فصلنامه جغرافیا و پایداری محیط
پیاپی 38 (بهار 1400)

Dust storms are one of the harmful climatic hazards that occur as a result of extensive ecological imbalances. Geographical location of Iran, i.e., the arid and semi-arid belt of the world, provides proper conditions for the occurrence of dust storm hazards. The present study aims to identify and spatially analyze the sources of Iran dust storms by using the National Centers for Environmental Prediction (NCEP/DOE); European Centre for Medium-Range Weather Forecasts operational (ECMWF) ERA-Interim reanalysis datasets and the records of 52 synoptic stations from 1984 to 2016. Since Dust storms occur in areas with disrupted ecological balance One-dimensional radiative-convective model (RCM) implemented to calculate the heat fluxes and heat balance at the ground. Finally, eight environmental parameters have been selected to identify dust sources in Iran. Combining the Analytic Hierarchy Process (AHP) and Index-Overlay (IO) method, the dust sources were detected and evaluated through the land-use maps and the Normalized Difference Vegetation Index (NDVI). The results indicated that before dust storm, the stored energy of the surface is high due to the increase in surface temperature and dry air. Therefore, the released energy through the surface instability forms severe winds in the stormy areas. After energy discharge, the amount of stored energy at the surface decreases drastically on the dusty day. In recent years, due to higher NDVI variations and exacerbation of soil erosion, higher potential dust sources have been found in the southern, southeastern and southwestern parts of Iran. In addition, considering the fundamental changes in NDVI, around the Lake Urmia in the northwest of Iran and eastern Caspian Sea, these areas also have more potential dust sources.

We gathered wind data (2009-2013), Digital Elevation Model and Land-use maps to assess wind potential. Topographic maps of the area with contour distances of 10 meters, surface roughness and obstacles in the stations were prepared to produce wind atlases. Then wind potential was calculated and evaluated using parameters including mean wind power density, most probable and maximum energy-carrying wind speed in 40 m AGL and 0.03 m surface roughness conditions. The comparison of wind properties in studied sites for 0.03m surface roughness and 40 m AGL conditions showed that mean wind speed is the highest in Gilanqarb and Tazehabad and the lowest in Kangavar and Sarpolzahab respectively. Besides, the investigation of shape reveals that wind speed is more uniform in Tazehabad, Somar and Gilanqarb, while it is less uniform in Kangavar and Ravansar. Most probable wind speed equals to 0 in Ravansar and Kangavar. Moreover, it is very low (less than 1 m/s) in Sapolzahab and Eslamabadqarb ,while it is the highest in Tazehabad (5.72m/s), Gilanqarb (5.61 m/s), and Somar (4.91 m/s). Although there is no notable spatial pattern for wind speed in Kermanshah province due to topographical complexity, sites with high wind energy potential is more frequent in west of this province (such as Tazehabad, Gilanqarb and Somar) compared to the east of it. Thus, the west of this area has more potential to use wind energy in general.

Considering the spatial patterns of ecosystem services plays an important role in evaluating land capacity to provide some benefits. Accordingly, the spatial variation of three ecosystem services, aesthetics value, recreation value and noise pollution reduction in the central part of Isfahan province was investigated applying statistical approaches of Local Moran’s I, and Getis-Ord Gi analysis. Then, spatial accuracy of the investigated algorithms was evaluated and compared using the Receiving Operator Characteristic method. The findings reveal that the spatial variation of the ecosystem services under study have High-cluster pattern. Based on both used approaches and for all three ecosystem services, the central part of the study area has a positive spatial correlation pattern, whereas the southern part of the study area has a negative spatial correlation pattern. Areas representing the high cluster pattern involved have the highest supply of ecosystem services in the study area, while in areas with low cluster pattern, the provision of ecosystem services is very low. Spatial accuracy evaluating of ecosystem services spatial correlation patterns indicate that returned the largest area under ROC curve of recreational value and Getis-Ord Gi analysis (0.972), whereas the lowest rate is found for the noise pollution reduction service and Local Moran’s I, (0.833). Overall, according to the results, the Getis-Ord Gi indicates higher spatial accuracy than Local Moran’s I method for three ecosystem services, and the Receiving Operator Characteristic is an appropriate index to evaluate the accuracy of the analytical approaches used in This research. The findings of this study can be applied by decision makers and land planner to identify and management spatial correlation patterns of ecosystem services.

It is obvious that the Covid-19 virus outbreak crisis has had many negative effects on tourism businesses worldwide; Therefore, it is necessary to study the adaptation behaviors to this crisis in order to reduce its adverse effects leading to manage the adaptive behaviors of this crisis by sharing experiences and to ensure the continuity of tourism businesses in times of crisis. The current study aims to investigate the behavioral adaptation model of tourism business owners in Kermanshah Province facing COVID-19 crisis. The population of this study includes business owners in the field of tourism in Kermanshah Province. Purposeful snowball sampling was used to select the interviewees. Sampling was continued until theoretical saturation was reached. In this study, theoretical saturation was obtained by examining 38 samples from the mentioned population. Data collection was done through open (unstructured) questionnaires, in-depth individual interviews, note-taking, document review, or often a combination of these methods. The results obtained from the research data in the open, axial and selective coding process were extracted and classified in the form of concepts. Based on the data; a "model of behavioral adaptation of tourism business owners in the face of the Covid-19 crisis" was designed and presented. In general, the results showed that the behaviors included two categories of reactive adaptive behaviors and planned adaptive behaviors that reactive adaptive behaviors are the basis for the destruction of businesses in the long run and planned adaptive behaviors paves the way for the growth and development of businesses in the long run. It is suggested that policymakers try to reduce reactive behaviors and improve planned behaviors in order to grow and develop businesses in the long run by adopting correct and principled policies.

Urban heat islands in hot and dry climates have adverse effects on the environment and human health. In this study, a method has been proposed to investigate the factors affecting the heat islands of Iran's central plateau climate. In the first step, after applying geometric, radiometric, atmospheric corrections and preparing Landsat 8 satellite images, including OLI-TIRS sensors, Tasseled Cap transformation is created. In the second step, the surface temperature of the earth is extracted using Split window algorithm. In the third step, in order to evaluate the heat islands, the Urban Thermal Field Variance Index is classified into six levels. Finally, using the correlation coefficient between TCT and Urban Thermal Field Variance Index indicators, the relationship of heat islands with the desert, urban areas, vegetation, and humidity is evaluated. In order to evaluate the proposed method, the city of Qom has been studied. The results of the proposed method show that heat islands are inversely related to the amount of vegetation (-0.613), water and humidity (-0.535) and directly related to the amount of soil and desert areas (0.709). Examining the Urban Thermal Field Variance Index, it was shown that the rate of this index in the core of the studied city is less than the outskirts of the city which can be due to the expansion and dispersion of the city, insulation of the roofs of residential houses, increasing the density of vegetation in the suburbs, river crossing through the city center, the presence of barren areas, ring roads, factories and industrial towns in the suburbs cited. The results reveal that the proposed method is an efficient method to analyze the factors affecting the phenomenon of heat islands.

Given the heterogeneous spatial distribution of opportunities for agricultural tourism development and the close relationship between this type of tourism and spatial conditions, this study is an attempt to fill the gap of spatial and location-based analysis in agricultural tourism studies as a strategy to solve agricultural and rural challenges. For the development and sustainability of agricultural tourism opportunities, it is necessary to recognize the existing capabilities in each geographical space and villages with more capabilities. This issue is especially important for planners, decision makers and investors, considering the risk of developing tourism businesses in different space units. Therefore, in support of spatial planning of agricultural tourism activities, the present study was conducted to make spatial zoning of agricultural tourism development capabilities and rank the villages targeted for agricultural tourism in susceptible areas. In this study, spatial data including natural resources, access, preconditions for tourism development, data related to rural capacities, which can be divided into three groups of agricultural capacity, services required by tourists and tourist attractions located in the villages of the study area, have been used. Spatial analysis was performed with Fuzzy-TOPSIS hierarchical analysis model. The results show that the criteria of distance from agricultural plots and distance from historical villages are the most important and the least important among the criteria used, respectively. Also, only 35% of the total area of the study area has high potential (suitable and very suitable class) for the development of agricultural tourism. The ranking of the villages located in the very suitable class reveal that Nasrabad village with a relative distance of 0.779 is the most capable village for the development of agricultural tourism. In general, the findings indicate that the susceptible areas to agricultural tourism development are stretched from the southeast to the northeast of the study area.

The risk of fires in natural areas is one of the most important climatic hazards in Lorestan Province. The current study aims to analyze the temporal series trend of wildfire events in natural areas and to reveal the spatial-temporal pattern of wildfire related to the type of land cover. In this regard, the data of MODIS fire product as well as land cover and vegetation product of MODIS during the 2000-2020 were used. Cross-tabulation matrix analysis and spatial correlation matrix were used to reveal the relationship between wildfire events and land cover. The findings showed that more than 70% of the total frequency of wildfire in natural resources (wildfire code 2) in Lorestan Province is related to June and then July. The results of cross-tabulation matrix analysis revealed that 3 uses of rangelands with medium canopy, low density forest lands and rainfed agricultural lands accounted for 0.75 of wildfires. More than 70% of the annual frequency of fire events in the two forest land cover classes was in June and July (June and July), which is significantly correlated with the density of vegetation in these classes two months ago (April and May). While the frequency of fires in rainfed agricultural lands was generally concentrated in August and September, it did not show a significant correlation with vegetation of any month. Practically, it can be said that the presence of vegetation density during April and May in the forest floors is one of the most important factors related to wildfire in following two months, June and July. Therefore, focusing on management over a 62-day period, from May 15 to July 16, 55% of fire incidents can be controlled in the area of ​​pastures and forest floors.