Earth Observation in Urban Monitoring: Techniques and Challenges

By Amit Kumar, Prashant K. Srivastava and Purabi Saikia

Earth Observation in Urban Monitoring: Techniques and Challenges presents the latest techniques of remote sensing in urban monitoring, along with methods for quantitative and qualitative assessment using state-of-the-art Earth observation technologies. The book details the advances of remote sensing technologies in urban environmental monitoring for a range of practical and research applications, Earth observation datasets, remote sensing of environmental considerations, geostatistical techniques and resilience perspectives. Chapters cover sensor applications, urban growth modelling, SAR applications, surveying techniques, satellite time series analysis and a variety of other remote sensing technologies for urban monitoring.

Each chapter includes detailed case studies at a variety of scales and from a variety of geographies, offering up-to-date, global, urban monitoring methodologies for researchers, scientists and academics in remote sensing, geospatial research, environmental science and sustainability.

• Focuses on a variety of interdisciplinary applications using Earth observation data, GIS and soft computing techniques to address various challenges in urban monitoring
• Provides numerous case studies at a variety of scales, from local to global, to aid readers in implementing urban monitoring techniques at any level
• Includes theoretical and applied research contributions along with background information on the use of concurrent technologies in the disciplines of urban studies

• Language: English
• Publisher: Elsevier Science
• Release date: Nov 9, 2023
• ISBN: 9780323957960

Book preview

Chapter 1

Introduction to earth observation in urban monitoring

Amit Kumar¹, Manjari Upreti¹, Pawan Ekka², Alisha Prasad¹, Purabi Saikia²* and Prashant K. Srivastava³,    ¹Department of Geoinformatics, Central University of Jharkhand, Ranchi, Jharkhand, India,    ²Department of Environmental Sciences, Central University of Jharkhand, Ranchi, Jharkhand, India,    ³Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India

Abstract

The rapid urban growth and increasing demand for natural resources lead to the considerable environmental degradation and habitat loss. The application of earth observation-based methods enables a better understanding of urban complexity with different spatio-spectral resolutions at temporal scales and contributes to addressing the adverse ecological impacts. There is an urgent need to understand the interdependencies of ecological conditions and changing urban growth to formulate a city-specific framework for sustainable urban development. Therefore the present chapter elucidates the fundamental components of the urban system emphasizing the earth observation-based technologies available to assess the urban environmental conditions that help in defining the settlement pattern and segregation of different land cover features. The inclusion of advanced artificial intelligence and machine learning algorithms provides precision analysis for future predictions and formulation of mitigation strategies. Furthermore, it helps in the determination of factors responsible for urban risk and of societal importance as well as in the implementation of significant policies and reforms for sustainable urban planning, monitoring, and management.

Keywords

Urban complexities; sustainable solutions; earth observation

1.1 Introduction

Towns and cities have acted as focal points in the cultural landscape of the world (Ramachandran, 1989). Moreover, cities are evolved due to a heuristic process of adaptation and response of human–environment interactions (Ramachandran, 1989). The urban growth patterns are linked with an increase in the built-up area in the horizontal and vertical planes (Jacquemin, 2019; Taubenböck & Esch, 2011). The changes in the urbanization process and urban growth are primarily influenced by the natural population growth and migration from rural to urban (Brockerhoff, 2000; Moore et al., 2003). Population growth is one of the major driving forces causing cities to expand beyond their borders, fragmenting their urban morphology and affecting the local ecology and environment (Ramachandra et al., 2014). Although urbanization is a global phenomenon, the trends and rates vary due to the variations in the topography, transportation connectivity, land use, social structure, and economy of a city (Hall, 2002; Li et al., 2003). Nevertheless, macro- and microeconomic, demographic, housing preferences, inner-city problems, employment opportunities, transportation, and regulatory frameworks governing the spatial distribution of urban setup in a geographical landscape are the other contributing variables influencing the trend and patterns of the urban growth (European Environment Agency, 2006; Taubenböck & Esch, 2011) (Fig. 1.1). Rapid urbanization is inducing the expansion of large cities and the proliferation of small cities around the world at an unprecedented rate (Schneider & Mertes, 2014). The physical or structural modification of built-up land over time has a significant impact on its operational efficacy and the urban scene of cities (Ali & Alzu’bi, 2021). The coverage of impervious surfaces and their linkages with their neighborhood spaces are crucial components for measuring urban sprawl, which defines the compactness and dispersion of built-up clusters (Li et al., 2020; Liu et al., 2019). The significant conversion of natural landscapes into impervious surfaces due to the growth of urban agglomeration in terms of buildings, road networks, or other infrastructure components (Marconcini et al., 2015; Song et al., 2015) resulted in the loss of productive agricultural lands, open green spaces, and surface water bodies and the depletion of groundwater (Rahman et al., 2010).

Figure 1.1 Drivers of urban development and urban sprawl. From European Environment Agency. (2006). Urban sprawl in Europe: The ignored challenge. 10, 1–57. Available at URL: https://www.eea.europa.eu’eea_report_10_2006. [Accessed on February 9, 2023]; Taubenböck, H., & Esch, T. (2011). Remote sensing – an effective data source for urban monitoring. Earthzine. Available at URL https://earthzine.org/remote-sensing-an-effective-data-source-for-urban-monitoring/ [Accessed on September 2, 2022].

There is no rigid definition of "urban" as it insignificantly varies across the country, and with periodic reclassification over time (Cohen, 2006). The degree of urbanization proposes primarily three classes viz., cities, towns, or semi-dense areas, and rural areas to reflect the urban–rural continuum (OECD/European Commission, 2020). The cities are categorized commonly into tier I, tier II, and tier III cities based on geographical location and size, economic status, and population density. The large cities viz., Mumbai, Delhi, Beijing, and Shanghai are classified as tier I cities, while Pune, Ahmedabad, Shenzhen, and Hangzhou are tier II cities. Moreover, Aurangabad, Vadodara, and Wuhan fall under the category of tier III cities. Urbanization and the spatial pattern of city growth, with cities being classified as compact or sprawling. Compact cities have high population densities to make better use of land and resources and to promote more livable urban forms, whereas sprawling cities have low population densities, separation of land uses, and more inefficient use of land and resources (Ardeshiri & Ardeshiri, 2011).

A notable difference in urban growth patterns across continents was observed over the last century (Li et al., 2021). Increased population density emerged as a major barrier to the development of sustainable economic possibilities, urban infrastructure, quality of life, and the existence of urban systems (Chen et al., 2014). Urban settlements have taken precedence over other types of human habitats in the 21st century (Rieniets, 2009), while the global population growth rates are projected to reach 68% and 85%, respectively, in 2050 and 2100 (UNDESA, 2014; United Nations, Department of Economic, & Social Affairs, Population Division, 2019). Northern America (82%), Latin America and the Caribbean (81%), Europe (74%), and Oceania (68%) are the most urbanized regions of the world (United Nation, 2018). Among all continents, Asia had the greatest urban area in 2010 (Fig. 1.2), and this tendency will persist even though urban areas would expand on all continents until 2100 (Zhou et al., 2018). While Africa is still predominantly rural, with only 43% of its population living in cities, while urbanization in Asia is currently approaching 50% (United Nation, 2018). Despite having a low proportion of its population living in urban areas, Africa will be a new driver of urban expansion in the second half of the century (i.e., after 2050) (Zhou et al., 2018). The relationship between urbanization and economic growth is having great scientific and societal importance due to its effects on the majority of the global population (Li et al., 2021). The Indian cities experienced significant urban growth along with population growth during the last three decades leading to significant fragmentation and degradation in the forested and nonforested land (Jat et al., 2008; Ji et al., 2006). By 2030, there will be 43 megacities in the globe, the majority of which will be in developing countries and have populations of more than 10 million (United Nation, 2018). However, some of the urban agglomerations with the fastest rates of growth are in smaller cities with fewer than one million people primarily located in Asia and Africa (United Nation, 2018). Numerous factors contribute to the challenges of unsustainability faced by major cities in developing countries, including the predominance of a small number of large cities, the uneven distribution and control of the economy, industries, and economic opportunities, as well as issues with housing, transportation, electricity, pollution, and congestion that impede the balanced regional development (Abdul et al., 2020).

Figure 1.2 Global map of urban land expansion rate (%) 2000–2100 ( Gao & O’Neill, 2020).

1.2 Urban complexities

Cities are complex and interdependent systems and are exposed to different natural and anthropogenic hazards due to specific geographic locations, which are expected to aggravate due to large population concentration and intensified anthropogenic activities (Wilbanks et al., 2007). The susceptibility to hazard-risk together with the typical urban-centric issues (Kumar et al., 2020) had a significant negative impact on the immediate environment and pose significant global challenges for sustainable development goals (von Weizsäcker & Wijkman, 2018). The combined effects of urbanization and climate change will have a disastrous impact on cities, with global warming projected to reach 1.5°C during 2030–52 and 3°C by 2100 (UN-Habitat, n.d.). On a local to a global scale, the implications of climate change have been seen as a rise in the frequency and severity of extreme weather events, particularly in coastal regions and cities in developing nations (Anand & Seetharam, 2011). Mostly the marginalized groups in low- and middle-income nations, with negligible contributions to climate change, are most at risk from major direct and indirect impacts of climate change (Satterthwaite et al., 2007). Climate change is visible in the form of sea level rise, heat/cold waves, floods, heat waves, tsunamis, storms, landslides, cloud bursts, and earthquakes (Awuor et al., 2008; Depietri et al., 2018; Kumar et al., 2020).

Moreover, the unprecedented challenges introduced and aggravated due to the vast urban growth including lack of housing and social services, sanitation, energy demand, the depletion of basic resources, economic hardships, the proliferation of vector-borne diseases, slums, traffic congestion, social inequality and conflicts, and environmental degradation (Aliyu & Amadu, 2017; Olalekan, 2014). In addition, urban areas are associated with a number of health risks for people, environmental hazards, native species extinction, invasion of exotic species, and loss of biodiversity (Nagendra et al., 2013). Approximately, 40% of global urban expansion live in slums (>1 billion people globally) under dilapidated conditions and making them vulnerable to violence, armed conflict, disasters, and extreme poverty, which cause malnutrition, disease, and hunger, affect the health/mortality of women and infants (Adeyeye et al., 2021; Duijsens, 2010).

The combined impact of unplanned and haphazard urban growth, land use transformation, industrial processes, transportation, and high energy consumption has several detrimental effects on the environment, human health, energy use, and climate change, which led to the loss of green space, agricultural land, habitat and accelerate food inflation, urban poverty, anthropogenic carbon dioxide emissions, urban heat island (UHI) effects (Lüttge & Buckeridge, 2020; Roth, 2002). It has also affected the water–carbon–nitrogen cycles, which has a major impact on the regional climate (Seto & Shepherd, 2009). The effects of climate change, which have been exacerbated by the emission of greenhouse gases (GHGs) from urban agricultural areas, industry, the burning of fossil fuels, pollution, and human-induced land use patterns, include shifting latitudes, altered global temperature patterns, monsoons, weather events, and the loss of species (Sadashivam, 2010a; Xu et al., 2009). Such changes, primarily climate change-induced crop failure (due to severe drought, floods, etc.) affect the livelihood security of neighborhood rural inhabitants and are compelled to migrate to cities (Sadashivam, 2010b). Wilderness areas, natural forests, woodlands, grasslands, coastal regions, and wetlands encroachment puts the urban environment close to the wildlife, causing conflict between the two and loss on both sides (Moore et al., 2003) (Fig. 1.3). It can also spread COVID-19-like pandemics and serious diseases by allowing the vectors to switch from their natural animal hosts to people (Lal et al., 2020; Robert et al., 2003).

Figure 1.3 (A) Schematic diagram of forest–agriculture–urban interface highlighting shrinking forests and sprawling cities; (B) forest cover in the vicinity of Ranchi city; (C) forest clearance at the urban fringe (Itki, Ranchi); (D) urban setting in a metropolitan city (Mumbai).

Urban growth and urbanization are inevitable processes for the economic development of a nation although having a number of socioecological difficulties and significant negative impacts on biodiversity and ecosystem services (Yuan et al., 2019). The economic contribution of cities to the long-term development of a country should be the main focus of the concept of urbanization (Sadashivam & Tabassu, 2016). However, a balance between economic growth and sustainable development in developing countries is required wherein public participation, awareness and environmental education, technology for consuming renewable energy, effective government policies, and future projections are important aspects (Meyer & Auriacombe, 2019). The provision of basic city services, such as green space, waste disposal and recycling, street lighting, road connectivity, and water supply, among others, presents challenges for the future urban settlements next to megacities in terms of municipal investment and creative plans (Keivani, 2010). Developing countries have faced ongoing challenges due to global concerns about food security, resource consumption, GHG emissions, pressure on remaining agricultural lands, and mitigating climate change (Sarkodie et al., 2019). The implementation of sound urban public policy and reforms in urban planning, finance, and management is becoming more difficult for the government given the current state of urban areas’ inability to manage air, water, and solid waste (Upadhyaya, 2014). The government has to deal with numerous political and administrative challenges due to lack of essential services like housing, electricity, water supply, and road connectivity (Chatterji, 2018).

1.3 Needs for urban monitoring

Monitoring changes to the landscape, rate of resource exploitation, and changes in ecological conditions brought on by rapid urban growth aid in evaluating the socioeconomic and environmental conditions to ensure their sustainability as well as the quality of life of city dwellers (McGrane, 2016; Ramachandra et al., 2014). Smart and sustainable city design requires an understanding of the interdependency of urban systems under various climatic conditions (Renard et al., 2019). Due to the complex urban morphology, various urban regions in the same city as well as different sections of the same city may experience varying microclimates (Alonso & Renard, 2020). The historical perspective of urban issues including growth patterns, pollution, and hazards in densely populated urban areas and their linkages with land use functions are pertinent to assess their effects on the environment and living health over generations (Power et al., 2018) and devising effective pollution control strategies (Goodsite & Hertel, 2012). The policy to incorporate additional natural infrastructures in the planning and design of cities associated with benefits to public health is being expanded with the help of socio-ecological and environmental indicators with a city-centric framework focusing on issues like poverty, sustainable human settlements, and biodiversity conservation (Sandifer et al., 2015) (Fig. 1.4). Understanding urban developments and guiding those changes toward more sustainable paths is extremely important from a societal perspective (Ghazaryan et al., 2021) and through careful monitoring, cutting-edge technologies, like integrated smart management control systems based on wireless sensor networks, can turn wasteful cities into sustainable ones (Kantarci & Oktug, 2018). It is possible to optimize sustainable urban development while taking into account shared socioeconomic pathways by using a multimodel approach to amplify the energy, land use, and emission trajectories (Riahi et al., 2017). The fundamental ideas should be put into practice in each city individually, to maximize the totality of environmental, social, and economic values (Riffat et al., 2016).

Figure 1.4 City-centric framework for sustainable urban development (UN-Habitat, 2020).

1.4 Earth observations in urban monitoring

Earth observation has emerged as a potential solution to address and counteract several challenges to urban research and to provide spatial support for the analysis of urban land use changes and urbanization trends. The capability of repetitive coverage, and the synoptic view that enables object identification, patterns, and monitoring of human–land–environment interactions in a spatiotemporal framework are the main advantages of remote sensing (Yang, 2011). The impact of human activities on the natural landscape, the impact of cities on the environment, the measurement of climatic variability, and effective urban and regional planning processes are all made possible by the use of spatiotemporal multiresolution satellite images (Kumar, 2016). Cities are increasingly using less expensive technologies and more efficient sensors to track and share data on urban scenarios including water, air, solid waste, infrastructure, energy, traffic, and public transportation, among other areas to mitigate any negative ecological impacts (World Cities Report, 2020). Statistical modeling, neural networking, and fuzzy classifiers have replaced the conventional methods of supervised and unsupervised classification of satellite datasets to identify land use changes in recent years (Taubenböck & Esch, 2011). The methods of multitemporal urban monitoring have also advanced from spectral mixture analysis, artificial neural networks, and curvelet-based change detection algorithms for radar images to image differencing, vegetation index differencing, principal component analysis, direct multidate unsupervised classification, postclassification change differencing, and a combination of image enhancement and postclassification comparison (Lu et al., 2004; Mas, 1999; Mather & Koch, 2011) (Fig. 1.5). Typically, different artificial and natural surface materials utilized in urban landscapes are evaluated using hyperspectral remote sensing images to understand the effect of these surfaces on regulating the ecological, climatic, and energy aspects of cities (Heiden et al., 2007; Taubenböck & Esch, 2011). While light detection and ranging (LIDAR) based digital surface models or stereo-imagery in combination with high-resolution multispectral images are extensively used to retrieve the structural parameters using 3D models of growing cities (Bochow et al., 2010; Taubenböck & Esch, 2011) including average building sizes, building density, floor space index, percentage of impervious surfaces, vegetation fraction, and dominant roof materials, or the mapping of urban biotopes (Chen et al., 2009; Schenk & Csathó, 2002; Wurm et al., 2011). By combining many disciplines with remote sensing datasets, such as the social sciences (Liverman et al., 1998), civil engineering (Taubenböck, Goseberg, et al., 2009; Taubenböck, Roth, et al., 2009), urban planning (Netzband et al., 2007), risk and vulnerability analysis (Lang et al., 2008), energy-relevant techniques (Geiß et al., 2011), or urban climate analysis (Voogt & Oke, 2003), cities may be monitored and understood (Taubenböck & Esch, 2011).

Figure 1.5 Schematic diagram representing the pathways from urban complexity to sustainable development through the integration of remote sensing and artificial intelligence markup language (AIML). After Alastal & Shaqfa (2022); UN-Habitat (2020); Vollmann (2018).

Urban areas are visually represented as cyan in the standard false color composite (FCC) of the multispectral satellite images (Table 1.1) and are separated from other land use features in Landsat TM (Aniello et al., 1995), Landsat ETM+ (Adinna et al., 2009), Landsat OLI (Poursanidis et al., 2015), MODIS (Schneider et al., 2010), and LISS (Gupta & Jain, 2005), while the pattern of settlement in an area can be determined with the aid of spatial resolution. Moreover, the LANDSAT satellite series offer the critical capability to observe long-term changes in the urban landscape with almost uniform spectral channels that enable comparative evaluation of the land surface process more effectively (Espey et al., 2017). Additionally, a lot of researchers use LANDSAT TM, ETM+, and LANDSAT 8 TIRS images to observe and model the biophysical characteristics of the land surface and to estimate land surface temperature (LST) (Voogt & Oke, 2003; Weng, 2001). It is also possible to study the spatiotemporal trends of monitoring surface UHI using thermal remote sensing, which provides LST information for large areas (Roth & Oke, 1989; Schwarz et al., 2011). The very high-resolution satellite datasets (viz., QuickBird, IKONOS) support in effective characterization of slums and enable their precise identification, and neighborhood thus contributing to slum renovation and rehabilitation (Livengood & Kunte, 2012; Rangwala et al., 2003). Terra-MODIS satellite sensors provide datasets for monitoring large-scale changes in minimum 8 days intervals (NASA, 2023). Global stratification of ecoregions based on climate, vegetation, and built-up facilitates the mapping of cities and towns at regional and global scales (Schneider et al., 2010). The Global Human Settlement Layer (GHSL) provide global spatial information for settlement and population operating in open and free access 2–1000 m spatial resolution (Joint Research Centre (JRC), European Commission, 2021). The GHS-Urban Center Database with multitemporal and multi-thematic information provides the basis to estimate sustainable development goals (SDGs) indicators including sets of geographical, socio-economic, and environmental attributes (Melchiorri, 2022).

Table 1.1

From Lehner, A., Naeimi, V., & Steinnocher, K. (2017). Sentinel-1 for urban areas-comparison between automatically derived settlement layers from sentinel-1 data and Copernicus high-resolution information layers. In: Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management, pp. 43–49. https://doi.org/10.5220/0006320800430049.https://www.goes-r.gov/mission/mission.html; https://eos.com/find-satellite/sentinel-2/; https://mausam.imd.gov.in/responsive/servicesSatMet.php.

Land use/land cover (LULC) classification use supervised classification, unsupervised classification, support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM), and Mahalanobis distance (MD) algorithms (Talukdar et al., 2020). The use of LULC classification extends beyond simply tracking spatiotemporal urban change to assessing the statistical and spatial extent of the effects of hazards and disasters on land cover (Adnan et al., 2020; Alam et al., 2020). The spatiotemporal LULC changes can be used for both past reconstruction and future projection, along with variables like population density, road networks and densities, slope, relief, geomorphology, soil, and geology (Zhou et al., 2019). The extraction of urban patterns based on the spectral property of the different landscape features is performed using several spectral ratio-based indices (Baranwal & Ahmad, 2021). The spectral urban indices employing unique responses of different spectral bands have been explored to study impervious surfaces more accurately and to understand their role as an ecological indicator within the urban landscape (Sekertekin et al., 2018) (Table 1.2). Applications of remote sensing based on temporal nighttime light observations in urban studies include monitoring urbanization and socioeconomic dynamics, assessing armed conflicts and disasters, calculating GHG emissions and energy use, and examining the effects of light pollution on human health (Zhao et al., 2019). The distribution of humans in a region can be found by analyzing the relationship between population density and nighttime satellite imagery derived from the visible near-infrared (IR) band (Amaral et al., 2005; Sutton et al., 1997). A different way to measure wealth distribution is the night light development index (NLDI), which uses population grids and nighttime light images based on the Lorenz curve and Gini coefficient (Ghosh et al., 2013). While object-based spatial cluster analysis maps urban landscape patterns (Yu et al., 2014), Zipf’s law model quantifies the fractal, self-organized, and agglomeration behaviors of cities (Wu et al., 2018). The other methods include local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM) (Dou et al., 2017), as well as an SVM-based method that extracts urban land using nighttime data (Cao et al., 2009).

Table 1.2

Cloud cover is a major difficulty for the optical satellite that synthetic aperture radar (SAR) images can overcome (Ager, 2013). Backscatter coefficients of SAR images are used to differentiate urban features from other LULC features and to track the spatiotemporal changes in the urban environment (Gamba & Aldrighi, 2012). The urban land cover was mapped by combining high-resolution SAR and optical imagery and using the Bayesian classification technique for improved classification (Amarsaikhan et al., 2010). The SAR-based interferometric technique, on the other hand, is used to map land subsidence and assess the harm and altered land cover caused by natural disasters like earthquakes (Liu et al., 2020; Zhang et al., 2011). The other technique includes eigenvalue-based urban area extraction for change detection (Quan et al., 2018). Real-time information helps with smart management, traffic control, and identifying the busiest times and routes (Koller et al., 1994; Robertson & Bretherton, 1991), while real-time GPS traffic management offers traffic updates and alternate routes (Rizwan et al., 2016). The most well-known method for feature identification using LIDAR and optical data is image segmentation (Chen et al., 2009). While the addition of vertical height provides the 3D perspective of the building, the analysis of 2D urban expansion provides a statistical area of the extent and sense of direction of expansion (Xu et al., 2019). Building coverage ratio (BCR), floor area ratio (FAR), and other building density indicators are computed using digital surface model (DSM) from airborne LIDAR data by extracting the objects, defining the boundaries of buildings at a specified height, and performing the procedures boundary tracing, object identification, morphological operation, and threshold-based segmentation (Yu et al., 2010). The historical LULC changes pattern, population density, road networks and density, slope, relief, geomorphology, soil, geology, and drainage density (Zhou et al., 2019) together with the growth trends of urban population and density change have been used for forecast of urban growth (Angel et al., 2011; Seto et al., 2012). The study projected a twofold increase in global population (2.8–6.4 billion) in 2000–50 where the large population concentration will occur in South and Central Asia, Sub-Saharan Africa, and Southeast Asia while the decreased concentration in Europe and Japan, Latin America and Caribbean regions (Angel et al., 2011) (Fig. 1.6).

Figure 1.6 Urban population projections for different world regions, 2000–50 (Angel et al., 2011).

1.5 Conclusions

Rapid urbanization processes and increasing population densities provide a high degree of complexity in urban challenges and risk, necessitating greater knowledge, planning, and management of resources and society to attain livability and sustainability. Urban expansion and structural transformation influence socioeconomic growth, variations in land use and topography, as well as the spatial distribution of urban population. The dynamic conversion of natural landscapes to impervious surfaces has a negative impact on biodiversity and ecosystem services with encroachment on wilderness and natural habitats. The growth of the city in horizontal and vertical planes is influenced by the influx of migrant population that hampers the economy and social status with the overutilization of resources, development of slums, food inflation, fragmentation of blue–green infrastructure, and transport congestion. The city-centric framework by the United Nations provides key principles focusing on society, economy, culture, governance, and environment to develop a new urban agenda for making cities inclusive, resilient, safe, and sustainable. The optimization of advanced earth observation-based techniques provides the platform for monitoring the spatiotemporal trend of urbanization from the local to a global level. The availability of multispectral to hyperspectral SAR remote sensing-based technology for determining the surface material properties, structural parameters, vegetation fraction, road networks, and densities are instrumental for sustainable urban planning and designs. The integrated modeling approaches for analyzing the urban climatic conditions in consideration of population, slope, relief, geomorphology, soil, geology, etc. support the identification of the potentially vulnerable zones to formulate disaster risk reduction policies. A considerable rise in the performance of the earth observation techniques has been observed with the introduction of artificial intelligence and machine learning-based algorithms with high accuracy-based classified products and modeling operations more specifically in open source environments. Earth observation of urban landscape provides significant assistance in good decision-making, policy formation, and corroborated effective implementation in light of creating livable cities.

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