Keyvan Ezimand; Hossein Aghighi; Davod Ashourloo; Alireza SHakiba
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 ...
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
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 ...
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.
Keyvan Ezimand; Hossein Aghighi; Davod Ashourloo; Aref Shahi Aqbelaghi
Introduction: Urbanization and urban growth have a significant impact on the natural and human environment as well as the climate at local and regional scales. For instance, the difference in the energy balance of the central and peripheral regions of cities stems from their physical characteristics ...
Introduction: Urbanization and urban growth have a significant impact on the natural and human environment as well as the climate at local and regional scales. For instance, the difference in the energy balance of the central and peripheral regions of cities stems from their physical characteristics and surface land cover. These characteristics in the temperate regions create the phenomenon of urban heat island, but they cause the phenomenon of the urban cold island in arid and semi-arid areas. The purpose of this study was to analyze the impacts of land-surface characteristics, land cover, built-up areas, and morphological characteristics on temperature changes in Zanjan city, Iran. Material and methods: The dataset used in this study included Landsat-5 TM sensor images in 2010 and 2011 as well as statistical information at the level of building blocks. The methodology used in this study was to investigate the effects of different land covers on surface temperature. Then, to demonstrate the effects of built-up areas on surface temperature, the IBI method and Otsu threshold were used. To investigate the effects of the configuration of built-up areas on land surface temperature variations, landscape metrics such as Landscape Division Index, Fractal Dimension Index, and Percent cover of class areas were used. Finally, urban morphology has been investigated using Plot size (PS). Results and discussion: The results of this research showed that among all seasons, the stronger cold island was detected in summer. Moreover, the results also showed that the cold island was much better presented in summer than other seasons. The scatter plots between the land surface temperature (LST) on one hand, and the built-up area as well as the vegetation land cover, on the other hand, illustrated indirect correlations where higher Pearson correlation coefficient was observed between LST and the built-up area (r = - 0.704). Among the landscape metrics, the highest positive correlation (r = 0.72) was observed between LST and the Landscape Division Index. Moreover, a high negative correlation was found between the characteristics of urban morphology or Plot size and the LST (r = - 0.73). The results of the Pearson correlation between land cover, configuration, and morphology characteristics and LST were quite significant (P≤0.01). Conclusion: From this research, it can be concluded that the configuration and morphology characteristics can model surface temperature variations better than the land cover.
Ali Akbar Matkan,; Mohammad Yazdi,; Davood Ashoorloo; Narges Sadati
Volume 9, Issue 4 , July 2012
The Siyah Bisheh area is located in the central part of Alborz zone, 40 km to the south of Amol. Rock units exposed in the area consist of sedimentary (carbonates, sandstone, siltstone), volcano-sedimentary (andesite to andesitic tuff, tuff), ignimbrite and basalt. Once erosion and tectonism have rendered ...
The Siyah Bisheh area is located in the central part of Alborz zone, 40 km to the south of Amol. Rock units exposed in the area consist of sedimentary (carbonates, sandstone, siltstone), volcano-sedimentary (andesite to andesitic tuff, tuff), ignimbrite and basalt. Once erosion and tectonism have rendered volcanic structures undetectable, remote sensing provides an invaluable tool for their identification and identifying the relationship between lithology and vegetation has shown that the integrated use of remote sensing techniques and field studies can be a powerful tool for distinguishing and mapping the relationships between rock units, structures and alteration zones associated with mineral deposits along the Seyih Bishe area. The main image analysis techniques involved in this study were principal component analysis (PCA) and false color composite (FCC).
Ali Akbar Matkan; Azadeh Kazemi; Mohmmad Reza Gilly; Davod Ashourloo
Volume 6, Issue 2 , January 2009
In order to estimate different parameters such as heavy metals that existing in soil, changing point information to area are binge used. In this case, different method are existed.In this study, we used the Ordinary Kriging method for estimating amount of cadmium in soil the Esfahan province. Then map ...
In order to estimate different parameters such as heavy metals that existing in soil, changing point information to area are binge used. In this case, different method are existed.In this study, we used the Ordinary Kriging method for estimating amount of cadmium in soil the Esfahan province. Then map for estimating spatial distribution of the total cadmium with use of GIS ability, it was classified for recognition of the polluted regions with the cadmium. Variance analysis test shown that land use is significant effect on total cadmium existed in soil. As, in urban and industry uses, mean of these heavy metals were much than their mean in the other uses. This explains that human activities are the most important factor for increase amount of heavy metals exists in soil. Examination results of total cadmium description statistics and compare those with suggested value by the other countries shown that soil of this region is contains pollution for purpose of cadmium quantity. With use of ETM+ sensor and spectral unmixing techniques, vegetation covering of region was extracted and up to date. Amount of pollution expansion distinguished in different regions of vegetation covering with fitness to map of pollution soil and vegetation covering.
Ali Akbar Matkan; Alireza Shakiba; Davoud Ashorlo
Volume 4, Issue 3 , April 2007
Moteza Ashorlo; Abbas Alimohammadi; Parviz Ziaeian; Davoud Ashorlo
Volume 4, Issue 2 , January 2007