Keyvan Ezimand; Hossein Aghighi; Davod Ashourloo; Alireza SHakiba
Abstract
Introduction: The urban heat island (UHI) as a climatic effect of urbanization can negatively impact the flora and fauna involved in urban and suburban ecosystem, the presence of pollutants, air quality, energy and water consumption, as well as human health and economy. Therefore, spatiotemporal analysis ...
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Introduction: The urban heat island (UHI) as a climatic effect of urbanization can negatively impact the flora and fauna involved in urban and suburban ecosystem, the presence of pollutants, air quality, energy and water consumption, as well as human health and economy. Therefore, spatiotemporal analysis of the urban heat island changes has been considered as an effective approach to understand the impact of urbanization on the urban and suburban ecosystem, which also can support sustainable urban development and planning. Accordingly, this study contributes a novel approach to identify the trend and predict the pattern of UHI changes using statistical analysis, Shannon's entropy and chi-score statistics.Material and methods: The study area of this research is the city of Rasht and its surroundings, a region located in the north of Iran. This research was implemented using remote sensing imaged from 1991 to 2021 that was collected by LANDSAT 5 and 8 with a fixed time interval of 10 years. All those images captured in summer. In order to conduct this research in the pre-foresight stage, first, the required preprocessing, including atmospheric and radiometric corrections applied to the satellite images. Then, the surface biophysical characteristics of the study area were extracted from the satellite images. In the third step, the land surface temperature was computed using satellite images in 2021. In the fourth step, Multivariate linear regression between surface biophysical characteristics and the land surface temperature in 2021 was applied and then cellular automata-Markov chain model was utilized to predict the land surface temperature for 2031. Finally, the pattern of changes in urban heat island of Rasht city was investigated using statistical analysis in different geographic directions and different time periods.Results and discussion: The results of this study indicate that the highest positive correlation (R=0.89) was between NDBI and LST. Moreover, the highest negative correlation (R=-0.81) was between the greenness and LST. Our results also showed that the lowest correlation (R=0.42) was between the brightness and LST.The predicted LST corresponding to surface biophysical characteristics using Multivariate linear regression model illustrates the low error of this approach (RMSE=1.33K) in 2021. This means that the predicted values in 2021 are close to the real values, and therefore, this model can be trusted to predict LST in 2031. Statistical analysis of the pattern of observed and expected changes of UHI clearly illustrated that Rasht urban expansion and the UHI expansion will consistency continue to increase from 1991 to 2031. However, the expansion rate changes over time and space. Moreover, these analyses also showed that the UHI of Rasht city have a high degree of freedom and a high degree of sprawl. Thus, and as a result, its degree of goodness is negative.Conclusion: The pattern of UHI changes is highly dependent on the pattern of built-up land changes: as a result, sustainable development, resilience and environmental Protection of Rasht requires to directly monitor and control the pattern of urban growth, such as prevent changes in built-up areas and agricultural lands in suburban areas by incorporating a vertical form of development as well as constructing green roofs and walls and using high-reflectance building materials.
Mohamad Reza Gili; Davoud Ashourloo; Hosein Aghighi; Ali Akbar Matkan; Alireza SHakiba
Abstract
Introduction:Given that agriculture has the most important role in ensuring food security (Johnston & Kilby,1989), it is necessary to prepare a map that shows the spatial distribution, land area, and type of crops cultivated with high accuracy (Cai et al., 2018). Agricultural land cover is relatively ...
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Introduction:Given that agriculture has the most important role in ensuring food security (Johnston & Kilby,1989), it is necessary to prepare a map that shows the spatial distribution, land area, and type of crops cultivated with high accuracy (Cai et al., 2018). Agricultural land cover is relatively dynamic and variable at relatively short intervals. This makes it difficult to classify crops on satellite imagery (Bargiel, 2017). The lack or absence of ground truth data is another cause. Therefore, methods that are less dependent on ground samples and use phenological features derived from time series of bands and vegetation indices to classify crops will be more appropriate (Ashourloo et al., 2020). The purpose of this study is to use a deep learning method based on convolutional networks to classify the crop types and improve the performance of this network by using feature channels as an input image to the network and increasing the classification accuracy. Materials and methods:In this study, the visible and near-infrared bands of Sentinel-2 satellite on 10 different dates from 2019 for an area in Idaho, USA, as an important agricultural area, and the cropland data layer for extracting the crop types ground labels was used (Han et al., 2012). Then, in MATLAB software, the time series of spectral bands were constructed and using them, temporal profiles of NDVI for any crop were extracted to identify the unique phenological features of crops. Then, the functions developed based on the phenological characteristics of crops were applied to the time series of the bands and a feature channel was obtained for each crop that in two separate processes, once bands and once again feature channels were used as input to the CNN and the network was trained and the results of network performance on crop classification in the test site, were compared.Results and discussion:In the first stage, the time series of bands formed the input of the deep convectional neural network and the network was trained in the training area, using the tempo-spectral information of bands as the input channels and crops ground samples as the related labels. Due to the spectral overlap of the crops in some time periods, network training was associated with a relatively high loss and therefore, for the test area, the overall classification accuracy was 69% (percent) and the kappa coefficient was 0.55. In the next step, the functions that were developed as phenological features for crops were applied on the time series of the bands, and for each crop, a feature channel was obtained as the special feature of that crop. Then the algorithm was implemented using these feature channels in the test area and the overall accuracy was upgraded to 86% and the kappa coefficient to 0.82 compared to which indicated a significant improvement in the results compared to the previous case.Conclusion:The deep convolutional neural network is very sensitive to the type of input channels for detecting agricultural crops and selecting the channels with suitable tempo-spectral characteristics for different types of crops, has a great impact on the accuracy of network training and can reduce the loss of training network and increase its efficiency in the classification of various crops.
Alireza SHakiba; Mitra Amini; GHolamreza Barati; Mohammad Moradi
Volume 16, Issue 2 , July 2018, , Pages 83-100
Abstract
Introduction: Sea Surface Temperature (SST) anomalies can greatly affect climate fluctuations of arid regions. The impact of SST on the amount of rainfall is not limited to coastal areas, but distant areas may also be affected by changes in patterns of SST areas. Understanding the correlation between ...
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Introduction: Sea Surface Temperature (SST) anomalies can greatly affect climate fluctuations of arid regions. The impact of SST on the amount of rainfall is not limited to coastal areas, but distant areas may also be affected by changes in patterns of SST areas. Understanding the correlation between SST and rainfall pattern has an effective role in prediction of drought and rain in areas that are influenced by water temperature fluctuations. Materials and methods: In this research, in order to investigate the relationship between anomalies of Mediterranean Sea SST (as one of the sources of rainfall in Iran) and rainfall in western regions of Iran, after statistical analysis and identification of hot and cold periods of water, which had a significant correlation with rainfall in the study area, two samples were selected and the way water temperature affects rainfall was analyzed using a Synoptic-Dynamic condition analysis. For this purpose, in selected regions, the following factors were depicted and examined in a GRADS environment: daily rainfall data; mean sea level pressure field maps; heights of 500, 700, and 850 hPa; temperature field at 850 and 1000 hPa; specific humidity field at 850 and 1000 hPa; wind and orbital components at 500-1000 hPa; vertical velocity field at 500 and 700 hPa; relative vorticity field and horizontal divergent of relative vorticity field at 500 hPa; and specific humidity field and horizontal divergent of specific humidity field at 800 and 1000 hPa. Results and discussion: The results showed that, although a statistically significant correlations exist in the whole study region between high SST and rising rainfall in April (95%) as well as low SST of the Eastern area decreasing rainfall in March (99%), according to a Synoptic-Dynamic analysis the system that brought rain to the region in April was the Sudanese system and the main source of humidity was the southern area. These synoptic systems, which are associated with atmospheric fronts, are formed or strengthened under the influence of a deepened trough at mid-level height, creation of a positive vorticity in the eastern part of a trough, existence of an upward movement, convergence at lower heights, and reduced pressure. By providing humidity at lower heights and upward movement, suitable conditions are provided for convective rainfall in the study area. Also, under the influence of the relatively stable presence of the Azores anticyclone over the Mediterranean Sea on the Earth's surface and the study area, reduced rainfall in May prevents the influence and expansion of the Sudanese system and the formation of Mediterranean cyclones at higher levels, deepening of a low height trough, and approaching western Iran. Although water temperature decreased in humidity advection from the Mediterranean Sea to the studied area, no change occurred and only humidity advection from southern area reached zero. According to Omega and vorticity maps, it is also observed that the presence of stable atmospheric conditions, vorticity advection, and downward movements in the region all reduced rainfall in this month. Conclusion: In general, the dynamic and thermodynamic complexity of weather from the Mediterranean Sea to western and north-western Iran prevents recovery of the effect of SST fluctuations caused by decreasing and increasing precipitation. Therefore, it seems that an increase and/or decrease in water temperature of the Mediterranean Sea has no impact on the amount of humidity advection and increase and/or decrease of rainfall in the study area. What is more, different atmospheric patterns accompanied by positive or negative SST abnormalities in different months, play a controlling role in humidity feeding through various sources and occurrence of rainfall.
Ali Akbar Matkan; Alireza Shakiba; eyed Hossein Pourali; Eisa Ebadi
Volume 6, Issue 3 , April 2009
Abstract
Multi store parking Site selection is one of the key problems in metropolitan city such as Tehran. This problem should be considered according to a series of criteria. The main aim of the paper is to determine proper sites for multi store parking using Fuzzy methods in GIS environment in first district ...
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Multi store parking Site selection is one of the key problems in metropolitan city such as Tehran. This problem should be considered according to a series of criteria. The main aim of the paper is to determine proper sites for multi store parking using Fuzzy methods in GIS environment in first district of Tehran. To achieve the aim, parameters such as; distance from trip attractive center, distance from routes and communication networks, land price, suitable land use for parking establishment, distance form faults, and some other criteria were put in the fuzzy model. The results of this research showed that the OWA method in low risk and somehow trade-off mode represented the best result in according to aim. In this state, 6239.1 square meter of study area was selected as the high-rank area for establishing multi-store parking lots.
Ali Akbar Matkan; Ali Reza Shakiba; Hossein Pourali; Hossein Nazmfar
Volume 6, Issue 2 , January 2009
Abstract
The healthy landfill of urban waste, such as every other engineering project, needs basic information and careful planning. Choosing different factors leads to diversity in data layers, consequently the attempts to find adequate solutions and make correct decisions directs the decision makers to apply ...
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The healthy landfill of urban waste, such as every other engineering project, needs basic information and careful planning. Choosing different factors leads to diversity in data layers, consequently the attempts to find adequate solutions and make correct decisions directs the decision makers to apply systems which not only have high accuracy but also are fast and easy to be used in operations. Today ''Geographical Information Systems'' (GIS) have the potentiality to be applied in environmental planning and engineering projects. In present study, in order to select sites for dispose of urban waste of Tabriz, in addition to SPOT images, the following data layers and maps have been utilized; the steep map of the area, the maps of land use, land slide, road network, soil, hydrographic, underground water, dominant wind aspect, and the layers related to the distance from city center, airports and other important suburban areas. The results of present research represents that the conditions in Boolean method has less certainty and regarding to definite limitations in this method, the sites selected according to Fuzzy have fewer parameters, however in studying the two Fuzzy methods applied in this study (OWA and WLC) it revealed that although Weighted Linear Combination (WLC) is simple, it has some deficiencies; one of them is "overestimating'', meanwhile Ordered Weight Analysis (OWA) ,by ordered weights, offers this chance to the decision maker to insert more important subjects which have greater role in site selection. Regarding to this ability the result of site selection by OWA has better resolution.
Ali Akbar Matkan; Alireza Shakiba; Davoud Ashorlo
Volume 4, Issue 3 , April 2007
