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The identification of spatio-temporal patterns of the urban growth phenomenon has become one of the most significant challenges in monitoring and assessing current and future trends of the urban growth issue. Therefore, spatio-temporal and quantitative techniques should be used hand in hand for a deeper understanding of various aspects of urban gro...
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... Land monitoring is important in supporting decision-making on land management [1]. The application may include monitoring in agriculture [2]- [4], forestry [5], [6], urban areas [7], [8], disaster susceptibility and hazards [9], [10]. In addition to monitoring the inner mainland, observation of the coastline and its change is also important to monitor the impacts of disturbances in coastal areas [11], [12]. ...
Continuous land monitoring in Indonesia using optical remote sensing satellites is difficult due to frequent clouds. Therefore, we studied the feasibility of monthly land monitoring during the second half of 2019, using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from Terra and Aqua satellites. We divide the Indonesian area into seven regions (Sumatra, Java, Kalimantan, Sulawesi, Nusa Tenggara, Maluku, and Papua) and examine NDVI data for each of the regions. We also calculated the cloud occurrence percentage every hour using Himawari-8 data to compare cloud conditions at different acquisition times. This research shows that Terra satellite provides more cloud-free pixels than Aqua while combining data from both significantly increase the cloud-free NDVI pixels. Monthly monitoring is feasible in most regions because the cloudy areas are less than 10%. However, in Sumatra, the cloudy area was more than 10% in October 2019. We need to include further data processing to improve the feasibility of continuous monitoring in Sumatra. This research concludes that monthly monitoring is still feasible in Indonesia, although some data require further processing. The use of additional data from other satellites in the monitoring can be an option for further research.
... It is mandatory to divide the study area into zones in order to compute the entropy value. According to (Aburas et al., 2018;Mohabey et al., 2023) the circle is a suitable shape used in understanding urban growth because it maintains equal distance in all directions. To characterize urban growth and identify urban sprawl, we calculated the absolute and the relative Shannon entropy according to the number of zones using the previously mentioned equations (Eq 1). ...
The assessment of land use and land cover (LULC) changes is crucial to understanding its impacts on the natural environment and resources. The dynamics of LULC might be a result of national legislation or unplanned development. This study utilizes remote sensing data to evaluate the LULC in Tolga Oasis resulting from the promulgation of agricultural development law in 1983. Four Landsat images from 1985, 2000, 2015, and 2023 were classified using the Support Vector Machine (SVM) and ArcGIS Pro software. The findings showed a continuous change in the built-up area and vegetation area. The increase in built-up area was in conjunction with the rise in vegetation area. A spatial direction approach and concentric circle approach were used to assess the change in each direction and to identify the zone experiencing the most change in the built-up and agricultural oasis expansion. Shannon’s entropy model was used to measure the dispersion and the compactness of urban growth. The overall outcomes revealed that all directions showed an increase in built-up and vegetation area. Total Shannon’s entropy values showed compact urban growth in 1985, while, a dispersed development was recorded in 2000, 2015, and 2023. Statistical analysis demonstrated a high correlation between date palm plantations and vegetation area by 0,928%, as well as a significant correlation between built-up areas and population growth by 0,926%. These results can be helpful for the local authorities and planners in order to make a sustainable urban development and protect the fragile oasis ecosystem.
... This approach holds a connecting potential when it comes to relating urban phenomena at different spatial levels, from individual plots to block groups and entire urban regions [21], as well as linking pixel-level information with landuse classification [22]. In this analytical domain, operational possibilities are recognized in Geographic Information System (GIS) research strategies that bridge top-down and bottomup approaches [23] and have integrative potential through the integration of GIS techniques with quantitative analysis [24], fieldwork surveys [25][26][27], remote sensing data [28], point pattern analysis [29], image classification techniques [23,30], predictive modeling [20], and space syntax [31]. The third research group highlights the challenge of quantifying urban morphological patterns and combining qualitative and quantitative approaches [25], showcasing research opportunities of automated classifications supported by machine learning [29] and deep learning techniques, including moving window approaches [32], to enhance classification and mapping protocols. ...
... In the context of analytical techniques, statistical analysis is recognized as the most represented within the analyzed scope of literature documents (a third of the studies employ some statistical method) through a wide range of applications that include (1) regression and predictive modeling: regression modeling [21,22,33], discriminant analysis [36], Bayesian stochastic approach [22]; (2) clustering: k-Means clustering [21,36], hierarchical clustering [37], Gaussian mixture modeling [38]; (3) dimension reduction: principal component analysis (PCA) [37], (4) density estimation: kernel density estimation (KDE) method [39]; and (5) statistical tests: correlation analysis [37,40], analysis of variance (ANOVA) [33], chi-square test [24], Kolmogorov-Smirnov (KS) statistical test [25]. The analyzed literature indicates the presence of specific methodological approaches for comprehensive evaluation of urban compactness using statistics-based methods beyond traditional entropy-based methods such as integration of multicriteria decision analysis, proximity analysis, and land use mix measurement [28]. ...
... A special domain of integration is reflected in combining statistical methods with visual/spatial analysis such as (1) employment of agreement-disagreement maps [33] in order to understand how morphological characteristics of built-up areas correspond to dataset congruency and influence change detection accuracy, and (2) performance of spatiotemporal analysis in order to establish relationships between growth patterns and their environmental, social, and economic impacts through statistical models [24,36]. ...
This study aims to bridge the fields of urban morphology and land use/land cover (LULC) mapping through a systematic analysis of their integration in recent research. The research employs systematic literature review (SLR) methodology combining quantitative and qualitative methods through four methodological steps: data search, data selection, data analysis, and data clustering. The analysis performed three distinct clustering patterns: (1) methods and tools, (2) data types, and (3) urban morphology aspects. The results reveal five distinct methodological approaches—Data-Driven Typological Decoding Approach, Quantitative Structural Metrics Approach, Predictive Spatiotemporal Transition Approach, Temporal Change Detection and Performance Approach, and Spatial Configuration and Density Analysis Approach—each contributing unique insights to urban form analysis. The findings demonstrate the multidimensional nature of urban form analysis, incorporating both social and temporal dimensions, while highlighting the essential role of change detection in understanding urban pattern evolution. This systematic review establishes a comprehensive framework for understanding the relationship between urban morphology and LULC mapping, providing valuable insights for future research integration.
... This data offers a nuanced understanding of urban phenomena, including traffic patterns, fluctuations in population density, and evolving environmental impacts over time [2], [3]. The effective utilization of spatio-temporal data * Corresponding author: Jiechao Gao is essential for informed decision-making aimed at promoting sustainability and resilience in urban development [4]. ...
The rapid acceleration of global urbanization has introduced novel challenges in enhancing urban infrastructure and services. Spatio-temporal data, integrating spatial and temporal dimensions, has emerged as a critical tool for understanding urban phenomena and promoting sustainability. In this context, Federated Learning (FL) has gained prominence as a distributed learning paradigm aligned with the privacy requirements of urban IoT environments. However, integrating traditional and deep learning models into the FL framework poses significant challenges, particularly in capturing complex spatio-temporal dependencies and adapting to diverse urban conditions. To address these challenges, we propose the Federated Local Data-Infused Graph Creation with Node-centric Model Refinement (Fed-LDR) algorithm. Fed-LDR leverages FL and Graph Convolutional Networks (GCN) to enhance spatio-temporal data analysis in urban environments. The algorithm comprises two key modules: (1) the Local Data-Infused Graph Creation (LDIGC) module, which dynamically reconfigures adjacency matrices to reflect evolving spatial relationships within urban environments, and (2) the Node-centric Model Refinement (NoMoR) module, which customizes model parameters for individual urban nodes to accommodate heterogeneity. Evaluations on the PeMSD4 and PeMSD8 datasets demonstrate Fed-LDR's superior performance over six baseline methods. Fed-LDR achieved the lowest Mean Absolute Error (MAE) values of 20.15 and 17.30, and the lowest Root Mean Square Error (RMSE) values of 32.30 and 27.15, respectively, while maintaining a high correlation coefficient of 0.96 across both datasets. Notably, on the PeMSD4 dataset, Fed-LDR reduced MAE and RMSE by up to 81\% and 78\%, respectively, compared to the best-performing baseline FedMedian.
... LULCC generally refers to physical changes on the Earth's surface. These transformations are often a result of human activities (Aburas et al., 2018). ...
Urban growth changes spatial uses over time due to different dynamics. These processes cause many physical, environmental, and socioeconomic problems, such as climate change, pollution, and population-related events. Therefore, it is essential to predict future urban expansion to produce effective policies in sustainable urban planning and make long-term plans. Many models, such as dynamic, statistical, and Cellular Automata and Markov Chain (CA-MC) models, are used in geographic information system (GIS) environments to meet the high-performance requirements of land use modeling. This study estimated the growth of settled areas in Eskişehir city center using models developed using two different methods. In this context, settled areas in the city center were examined within the scope of 1990–2018, and the growth areas of settled areas in 2046 were predicted using the CA-Markov method in Model 1: Quantum GIS (QGIS) MOLUSCE plugin and Model 2: IDRISI Selva. While settled areas are continuously increasing, other urban areas are decreasing. Model 1 predicts an increase of 1195 ha in settled areas by 2046, while Model 2 predicts an increase of 45,022 ha. At the same time, it is concluded that settled areas will grow in a central location in Model 1, while they will spread in an east-west extension in Model 2. The study results show that QGIS-based modeling predicts more limited spatial growth than IDRISI Selva. The research interprets growth in terms of the staging of urban services, the population size of neighboring cities, distances, and income levels based on the internal and external dynamics of the city.
... While the land use alteration is often associated with how human behavior affects physical changes on the Earth's surface. Land use change typically has some impact on land cover change (Aburas et al., 2018). LULC Sustainability is largely concerned with changes in the environment. ...
The paper aims to evaluate the effectiveness of the multi-layer perceptron-Markov chain analysis (MLP-MCA) integrated method in predicting future Land Use and Land Cover (LULC) change scenarios in Fayoum due to rapid urbanization. The study employed machine learning algorithms for image classification using Google Earth Engine (GEE) for classification techniques to derive LULC maps from Landsat imagery taken in 2001, 2011, and 2021. The 2001 and 2011 LULC maps were used to predict the LULC scenario for 2021 using MLP-MCA, and the predicted result was validated against the observed 2021 LULC map using Area under the curve (AUC) that was derived from the Receiver Operating Characteristics (ROC). Subsequently, the study predicted future LULC changes for 2031 using two sub-models; sub- Agri and sub-built. The results show that a rapid growth in both built and agricultural area. The findings of this study highlight the potential of the MLP-MCA method in predicting future LULC changes due to urbanization.
... It serves as an important indicator of urban development and urbanization. This measure is often based on both the total population of an urban area and the overall built-up area of the city [20][21][22][23][24]. Furthermore, the non-agricultural population proportion and urban population density are also used to depict the urban scale [20,25,26]. ...
Environmental pollution significantly impacts the urbanization process. Despite the well-documented influence of urban scale on pollution, understanding of the specific effects of pollution at the urban scale remains limited. This study aims to further the understanding of the impact of pollution on urban scales by analyzing pollution variations and mechanisms. This study investigated city-level panel data in China, specifically assessing different pollutant emissions and their linkage to resident health. This study found that pollution has contrasting effects on urban land and population scales. It leads to expansion in urban land but has crowding-out effects on population scales. Notably, pollution from haze was found to increase urban mortality to a greater extent than pollution from industrial sources. Furthermore, this research found that increasing healthcare expenditures for urban residents can offset the negative impact of pollution on population growth and promote coordinated urbanization. This study emphasizes the importance of local government investment in medical services and public expenditures to mitigate the harmful effects of pollution on health, which can substantially prevent population outflows. Furthermore, stronger environmental protection measures can prevent urban land development sprawl resulting from pollution. In conclusion, this study highlights the need for a balanced approach to pollution control and urban development to achieve sustainable and high-quality urbanization.
... Urban built-up areas are highly intensive areas of urban construction land and buildings, reflecting the size and density of buildings [37]. This content is an important basis for defining the boundaries of our research and completing data processing We refer to the 2020 China urban built-up area dataset shared by Li et al. in the Science Data Bank (https://www.scidb.cn/ ...
Urban green space (UGS) is an important public infrastructure. However, the rapid development of cities and the insufficient supply and uneven distribution of UGSs have led to a mismatch between them and various needs, which has seriously affected environmental justice and social equity. This study discusses the fairness of UGS from the perspective of supply and demand and improves the theoretical framework of supply and demand combination evaluation. First, this study analyzes the supply of UGSs through accessibility analysis and evaluates their demand from both subjective and objective dimensions. Second, the fairness of UGS is analyzed from a Gini coefficient and spatial evaluation perspective. Finally, the location optimization of UGSs is performed, which provides strategic guidance for the improvement of UGSs in Chengdu. The research results based on Chengdu City show that the travel mode directly affects the supply range of UGSs and is reflected in their fairness. At the same time, in the case of a highly dense population, UGSs in the city are in short supply; that is, the equity is negatively correlated with the population. This study provides a new perspective to evaluate UGS fairness and can be a reference for UGS optimization decisions.
... The spread of built-up areas in these cities shows the potential impacts of development and increased human activities associated with rapid urbanization on vulnerable ecosystems such as urban forests, water and soil. As Aburas et al. (2018) reported, integrating Remote Sensing (RS) and Geographic Information Systems (GIS) into the management of urban ecosystems would help urban managers/ planners to monitor urban development processes constantly and efficiently. Urban forests provide several ecosystem services beneficial to both humans and the environment. ...
Urban ecosystems face numerous challenges due to rapid urbanization and population growth. Effective management of these ecosystems is crucial to ensure their sustainability and the well-being of urban residents. Remote sensing (RS) and Geographic Information Systems (GIS) have emerged as valuable tools for understanding and managing urban ecosystems. The integration of remote sensing and GIS technologies facilitate the monitoring and assessment of urban biodiversity, aiding in the conservation and restoration of ecological habitats. With this mind, the objective of this study was to investigate the integration of remote sensing and GIS technologies for real-time monitoring and assessment of environmental parameters in urban ecosystems, and their role in supporting sustainable urban ecosystem conservation efforts. Landsat 8 Operational Land Imager (OLI) images were acquired between January 2nd and April 5th 2020 to assess and monitor the dynamics in urban ecosystems in Abidjan, Accra, and Lagos. The Normalized Difference Built-up index was used to detect areas covered with concrete structures and impervious surfaces, while the Normalized Difference Vegetation Index and Normalized Difference Water Index were used to detect areas covered with vegetation and water bodies, respectively. Results of the study show that Abidjan, Accra, and Lagos experienced increased built-up areas at the expense of other land uses such as forests. Remote Sensing and GIS technologies provide valuable insights into the spatial and temporal dynamics of urban environments, supporting evidence-based decision-making and sustainable urban planning and development.
... For the last 300 years, the trend of LULC has been characterized by deforestation and the expansion of agricultural land globally [124]. However, in recent decades, developing countries have experienced a reduction in rural land and an expansion of urban areas through urbanization [125][126][127][128]. Urban growth phenomena have become unsustainable in many cities worldwide [129]. In fact, urbanization itself is a globally concerning topic, where people are leaving rural areas and moving to big cities [130]. ...
This dissertation aims to investigate the factors behind flash flooding in Erbil's central district, located in the Kurdistan Region of Iraq, and develop a methodology for assessing flood hazards in the city, despite limited data accessibility. In this thesis, each factor was investigated, including analyzing extreme precipitation events in the last two decades, including their spatial and temporal distribution of rainfall, intensity, and exceedance probability, and examines the impact of changes in Land Use and Land Cover (LULC) on the hydrological response of the Erbil basin. The hydrodynamic model's input data were generated using GIS-based modeling interface. HEC-RAS 2-D software package's suitability was ensured by evaluating two building representation techniques and two mathematical models (Diffusion-Wave Equations (DWE) and Shallow-Water Equations (SWE)) using the Toce River urban flood experimental model. The study utilized a two-dimensional hydrodynamic model (HEC-RAS 2-D) to assess the susceptibility, vulnerability, and socioeconomic impact of flooding in the study area. Using the model, flood hazard maps were created to show the extent of potential flooding in the study area during various rainfall events and return periods. Ultimately, the study concludes that without essential engineering measures, there is an increased probability of flooding in the center of Erbil.
