فصلنامه سنجش از دور و GIS ایران
سال دوازدهم شماره 1 (پیاپی 45، بهار 1399)

The models of the association between land use and air pollution have wide applications in urban studies, but the land-use role and its different parameters effective on the variability of air pollution concentration in various hours can be used for more accurate Spatio-temporal prediction of pollution. In this study, to make Spatio-temporal prediction of CO pollutants using hourly land-use regression (LUR), the effective parameters on Spatio-temporal variation of this pollutant are investigated during the day and night. The hourly data are collected from 21 air pollution monitoring stations for the summer  in Tehran and the predictive parameters including density and distance from different variables such as road network, vegetation, elevation, and different land-use are generated in the geographic information system (GIS). A general model and 8 hourly models are created at 3 am, 6 am, 9 am, 12 noon, 3 pm, 6 pm and 12 midnight. The coefficient of determination (R2) of the created model is equal to 0.7898, and it shows that the model has an  outstanding performance. By analyzing the generated hourly models, because of the differences in the parameters used in these models, it is denoted that both temporal variability and spatial variability play effective roles in forming the models during the day and night. The coefficient of determination (R2) of the hourly models ranges from 0.51 to 0.92 in which the lowest one and the highest one are related to the noon hours’ models and the nocturnal hours’ models, respectively. The parameters including local access roads and official/commercial areas have the most effect on increasing CO pollutants during the day and night, and the parameters including green space, sports, and medical centers lead to the locations with lower CO pollutants concentration.

Dust storm increased in both spatial and temporal aspects during last decade. Middle East dust storms have caused countless social, economic and environmental damages for the residents of South and Southwest regions of Iran. MODIS satellite imagery has certain advantages, including available and useful spectral bands, with high spatial and radiation resolution and MODIS data are used in the present study. In this study, two MODIS datasets were used. Part one, model development data (January 18-21, 2018) and part two, model evaluation data. Metrological data are collected with respect to time interval studied. After preprocessing MODIS data and preparing field observations, features (artificial neural network input) were generated by proposed method from MODIS data. A model through artificial neural network analysis was developed. This model extracts dust storm and estmates visibility. Model outputs were compared visually with NDDI outputs.To evaluate the effectiveness of the proposed method, the developed model was tested with other time data. Model outputs were compared visually with NDDI outputs.Eventually, in order to reveal the strengths and weaknesses of the proposed method, an accuracy assessment has been carried out by comparing the models output with visibility parameter of synoptic stations. The observation root mean squared error are10%, 10%, 15% and 10% related to January 18th, January 19th, January 20th and 21th, and also, 20% and 25% related to January 26th, 2019 and October 28th, 2018, respectively.

Evapotranspiration is significantly affected by global climate changes as an essential component of both climate and hydrological cycles. Comprehensive analyses of the spatiotemporal changes of ETo enhance the understanding of hydrological processes and improve water resource management. The main objective of this study is to investigate and predict the temporal trend and spatial distributions of the mean maximum temperature (Tmax), the mean minimum temperature (Tmin) and reference evapotranspiration (ETo) during 1961-2014, 2021-2050 and 2051-2080 over Khorasan Razavi Province using CRUTS3.23 dataset and the outputs of four CMIP5 climate models. The results were as follows: (i) the ability of CRU dataset in simulating monthly mean of Tmax and Tmin is suitable, (ii) generally, ETo increased from north to south across the province (ii) from 1961 to 2014, annual ETo exhibited an increasing continuous trend across the area under study (iii) the mean annual minimum temperature projected to increase by 1.6 under RCP4.5 and RCP8.5 scenarios during two future periods. During 2051-2080, this variable will have an increase by 3ᵒ C under RCP8.5 scenario. The maximum temperature will increase by 4ᵒ C during the middle future period under RCP8.5 scenario. (v) The difference between mean annual ETo values of two periods was statistically significant in all grid points covering this province. The results showed that these increases may lead to the increase in crop water requirements and aggravate the water shortage in this area in view of the increase in ETo in response to ongoing climate change.

In this study, meteorological radar images were used to calculate intensity - amount and distribution of precipitation. Spatial resolution of 500 m radial and temporal resolution of 15 min were calculated. Floods in three basins of Sermo, Zrinigol and Ramian were studied. Results showed that intensity, duration and location of precipitation determined the amount of runoff in the basin. Flood time differed with the concentration and maximum runoff time of the basin. Investigating the radar images revealed that the maximum runoff in addition to the sum of precipitation on the basin was also dependent on the distribution of precipitation. If the sum and distribution of precipitation were consistent, intensity of flooding was significantly increased, if intensity and sum of precipitation were inconsistent, flood intensity was lower. Maximum runoff time was different in each basin depending on location of rainfall intensity and the distribution of rainfall. The results showed that use of radar data was more accurate than experimental methods to predict flood and maximum runoff.

Full polarimetric SAR sensors can capture full polarimetric characteristics of targets. Therefore, in comparison with single and dual polarimetric sensors they offer more capabilities in target detection. However, operation in full polarimetric mode increases complexity, data volume and need more power. Full polarimetric sensors acquire images with less swath compared to dual mode. As a result, most of SAR sensors operate in dual mode and provide dual polarimetric images. Due to high availability, dual polarimetric images are increasingly being used in many researches. In this research, the efficiency of dual polarimetric images is compared with full polarimetric mode. The main goal is to find the best combination of two polarimetric bands which has the nearest results to full polarimetric mode. One Advanced Land Observing Satellite / Phased Array L-band Synthetic Aperture Radar scene had been processed. The scene was multi-looked and converted to the backscattering coefficient (sigma nought, dB). The image was decomposed by cluode-pottier method into alpha and entropy components. Three different combination of two polarimetric bands were considered; HH-HV; HH-VV and HV-VV. Alpha and entropy of each dual polarimetric mode were also computed. Then alpha and entropy driven from full-polarimetric mode were separately compared with alpha and entropy of each dual mode. Since different land cover types (i.e. built-up, cropland, bare land and water) exist in the scene, the computations were done separately for each land cover type. The comparison among alpha values from full polarimetric mode and dual polarimetric mode reveals that HH-HV combination shows the best conformity with full polarimetric mode. HH-VV dual mode has the poorest results. Entropy values of HH-HV mode had the least difference with full polarimetric mode. Entropy values of HH-VV shows the weakest similarity. The MAE values of HH-HV, HH-VV and HV-VV were 0.06, 0.22 and 0.17, respectively. The findings of this research shows that polarimetric features driven from HH-HV combination are more compatible with full-polarimetric mode. In case, no full polarimetric image is available, this dual combination can be substituted. Based on quantitative results, HH-HV combination is recommended to be used in case no full polarimetric image is availableOne Advanced Land Observing Satellite / Phased Array L-band Synthetic Aperture Radar scene had been processed. The scene was multi-looked and converted to the backscattering coefficient (sigma nought, dB). The image was decomposed by cluode-pottier method into alpha and entropy components. Three different combination of two polarimetric bands were considered; HH-HV; HH-VV and HV-VV. Alpha and entropy of each dual polarimetric mode were also computed. Then alpha and entropy driven from full-polarimetric mode were separately compared with alpha and entropy of each dual mode. Since different land cover types (i.e. built-up, cropland, bare land and water) exist in the scene, the computations were done separately for each land cover type. The comparison among alpha values from full polarimetric mode and dual polarimetric mode reveals that HH-HV combination shows the best conformity with full polarimetric mode. HH-VV dual mode has the poorest results. Entropy values of HH-HV mode had the least difference with full polarimetric mode. Entropy values of HH-VV shows the weakest similarity. The MAE values of HH-HV, HH-VV and HV-VV were 0.06, 0.22 and 0.17, respectively. The findings of this research shows that polarimetric features driven from HH-HV combination are more compatible with full-polarimetric mode. In case, no full polarimetric image is available, this dual combination can be substituted. Based on quantitative results, HH-HV combination is recommended to be used in case no full polarimetric image is available.

In recent years, the phenomenon of climate change and drought has become a global problem in the arid and semi-arid regions of the world. Climate change as a problem in the annual bio-farming cycle causes extinction of plant and animal species, reduced vegetation richness, impaired and reduced fertility severity in animals, changes in the pattern of migration of birds and animals (due to new habitats or food sources New) and changes in the spawning pattern of fish. Droughts and floods are one of the most severe climatic events that are likely to change faster than the average climate of any region. Today, access to freshwater resources is a very important issue in most countries, including the Middle East and Iran, according to FAO statistics, while the Middle East accounts for 14 percent of the Earth's surface, accounting for only 2 percent of water resources. The drying up of internationally valuable lakes and wetlands, the lowering of rivers to crisis levels, and the exposure of people in 12 provinces to drinking water shortages are among the consequences of a nationwide drought. Droughts have been particularly prevalent in the tropical and subtropical regions since the 1970s. Reduced ground precipitation and increased temperatures, which increase evaporation and decrease soil moisture, are important factors that have led to more drought zones. Recent droughts have emphasized the need for more research into the causes and effects of droughts and the need for additional planning to help reduce the potential consequences of future droughts. On the other hand, some studies consider the increase in greenhouse gases and disruption of sunlight transfer to and from the earth to the atmosphere as a reason for the recent drought. In the present study, monthly changes of atmospheric carbon dioxide and monthly changes of total water storage in the period 2003-2015 in Iran were investigated. Combined data with the Obsm4MIPs algorithm of GOSAT satellite and SCIAMACHY sensor were used to obtain the trend of changes in carbon dioxide concentration and GRACE satellite data for changes in total water storage from 2003 to 2015. The results of the canonical correlation show a strong relationship between carbon dioxide concentration and changes in total water storage. Stepwise regression model was used to model the relationship between changes in total water storage with CO2, discharge rate and groundwater consumption. The results of regression model showed that carbon dioxide with R2 = 0.91 had the highest relationship with total water reservoir changes in the model. It is noteworthy that the identification of these relationships on a large scale is tangible and at the local scale management practices are more influential in changing water resources, especially groundwater.