جستجوی مقالات مرتبط با کلیدواژه « شناسایی تغییرات » در نشریات گروه « علوم انسانی »

The urban heat islands are hazardous to the health of urban residence, their activities, lifestyle and the quality of their life. This phenomenon occurs, in particular, as a result of the urbanization process, land use/cover changes and the rate of impervious surface coverage. Since early 1970s, the urban heat islands have been studied using remotely sensed data; because this approach is cheaper, more efficient and faster than traditional techniques to detect the heat islands as well as to examine the severity of them. However, less attention has been paid on the relationship between urban heat island (UHI) and impervious surface patterns. Therefore, this work aims to study UHI based on the analysis of land-surface temperature (LST) and impervious surface patterns (ISP) retrieved from remote sensing data covering a 29-year period.

In this research, the city of Rasht as the center of Gilan province, Iran, is taken as the study area. Rasht is the largest city in the South Cost of Caspian Sea. In order to study the relationship between UHI and both LST and ISP, the time series of Landsat-5 / Thematic Mapper (TM) sensor, Landsat-7 / Enhanced Thematic Mapper Plus (ETM+) sensor and Landsat-8 / Operational Land Imager (OLI) sensor as well as Thermal Infrared Sensors (TIRS) of Landsat-8 from 1989 to 2018 have been utilized. Then preprocessing of satellite images including geometric correction and image referencing, radiometric corrections, and atmospheric corrections were applied on the images prior to other image processing steps. Then, by using the Normalized Difference Impervious Surface Index (NDISI), the impervious urban surfaces classified. The Otsu thresholding method was employed to determine a threshold value for the separation between impenetrable surfaces (constructed) and permeable surfaces (not constructed) in each utilized image. The classification accuracy was evaluated considering 300 randomly selected points. After mapping land use change over the years from 1989 to 2018, the future land use changes in the impenetrable urban areas were simulated to the year 2036 using CA-Markov model. Finally, the relationship between urban impermeable coverage and thermal island intensity changes were studied.

The results of this study showed an overall accuracy of 84.5 percent to 90 percent for impervious surface classification using the NDISI method and the Otsu threshold. The results of the CA-Markov's model also indicate overall accuracy of 83.6 percent for impervious surface prediction. The difference in CA-Markov's prediction map with a reality map was less than 8 percent; hence, CA-Markov can be considered as reliable method in predicting land-use change in Rasht. Moreover, the obvious peaks and valleys values can be seen in the histogram of NDISI index; therefore, the determined threshold has well been able to classify the impervious surface. The spatio-temporal change of impervious surface showed an increasing trend, more than double over the city, from 1989 to 2018. Moreover, the prediction results of CA-Markov model indicates that the impervious area would double again within the next 18 years. The highest levels of urban impervious are located at an average distance of 5,000 meters from the city center, which has had an important impact on the thermal island's severity. The relationship between impervious surface and thermal islands showed a positive and strong correlation coefficient between 0.69 and 0.89 for various years. Furthermore, the pattern of urban impervious surface growth and thermal island intensity changes coincided with each other. The spatio-temporal change of UHI showed that the spatial extent of heat islands in Rasht was increasing with time and the temporal trend of UHI was also increased. Moreover, the trend of heat island changes illustrated that area of regions with very low and low temperature were decreased. On the other hand, the coverage of regions with medium, high and very high temperature were increased.

The time series of Landsat images along with spectral indices are the convenient dataset to classify the impervious surface of the city with proper accuracy. The produced land cover map can also be employed as a proper input data for prediction models. The spatio-temporal analysis of urban heat island in Rasht illustrated that the urban heat intensity was increased. This trend was because of increasing rate of impervious urban surface. Ultimately, in order to control the heat island, it is required to prevent the unplanned urban construction and developments.

this study examines and compares the performance of seven spectral indices in the classification and change detection of built-up lands from Landsat-7 ETM (Enhanced Thematic Mapper Plus) and Landsat-8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) imageries. The Study site with an area of 68,995 hectares in this study in the Tehran It includes three mid-infrared (MIR)-based indices, i.e. the urban index (UI), the normalized difference built-up index (NDBI), and the index-based built-up index (IBI), two proposed visible (Vis)-based indices, i.e. the VrNIR-BI and VgNIR-BI or the visible red/green-based built-up indices, ,one thermal infrared (TIR)-based index, i.e. the normalized difference impervious surface index (NDISI) and visible blue/ mid-infrared (SWIR1) based built-up indices (VbSWIR1). In addition, a water index, i.e. the modified normalized difference water index (MNDWI), was also derived. Otsu’s method was used to separate water from the non-water areas on the MNDWI map. Subsequently, a water mask was produced and used to mask all the built-up index maps, leaving only the non-water areas. Using the same thresholding method, the non-water areas of all the built-up index maps were classified into built-up and non-built-up classes. The classification accuracy was assessed using 3500 reference points for each image The results show that the VbSWIR1-BI, with an overall accuracy of 92.88% (Landsat-7) and 91.68% (Landsat-8), were more robust and superior. The results also show indications that the detected spatiotemporal urban LULC changes based on the VbSWIR1-BI were also the most accurate.

This research studies the suitable process of change detection at at an Agricultural areas by focusing on object based method and color fusion. In order to obtain this goal, it is benefit from Landsat7 images. The main idea of offering object based method is a modern algorithm i.e. Double-layer image are combined and An image of the entire layer is formed. Then by selecting suitable parameters a single image is separated in to several parts and by color fusion and object based classification method the changed and unchanged parts are classified. In fact, color fusion is determined by creating different color areas with elementary images that determines changed parts on visual basics and then by using object based classification method and selecting some parts by the user, the total parts of image is determined. Finally, by selecting training samples only one part of image is labeled and its classification is determined and the ultimate map of changes is obtained. Results show that this method is suitable for reducing training samples, increasing exactness (3%-2.5%), speed and increasing information for classification of spatial information and structure and in addition to spectral information it is better than ordinary methods of change detection from comparing 2 multi-temporal images.

In the present study, land use changes in Babavali Basin watershed have been evaluated over a 13- years period. For this research, 2000 Landsat 7 satellite ETM + images and Landsat 8 satellite imagery have been used. After performing the required actions in the preprocessing stage, the maximum probability of the images is classified. The land use map of the region was obtained in 2000 and 2013 the validation results with ground realities and field visits indicate the total accuracy is equal to 0.89 and 0.88. The Kappa coefficient is equal 0.88 and 0.88 in the ETM + images of 2000. Therefore the Landsat 8 of 2013 was acceptable.

Analyzing multi-temporal remotely sensed images is a powerful technique for monitoring land use and land cover changes in urban areas. Apart from the change detection (CD) technique, the features space have an enormous impact on the CD accuracy.To achieve satisfactory CD results in urban area, optimum selection of textural and spectral features is necessary. Although an exhaustive search guarantees the optimality of the selected features, but it is computationally prohibitive. Data reduction techniques such as PCA considers the independence of the data to find a smaller set of variables with less redundancy without intending to improve the CD accuracy. Difficulty in setting the best threshold for JM distance in separability analysis algorithm (SAA) reduces its efficiency. The aim of this paper is finding the optimal textural and spectral features to enhance the CD accuracy using genetic algorithms (GA) and Bayesian classifier. To evaluate the effectiveness of the proposed approach, a case study using IRS-P6 and GeoEye1 satellite imagery acquired on July 15, 2006, and September 1, 2013, respectively, from Sahand New Town (Northwest of Iran) was performed. All the mentioned feature selection methods (PCA, SAA and proposed GA-based method) were implemented in MATLAB R2013a. Results show that, textural features provides a complementary source of data for CD in urban areas. Results show that features selection is an effective procedure in change detection based on textural and spectral features. Each of feature selection methods has its own limitation and advantages, but in general they increase the CD accuracy. The proposed GA-based feature selection approach was found to be relatively effective when compared to PCA and SSA approaches. Overall accuracy and Kappa coefficient of CD were increased from 53.66% to 88.49% and 58.94% to 90.39%, respectively using proposed method in compared with that using only spectral information.

Increasing technology and population in the two recent decades have led to destructing nature and changing its land cover. Remote sensing is one of those new technologies which provide accessibility for managing earth resources. the dynamic information of remote sensing can play an effective role for monitoring and analysis of land use change using digital change detection techniques. Method used for the rate of development of remote sensing, is on the basis geometric correction, spatial and spectral enhancement of available satellite images with regard to the maximum likelihood supervised classification and post classification method. At first we produced land use maps by using the satellite images of landsat. The results of this study are, in addition to the impacts of environmental sensitivity visualization development trend based on the visual display, calculate of the land use changes together in developed area and production of digital map changes, to support land management.

Because of rapid and excessive expansion of cities in recent decades, urban land usehas been the focus of attention of many geographers.In Iran, Tehran as capital of the country and its municipal zone 6 as the city sbackbone and centre of administrative and commercial activities CBD has been ofparticular importance.The aim of the present study has been to investigate the causeand effect of tremendous changes that have taken place in this zone.The methodadopted in this research has been analytical intersection and spatial analyst and themain source of information has been 1995 census reports plus cadastral maps of thattime and their com prison with those of the present.The results of the study have indicated considerable changes in land use over thepast fifteen years, as shown below: the total land area devoted to human settlement in1985 being some 754.13 hats. Has been reduced to 679. 25 hats In 2010 whereas thearea under CBD being 167.78 hats. In 1985 has increased to 213.80 hats in 2010 itselfindicating the extent of change of human settlement land to none human settlementnamely various kinds of business activities.A study of the population of the zone in the same period shows clearly that nonesettlement and none permanent (floating) population has been increasing, particularlyin the middle sections the zone. Thus briefly speaking the interchange of permanentand none permanent population has created numerous difficulties for the zone such astraffic, air pollution, none coherent appearance, none accessibilities, city traffic project, rise of land price, loss of identity, reduction of resident population etc. etc.