Abdolhossein ZarifianMehr; Laala Jahanshahloo; Hossein Zabihi; Bohloul Alijani
Abstract
Extended Abstract Introduction Obtaining reliable environmental values in vast geographic areas is usually costly and difficult; therefore, the ability to predict unknown values or in other words, the use of better interpolation methods is very important. Interpolation methods utilize a set of different ...
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Extended Abstract Introduction Obtaining reliable environmental values in vast geographic areas is usually costly and difficult; therefore, the ability to predict unknown values or in other words, the use of better interpolation methods is very important. Interpolation methods utilize a set of different mathematical and statistical models to predict the unknown values. The similarity of the unknown points to the nearest known points or the principle of the nearest neighbor is the basis of interpolation methods, and how this principle is used depends on the selected model. In a general classification, interpolation methods are divided into two large classes. The first method is deterministic, in which interpolation is carried out based on determining the level of sampled points and also based on the similarities such as Inverse Distance Weighting (IDW) method or Radial Basis Function (RBFs). In the second method, interpolation is probabilistic – geostatistical, that is done based on the statistical properties of the sampled points. On the other hand, due to the growing increase in the problems of urbanization and urban heat islands, current cities need to have a detailed planning for future developments and preserving the quality of urban environment. Also, the geometry of urban valleys, which is defined by changing the height, length and distance of buildings, has a significant impact on the energy exchange and thus, the temperature of urban areas. But, this temperature, in turn, depends on a number of geographical - geometric factors (such as SVF) and meteorological variables. The Sky View Factor (SVF), as one of the usual indicators of describing urban geometry that refers to the amount of sky observable from a point on the Earth, has become one of the most important predictors of UHI due to its applicability in the urban climate, its contribution to the spatial data, and the existence of available techniques. In the climatic studies, the SVF is also considered as an important geometric parameter due to its correlation with the local temperature performance and its potential importance in the urban design process.Although urban Climatologists know this indicator well, it is not that much known among the urban designers and planners. This issue has not progressed much in Iran and there are no reliable sources about it. Despite the fact that different methods and models have been introduced for interpolation of Point data, no specific method has been proposed for estimating this index. Hence, this study has empirically compared the interpolation models with an emphasis on the Empirical Bayesian Kriging (EBK). This comparison is important since EBK has automated the most difficult aspects of the construction of a kriging model. This is while in other Kriging methods, the parameters are adjusted manually to obtain accurate results. EBK automatically simulates and calculates these parameters through a setup process. In classical kriging, it is also assumed that the estimated semivariogram is a true semivariogram of the observed data. This means that the data are generated from Gaussian distribution with the correlation structure defined by the estimated semivariogram. This is a very strong assumption, and it rarely holds true in practice. Accordingly, measures should be taken to make the statistical model more realistic. Materials & Methods The present study is an applied research in terms of its objective and it is quantitative in terms of the data analysis method. The study area is district 6 of Shiraz Municipality (496 hectares). Due to the multiplicity of interpolation methods and techniques as well as kernel functions and model fit functions, about 138 interpolation scenarios arewereimplemented. Also, four indices of Root-Mean-Square (RMS), Mean Standardized (MS), Root-Mean-Square Standardized (RMSS) and Average Standard Error (ASE) have been used for evaluating the models. The input data (sample) contains 6157 points, measured at intervals of 30 m distances in the study area. These points are werecreated based on the SVF calculation software method and using the GIS base model in ArcGIS10.6. Results & Discussion Out of 138 scenarios, seven scenarios with the lowest RMS values arewereseparately examined in detail taking into account three other indicators. Another variable called “Neighborhood type” iswas added to the surveys in two standard and smooth modes. The results show that simple kriging and EBK have better results than the other models. Also, among the simple Kriging fitted models, the RQ model shows better results than other fitting models. Conclusion Based on the RMS index, EBK is one of the best reliable automatic interpolation models (ranked second) for estimating the SVF. In general, based on RMS, MS, RMSS, it is the best automatic interpolation model for estimating SVF.
Hamid Ebrahimy; Aliakbar Rasuly; Ahmad Ahmadpour
Abstract
Extended Abstract
Introduction
Land use is one of the most important indicators of economic and social development in urban areas, and has resulted in extensive changes in available structures and procedures of these areas. Therefore, human activities are known as one of the main principles and components ...
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Extended Abstract
Introduction
Land use is one of the most important indicators of economic and social development in urban areas, and has resulted in extensive changes in available structures and procedures of these areas. Therefore, human activities are known as one of the main principles and components of change in land use. Generally, land use changes are inevitable product of interactions between human activities and environmental elements. Remote sensing technology with capabilities such as providing update and reliable information about natural and urban areas, digital processing of satellite imageries, providing the possibility of temporal and spatial comparing of different phenomena, diversity of products, and etc. is considered to be a powerful tool in improving the efficiency of urban management. Consequently, remote sensing data are used to determine type, quantity and location of land use changes. Moreover, remote sensing technology is used extensively in land use maps all over the world. Many models have been applied to predict land use changes, which due to the complex, dynamic, and non-linear nature of the issue gained little attention. However, CA-Markov model, which is a combination of Markov chain and cellular automata, is commonly considered to be an appropriate and good method for spatial-temporal modelling of land use changes. In the present study, land use changes were investigated for a 15-year period in Shiraz using object- based image analysis. Then, a land use map was produced using cellular automata-Markov (CA-Markov) model to predict land use changes in the study area in 2020.
Material & Methods
The present study includes two main phases. In the first phase, land use map of Shiraz was produced using Fuzzy object based analysis of satellite imageries. In the second phase, modeling and predicting of land use changes in 2020 were performed. Landsat imageries of the study area in 2005, 2010 & 2015 were used in this research. After preprocessing and preparing the imageries, segmentation procedure was performed as the first stage of object based classification using multiresolution segmentation algorithm. The nearest neighbor algorithm was used for object based classification of satellite imageries. Classification conditions were defined in accordance with each class properties, and classification was performed based on fuzzy operators of the classification operation. In CA-Markov model, the possibility of changing from one class of land use to another was calculated using transfer matrix table. Then, land use map of future years will be predictable in accordance with the transfer probability matrix, and desired time interval.
Result & Discussion
In this study, scale parameter of 10, shape index of 0.4, and compactness index of 0.2 were extracted as the optimum conditions for segmentation. Apart from spectral data, information regarding the location, context, texture, normalized difference vegetation index, enhanced vegetation index, and digital elevation model were also used to improve the efficiency of classification phase. The results of model validation shows an overall accuracy of 89% and kappa coefficient of 0.87. Therefore, the results of CA-Markov model shows a very good potential for predicting land use changes in Shiraz. Thus with the adjustment and calibration of model parameters and based on land use maps of 2010 and 2015, Shiraz land use in 2020 was predicted.
Conclusion
Due to the complexity of modeling dynamic changes in urban land use, utilizing efficient and update methods of data analysis is crucial. Therefore, satellite imageries and object based image analysis techniques were used to prepare land use map of Shiraz based on the data collected over a 15 year period. By considering the defined land use classes (residential area, barren lands, street network and urban green space), optimum image segmentation parameters were found. Then, classification conditions were defined for each class using the nearest neighbor algorithm and fuzzy operators. In this way, image classification was performed. By analyzing land use changes during the 20-year period, we understand that residential area has increased from 38 square kilometers in 2005 to 142 square kilometer in 2020. Additionally, green space area faced a reduction of 4 km in the first 5 years of the period, while in the next 15 years green space area shows an increasing trend.
Saharnaz Shekoohizadegan; Hassan Khosravi; Hossein Azarnivand; Gholamreza Zehtabian; Behzad Raygani
Abstract
Abstract
Desertification means land degradation in arid, semi-arid and dry sub-humid regions in result of climate variability and human-activity. Desertification is the third major challenge for international community in twenty-first century after the two challenges of climate change and scarcity of ...
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Abstract
Desertification means land degradation in arid, semi-arid and dry sub-humid regions in result of climate variability and human-activity. Desertification is the third major challenge for international community in twenty-first century after the two challenges of climate change and scarcity of fresh water.This phenomenon has been raised as one of the most striking aspect of environmental degradation and destruction of natural resources in the world.Desertification, byaffecting vegetation cover, water and soil, is a serious factor threatening national parks in arid and semi-arid regions including Iran.Executive actions related to desertification control must be based on the recognition of the current state of desertification and its intensity.The aim of this study was to evaluate and monitor desertification by usingvegetation indices (NDVI and EVI) extracted from MODIS satellite imagery and classification of desertification by using fuzzy logic.
Materials and Methods
The study area covers an area with about 47,244 hectares, which has been named as Bamou National Park.The height distribution of Bamou National Park shows that most of the area is locatedbetween 1700 and 1900 meters altitude and a maximum height of the study area is 2700 meters above the sea level.The average annual rainfall in the main station area representing the Shiraz station is 392.9 mm with a mean annual temperature of 17.9°C.Based on Domarten developed method, Bamou National Park has a semi-arid climate and is cold with winter rains.
In this research, to monitor and evaluate desertification in Shiraz Bamou national park, the annual changes in vegetation cover were studied during the period of 2000 - 2014. On the other hand, this paper tries to monitor desertification changes using long term-time series analysis of satellite data and vegetationcover indices (EVI & NDVI).Therefore, in this study, profile and map of annual changes were prepared on IDRISI Selva and then analyzed using the MOD13A1product, MODIS sensor, Terra satellite and Aqua system. Finally, using fuzzy logic, profile and desertification intensity map were prepared for 2000-2014. According to the climatic conditions of the region and based on expert opinion, the value of fuzzy classes index changes, the software IDRIDIselva and Arc GIS 10.2 severity of desertification on each indicator based on fuzzy logic was prepared.
Discussion and results
Based on the results of EVI & NDVI, vegetation destruction and desertification intensity have been more in the north west of the study area. The reason for this destruction and desertification is the construction of the new city of Sadra in part of the North West and the west of this park. It can be said that, this degradation is a new form of desertification entitled anthropogenic desertification.As a result of the construction of Sadra city in the western area of the park, it is practically impossible to protect this area.The results show that EVI is more sensitive than NDVI for monitoring parameters such as canopy cover, leaf area index, canopy structure, phenology, and stress plants. The EVI index due to greater sensitivity to changes in areas with high biomass (vegetation growth season) and mitigating the effects of atmospheric conditions on vegetation index values is more applicable to monitor vegetation changes than NDVI.This paper introduces fuzzy logicas one of the methods for classifying the severity of desertification. Fuzzy logic can be used to determine the boundaries of class and privilege of desertification indicators and explain the process. Fuzzy sets, or classes of fuzzy are no sharply defined boundaries and membership or non- membership of a place in particular.The severity of desertification in the form of fuzzy maps based on each available indicator provided the values between 0 and 1 as the classes of desertificationon the map.It can be concluded that for better management of desertification it is necessary to prioritize areas affected by desertification according to its severity.As a result, we can say that accurate desertification classification can be helped to manage this phenomenon. In fact, it is a set of unpleasant consequences that human environment brings. Hence, monitoring and evaluation of the severity of desertification and mapping always isone of the most important management andplanning tools to achieve sustainable development in the field of natural resources.
Hossein Mohammadi; Mohammad Hasan Mahoutchi; Mahdi Khazaei; Esmaeil Abbasi
Abstract
Probability analyses are useful methods for recognizing and predicting phenomena such as precipitation. One of these methods is the Markov chain. The Markov chain model is a special state of the models in which the current state of a system depends on its previous states. With this method, it is possible ...
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Probability analyses are useful methods for recognizing and predicting phenomena such as precipitation. One of these methods is the Markov chain. The Markov chain model is a special state of the models in which the current state of a system depends on its previous states. With this method, it is possible to calculate the probability of the occurrence andthe return period of climatic phenomena such as precipitation.Therefore, in the present research,using the 58 year daily precipitation data (1956 - 2013) ofShiraz synoptic station, the frequency and the continuity of rainy days in this city were studied by the use of the Markov chain model. The above statisticswere arranged based on the matrix of counting the changes of the occurrence states of the dry and wet days (days without precipitation and precipitation days), then, the situation change matrix was calculated based on the maximum likelihood estimation method. The matrix was evaluated and analyzed with repeated, constant power, and daily rainfall return period. Next, the return periods of 2 to 5 day rainfall days and the return period of1 day dry days, were also evaluated. Then,the return period of the continuation of 2 to 5 day precipitation days for twelve months of the year was calculated. The results showed that the probability of precipitation occurrence (wet days) per day was %0.1167 and the probability of no precipitation occurrence (drydays) was %0.8833. It was also determined that the most probable occurrence of rainy days was during the winter, especially in January and February. For example, the return period of 2 consecutive rainy days in January was estimated to be nearly 5 days. Therefore, it was observed that Shiraz precipitation has a heterogeneoustime distribution. In other words, precipitation is not uniform and concentrated in Shiraz.
Behnam Moghani Rahimi; Zahra Porbar
Volume 22, Issue 87 , November 2013, , Pages 64-67
Abstract
The importance of climatic effects on architecture necessitates exclusive studies and researches, especially in our country which possesses climatic diversity and diverse architecture. Climate can be defined as an organized collection of vegetation, precipitation, heat, temperature, and sunlight in the ...
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The importance of climatic effects on architecture necessitates exclusive studies and researches, especially in our country which possesses climatic diversity and diverse architecture. Climate can be defined as an organized collection of vegetation, precipitation, heat, temperature, and sunlight in the area. In order to identify local architectural structures and increase the level of comfort among its inhabitants, we inevitably have to consider local climatic features.
If we reach an awareness of climatic elements and direction of buildings in Shiraz, recognition of appropriate materials and choosing appropriate size for windows, we can think of measures so that its inhabitants feel comfortable and can supply the necessary cooling and heating for their house. Information collection was performed in the form of a secondary, documentary and survey research.
