also increasing, but the rate of increase in maximum temperature is less than the minimum temperature. The increasing rate per decade calculated to be between 0.08 and 0.3  degree of Celsius in Zanjan and Ahwaz, alternativelyPrecipitation: The 60, 30, and 10-years averaged annual precipitation of Iran calculated to be 230.8, 222.4 and 199.3 mm, respectively. The results showed that during the 60-year period, the average rainfall of the Iran decreases, with a rate of 0.43 mm per year (4.3 mm per decade), although the precipitation decline is not significant at 95% level.However, during the last 30 years (2017-1988), the average precipitation of the country has dropped by 2.2 millimeter per year (22 mm per decade), which is significant in 95% confidence level; meaning that precipitation reduction in the most recent 30 years is about four times higher than that of past 60 years.In Figures 2 and 3, the all-country time series of rainfall and temperature changes are shown in the 60- and 30-yearly basis.The country's declining precipitation for the most recent 30 years is about four times faster than the decline of most recent 60 years. The rapid decline in country precipitation over the past 30 years, which is significant   in 95% level, is consistent with the intensification of global warming in the most recent 30 years period.Other parameters: The average wind speed in many parts of the country has increasing trend, which is significant in many stations located in the west, center, and northern part of the country. Average relative humidity has decreased in many regions of the country. The most decreasing trend was observed in southwest and west of the country. Of course, in a few cases such as Gorgan and Rasht, there was an increase, which was not statistically significant. Sunny hours trend was calculated in the period of 1992-1992, and interestingly, seasonal and annual trends at most stations indicate an increase in the number of sunny hours. The total number of days with sky overcast in the west of the Caspian Sea, western part of the country, and stations such as Kerman, Sabzevar and Shahrood has decreased significantly..

Landslides are one of the most common and dangerous threats in the world that generate considerable damage and economic losses. Landslides detection and monitoring are two important research aspects of landslides analysis. There are different geodetic and non-geodetic methods to measure slope instability. Geodetic methods includes ground observations via GPS, Total Station and laser scanners. Observation based on mentioned methods provide accurate and continuous measurements at limited points in unstable regions, but none of them has the ability to determine the extent and pattern of spatial unstable regions. On the other hand, the repetition of each of these methods, especially when it is intended for a large area, is very costly and time-consuming. The existence of such constraints has always been one of the fundamental challenges faced by researchers in relation to precise measurements and spatial monitoring of land surface changes. Due to the fact that landslides directly affect the surface of the earth, the use of remote sensing techniques in instability studies of the slopes seems to be very suitable. Among the remote sensing techniques, radar interferometry (InSAR), capable of working in all weather conditions and the duration of night and day, is one of the most effective and efficient techniques for detecting and monitoring the steady change of the earth's surface. Of course, the lack of spatial and temporal correlations in available radar images limits the use of conventional radar interferometry to monitor the displacement of ground level. Persistent Scatter Interferometry (PSI) is the advanced InSAR technique which has significantly improved upon traditional InSAR methods by increasing the accuracy of results (millimeter scale precision). The main objective of this project was to detection and monitoring of Ahar Chai sub basin landslides based on PSI technique.

This landslide with an area of about 42 hectares is located near the village of Dupiagh, located in Ahar city and 22 km on the Ahar-Varzaghan axis, in East Azarbaijan province, Iran. The geographical coordinates of the study area is N38 29´ and E46 49´. For detection and monitoring of the landslide, the PSIn-SAR method was implemented on 22 ASAR images (with descending orbital modes) recorded between October, 20030502 and 20100709 by ENVISAT satellite in VV Polarization. For detection and monitoring of Dupiagh landslide, the PSInSAR method was implemented on mentioned ASAR images. SARscape5.2 software on the platform of ENVI 5.3 was employed to process the radar images and to extract the persistent scatterers. The PSInSAR processing technique was performed within five stages including: (1) selection of master image or connection graph selection. (2) Interferometry or interferometric workflow stage consisting of co-registration and differential interferogram generation parts. (3) First step inversion for selection of the candidate persistent scatterer points. (4) Inversion: second step including phase unwrapping and filter implementation stages. (5) Geocoding or conversion of the phase into displacement. Finally, The PSInSAR method results compared with dual-frequency -GPS measurement.

Based on the ASAR images processing by PSInSAR method, results showed that some parts of Dupiagh landslide are still active and displacement rate of the this landslide was obtained 12.4 mm/year duration of 2003 to 2010. While, the ground surface displacement velocity was estimated between 58 to -22.5 mm/year in the Ahar Chaiy sub basin duration the mentioned period. The results of GPS measurement indicated that the Dupiagh landslide was inactive duration of observation baseline. Differential RADAR Interferometry (DInSAR) method in detection and monitoring of landslides has been reported in numerous studies. Also, the study suggests that PSInSAR is a powerful technique to determine displacement and spatial pattern of landslides.

 Nowadays,satellite imagery is used as a suitable toolforproduction of land use maps. It is also considered to be an important resource used for urban and rural land use planning. Due to the general coverage of different phenomena and natural resources, satellite imageriesplay a major role in spatial and temporal analysis. Using these images in various fields can show us their capabilities and limitations. The important point is to consider increasing advances in their spectral and spatial capabilities. Systematicexploitation of natural resources requires patterns and models of the region, so that related regulations are observedand sustainable utilization is also considered.Obviously,exact, accurate, fast and economic estimate of these changes is impossible without modern technologiesused for regional and environmental studies.Land use change modelingis an indispensable tool for environmental analysis, planning and management. Eastern parts of Tehran metropolis are among regions facing unstructuredand unscheduled constructions in Iran. Urban development and population growth have led to rapid changes in spatial patterns and have severely affected land use and natural resources.  

In order to investigate land use changes, the present study takes advantage of satellite imageries, remote sensing techniques and spatial information systems.The trend of land use changeswas separately extracted from satellite imageries received in1986, 2002, and 2018.After visual interpretation and error correction,four categories were selected (residential and non-residential construction, vegetation, mountain and grassland) based on which changes were investigated. After data collection (including imageries received from Landsat satellite and TM, ETM and OLI sensors) classification and detection commenced.Then, suitable band was selected for classification, spectral reflectance curves of each land use class were evaluated and bands correlation histograms were compared.since changing bandsgives a comprehensive understanding of the classes, their relations and resolution, two-band diagram of pixels’ distribution in two different bands was used.Properties of the texture were extracted using GLCM matrix and principal component analysis was performed. Support Vector Machine was selected as an optimal classification method. Feature vectors and the training rangeweregiven to this algorithm as its input.Markov chain works well in predicting probability of change, and especiallyland use changes. Cellular automaton is also a powerful method used for detecting changes in spatial component. Thus,Markov chain and automated cells model were both used in order to predict changes in quantity and space, and land use map was predicted and simulated for 2050.Results indicate that Markov models provide useful information which can be beneficial for future land use planning.  

Calculations indicate thatdue to creeping discrete growth and in some areas continuous growth, most changes in Damavand (in Tehran)have happened in the category of residential construction (9.06%) and road (1%).This increasing trend has reduced two classes of mountain/grassland and vegetation cover by 9.07% and 0.1%, respectively. After field operations and sampling with dual-frequency GPS receivers, data was introduced to software and classification was performed using support vector machines with an average overall accuracy of 96.62% and a mean kappa coefficient of 85.33%. Change detection studiesindicate that in time period of 1986 to 2002,most changes have occurred in residential and non-residential construction category. In fact, ​​residential and non-residential construction has reached from 3.1% in 1986 to 6.1% in 2002 year, while mountain and grassland category has faced 2.96% decrease. Also, vegetation cover has decreased by 0.76%.Likewise, we also saw a 6.15% increase in residential and non-residential construction, a 6.11% decrease in mountain and grassland and a 0.22% decrease in vegetation cover of the study area in the time period of 2002 to 2018.Road category had an 81% increase in the first time period and an 18% increase in the second time period. Overall, residential/non-residential construction and roads have increased, while mountains/grassland and vegetation cover have decreasedin the time period of 1986 to 2018. Due to population overflow in recent decades, and unplanned construction, land uses like vegetation cover and grassland have changed into residential construction, and especially industrial land use in the area under study (Jajrood, Kamard, KhorramDasht, Shamsabad, Mehrabad, Pardis and Siasang).  

While investigating spatial evolution and agricultural land use changes, it is important to distinguish betweenrapidly changing phenomenon, and slowly changing one.Results of the present study indicate that compared to other land uses,vegetation cove has changed more severely. Therefore, without necessary policies and actions to prevent this process,pressure on naturalresources, land use changes, and consequently destruction of valuable resourceswill result in harmful environmental impacts. This will also change the economic performance of the villages, and have many negative spatial, socio-economic consequences.

Purposes of this research are introduction acceptable index for detection of dust storms and also identifying sources and pathways affected by this phenomenon in the East of Iran by studying a the dust storm using MODIS satellite images. For this purpose were used dust storm hourly data, satellite images of Modis and also digital and visual indexes including NDDI, BTDI, BTD, LRDI and false color composing for identifying dust storms. Finally, the studied dust storms were monitored using satellite images. Results indicated that LRDI index and false color composing have the higher resolution than the other indexes for detection of dust storms. Results monitoring dust storm event in the eastern Iran in summer indicated that storms sources of east area are dry beds of Hamoon lakes, Rigistan, Margo and Khas deserts at the convergence of the borders of Iran, Afghanistan and Pakistan and central Iran. The results of this research can be used in identifying of dust sources and pathways affected from this phenomenon by organizations and institutions to national and international planning.

Cities are always influenced by various forces and factors. With social changes, demographic displacement, economic change, and technological innovation, they are transformed. As the population grows, activity and investment are greatly expanded, and the physical system of the cities undergoes fundamental changes.Along with the rapid urbanization process, a large amount of natural lands, such as forests and wetlands, has turned into agricultural land and residential areas. Quick land use changes have had profound effects on natural and human environments. For example, agricultural development and structures lead to deforestation, soil erosion, water basin degradation, and biodiversity loss and pollution. In addition, changes in the use of agricultural land and the acceleration of urbanization have led to an increase in impenetrable levels, which has led to the development of a transport network and the accumulation of non-disturbing contaminations associated with surface runoff. Due to these great effects, the detection and anticipation of land use change has become an important topic in environmental management and land use planning.
At their initial stagesof formation, most of the cities in Iran were established near or among thehigh-quality agricultural lands with the purpose of using high-quality soil foragriculture and then these lands were gradually buried under the cities throughvillages development and changing into cities and then development of thecities. Accordingly agricultural activities were inevitably receded to the poorlands.
To access fundamental maps for analysis of data and use of different methods to achieve the goal of this study, satellite images related to years 1987,2000 and 2010 are used. Topographic maps of 1:50000 scales obtained from the army geographical organization are used for geometric correction. At this stage geometric correction was performed on the images using image sensor TM of the year 2010 image - vector, were georeferenced. To perform this task, 42ground control points with appropriate distribution in road junctions, water channels and etc were used.To process data, make models and analyze output…,In this research, land cover maps produced in the years 1987 and 2014 as inputs of the LCM model were selected to analyze the changes in the region and predict land use changes in the year 1404. The LCM model requires two maps covering land belonging to different times as inputs . In this study, Gain and losses, net change, unchanged regions, Transition, from each user to another in different classes of land cover, was mapped and mapped to the model analysis section of the model.ENVI, IDRISI Selva and ARCGIS10 are used and to categorize uses most-probability-models and methods are used and finally Ca_Markov model was used to predict and calculate changes in 2025,2035 and 2045.
Multitemporal images used in this study were used in mapping land coverafter geometric correction. With regard to existing images and maps and the condition of the area under investigation and field visit for mapping land cover, five types of applications are discovered for land namely, residential land,irrigated land, rain-fed land, sterile lands, parks and gardens. Altogether during this time,27ys, agricultural land and residential land cover has increased and sterile land and rain-fed land cover has decreased. Agricultural lands consume a huge amount of water due to exploiting water from deep holes and land overuse that has turned rain-fed lands and sterile lands into water-fed and residential lands.
As the table of predicting areas indicates the greatest increase about 1744/74 hectares belongs to agricultural lands and 1741/79 hectares belong to urban lands which includes: residential lands, trade centers, military areas, hospitals, higher education institutes and etc. The least change, 274/18 hectares belong to parks and gardens in and around the cities. The most decline 2261/59 hectares, is observed in sterile lands. Of the total net changes, one can conclude that urban use has increased and all land cover has become largely urbanized, as well as water lands with the rise and development of deep wells ... The need to preserve these lands from the physical development of the city in this direction is essential in order to develop the sustainable development of the city. There are many undeveloped lands in the old days, due to the lack of water and the lack of facilities that, with the advancement of agriculture, turned these lands into agricultural lands, as well as landless areas today are mostly in the suburbs or around the city, mainly because Farmers, in hopes of landing and declaring a few years away, leave the land in a state of desert, in order to become urban development, and to see huge profits, which is one of the most important issues in our country, and many offenses in This is the case where the city of Gonbad-Kavas is not an exception to this rule. Parks and Gardens also have a rational increase in the city, and in urban development projects, parks have been created and the size of the garden is very low in the city of Gonbad-Kavoos.
By predicting changes in usages, it is concluded that the most change will take place in urban usages and rain-fed and sterile lands with dramatic increase and decrease respectively and with Gonbad e Kavoos population growing, some steps should be taken to develop the spatial area of the city so as to prevent destruction of fertile lands for the sake of human construction.
In this study the effect of physical expansion of Gonbad e Kavoos city on agricultural lands is investigated. Findings indicate that during 45 years around 1880 hectares of fertile farmlands surrounding the city are destroyed. The main reason behind this destruction is the horizontal expansion of the city. Hence, as one of the fundamental bases of sustainable urban development is the increase of city density, it is concluded that horizontal expansion of the city is totally in contradiction to sustainable development and it leads to more instability of the city.

Floods are one of the most destructive atmospheric hazards which push different communities all over the world to suffer from life and financial damages. The present research has the aim of detection of synoptic and hydrometeorology features of flood in Herochay. For this goal, the data from daily rainfall of stations in Khalkhal, Khanghah, Arpachayi and Lamber and the data from Hydrometer station in River Kivichay was used to detect the rainfall and flood happened and for synoptic inspection of this phenomenon, the data from Sea level pressure, upper layers of atmosphere like Geopotential high, Omega, Orbital wind, Meridional wind and Relative humidity in levels of 500 to 1000 HPA was used. The obtained results show that presence of low-pressure system on the surface of ground, occurrence of blocking and located of the study area on the east of the trough on the upper levels, negativity of atmospheric eddies and omega on different levels with humid supply of Black Sea and Mediterranean sea on the levels of 500 to 700 HPA, Red sea and Persian Gulf and Oman Sea on the levels of 850 and Caspian Sea on the levels of 925 and 1000 HPA have prepared the conditions needed for generation of instability over the atmosphere of the studied area. The mentioned conditions have caused intense thunderstorm and increase of Herochay discharge and generation of flood in that region.

Nowadays, modeling and simulation of urban growth using satellite images can be a very useful tool for describing the interactions between man-made and natural environments to help planners make decisions in complex situations. Inrecent years, many lands of Iran cities, especially marginallands of cities affected bythe processof urbanization andthe citizens need tonew house,landusechanged and have been converted to made areas.Given that, The proper detection of changes in surface forms provides a better understanding of the relationships and interactions among human and natural resources to enhance management of the resources. There are several methods for detecting land use and land cover changes. Post- classification is one of the most effective ways to detect the changes. The purpose of this study is to detect land cover change in Gonbad Kavoos province. For this purpose satellite images for the years 1987-2000 and 2010 were prepared and for processing of these images and obtaining the subsequent data, a number of softwares including GIS and ENVI, IDRISI were employed. This study sought to assess changes in land use in Gonbad Kavoos province during the years 1987, 2000 and 2010 and then anticipate the development of the province using CA Markov model. The results of the study revealed that the growth changes between 1987 and 2010 were for urban users 217.3%, irrigated land 53.5%, rainfed land -40.4%, Barren lands 87.7%and parks 198.6%. The growth of Gonbad Kavoos over the coming years irrigated lands in the south, the southeast and the east of the province will be took over by the growth of the Gonabd Kavoos city,

Today, global warming effects on various aspects of the Earth are no secret to anyone. Because of this, the research ahead is done for the detection of global warming on minimum temperatures, monthly and periodic (hot and cold) as well. For this study, two groups of data, temperature data of 17 synoptic stations and corresponding amounts of data in global temperature anomalies were figured out over 60 years period of time (1951 to 2010). Goals, the Pearson correlation method for detecting relationships between data, linear and polynomial regression for trend analysis time series data , To illustrate the correlation between the spatial distribution of temperature data with global warming stations nationwide Geostatistical model Finally, non-parametric test for detecting significant temperature change Man - Kendall were used. Based on the results, apart from Khorramabad and urmia stations that have negative relation with global warming and Hamadan and Kerman stations that do not show any significant relationship with global warming, global warming is seen as a positive influence on the other stations. Caspian Bank stations than any other stations in the cold months of global warming have much more influence. Checking the changes of minimum temperature trend showed a significant change in several months. In the warm months the maximum temperature variability is seen in the southern stations of Ahwaz, Abadan, Bushehr and Shiraz. Results obtained from the survey period (hot and cold) minimum temperature, indicate a greater influence global temperature anomaly on the minimum temperatures are warm period of year. During the warm period, southern stations have had the highest influence on the station and during the cold period Caspian Bank stations have had the highest relationship with it. The changes were made based on both periods the obtained results are clarity significant.

Understanding the changes in extreme precipitation over a region is very important for adaptation strategies to climate change. One of the most important topics in this field is detection and attribution of climate change. Over the past two decades, there has been an increasing interest for scientists, engineers and policy makers to study about the effects of external forcing to the climatic variables and associated natural resources and human systems and whether such effects have surpassed the influence of the climate’s natural internal variability. The definitions used in the 5th assessment report were taken from the IPCC guidance paper on detection and attribution, and were stated as follows: “Detection of change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense without providing a reason for that change. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small. Attribution is defined as the process of evaluating the relative contributions of multiple causal factors to a change or event with an assignment of statistical confidence”. Detection and attribution of human-induced climate change provide a formal tool to decipher the complex causes of climate change. In this study the optimal fingerprinting detection and attribution have been attempted to investigate the changes in the annual maximum of daily precipitation and the annual maximum of 5-day consecutive precipitation amount over the southwest of Iran.
This is achieved through the use of the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources Project(APHRODITE) dataset as observation, a climate model runs and the standard optimal fingerprint method. To evaluate the response of climate to external forcing and to estimate the internal variability of the climate system from pre-industrial runs, the Norwegian Climate Center’s Earth System Model- NorESM1-M was used. We used up scaling to remap both grid data of observations and simulations to a large pixel. This remapped pixel coverages the area of the southwest of Iran. The optimal finger printing method needs standardized values like probability index(PI) or anomalies as input data, since the magnitude of precipitation varied highly from one region to another. The General Extreme Value distribution (GEV) is used to convert time series of the Rx1day and Rx5day into corresponding time series of PI. Then we calculated non-overlapping 5-year mean PI time series over the area study. In this research, we applied optimal fingerprinting method by using empirical orthogonal functions. The implementation of optimal fingerprinting often involves projecting onto k leading EOFs in order to decrease the dimension of the data and improve the estimate of internal climate variability. A residual consistency test used to check if the estimated residuals in regression algorithm are consistent with the assumed internal climate variability. Indeed, as the covariance matrix of internal variability is assumed to be known in these statistical models, it is important to check whether the inferred residuals are consistent with it; such that they are a typical realization of such variability. If this test is passed, the overall statistical model can be considered suitable.
Results obtained for response to anthropogenic and natural forcing combined forcing (ALL) for Rx1day and Rx5day show that scaling factors are significantly greater than zero and consistent with unit. These results indicate that the simulated ALL response is consistent with Rx1day observed changes. Also, it is found that the changes in observed extreme precipitation during 1951-2005 lie outside the range that is expected from natural internal variability of climate alone and greenhouse gasses alone, based on NorESM1-M climate model. Such changes are consistent with those expected from anthropogenic forcing alone. The detection results are sensitive to EOFs. We estimate the anthropogenic and natural forcing combined attributable change in PI over 1951–2005 to be 1.64% [0.18%, 3.1%, >90% confidence interval] for RX1day and 2.5% [1%,4%] for RX5day.

Change detection and identification Terrain is essential for understand of human interaction and the environment, that Aware of the cause is correct planning for sustainable development. Today, land use mapping manually is difficult with traditional methods and, remote sensing can help for Engineers in Land use mapping accurately and more quickly, and then assess the region changes. The purpose of this study to explore changes in land cover and land use by using Different methods of statistical training theory. In this study the process of preprocessing and data preparation to extract accurate information have been evaluated of three methods, Maximum likelihood and minimum distance and support vector machines by using the kappa coefficient. The results show that the maximum likelihood method with kappa 0/79, and overall accuracy of 29/83 than the minimum distance and support vector machine methods more accurately for made land use map .Then was produced land use map using by maximum likelihood method for the years 1988-200-2006 detection and evaluation the changes occurring by comparison post classification method. The most important changes to increased area of arable land, orchards increased of dam construction, urban land increased during the study period is 18 years.

Uncertainties discussed in our country in recent years in the study of climate change, particularly temperature and precipitation are considered. In this study, the uncertainty of climate models and hydrological parameters annual temperature maximum temperature and minimum temperature in the station area has been Karkhe. In this regard, the 41-year period (1970 -2010), 11 stations Karkhe basin is studied. In this study the detection of climate change, annual variations in temperature, maximum temperature, minimum temperature stations using climate models Parametric (Mann-Kendall (MK), Kendal Mann rank, test San (Q), regression (LR), Spearman rank correlation coefficient (SRC), testing Pre-whitening, standard deviation analysis (StD), test (t test) and model TFPW. Parametric modeling results show that the mean annual temperature change of station (9 stations) was 82 percent. This significant increase in the average temperature in most parts of the basin there. Stations in the south (downstream) basin at a rate greater than the other trend are Karkhe. T model results in a significant increase in the percentage of stations in Ahwaz, Kermanshah, Hamidieh and Hamadan, Dezful show and at 5% for the four bridges is positive. Contrary to the trend at a maximum average temperature in the northern parts of the southern part of the basin is more extensive. So that the model used, the northern and central stations Hamadan, four bridges, canals and Khorramabad Varynh significant positive trend at different levels are high. The minimum temperature varies significantly increased severe Karkhe station area Ahvaz Parametric models (I Kendal, age, Spearman linear regression) at a percentage of 10% is PW. Parametric and nonparametric models to detect significant trends stations have the same share. A total of 8 stations Karkhe (73%), a positive trend has been recorded minimum temperature climate models. In 37 percent of the basin's 4 stations from eleven stations Parametric test procedure was not seen. . This increase and decrease in some variable rainfall, in some stations are different models used. Overall performance in the test not matches the temperature variables that indicate the uncertainty in the model's output.

The aim of this study was to investigate the effects of changing precipitation in Debi on the Urmia Lake’s surface. Calculation and analysis on the Urmia basin’s average rainfall and discharge is performed. In this study, a 42-year period from 1345 until 1386 took place for precipitation factors and Debbi. We used the SPI model to identify precipitation fluctuations and drought periods and then using Spearman correlation test the relationship between precipitation and Debi in different stations were displayed. Graphical diagrams analysis shows that fluctuations in rainfall caused swings in Debi and in those years that SPI model shows a drought the average flow rate in Debi reduces the average level prevailingand based on phases of increasing and decreasing rainfall in between 1365-1385, Debi was impacted by these phases. Lands intense rainfall and its effect on runoff caused a mutation in Man Kendall test and this model also shows the reduction rate in Debi. This mutant formed in 1374 and has continued until the end of the study period. By reducing the amount of precipitation the standard deviation and average Debi in flow stations was reduced and linear equation in forward rates was declined and the decline has continued until today.

Multispectral remote sensing data is an important informational resource used for recognizing surface changes. To the extent that today, remote sensing images can provide the latest information on vegetation and land use. The present study seeks to detect changes in vegetation and land use across Taleqan area in time period between 1988 and 2007 using remote sensing. Taleqan is located in Alborz province (Karaj) and Taleqan basin. Results indicate that area dedicated to gardening has increased to 2.28 percent, while agricultural lands have faced a 15.05 decrease. On the other hand, rangelands have decreased to 16.25 percent and bare lands have increased to 28.08 percent. The most important change happened with the construction of Taleqan storage dam in 1999 which submerged more than 1100 hectares of the most desirable lands in the area. Since bare lands have increased and rangelands have decreased, thus from an ecological viewpoint it is possible to say that vegetation is degrading. Therefore, in order to restore bare lands, performing rangeland plans and avoiding unplanned changes can be suggested.