Journal of Radar and Optical Remote Sensing
Volume:5 Issue: 4, Autumn 2022

Land-use change has significant impacts on environmental and natural resources, including water quality, air and terrestrial resources, ecosystem processes and functions, and climate systems. Therefore, accurate and timely detection of land-use changes is crucial for understanding the interactions between humans and natural phenomena and managing natural resources effectively. This study aimed to monitor land-use changes in Damavand city using remote sensing techniques. Two Landsat 5 and 8 satellite images from 1996 and 2018 were used after applying radiometric and atmospheric corrections. Four methods, including band differentiation, band ratio, principal component analysis, and post-classification image detection were employed to detect land-use changes. The results showed that man-made areas increased by 7288 hectares due to construction activities in agricultural fields, leading to a reduction of 4047 hectares of agricultural lands. Additionally, 10324 hectares of rich rangeland cover were transformed into poor pastures. The principal component analysis method using band 3 and the band difference method using band 5 effectively detected the changes in the region; however, the band ratio method did not perform well. The findings of this study can help policymakers make informed decisions about land use planning in Damavand city.

Urban land use maps, in addition to different classes of land use with spatial patterns, specify the type and intensity of land use; therefore, they can be used for current and future planning of urban land. In this study, land use changes in Lali city in 30 years (1987-2017) were investigated. To evaluate the land use changes in this time interval, several spectral images of Landsat satellites 5, 7, and 8 from the years 1987, 2001 and 2017 were utilized. After collecting data and the application of necessary pre-processing on them, also for the preparation of land use maps for the specified time intervals, data analysis was carried out by Maximum Likelihood Classification Algorithm. The findings obtained each year were monitored and controlled through field operations, and land use maps in 7 classes of agriculture, rangeland, forest, mountain, residential, river, and other areas were produced. Then, the changes in each land use were determined in the specified periods during 1987 to 2001, 2001 to 2017, and eventually 1987 to 2017. While the results obtained from the final changes illustrate that the overall level of vegetation compared to the beginning of the period has declined markedly which is an indication of deforestation in the region, urban areas, agriculture, and rangelands have maintained an ascending trend which can be due to increasing urban development and rural expansion, and the growing need of residents for housing, agriculture, and gardens.

we have made a comparative-statistical explanation of the perception of a number of visitors and tourists based on the results of a questionnaire and a completed survey of tourist attractions. This study finally presents four main findings First: The factor analysis model of this study showed that the perception of tourists is based on the components of urban regeneration in four different levels of tourist attractions. These four levels are the historical and ancient core of the city, complementary historical attractions, the city's tourism belt and the general urban context. Second: Elements associated with the city's tourism belt, such as territories and public spaces of the urban environment, are considered as the most important tourist attractions and tourists. Third, a comparison of the average value of the indicators shows that tourists who have used the Internet and social media in their searches, in the first place, they react more to the historical core of the city, in the second place, to the levels of public urban areas and territories, and in the third place, to the complementary tourist attractions. Fourth: In this study, it was found that among the components of urban regeneration, the component of development and promotion of public spaces and territories, have the greatest impact on the development of urban tourism. Since public areas and urban spaces play an essential role in tourist attractions, it is recommended that this issue be included in urban development strategies when planning cities with a tourism base.

Floods are among the most devastating and widespread natural disasters, resulting in loss of life and significant financial damage. Therefore, flood control poses a significant challenge worldwide, including in our country. One effective approach to mitigating this risk is through the implementation of zoning strategies. Consequently, the objective of this study is to conduct a spatial analysis of flooding in the Nekarud region. For this research, a 2-kilometer stretch along the downstream section of the Nekarud River, near Neka city, was selected. Spatial data, including geographical maps and reference land information, were utilized and processed to facilitate the analysis. The study focused on simulating flood-prone areas along the main route for return periods of 10, 25, 50, 100, 200, and 500 years. The resulting flood map illustrates the spatial extent of potential flooding under different scenarios. The study employed elevation data at a scale of 1:1000, as well as river discharge data specific to the study area, to construct a base map. In a GIS environment, topographic data was extracted and used to generate a triangulated irregular network (TIN). Additionally, by utilizing a database that incorporates information on agricultural land use and flood zoning within the study area, the researchers calculated the extent of damage caused by flooding. Based on the land use map within the GIS environment, the study estimated that, during different return periods, the affected land area within the study region would measure 144.17 hectares, 175.14 hectares, 182.56 hectares, 190.14 hectares, 193.97 hectares, and 198.298 hectares, respectively.

Creative cities generate high levels of innovation and economic growth, and also vibrant places to live. It is not certain that creative cities will show high levels of inclusiveness. Inclusiveness is a multi-dimensional concept and creativity is definitely established. When cities decide to be both as creative city and an inclusive city, various tensions may arise. Inclusive city branding and urban policy initiatives are a contentious issue We have chosen Dubai, Amsterdam and Toronto as case studies because all three have good situation in both. Toronto in religious and racism issues, Amsterdam in political initiatives related to age and ability to perform have shown weakness. Dubai performs poorly in political initiatives and geographical discrimination, issues of income and wealth in many ways there is proportionality between creativity, but there is also an inconsistency. Dubai has consistency aspects with local cultures and ignoring elements contrary to traditional norms such as classism, religionism, sexism and racism. In Amsterdam, there is little attention to economic growth, income generation, and support for the elderly and disabled people. In Toronto, there is less tension between the inclusive city aspect and the religionism, racism and discrimination based on income, wealth, and location.

Soil erosion is one of the environmental hazards. The aim of this study was to predict water erosion and evaluate it with CORINE and ICONA models in the study area of ​​Talesh city. In CORINE model, parameters such as topography, erodibility, erosion and land use and vegetation are required, and in ICONA model, slope, geology, land use, vegetation are required. The results showed that the highest area in the erosion risk map of 2000 is the CORINE model of the middle erosion class with an area of ​​63.22%. In 2020, the highest area is related to high erosion class with an area of ​​45.67%. These areas are seen in the western, eastern and central parts of the range. The results of the ICONA model for the period 2000 to 2020 showed that the high and very high risk classes in 2000 were 8.01 and 2.31 percent, respectively, while in 2020 these figures reached 8.80 and 3.44 percent. The results of the evaluation of the final zoning of the erosion risk map of the area, the study area with terrestrial realities, showed that in the best case, the overall image zoning accuracy of 2000 and Landsat 8 of 2020 CORINE model are equal to 0.87 and 0.91 and kappa coefficient of 0.86 and 0.89, respectively. It was estimated to be acceptable. In the ICONA model, the kappa coefficient and overall accuracy of 2000 were 0.85 and 0.86, and for 2020, 0.87 and 0.88, which were acceptable.