Journal of Spatial Analysis

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 Investigation and monitoring of desertification in arid and semi-arid regions is a major concern for societies and governments due to its increasing rate. It is essential to identify areas at risk of desertification to manage and control this phenomenon in the shortest possible time and at minimum cost. The objective of this study is to create a map of desertification intensity in the MoradAbad plain of Saravan using the Albedo-NDVI model, which is based on remote sensing. Two Albedo and NDVI indicators were extracted from Landsat 8 satellite images in Erdas Imaging software after necessary corrections. A linear regression was formed between the two indicators by selecting 200 pixels corresponding to each indicator. Based on the slope coefficient of the line obtained from linear regression, the equation for determining the intensity of desertification was obtained. A map of the intensity of desertification was prepared based on Jenks’ natural refractive index. To evaluate the accuracy of the model, a clutter matrix was formed between 100 corresponding points. The results of linear regression between NDVI and Albedo indices showed that these two indices have a high negative correlation with each other (R = -0.85). The results of the desertification severity classification based on this model showed that 35% of the area is in the very severe class and only 5% of the area is without degradation. The model’s accuracy value was obtained with a kappa coefficient equal to 0.58, indicating good accuracy of the model.
 

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Cold and frost are one of the climatic hazards that cause damage to various activities every year. Climate change, on the other hand, causes spatial and temporal changes in glaciation. The purpose of this study is to analyze the temporal-spatial changes and predict the future of glaciers in Hamadan province. CanESM2 model was used to predict the minimum daily temperature in the province. Data mining of general circulation models was Downscaling using LARS-WG model. The above parameters were simulated for a period of 30 years (2050-2021) under three scenarios RCP2.6, RCP4.5 and RCP8.5 for selected stations. The results of the monthly minimum temperature survey in the study stations of the province showed that the minimum temperature in the period (2050-2021) in all studied stations according to all three scenarios will increase in all months of the year compared to the base period. The average minimum temperature of the province is equal to 2.5 degrees Celsius, which in the coming decades based on the scenarios of RCP2.6, RCP4.5 and RCP8.5 will reach 6, 6.2 and 6.3 degrees Celsius, respectively, which is the highest The changes are related to Nojeh station and the lowest is related to Hamedan. The spatial distribution of the beginning and end of freezing in the future period indicates that freezing in the northeastern and northern parts of the province starts earlier and ends later than in other parts of the province, while in the southern parts of the province it starts later and ends earlier. The results of examining the changes in the onset of frost in the next decade compared to the base period showed that in all stations studied the onset of frost will decrease between 3 to 11 days.
 

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Coastal areas are constantly changing physically and ecologically, depending on natural and human factors. The natural causes of coastline changes are assessed in three ways: short-term changes including the effects of up and down currents, long-term changes including climate change, periodic storms and waves, and accidental changes including sudden natural events. Today, coastal tourism is considered as one of the important factors in the development of coastal areas. In this regard, the Caspian Sea, with many tourist attractions such as lush forests, accessible foothills and mountains, historical monuments and appropriate welfare facilities, benefits from the sea and beaches. The coastal area of Babolsar City, due to its many facilities and capabilities to attract tourists, much of which is due to natural and environmental attractions, every year, hosts a large number of tourists who come to this area to take advantage of its facilities and attractions, including the beautiful beach and very beautiful forests. This coastal area because its dynamic nature, is exposed to permanent erosion and variability due to processes such as river, wind, tectonic, wave and tide and marine transgression-regression in the area causes the destruction of coastal facilities and recreational places. Therefore, any planning to change the land use and construction in this coastal area should be considered in terms of the sea water fluctuation impacts on the shoreline position. Coastal environmental degradation as a result of Caspian Sea water level fluctuation are very probable and human behaviors in non-optimal choice of the land use locate intensify these losses. Coastal tourism, as one of the coastal land uses is heavily influenced by fluctuations in sea level in both marine transgression-regression statuses.
 

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Nowadays, deep learning as a branch of artificial intelligence acts as an alternative for human with hopeful outcomes. Open Street Map as the biggest open source data is used as a complementary data sources for spatial projects. It is notable that is some advanced counties the accuracy of VGI data is higher than governmental official data. This research aims to use artificial intelligence to produce and subsequently promote completeness of OSM data. Res_UNet architecture was utilized to train landuse categories to the network. The result shows that IoU metric is about 83 percent that implies a high accuracy paradigm. Then, united-based method was used to calculated completeness of OSM data. The unit-based results show that completeness of building blocks, forest, fruits garden and agriculture land are: 3.6, 9.7, 90.4 and 81.88 respectively. It shows the low volunteer  participation rate to produce OSM data. On the other side the high accuracy achieved by deep learning leads us to complete OSM data by artificial intelligence instead of human prepared data. The advantage of using machine rather than human may be utilized in undeveloped countries or low density population regions as well as inaccessible areas.
 

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This study aimed to systematically explain the potential of the landslide occurrence to provide a prediction model of the possibility of this phenomenon in the Yamchi catchment in Ardebil province. In this regard, both approaches of discrete and continuous variables were used by means of overlay and logistic regression, respectively. Independent variables included elevation, slope, aspect, lithology, annual rainfall, roughness, general curvature, topographic wetness index, vegetation index, distance to fault, distance to stream and distance to road. The results, firstly, revealed the areas with high landslide potential by the matching layers of independent variables with the landslide layer in the geographical information system (GIS). These areas were in the middle elevation, high slopes, northern slope, high roughness, erodible formations, high rainfall, medium vegetation, surroundings of faults and rivers. Secondly, the results of the logistics regression model by providing a prediction equation of probability of landslide occurrence showed that the resulting model with pseudo r² and ROC 0.22 and 0.86, respectively, had good power and efficiency to predict landslide through the catchment. In addition, the resulting beta coefficients for independent variables indicated that the importance of the variables was as follows: vegetation index distance to road, rain, lithology, distance to fault, elevation, topographic wetness index, roughness index, aspect, slope, and distance to river. In the end, the need to pay serious attention to the supporting and protection of vegetation cover of the mid -range and upstream of the catchment was determined because of unstable geomorphic conditions of these areas.
 

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The process of erosion and sedimentation on river banks is often accelerated by natural events and human activities, which lead to natural hazards. This process has also faced serious risks in Jajrud river, urban and rural settlements, agricultural lands, construction structures around the river. The Jajroud River has important importance and effects in securing agricultural water rights, feeding underground aquifers, providing drinking water in the region and the green and natural spaces of Fashm, Migun, Lavasanat and downstream, which has been subject to many changes in recent years due to direct and indirect human interference. including the expansion of urban and rural construction, river engineering operations and wall construction along the river, encroachment on the river boundary and changing the course of the river channels, the use of non-native vegetation on the banks of the river, and as a result, the eco-hydro geomorphological conditions of the river have become unstable. The phenomenon of erosion and sedimentation in Jajroud river has brought negative consequences such as changing the bed, accumulation and settling of large amounts of sediment and overflowing of canal water and as a result the risk of flooding.
 

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Evapotranspiration is one of the main elements of hydrologic cycle. Accurate determination of reference crop potential evapotranspiration (ET₀) is crucial in efficient use of water in irrigation practices. ET₀ can be measured directly by lysimeters or estimated indirectly by many different empirical methods. Direct measurement is cumbersome, needs for more time and costly. Therefore, many investigators used empirical methods instead of direct measurements to estimate ET₀. Nowadays, the FAO-56 Penman Monteith (PMF56) method is known a bench mark for comparing the other empirical methods. For example, in the works of Zare Abyaneh et al. (2016), Biazar et al. (2019), Dinpashoh et al. (2021) and Dinpashoh et al. (2011) PMF56 method was used to estimate ET₀ and comparing the outputs of other empirical methods. Many researchers analyzed trends in ET₀ time series in different sites around the Earth. Among them it can be referred to the works of Sabziparvar et al. (2008), Babamiri & Dinpashoh (2015), Dinpashoh et al. (2021), Dinpashoh  (2026) and Tabari et al. (2013). ET₀ can be affected by many different climatic factors such as maximum air temperature (Tmax), minimum air temperature (Tmin), relative humidity (RH), wind speed, and actual sunshine hours. Factor analysis (FA) is a multivariate method that reduces data dimensionality. In general, climatic variables have high correlation with each other. On the other hand, these variables affect ET₀. The FA can be used to reduce data dimensionality in which correlated variables converted to few uncorrelated factors.
 

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Gully erosion is one of the advanced forms of soil erosion, which needs to be analyzed and identified in order to protect the soil. In this research, according to the complex system of factors influencing the creation of ditch erosion, 23 factors were analyzed in the two famous Dempster-Schiffer models and the entropy model, and using Google Earth images and field visits, 331 ditch points were identified, recorded, and a ditch distribution map was prepared. Spatial data of gully erosion distribution were divided into two random training (70%) and experimental (30%) groups. In this research, two indicators of tolerance coefficient and variance inflation factor were used to check the collinearity test, and as a result, two indicators of waterway density and relative humidity index were removed and 21 factors were used in the modeling process. The output results of the layers, weighting and classification and integration in two Dempster-Schiffer and entropy models are the extraction of the zoning map of the gully's erodibility sensitivity. and 30% of the calibration and validation of the models, the area under the ROC system performance characteristic curve and the area under the AUC diagram of the Dempster-Schiffer model with an explanation factor of 0.934 and the maximum entropy model with an explanation factor of 0.936, both models have an acceptable percentage of the area under the curve were that this issue shows the high performance of both models in the region. Among other results of statistical analysis, the prioritization of the impact of 21 factors in causing ditch erosion in the region was determined. The scientific results of the research can be promoted and taught, and from the practical point of view, the relevant executive body to control ditch erosion can take the necessary measures using the results of this research.
 

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Developing roads and constructing new highway are urban policices contributing to solve transportation problems in cities. These projects often being passed through urban fabrics, so it is nessessery to buy and demolish buildings from their owners including individuals or governmental and public institutions to imply the projects. However, acquiring land is not an easy task and completing these projects may hit with long-term delays. This paper aimed to analyze the impacts of delaying in constructing Shoosh Highway in Tehran. The investigated impacts originate from land acquisition problems. The research data was obtained from many sources including documents and research reports, a survey, and interviews with Tehran Municipality managers. The One Sample T-Test in SPSS software was performed to analyze the data obtained form the survey. Findings indicate that the residents are often dissatisfied with the project because since the beginning of the project, social security decreased a lot and people are less likely to respect citizens' rights than before, recreational sites are often demolished, the value of residential buildings slowed down significantly, living costs incresed, and businesses were stagnant. In conclusion, if urban highways are not contributing to proper planning and site selection, they will disrupt the physical, social, and economic structures of urban neighborhoods and cause to many environmental problemes including air pollution. To avoid these adverse outcomes, it should be thought in advance about sufficient financial resources and possible practical methods to acquire land for projects. These consequences are reduced by studying and managing the risk of projects.
 

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Forest fire in many ecosystems is a natural phenomenon, but also a serious and dangerous threat with environmental, ecological, and physical effects. Therefore, this research investigated the risk areas of fire in Zagros forests identification to evaluate the changes in the time series of deals with a potential fire hazard. To achieve this goal fuzzy layers of analysis network process and order weighted average method were used regularly. For this purpose, fire Zagros forests using satellite images Landsat and MODIS Lordegan city in the period between 2000, 2007, and 2014 and the factors affecting fire are examined. The high-risk areas based on classification utility area and the number of zones were identified as fire-prone areas. In the analytical network process procedure, the largest weighs were assigned to the distance from residential areas and roads, GVMI index, and maximum daily air temperature factors which were 0.209, 0.198, 0.09, and 0.0716, respectively. Time series analysis map showing the extent of critical areas from 2000 to 2014 decreased by investigating the factors affecting fire occurrence in critical areas, distance for roads and residential areas, slope, aspect, GVMI index, and NDVI and maximum temperatures have the greatest impact were on fire. The low-risk scenario and a small amount of compensation with the ROC higher than 0.7 as the best model was the estimated risk of forest fires. The preparation of a map of areas susceptible to fire, as well as analyzing and analyzing the time series of factors affecting the fire in different years, is an effective step in helping forest managers to plan and implement preventive operations in high-risk areas.
 

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Abstract
Dust storm is a complex process that it was affected by relation between earth-atmophere system and point of veiw climatologist and meteorologist that they assessing atmospheric and climatic change, in general of world veiw, monitoring from dust cover is a need structures.
The western region of Iran is the study area. The data used in this study are divided into two categories: ground-based observations in 27 synoptic stations extracted from the Iran’s Meteorological Organization during period (1998-2010) and satellite MODIS images during the first to fourth days of July 2008. Finally, the aim has analyzed using Arc GIS and ENVI softwares and NDDI index.
According to results, interpolated map for the number of dusty days during the study period over the western half of Iran showed that extent of case study have not a equal system aspect quantity of occuring from dust phenomenon and how is it trend. The number of dust days increase from north toward south and sites located in northen proprotion of studied area have experienced a lower dust events. While, maximum hotspots are occuring over southwestern sites such as: Ahvaz, Ilam, Boushehr and Shiraz. Therefore, principle offspring of dust input has been out of country boundaries and arrived far way area. On based resultes obtined on satellite images using NDDI index also idicate that maximun of intense cover dust is observed over Fars, Ilam, Boushehr and Ahvaz provinces on the first, second, thrid and forth of July. But, the lowest rate of index situated in extent far such as: Eastern Azarbayjan, Western Azarbayjan provinces. Thus, parts located on the north of the study area experienced less dusty days and the maximum dust core was located in the southwestern (mostly ahvaz). The long-term result was consistent with the use of NDDI index and the daily average of NDDI index in the whole study area indicated the hotspot areas (Ilam, Ahvaz, Omidieh, Bushehr and Shiraz) during the first to fourth days July 2008. However, in the region has reduced the level of dust cover when a wet and cloudy synoptic system pass over the central and northwestern parts of the study area.

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Iran is a country with low rainfall and high-intensity rainfall that is affected by various synoptic systems, the most important of these systems is Sudan low pressure, Therefore, recognizing the low pressures of the Sudan region is of particular importance, The purpose of this study is to gather a complete and comprehensive knowledge of the set of studies conducted about this low pressure, structure and formation and its effects on the surrounding climate. The present study was conducted using the library method and a search for authoritative scientific and research sources in connection with research on low pressure in Sudan and no data processing was performed in it. Thus, it has studied and analyzed the temporal and spatial changes of Sudan's low pressure over several years and its effect on the climate of the surrounding areas, especially Iran. In general, the results of this study can be divided into several categories, including studies on the recognition and study of Sudan low pressure, its structure and formation over time, pressure patterns affecting it at different atmospheric levels, and its effects on the climate of surrounding areas, especially Iran. Has been studied, The effect of this low pressure on seasonal and spring rainfall in Iran, snow and hail, floods, thunderstorms and also the effect of remote connection patterns on this low-pressure system have been studied, and finally, the analysis of these findings has been studied. It can be concluded that the Sudanese low-pressure system is a Low-pressure reverse in the region of Northeast Africa and southwest of the Middle East, which is strengthened and displaced in the upper levels of the Mediterranean and Subtropical jet stream and in the lower surface moisture injection from the Arabian Sea and Oman through high pressure. Saudi Arabia is inwardly the cause of severe instability in Iran and a major cause of heavy rainfall in various parts of the country.

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Assessment of the measurement statistics of model accuracy and the appropriate
use of them (Case study: Interpolation of Precipitation in Fars province)
Abstract
In many scientific researches, error measurement statistics are often used without taking notices into account
when selecting a model or method for the spatial analysis of environmental hazards. In order to assess the
accuracy of precipitation interpolation methods in Fars province, the performance of widely used error
measurement statistics and some comments were implemented. Spatial interpolation of precipitation was
accomplished using inverse distance weighting, kriging, co-kriging, and radial basis functions methods with 161
weather stations (22 synoptic and 139 rain gauge stations) for 2018 as a rainy year. The results of MBE statistic
evaluation indicated that the researcher may have chosen the incorrect interpolation method in certain cases
where the sum of the positive and negative values became zero. In addition, this statistic is limited to indicating
overestimation or underestimation and should not be used for assessing accuracy or selecting interpolation
techniques. Regarding the coefficient of determination (r 2 ), the results revealed that due to the lack of
compatibility in the magnitude of the range of this coefficient (0 to 1) with error values (100 to 400 mm for the
interpolation of precipitation in Fars province), its use in evaluation of the accuracy of a method is not
recommended. In terms of NRMSE, the results showed that samples with a small number of observations (n=3),
its value increased excessively (NRMSE=0.35) when compared to samples with a bigger number of data (n=20,
NRMSE=0.097). Therefore, it is not advised to use this statistic. In conclusion, since MAE and RMSE statistics
provide a more realistic error value, it is advised to use them for assessing the accuracy of interpolation
methods.
Keywords: Precipitation, Error evaluation statistics, Interpolation methods, Fars province

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Dust storms affect different regions of the globe countries and Iran. The dust storm events were considered as one of the climatic components in arid and semi-arid regions and it is called one of the most important environmental problems of these regions. Therefore, in this research, the activity class of sand dunes were investigated based on the Lancaster index in Iranshahr city in Sistan and Baluchistan province. The aim of this study is to investigate the climatic different conditions on the mobility of sand dunes in Iranshahr city. Meteorological data for synoptic station of Iranshahr were collected from the Iranian Meteorological Organization for 15 years (2003 to 2018). After examining the wind speed velocity in Iranshahr, and extracting the seasonal and annual Wind rose diagrams, Dust Storm Index (DSI) was calculated.  Finally, the effects of possible changes in climatic elements on the mobility of sand dunes were predicted. The results showed a significant correlation between the Lancaster index and the amount of annual rainfall, wind and drought index. The results of the sensitivity analysis also showed that if the frequency of erosive winds and potential evaporation and transpiration increases to 30%, the activity of sand dunes in Iranshahr station increases by 25 and 23%, respectively, on the other hand, with a 30% increase in rainfall, the activity of sand dunes decreases by 30%.
 

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Air pollution can have serious negative effects on human health, including cardiovascular and respiratory diseases. Monitoring and controlling air pollutants is very important to protect public health and the environment. Like many developing countries, Iran is facing air pollution, especially in its big cities and industrial cities. One of the powerful tools in air pollution monitoring is remote sensing methods. The aim of this study is to use relatively high-resolution satellite data to monitor air quality and air pollution using Sentinel-5 (Sentinel-5P) sensor images. In this study, a comprehensive monitoring based on the values of some of the most important air pollutants (including AI, O3, NO2, SO2, CH4 and CO) has been done using Sentinel-5 satellite images for Iran in 2019-2023. The results of this research showed that the emission of carbon monoxide and sulfur dioxide gases had a decreasing trend (in the months of June as an example of the examined month), but nitrogen dioxide gas, methane gas, ozone gas and aerosols had an increasing trend during the month. from June 2021 to 2023. In general, air pollution is more serious in the northern parts of the country, especially in big cities and several large urban gatherings. In this study, it was investigated how the levels of six air pollutants in Iran vary and differ from June 2019 to 2023. Another important result of this research is that the total amount of air pollution in 2020-2023 has faced an increasing trend compared to 2019. Also, the monitoring by Sentinel-5 satellite images shows that in recent years, Tehran has had the most polluted air in terms of carbon monoxide, nitrogen dioxide, sulfur dioxide and suspended particles (dust). Also, changes in the concentration of pollutants do not follow a specific pattern. It was also found that the GEE system is able to process a large amount of data in a very short time with high accuracy.
 

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The zoning of phenomena in order to recognize and evaluate the current situation as a basis for decision-making is one of the of scientific management essentials of natural resources. In the management of desert areas to prevent paying exorbitant costs and making incorrect decisions, strong and well-reasoned methods are needed to understand the current situation and the development process. Therefore, the main goal of the current research is to provide a suitable model for zoning the spatial distribution of desertification risk by using Moora, Aras scoring techniques and the use of geographic information system in Yazd-Khizrabad sub-basin. At first, after determining the working units by geomorphological method, the importance of indicators in each unit was obtained based on the Delphi method. Shannon's entropy method was used for the relative importance of indicators. Then the decision matrix was formed and after weighting, the potential of desertification intensity was estimated by calculating the desirability coefficient from Moora and Aras scoring method. The results of the analysis of both models showed that the land units of Mountain Agricultural Grounds (MAG) and Plan Agricultural Grounds (PAG) have the highest desertification potential, which covers 7135 ha (9.11%) of the entire study area and Quantitative value of desertification potential for the whole region based on the all of indicators was placed in the middle class (IV). It is suggested that the obtained results and ranking should be taken into consideration in the plans for controlling and reducing the effects of desertification and rehabilitating degraded lands.
 

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The growth of settlements and the increase of human activities in the floodplains, especially the banks of rivers and flood-prone places, have increased the amount of capital caused by this risk. Therefore, it is very important to determine the extent of the watershed in order to increase risk reduction planning, preparedness and response and reopening of this risk. The present study uses the common pattern of the machine and the classification of Sentinel 2 images to produce land cover maps, in order to construct sandy areas and determine land issues affected by the flood of March 2018 in Aqqla city. Also, in order to check and increase the accuracy of the algorithms, three software indices of vegetation cover (NDVI), water areas (MNDWI) and built-up land (NDBI) were used using images. The different sets of setting of each algorithm were evaluated by cross-validation method in order to determine their effect on the accuracy of the results and prevent the optimistic acquisition of spatial correlation from the training and test samples. The results show that the combination of different indices in order to increase the overall accuracy of the algorithms and to produce land cover maps, the forest algorithm is used with an accuracy of 83.08% due to the use of the collection method of higher accuracy and generalizability than compared to. Other algorithms of support vector machine and neural network with accuracy of 79.11% and 75.44% of attention respectively. After determining the most accurate algorithm, the map of flood zones was produced using the forest algorithm in two classes of irrigated and non-irrigated lands, and the overall accuracy of the algorithm in the most optimal models and by combining vegetation indices (MNDWI) was 93.40%. Then, with overlapping maps of land cover and flood plains, the surface of built-up land, agricultural land and green space covered by flood was 4.2008 and 41.0772 square kilometers, respectively.
 

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The purpose of this study is to investigate one of the supporting services of the ecosystem, called habitat quality, and to assess the spatial vulnerability of natural landscapes in the city of Khorramabad using the InVEST habitat quality module. To achieve this objective, it is essential to identify the sources of threats, the relative weight of each threat, the maximum distance of its effect in space, the types of habitats, and the sensitivity of each to the source of the threat. In addition, one of the most important inputs of InVEST is the land use/land cover (LULC) of the study area. The Local Climate Zone classification method has been used for the creation of this map. Finally, the model generates spatial distribution maps of habitat quality and degradation across the landscape by combining LULC and different threat sources. The findings indicated that the city, airport, and highway factors are the most destructive threat factors to all habitats with an average score of 0.51, 0.345, and 0.33, respectively. Compared to other LULC, water, dense trees, and scattered trees suffered the most destruction, with sensitivity ratings of 0.47, 0.39, and 0.36, respectively. The results also revealed that habitat quality in Khorramabad is at a medium to low level, and the habitat quality is in very good condition in only 3% of the landscape area.