JOURNAL OF NATURAL ENVIRONMENT HAZARDS
Year:2022 | Volume:10 | Issue:30 (پیاپی 4)|Papers Count:12

The use of remote sensing is particularly important in identifying burned areas due to its extensive spatial coverage and the provision of information at different times. Today, Modis fire products are widely used for this purpose. The purpose of this study is to evaluate the capability of Modis MOD14, MOD14A2 (Terra), and MYD14, MYD14A2 (Aqua) fire detector products and to identify fire areas in Golestan state. First, a point map of all the images was generated, then to evaluate the accuracy of the fire products, the prepared point map for the products was compared with terrestrial reality data. If the location of each of the reported fires is consistent with the fires identified by the products, that location was correctly identified as the fire. Landsat images were used as a ground accuracy map to evaluate the accuracy of Modis images. The results showed that six regions identified by level 2 fire products and eight regions were detected by level 3 fire products were identified. The results show the accuracy of the images with a coefficient of R ^ 2 of 0.94 and a coefficient of RMSE of 426.12 ha. The studies conducted in this study show that to improve the performance of the text fire detection algorithm, this algorithm is proposed for the forests of Golestan province and following the conditions and characteristics of the fire area, its intensity, and area. Be developed to provide better results.

Prediction of occurrence of gully erosion through the use of models and output them to the hazard mapping of gully erosion, most appropriate strategy for land management planning in watersheds prevent the occurrence of erosion. in this research, the zoning of gully erosion in the Quyjoq watershed Golestan province of Multilayer Perception neural network structure and the use of variables the selected suitable factors are: slope, aspect, elevation, land unit, land use, distance to river, lithology, distance to road. SPSS modeler software and MLP method were used to perform the neural network. The method of using layers in MLP method was 1-8-9. It includes 9 input layers, 8 hidden layers and 1 output or target layer. Results of the study show that 20, 30, 24, 16 and 10 percent of the region form the areas with very high, high, medium, low and very low risk of erosion. This finding is primarily related to streams, roads and geology. The produced gully erosion susceptibility maps can be helpful to make decisions for soil and water planning and management and finally sustainable development in the Quyjoq watershed.

Existence of important sources of pollutants and points of human activities on the ground and the penetration of these pollutants into aquifers, reduces the quality of groundwater, so in the management of groundwater resources, pollution prevention these waters are essential. The groundwater of Karun Township in Khuzestan province is always exposed to pollution due to the prosperity of agricultural and industrial activities in that area. Therefore, the aim of this study is to evaluate and zoning the aquifer vulnerability of the region using DRASTIC and SINTACS models. By mapping and combining hydrogeological parameters effective in the transfer of contamination to the aquifer, vulnerability maps of the two models were prepared in GIS software environment. To validate the models used, the measured amounts of nitrate in the wells in the area were used and the Pearson correlation coefficient between the models and the nitrate layer was calculated and determined. The results showed that the vulnerability index of the DRASTIC model varies Between 68 to 215 and the vulnerability index of the SINTACS model varies between 52 and 195. In the DRASTIC method, 485.2 hectares of the area without risk of pollution, 751.6 hectares with very low vulnerability, 3305.5 hectares with low vulnerability, 12411.7 hectares with moderate vulnerability, 11191.2 hectares with Moderate to high vulnerability, 9427.3 hectares with high vulnerability, 58861 hectares with very high vulnerability and 478.5 hectares are completely susceptible to infection. In the SINTACS method, 455.4 hectares of the area without risk of pollution, 789.1 hectares with very low vulnerability, 32281 hectares with low vulnerability, 12426.7 hectares with medium vulnerability, 11169.4 hectares with Moderate to high vulnerability, 9449.3 hectares with high vulnerability, 5844.2 hectares with very high vulnerability and 495.7 hectares are completely susceptible to infection. Also, the correlation of groundwater nitrate map with DRASTIC and SINTACS models was 0.68 and 0.51, respectively, which indicates the higher capability of the DRASTIC model than the SINTACS model for the aquifer in the study area.

Oke’s model is one of the most successful models presented to simulate maximum nocturnal urban heat island’s intensity (UHI) based on the urban canyons’ aspect ratio parameter. The aspect ratio parameter is known as one of the indicators of urban geometry. Since this simulation requires various spatial and descriptive analyzes (especially topological analyzes), the use of geospatial information systems is inevitable. In this study, the efficiency of Oke’s model is evaluated using regression analysis and land surface temperature (LST) calculated from ASTER data and single-channel algorithm (SCA), and a local model is presented to simulate the maximum nocturnal urban heat island intensity of the area of study. The coefficient of determination and correlation calculated based on regression analysis are 0.74 and 0.86, respectively. These quantities show a relatively strong linear relationship between the urban geometry index and nocturnal urban heat island’s intensity and the significant effect of urban geometry on nocturnal urban heat island intensity. The root mean squared error (RMSE) and the mean absolute error (MAE) of the presented local model are ±0.80 and 0.67, respectively, showing the acceptable accuracy of the presented local model in simulation of UHI intensity. Two-variable regression analysis shows a greater effect of the height of buildings on UHI intensity’s changes compared to the width of the streets. The sign of the coefficients above shows this effect is increasing in terms of the height of the buildings and decreasing in terms of the width of the streets.

Mangrove forests are always exposed to damages caused by various natural and anthropogenic hazards, so planning is necessary to provide appropriate tools to prevent or reduce the effects of these hazards. Therefore, in this study, natural and anthropogenic hazards to mangrove forests in Hormozgan province have been conducted with the aim of identifying and introducing them to management sections in order to reduce risk and prevention of risk factors. The initial list of threats was identified by reviewing internal and external sources and interviews with experts in the area and 34 threat factors were identified. Based on the identified threats, a questionnaire was prepared and its qualitative and quantitative validities were determined. According to relative validity method, 8 criteria (Lopping, reduction of basin environmental water right, oil leakage from vessels, development of ports and offshore structures, coastal erosion, grazing the wandering camels, Changes in precipitation pattern and water salinity pattern) had the least validity. The final list of threats was determined by Delphi method and the weighting of each was done by Analytical Hierarchy Process (AHP) method. Prioritization of threats showed that lopping, oil leakage from vessels and coastal erosion are the most important threats to mangrove forests in Hormozgan Province.

Due to declining rainfall in the last two decades, drought has become a major problem in the world, especially in arid and semi-arid regions such as Iran, so monitoring and managing it is important. Remote sensing and geographic information system (GIS) and remote sensing (RS) provide the ability to study various indicators to evaluate the types of droughts. So, in the present study, the drought of Iran using multi remote sensing indicators including precipitation condition index (PCI), temperature condition index (TCI), Vegetation Conditions Index (VCI), and the integrated under the heading the scaled drought condition Index (SDCI) during the statistical period 2000 to 2018 were evaluated. To evaluate the accuracy of the obtained results, these results were compared with the standardized precipitation-evapotranspiration index (SPEI). The results of this study showed that the three indices of PCI, VCI, and TCI are well matched. The results of the SDCI index indicated that severe droughts occurred in 2000, 2008, and 2017, which are consistent with SPEI index. It should be noted that minor differences between the two indicators (SDCI and SPEI) can be justified by the fact that the SPEI index is a climatic index that considers two parameters of temperature and precipitation for annual drought assessment, while the SDCI index in addition assessment to temperature and precipitation factors (‎meteorological drought), it also considers ‎agriculture drought and more comprehensively evaluates drought. Finally, it can be mentioned that based on the calculations performed, the SDCI has been more effective in assessing drought than other indicators used.

The ecological balance in nature is integrated with forest safety. Wildfire causes irreparable effects on the ecological functions of the forest and disrupts the production of oxygen and carbon sequestration, reducing biodiversity, expanding pests and diseases, and eventually breaking the balance of nature, which is the unique role of the forest. The purpose of this study was to identify the factors affecting the occurrence and zoning of fire hazards in Sardasht forests in the area of 1784.8 hectares to achieve control and counteraction strategies. At first, layers of elevation, slope, aspect, precipitation, temperature, organic carbon, soil texture, and vegetation cover were mapped for the study area. AHP was used for prioritization, evaluation criteria, and weighting of the layers. Finally, the classifications of the layer were done using GIS, and hazard scores were classified at four classes included; Safe, low risk, medium risk, and high risk. The results showed that 12.7% of the study area is at high risk, 39.9% has moderate risk, 31.9% is in the low-risk area and 15.5% is located in the safe area. Also, the Angstrom index was showed that wildfire occurring has maximum possibility between June to September. It was also found that the north-east of the study area is more at risk of fire due to the denser vegetation than other areas. The minimum fire hazard was observed in the north, part in the south, and the narrow strip in the central regions of the study area.

Due to a better understanding of the status of resilience and optimal management of distribution services, it will be essential to analysis of spatial resilience patterns and examine the spatial relationships between resilience and influencing factors, including distributive justice. Therefore, the purpose of this descriptive-analytical study is to investigate distributive justice and its effect on the resilience of villages west of Lake Urmia to drought. The statistical population of the present study was all villages located within 10 km of the shore of Lake Urmia, which were selected 53 villages as a research environment and finally 380 households as the human analysis units using the Cochran's formula and by multi-stage random sampling method. The research instrument was a researcher-made questionnaire whose validity and reliability were confirmed using Cronbach's alpha coefficient. In this study, the effective factors (distributive justice indices) on the resilience of the studied villages were investigated by geographic weighted regression (GWR). The results of GWR regression showed that the variables entered in the model explain 47.8% of the dependent variable. Also, the results of regression model (OLS) showed that infrastructure, trade-service and economic indicators have the greatest impact on the resilience to drought in the studied villages.

Increased waste production due to population growth and improper landfilling is one of the most significant problems in urban communities. Environmental impact assessment is one of the tools that has been introduced to predict and reduce the destructive and harmful effects of construction projects. Therefore, in the present study, the impacts of unprincipled exploitation of Quchan’s landfill on the environment (EIA) was assessed using existing reports and satellite images, field studies, and considering the influential factors of environmental geotechnics in Iranian matrix. Analyzing the Iranian matrix shows that the number of effects and consequences of the negative algebraic mean is equal to 3 and 5, respectively. However, the negative effect and consequence less than -3.1 in the column and row are related to leveling and ground preparation, bed rupture and wall stability, respectively. More investigation on geotechnical parameters indicated that the factor of safety (FS) of gravel walls needs improvement. It was shown that in Quchan’s landfill, in case of excavation with a slope of less than 35 degrees and excavation height of about 4 meters, the existing trenches will not slip. Therefore, the necessary modification and improvement is necessary for the wall retaining and appropriate results and solutions have been provided to improve the conditions.

After desertification mapping for proper management is very important to recognize the similar work units. Because, the two geomorphological units or two work units that have the same desertification intensity classes, do not necessarily require the same management. In this study, to identify the similar work units that need the same management, at first the intensity of desertification was evaluated according to 4 important criteria in the region (climate, soil, vegetation, and wind erosion) based on the IMDPA model and then classified and identified the similar work units using Cluster analysis method. The results of desertification intensity based on the IMDPA model showed that there are two classes of extreme and medium with the areas about 59.32 and 40.68 percent respectively. This is while the results of cluster analysis showed that all geomorphological units are classified in six different clusters. According to the results, two work units with the same desertification intensity are not necessarily in the same cluster, and work units with the same intensity are in different clusters. Therefore it can be said using the clustering method with desertification models to identify similar units for planning and implementation of management programs will have well efficiency.

It is well-known that climate is changing continuously under the intricate influences of natural and artificial factors at global and regional scales. The global Coupled Model Intercomparison Project (CMIP) already provides multi model data resources in order to improve the scientific research for investigating the vulnerability of climate change and future climate risk at regional or local scales and then developing the corresponding adaptation strategies. Global climate models (GCMs) have proven to be unable to resolve the details of regional climate change features because of the limitation of their coarse resolution. To bridge these gaps, downscaling methods, that is, statistical and dynamical downscaling, are multi method ways to get fine resolution projections of GCMs.Since the provision of robust climate information with a multimodel, multimethod, and multiscale (M5S) method can assist decision-making responding to climate change in agriculutral and water sectors, this study aims to provide the climate change scenarios of temperature and precipitation over Kashafrood Basin (KB) using three downsclaing methods. In this study the CanESM model outputs have been downscaled using two statistical downscaling methods (BCSD and SDSM) and one regional climate model (RegCM) during the period of 1984-2005 and the near future period (2021-2050) under RCP4.5. Results show that the mean temperature is projected to increase in the Kashafrood basin throughout all seasons. Precipitation changes exhibit a larger variability. By the end of the near future, an annual precipitation decrease by 4% and 9% are projected under RCP4.5 based on SDSM and RegCM model respectively in Mashad station, while an increase of over 24% is projected using BCSD downscaling method which is statistically significant.

Dust storms are natural hazards that cause economic and social damage in the Sistan region. Because the investigation of the impacts of dust storms in all aspects is difficult, in this study a part of the impacts of dust storms was assessed and assessment was limited to extent of damage to roads for the period 2017-2019. The damages cost of dust storms on roads includes damages to the road surface asphalt, signposts, and road maintenance. For estimation of damage cost and obtaining the required information, a series of tables were provided and completed by Road Maintenance and Transportation organizations of Zabol, Zahak, and Hirman. Analyzing dust storms was carried out through recorded wind speed and visibility data at Zabol meteorological station. The results showed that dust storms impacted the signposts and damaged the road surface for which their maintenance and repairmen cost increased. A good agreement was found between the monthly and annual frequency of dust storms and accumulated sediment. The greatest amount of accumulated sediment on the road was observed during the first half of the year, in June and 2018 according to the highest frequency of dust storms. The total damage cost of dust storms on road was obtained about 32.5 milliard Tomans for the period 2017-2019.