Urban growth modeling using cellular automata model and AHP (case study: Qazvin city)

Remote sensing-based seasonal surface urban heat island analysis in the mining and industrial environment

Cooling spaces have an optimistic influence on surface urban heat islands (SUHI). Blue spaces benefit from balancing the changing climate and heat variations. Because of the rapid deforestation and SUHI increase, the climate is gradually changing in Paschim Bardhhaman, West Bengal state, India. Paschim Bardhhaman has two sectors: specifically, Durgapur is the main industrial centre and Asansol has coal mines. This investigation aims to categorize spatiotemporal variations and seasonal differences in cooling spaces and their influence on SUHI, land use and land cover (LULC), and thermal differences using Landsat datasets for the years 1992, 2004, 2012, and 2022 in summer and winter. The coal mining and industrial range decreased from 10,391.92 (1992) to 3591.1 ha (2022), respectively. Open pit mining distresses fresh water by heavy water uses in ore processing, and mining water was applied to excerpt minerals. Among the two sub-divisions, the blue space amount was higher in Asansol because mining actions were higher in Asansol than in Durgapur. The open vegetation volume has reduced from 46,441.03 (1992) to 25,827.55 ha (2022) and dense vegetation has erased from 7368.02 (1992) to 15,608.56 ha (2022). Dense vegetation improved because of heavy precipitation in those regions. Mostly, Raghunathpur, Saraswatiganja, Bhagabanpur, Bistupur, Paschim Gangaram, Garkilla Kherobari, and Gourbazar have dense vegetation. The outcomes similarly demonstrate that the total built-up part has increased by 8412.82 ha in between 30 years. The built-up zone changes near the southeast and western Paschim Bardhhaman district. Those region needs appropriate attention and planning to survive soon.

Projection of bioclimatic patterns via CMIP6 in the Southeast Region of Türkiye: A guidance for adaptation strategies for climate policy

Over the past three decades, global urbanization and climate change have caused significant differences in climate conditions between urban and rural environments. The effects of global warming affect the climatic values in the urban area. The bioclimatic comfort in an area effectively chooses a site regarding the urban quality of life and activities. This study aims to predict the temporal and spatial changes of the bioclimatic comfort zones of Gaziantep province in terms of climate comfort in the context of long-term global scenarios. The future climate simulation maps were produced and analyzed comparing comfort conditions according to Shared Socioeconomic Pathways (SSPs) 245 and 585 scenarios of the Intergovernmental Panel on Climate Change's (IPCC) Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6). Spatio-temporal changes in temperature, humidity, and bioclimatic comfort areas were analyzed to inform these efforts according to Thom's discomfort index (DI) and effective temperature-taking wind velocity (ETv). The current situation of bioclimatic comfort areas to examine their synergy under extreme hot weather throughout the province and their possible concerns in 2040, 2060, 2080, and 2100 were modeled using ArcGIS 10.8 software. SSP585/2100 will create hot (84%) areas, according to DI, and warm (29%) areas, according to ETv. The spatial results of the research are discussed, and some strategies are produced in terms of urban planning, design, and engineering.

Change detection in a rural landscape: A case study of processes and main driving factors along with its response to thermal environment in Farim, Iran

This study aims to investigate the alteration of Land Use/Land Cover (LULC) change and its response to changes in land surface temperature (LST) and heat island phenomena of a rural district known as Farim in the north of Iran from 1990 to 2020 using multi-date Landsat data. The random forest-based algorithm, supported by Google Earth Engine, is used to execute the LULC classification with an overall accuracy of more than 92%. Based on the LULC results, in terms of area changes, the classes of bare land, rice fields, and water bodies encountered an increase, but woods and dry farms decreased. The present study also incorporates the trends of land cover change that are analyzed using regression based on the temporal datasets of the three leading driving factors: temperature, precipitation, and population. The result demonstrates that the main changing factors of the mostly changed class (bare land) are population/precipitation and temperature/population. Additionally, the effect of LULC change on seasonal LST and urban heat island (UHI) is also analyzed in this study. The result witnessed a significant LST rise in the summer and winter seasons of about 12.87 °C and 14.2 °C, respectively over the study period. The Urban Thermal Field Variance Index (UTFVI), characterizing the heat island phenomenon, shows that the strongest UTFVI zone is in the central area and the none UTFVI zone is in the surrounding region. Moreover, both seasons have seen a significant rise in none UTFVI zones compared to decreasing strongest UTFVI zone. The result of the present study will be helpful for urban planners and climate researchers who study future land cover change and its associated driving factors.

Environmental impact assessment of transportation and land alteration using Earth observational datasets: Comparative study between cities in Asia and Europe

Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991-2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.

Application of urban growth boundary delineation based on a neural network approach and landscape metrics for Khulna City, Bangladesh

The rapid and unprecedented urban growth in Khulna, Bangladesh is making it difficult to implement measures to limit further expansion and define clear administrative boundaries, which is posing a significant threat to the environment and ecological sustainability. Using an Artificial Neural Network (ANN) based urban growth simulation model and landscape metrics, this study aims to evaluate the spatial extent and direction of urban growth and demarcate an Urban Growth Boundary (UGB) by examining the future contiguous expansion of the city for implementing effective land use provision. Utilizing data on biophysical, proximity, neighborhood, and market factors over the past twenty years, the neural network with Markov chain model allocates the land demand for buildup area by 2020 and 2030, concerning twelve explanatory variables. The simulated map of the urban area is further used by landscape metrics to quantify local-level urban patch information viz. landscape pattern, size, aggregation, etc. The compact patch characteristics are mostly found under the Kotwali thana, while, fragmented and unstructured patches are prevailing between urban-rural interfaces. Finally, there has around 95 km² gap between the existing service provided by KCC and the future demand of Khulna city, creating an imbalance between the supply and demand of urban services. Hence, restricted urban growth would make government investment in service facilities cost-effective and enable planners and decision-makers to intend a feasible trade-off between future land demand and the protection of natural resources.

Monitoring and estimating spatial-temporary land use changes of the Aegean region with remotely sensed data

Natural resources are affected by parameters such as natural events, global warming, or irregular urbanization, and land use classes are changing. Faulty practices or wrong approaches to land uses can also lead to deterioration and destruction in the structure of land use classes. For this reason, it should be monitored regularly, and plans for the future should be made. Aegean region, covering 12% of Turkey, is one of the most important regions in terms of tourism, agriculture, and industry. Based on the previous research results, although the spatial change on the basis of cities has been examined, the spatial change of the entire Aegean region has not been examined so far and predictions for the future have not been made. This study aims to examine the change in land use classes of the Aegean Region between 2001 and 2019 using MCD12Q1.006 MODIS Land Cover Type Yearly Global 500 m data. In addition, an estimation study was made using these data for the year 2030. The results showed an increase in the urban area, forest, savannas, wetlands, and ice/snow between 2001 and 2019. On the other hand, a decrease was detected in agricultural areas, water bodies, grasslands, bare lands, and shrubs. Using the cellular automata (CA) method for estimation, first of all, the accuracy of the model was determined by estimating the year 2019. Then, using the same model, an estimation study was carried out for the year 2030. When the estimation results for 2030 are examined, an increase is detected in urban areas; it has been determined that there is a decrease in agricultural areas. This study has demonstrated the successful usability of MODIS data in spatial change estimation. In addition, the results obtained have revealed a comprehensive foresight that can be used in urban planning in order to ensure the sustainable development of the Aegean region in the future.

Delineation of urban expansion influences urban heat islands and natural environment using remote sensing and GIS-based in industrial area

Land transformation monitoring is essential for controlling the anthropogenic activities that could cause the degradation of natural environment. This study investigated the urban heat island (UHI) effect at the Asansol and Kulti blocks of Paschim Bardhaman district, India. The increasing land surface temperature (LST) can cause the UHI effect and affect the environmental conditions in the urban area. The vulnerability of the UHI effect was measured quantitatively and qualitatively by using the urban thermal field variation index (UTFVI). The land use and land cover (LULC) dynamics are identified by utilizing the remote sensing and maximum likelihood supervised classification techniques for the years 1990, 2000, 2010, and 2020, respectively. The results indicated a decrease around 19.05 km², 15.47 km², and 9.86 km² for vegetation, agricultural land, and grassland, respectively. Meanwhile, there is an increase of 35.69 km² of the built-up area from the year 1990 to 2020. The highest LST has increased by 11.55 °C, while the lowest LST increased by 8.35 °C from 1990 to 2020. The correlation analyses showed negative relationship between LST and vegetation index, while positive correlation was observed for built-up index. Hotspot maps have identified the spatio-temporal thermal variations in Mohanpur, Lohat, Ramnagar, Madhabpur, and Hansdiha where these cities are mostly affected by the urban expansion and industrialization developments. This study will be helpful to urban planners, stakeholders, and administrators for monitoring the anthropological activities and thus ensuring a sustainable urban development.

Effect of outdoor thermal comfort condition on visit of tourists in historical urban plazas of Sevilla and Madrid

The tourism plays a significant role in economics and development of any country through revenue generation from multiple sources. Spain has been tourist place and host million foreign tourist. The tourism is highly depended on climate and thermal comfortness of the visiting place. This present research aimed to analyze the outdoor thermal comfort conditions in microclimates of the urban ancient plazas of the two important cities of Spain, namely Sevilla and Madrid on a hot humid stress day of the year. The microclimatic measurement, questionnaire survey, and simulation results were examined to evaluate the thermal comfort condition of six different urban plazas in Sevilla and Madrid to distinguish the supreme time to visit each ancient site. The results have suggested that the outdoor thermal comfort range for the tourist in the historical plazas of Sevilla and Madrid varies from 28.42 to 30.87 °C and 24.5 to 29.82 °C in the hot summer. Despite the high heat stress condition, the result of questionnaires survey shows that about 38.11% and 28.09% of tourists in Sevilla and Madrid, respectively, were satisfied with the thermal conditions. As witnessed from the result of the Envi-met simulation, Plaza de Santa Ana of Madrid and Plaza Nueva of Sevilla is the best place for visitors in the early morning hours. Additionally, during the peak hours, the thermal comfort of Alameda de Hercules of Sevilla and Plaza de Santa Ana of Madrid is the most suitable historical places for visitors, whereas in the evening hours, Plaza Nueva of Sevilla and Plaza de Mayor of Madrid with wider semi-open spaces and relatively suitable vegetation bring more favorable conditions for visitors. The comparison of the simulation result with the questionnaire reveals that the urban plazas with relatively high thermal stresses have a higher rate of thermal dissatisfaction.

Urban Expansion Simulated by Integrated Cellular Automata and Agent-Based Models; An Example of Tallinn, Estonia

From 1990 to 2018, built-up areas in Tallinn, Estonia’s capital city, increased by 25.03%, while its population decreased by −10.19%. Investigating the factors affecting urban expansion and modeling it are critical steps to detect future expansion trends and plan for a more sustainable environment. Different models have been used to investigate, predict, and simulate urban expansion in recent years. In this paper, we coupled the cellular automata, agent-based, and Markov models (CA–Agent model) in a novel manner to address the complexity of the dynamic simulation, generate heterogeneity in space, define more complicated rules, and employ the suitability analysis. In the CA–Agent model, cells are dynamic agents, and the model’s outcome emerges from cellular agents’ interactions over time using the rules of behavior and their decisions concerning the adjacent neighboring cells and probabilities of spatial changes. We performed the CA–Agent model run two times for 2018 and 2030. The first simulated results were used to validate the performance of the model. Kappa showed 0.86, indicating a relatively high model fit, so we conducted the second 12-year run up to the year 2030. The results illustrated that using these model parameters, the overall built-up areas will reach 175.24 sq. km with an increase of 30.25% in total from 1990 to 2030. Thus, implementing the CA–Agent model in the study area illustrated the temporal changes of land conversion and represented the present spatial planning results requiring regulation of urban expansion encroachment on agricultural and forest lands.

Cellular automata predictive model for man-made environment growth in a Brazilian semi-arid watershed

The current study implements a cellular automata-based model for the development of land use/land cover (LULC) future scenarios using a Remote Sensing (RS) Imagery series (1985 to 2018) as data input and focusing on human activities drivers in a 6700-km² watershed vital for the water security of Paraiba state, Brazil. The methodology has three stages: the first stage is the pre-processing of images and preparing them as data input for the cellular automata land use model built in the R software environment (SIMLANDER); the stage of calibration establishes the variables and verifies the influence of each one on the LULC of the region; the last step corresponds to the validation procedures. After model calibration, land use maps for future scenarios (2019 to 2045) were simulated. The results estimate a reduction of 737 km² of natural land cover between the years 2019 and 2045. The spatial distribution of anthropogenic interference predicted a more significant degradation in the central region of the basin. This fact can be potentially attributed by the water availability increasing from the São Francisco River diversion. It is possible to identify an ascending trend of anthropogenic actions in the semi-arid region, which host the exclusively Brazilian biome-Caatinga-and contains biodiversity that cannot be found anywhere else on the Earth. The model helps large-scale LULC modelling based on RS products and expands the possibilities of hydrological, urban and social modelling in the Brazilian context.