Urban Extent Research Articles

Urban Sprawling and Agriculture: A Review of Issues

There has been unprecedented growth in urbanization worldwide in recent times because of high population growth and concomitant economic development. India has recently become the most populated nation in the world, surpassing China. Furthermore, it is the second-largest urban system in the world, with almost 11per cent of the global urban population. Urban expansion inevitably leads to changed land use both within cities and on the urban fringes. It has serious implications for the agriculture sector and land markets around towns and cities. This review article seeks to examine the issues related to urban sprawl and its associated implications for the agriculture sector. The review revealed that the extent of urbanization, especially in the developing world, was largely unacknowledged and unaddressed. Moreover, the changes in land use were not being effectively captured and documented. Urban sprawl had both positive and negative aspects concerning food security and livelihood generation. Research focusing on these aspects within the context of rural-urban interactions, especially in hilly regions, was generally lacking and thus required greater location-specific attention. Additionally, land regulation frameworks were poorly defined and often abused. This situation calls for strict land use policy regulations and their enforcement in both rural and urban areas.

Urbanization alters sandy beach scavenging assemblages and dogs suppress ecosystem function

AbstractUrbanization is rapidly transforming coastal landscapes around the world, altering the structure and function of marine, intertidal, and terrestrial ecosystems. In tandem, coastal areas are hotspots for human recreation, leading to shifts in wildlife behavior and activity patterns. Together, urban development and recreational use of wildlife habitats can shape wildlife behavior, abundance, and ecosystem dynamics at different spatial and temporal scales. In this study, we explore the impact of urbanization and human and domestic dog activity on the structure of vertebrate scavenging assemblages and the ecosystem functions they provide in sandy beach ecosystems across 40 km of the central California coast, USA. We surveyed vertebrate scavenging assemblages using baited camera traps on 17 beaches spanning a gradient of coastal urbanization. We found that urbanization extent within small spatial scales (i.e., 1 or 3 km radii of each site) and the rate of beach visitation by domestic dogs or humans were the best additive predictors of assemblage structure. We identified pronounced urbanization‐associated shifts in the composition of vertebrate scavenger guilds but found that the rate of carrion processing was more strongly influenced by domestic dog habitat use and diel period. Scavenging activity was substantially lower on beaches with more domestic dogs, suggesting that dogs interfere with critical scavenging ecosystem functions on sandy beaches. Our results underscore the pervasive and nuanced effects of urbanization and recreation on the dynamics of land–sea connectivity and suggest a need for comprehensive consideration of cross‐ecosystem linkages in ongoing shoreline conservation and development efforts.

Running out of empty space: environmental lidar and the crowded ancient landscape of Campeche, Mexico

As airborne lidar surveys reveal a growing sample of urbanised tropical landscapes, questions linger about the sampling bias of such research leading to inflated estimates of urban extent and population magnitude. ‘Found’ datasets from remote sensing conducted for non-archaeological purposes and thus not subject to archaeological site bias, provide an opportunity to address these concerns through pseudorandom sampling. Here, the authors present their analysis of an environmental lidar dataset from Campeche, Mexico, which reveals previously unrecorded urbanism and dense regional-scale settlement. Both characteristics, the authors argue, are therefore demonstrably ubiquitous across the central Maya Lowlands.

Measuring Change in Urban Land Consumption: A Global Analysis

An issue of concern in landscape and urban planning, articulated in the United Nation’s (UN’s) Sustainable Development Goals (SDGs), is the increase in urban land consumption over time. Indicator 11.3.1 of the SDGs is dedicated to measuring it, underlining the importance of decreasing urban land consumption per person, a strategy that is understood to contribute positively to climate mitigation and to a host of other social, economic, and environmental objectives. This article aims to explore the practical implications of the official methods for measuring Indicator 11.3.1, as well as two alternatives, and to calculate and compare the global and regional trends of these indicators for the 2000–2020 period for a universe of 3470 cities and metropolitan areas that had 100,000 people or more in the year 2020. Built-up area and population data for this universe were obtained from the Global Human Settlements Layer (GHS-BUILT-S and GHS-POP) published by the European Commission. We applied methods adapted from New York University’s Atlas of Urban Expansion to map the urban extents of all cities in 2000 and 2020, and then we used these urban extents, the built-up areas, and population estimates within them to calculate values for Indicator 11.3.1 and for two alternative indicators for the 2000–2020 period. We found that the current definition of Indicator 11.3.1 of the SDGs—“Ratio of land consumption rate to population growth rate”—has significant limitations in conveying meaningful information and interpretability for practical applications. We suggest two alternative indicators that address these shortcomings: the rate of change of land consumption per person and the rate of density change. Our analysis found that, for the world at large, urban densities declined at an annual rate of 0.5–0.7% between 2000 and 2020, with significant variation in the direction and magnitude of density trends by world region. Additionally, we found density declines to be faster in smaller cities than in larger ones and faster in cities with slower population growth or population declines compared to those with more rapid population growth.

Water Environmental Risks Encountered during Urbanization in Valley Areas and the Potential Mitigation Effects of Utilizing Reclaimed Water

Since urbanization poses risks to regional environments, assessing and mitigating these risks is a challenge inherent to the urbanization process. The present study employs an adapted methodology based on the water environmental capacity (WEC) to assess the risks associated with the water environment in an urbanizing area located in a valley of the Chongqing municipality in Southwestern China, with a particular emphasis on its representative regional river. By utilizing hydrological data and planned parameters specific to this region, this study estimates the spatial and temporal distribution of the WEC and pollution loads in the river under various urbanization scenarios. It also explores the potential of the utilization of reclaimed water to mitigate these water environmental risks. The results indicate that the extent of urbanization exerts significant influences on the water environment and, therefore, requires meticulous planning. Areas experiencing higher levels of urbanization in the middle reaches of the river are associated with greater environmental risks compared to those observed in the lower reaches, while minimal risk is observed in the headwater. Due to the pollution loads brought about by precipitation, there is a higher level of environmental risk during the wet season (May–October) compared to the dry season. Utilizing reclaimed water can effectively reduce the pollution loads discharged into the river and is the most efficient means of mitigating environmental risks. To effectively manage environmental risks during urbanization in this valley area, further studies should prioritize examining the correlation between the scale of urban expansion and the river’s environmental carrying capacity. Additionally, it is imperative to establish realistic targets for the river water’s quality and discharge standards for the effluent from wastewater treatment plants. Furthermore, it is crucial to determine an appropriate level of utilization of reclaimed water and delve into the mechanisms behind the method used to assess the water environmental capacity. Despite these challenges, analyzing the spatial and temporal distribution of risk during urbanization and evaluating the mitigative effect of utilizing reclaimed water based on adapted assessments of the water environmental capacity hold reference value for the formulation of environmental management strategies and environmental planning. There is significant potential for this approach to have various applications.

Assessing the suitability of the SLEUTH cellular automata model for capturing heterogeneous urban growth in developing cities: A case study in Northern Nigeria

Cellular automata (CA) models like SLEUTH (an acronym for slope, land use, excluded area, urban extent, transport-network and hill shade) have predominantly been developed and applied in developed countries. Modeling can serve as a tool to guide policy measures in facing urbanization challenges. However, developing cities have peculiar differences (heterogeneity, poor planning, and low infrastructure) thus the existing modeling approaches may not be able to apprehend heterogeneous urban growth. This research will use selected cities with similar spatial extents as controls but disparate urban extents, and growth indices to analyze the performance of SLEUTH simulations. Presumably, a comparison of the model simulations of the cities would display some significant differences, due to these variations and the scale of observation that has to be used for the model simulations. The results for the successfully calibrated cities (Kano/Funtua couple: 0.48/0.02. Katsina/Kaduna: 0.48/0.83 respectively) showed that in each city couple, the more expansive city with the most compact urban settlement pattern had a higher prediction accuracy, also predicted images of the cities showed underestimation of the urban areas over the years with the exception of Katsina city. The study further showed the model's effectiveness in modeling cities in developing countries, such as Nigeria. It is recommended that the type of urban growth experienced by cities be taken into consideration when implementing SLEUTH. Limitations of the study are centered on the inherent limitations of the model, the possibility of the occurrence of errors in data preparation, the scale and urban settlement type, which play an important role in the success of the calibration. Future research could be focused on adding other relevant inputs to the model and creating a metric that ascertains the best satellite image resolutions for a particular study area's growth coefficient values.

Dwelling conversion and energy retrofit modify building anthropogenic heat emission under past and future climates: A case study of London terraced houses

Energy demand per capita is expected to increase with smaller dwelling units in Europe, as less energy is shared linked to the trend of fewer people per household. To meet the demand for more smaller units, a popular retrofitting approach is to split existing large dwellings. As this type of dwelling conversion (DC) affects both household size (HHS) and therefore the likely energy use behaviour of residents, building thermal performance and anthropogenic heat emission (QF,B) to outdoor environment are impacted. Here, the UK time use survey (TUS) provides activity information to allow comparison of the implications of DC to energy conservation measures (ECM) for terraced houses in both past and future London climates. Our results show that ECM can substantially reduce both heating energy demand and QF,B during cold seasons, whilst due to the absence of space cooling in UK residential buildings the ECM ineffectively diminishes summer demands. Further to this, the increased occupancy density resulting from DC increases summer peak QF,B by 53.8% at 17:00, which could intensify canopy-layer urban heat island effects. Although climate projected for the 2050′s should result in a decreased wintertime QF,B, the potential increase in summertime space cooling energy demand will see an associated increase in summertime QF,B. Occupancy patterns need to be considered as part of retrofitting assessments and climate change impacts due to their influence on HVAC usage schedule. The role of occupancy behaviour extends beyond retrofit strategies themselves, to larger urban extents (e.g. planning, policy making, urban weather/climate feedbacks) to ensure both energy saving and urban heat mitigation.

Low-intensity urbanisation alters community composition across multiple trophic levels on Lipsi Island, Greece.

Urbanisation has reduced the abundance and diversity of many taxonomic groups, and the effects may be more pronounced on islands, which have a smaller regional species pool to compensate. Green spaces within urban environments may help to safeguard wildlife assemblages, and the associated habitat heterogeneity can even increase species diversity. Here, total abundance and species diversity of butterflies, birds, and vegetation at nine rural and nine urban locations were quantified on Lipsi Island, Greece. Sites were assessed using Pollard walks for butterflies, point-count surveys for birds, and quadrats for vegetation. There was no significant difference in the abundance or species diversity of butterflies or vegetation among rural and urban locations, which could pertain to the low building density within urbanised areas and the minimal extent of urbanisation on the island. However, urban areas hosted a significantly greater abundance, richness, and diversity of birds compared to rural sites. The community composition of butterflies, birds, and vegetation also differed significantly between urban and rural locations, highlighting the impact of urbanisation on species across a broad range of trophic groups. This study contributes to ecological knowledge on the impacts of urbanisation across multiple trophic levels in island ecosystems, with comparisons across a gradient of island size and urbanisation intensity needed in future research.

Quantifying urban flood extent using satellite imagery and machine learning

Quantifying urban flood extent using satellite imagery and machine learning

Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood

Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood

Urban development and the loss of natural streams leads to increased flooding

Developing countries with hard pressed economies and a rapidly growing population have a myriad of issues to face. Karachi is one of the largest cities of Pakistan and is rapidly growing. The city has been hit by floods in the year 2016, 2018, 2019 and 2020, recently. Since the early 1990s Karachi has seen a rapid spike in development and in the process many natural streams have been built over. This has led reduced space for rainwater runoff and has created problems for urban flood managers. To ascertain this, topographic records of 1940s and 1980s and Landsat imagery of 2000, 2008, 2015 and 2020 were used to map historical urban extent. Sentinel—1, Synthetic Aperture Radar (SAR) imagery was used to extract flooded areas during 2015, 2017, 2019 and 2020 floods. In the year 2020, 64.3 km2 of area was flooded which was the highest. There has been a growth of 286% in urban area observed between 1980 and 2000. The city was built with several natural flood water carrying streams, north and west of it. It was found that that out of 3600 km of these streams, 870 km of streams have either been blocked or removed and a 100% in core urban area. DEM of difference (DOD) prepared by subtracting 1945 DEM from SRTM DEM indicated that most of embankments along these streams were in the areas that show erosion in DOD surface, thus explaining the expansion of built areas along historical stream channels. Forcing floods to enter these built areas.

Spatially explicit downscaling and projection of population in mainland China

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

Estimating Combined Effects of Climate Change and Land Cover Change on Water Regulation Services of Urban Wetlands in Valdivia, Chile

AbstractThe relationship between cities and wetland cover varies across the globe, with some cities converting wetlands to low‐ and high‐density urban cover and others preserving, conserving, or restoring wetlands, or constructing new ones. However, the scientific literature lacks studies relating changes in systemic flood risk in an urban stormwater management systems to changes in wetland cover. Furthermore, whether and how such relationships are affected by changing storm intensity under climate change is unknown. We present a case study on the effects of changes in urban wetland extent and storm intensity on flooding in an urban drainage system in Valdivia, Chile, under several co‐produced future scenarios and historical trends of development. We used data derived from stakeholder workshops and historical landcover to determine four plausible scenarios of urban development, plus one business‐as‐usual scenario, in Valdivia through the year 2080. Additionally, we used historical precipitation data and downscaled climate data to estimate event rainfall from extreme storms in the year 2080. We found that system flood volume and time the system was flooded increased with increasing wetland loss and rainfall volume. Mean rate and hour of peak discharge were unaffected by wetland loss. Infiltration's relative role in reducing flooding diminished as wetland loss increased. Cities may still experience dangerous and/or unacceptable flooding even with extensive wetland coverage and will likely need to pair conservation with additional improvements in their stormwater management systems and contributing watersheds.

Modeling urban expansion in Zahedan’s dry climate: insights from the SLEUTH model

IntroductionRapid uncontrolled growth of build-up areas has increasingly challenged the sustainable use of urban area simulating the growth patterns of fastest-growing cities is more necessary in dry climates, due to low ecological suitability for urban development and meeting the needs of citizens. Therefore, this research conducted aiming at predicting the expansion of urban land use in Zahedan City, Iran, which has a dry climate with an evenness landscape.MethodsUrban Expansion in Zahedan Modeled using SLEUTH (slope, land use, exclusion, urban extent, transportation and hillshade) in two historical and environmental scenarios until 2050. The input data were extracted from processing on DEM and remote sensing data and the SLEUTH model was calibrated in four stages from 1990 to 2020.Results and discussionThe results showed that the increase in Ahead extent in 2050 is more than twice as much as in 2020, and this increase was associated with a less dispersion of urban patches in the environmental scenario compared to the historical scenario. Also, the results clarified that the developable spaces are saturated in terms of slope in the east and there is the lack of urban green spaces. These results reveal the need for the attention of city managers in predicting the urban green space in the expected growth areas and compensating for the lack of vegetation cover in the former urban areas. Geographic extension of predicted urban land can be used in future environmental planning and urban developing strategies, as well as it is suggested to adopt this approach as a plan for urban planning in dry climates.

Spatial constraints in cellular automata-based urban growth models: A systematic comparison of classifiers and input urban maps

Spatial constraints are fundamental to integrating the spatial suitability to urbanization into Cellular Automata-based (CA) urban growth models, but there is a lack of consensus on the optimal methods for this purpose. This study compared the performance of three probabilistic classifiers to generate suitability surfaces for CA-based urban growth models: Logistic Regression using Generalized Linear Model (LR-GLM), Logistic Regression using Generalized Additive Model (LR-GAM), and Random Forest (RF). The study also evaluated the sensitivity of these classifiers to the input urban map adopted as a dependent variable. For this analysis, seven maps were tested: the historical urban map containing the entire extent of the urban footprint, and six additional maps containing only the recently urbanized areas over timeframes ranging from one year up to two decades. The comparison evaluated the goodness of fit of the suitability surfaces and the spatial accuracy of the urban growth simulations, using five large Brazilian cities as case study areas. The results revealed that the RF classifier significantly outperformed the LR-based classifiers. However, this overperformance was more prominent when incorporating the new urban cells over the last one to two decades of growth as input urban maps. In addition, the sensitivity analysis of the input urban maps emphasized the benefits of calibrating the classifier using the recently urbanized cells rather than the historical urban extent. We consistently observed these results concerning classifiers and input urban maps across all five case study areas. Thus, the RF classifier combined with a training dataset containing the newly urbanized areas over at least the last 10 years systematically resulted in the suitability surfaces with the highest predictability among all tested scenarios.

Urbanization enhances channel and surface runoff: A quantitative analysis using both physical and empirical models over the Yangtze River basin

Urbanization has become an irreversible trend and is even more rapid in flood-prone area. However, the responses of both channel runoff and surface runoff to the urbanization and the underlying mechanisms still deserve more in-depth exploration within urban extents with limited data availability. Accordingly, we conducted a comprehensive quantitative analysis of the urbanization effects on channel and surface runoff from both statistical and model-based perspectives over three major urban agglomerations (i.e., Chengyu (CY), middle region of Yangtze River (MRYR), Yangtze River Delta (YRD)) in the Yangtze River Basin. The long-term high-resolution channel and surface runoff under different urbanization scenarios were simulated using both WRF-Hydro and SCS-CN models by integrating multiple remotely sensed, reanalysis, and in-situ datasets. Results demonstrated that the channel runoff increased by 4.4%, 5%, and 12.08% in CY, MRYR, and YRD urban agglomerations, respectively. Meanwhile, the urbanization also contributed to 30%, 21.4%, and 17.7% of the surface runoff changes in three urban agglomerations. Based on the high-resolution local climate zone (LCZ), our results highlighted that the high-rise and middle-rise buildings and compact-arranged regions were more prone to accumulating surface runoff and trigger flooding events due to the weaker infiltration capacity of land surface with the densely distribution of impervious surface. Further machine learning based investigation and case analysis also explored the potential influencing factors and mechanisms of the impact of urbanization on channel and surface runoff. Results in the current research show further evidence that policy makers of urban planning and management need to consider and ensure societal preparedness for extreme runoff and flooding events.

Green urbanization’s role in mitigating oil market shocks on China’s macroeconomy: evidence from panel data analyses

ABSTRACT Our study assesses the extent of green urbanization in China’s provinces. We conduct an empirical analysis at the provincial level, using the IPVAR model to explore the relationship between oil market supply shocks and macroeconomic impacts in the context of local green urbanization levels. The findings indicate that increased green urbanization at the local level can mitigate the macroeconomic impact of oil market disruptions. Furthermore, this mitigating effect tends to strengthen over time.

Characterization of temperature and humidity effects on extreme heat stress under global warming and urban growth in the Pearl and Yangtze River Deltas of China

With global warming accelerating, the heavily populated and rapidly urbanized coastal regions of the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) stand as representative areas with mounting concerns about extreme heat stress. This study analyzes differentiated effects of temperature (TAS) and relative humidity (RH) on human heat stress measured by wet-bulb globe temperature (WBGT) in those urban regions based on machine learning and mathematical derivation, while also examining the impacts of global warming and urbanization on prospective heat risks. To generate fine-scale climate projections targeted at the PRD and YRD, two global projections forced by Representative Concentration Pathway (RCP) 8.5 scenario are dynamically downscaled using non-hydrostatic Regional Climate Model version 4.7 (RegCM4), with the urban density and extent updated every year based on Shared Socioeconomic Pathways 5-8.5 (SSP5) scenario, thereby incorporating the transient urban growth into future projections. The bias-corrected downscaled simulations effectively capture the distinct interdependencies between TAS and RH on WBGT across different regions, similar to the observed patterns during the historical period. While the absolute contribution of TAS to WBGT is larger than RH regardless of warming levels and regions, the relative increase in RH becomes more pronounced with warming. Under RCP8.5 scenario, unprecedentedly extreme WBGT is projected to emerge in the far future (2080–2099). In contrast, the effect of urbanization appears to be more dominant in the near future (2030–2049) as urban density under SSP5 scenario is projected to peak around the 2040s and gradually decrease afterwards. The reduction of RH is found in the intensely urbanized areas locally, but it does not significantly lower WBGT because the positive contribution of increased TAS is more dominant. As a result, highly urbanized regions still exhibit higher WBGT compared to other areas. In addition, urban heat island effect is more pronounced for compact areas with high urban density (i.e., PRD) and at night. Despite the smaller temperature increase from urban heat island effect compared to global warming, it can play a critical role in exacerbating heat stress, adding to the already dangerous humid and hot conditions.

Global Mapping of Three-Dimensional (3D) Urban Structures Reveals Escalating Utilization in the Vertical Dimension and Pronounced Building Space Inequality

Three-dimensional (3D) urban structures play a critical role in informing climate mitigation strategies aimed at the built environment and facilitating sustainable urban development. Regrettably, there exists a significant gap in detailed and consistent data on 3D building space structures with global coverage due to the challenges inherent in the data collection and model calibration processes. In this study, we constructed a global urban structure dataset (GUS-3D), including building volume, height, and footprint information, at a 500 m spatial resolution using extensive satellite observation products and numerous reference building samples. Our analysis indicated that the total volume of buildings worldwide in 2015 exceeded 1 × 1012 m3. Over the 1985 to 2015 period, we observed a slight increase in the magnitude of 3D building volume growth (i.e., it increased from 166.02 km3 during the 1985–2000 period to 175.08 km3 during the 2000–2015 period), while the expansion magnitudes of the two-dimensional (2D) building footprint (22.51 × 103 km2 vs. 13.29 × 103 km2) and urban extent (157 × 103 km2 vs. 133.8 × 103 km2) notably decreased. This trend highlights the significant increase in intensive vertical utilization of urban land. Furthermore, we identified significant heterogeneity in building space provision and inequality across cities worldwide. This inequality is particularly pronounced in many populous Asian cities, which has been overlooked in previous studies on economic inequality. The GUS-3D dataset shows great potential to deepen our understanding of the urban environment and creates new horizons for numerous 3D urban studies.

Economic value of the hot-day cooling provided by urban green and blue space

Increasing high temperatures due to climate change are exacerbated by urban heat island effects, resulting in a range of human health and economic impacts. The green and blue infrastructure (GBI) in cities that underpins nature-based solutions (NBS) can help alleviate hot-day temperatures. In this study we bring together multiple data sources to evaluate the cooling benefit provided by urban GBI in terms of avoided losses in labour productivity, for eleven City Regions in Great Britain, over a ten-year period. We defined the urban extent to include the green (woodland, grassland and parks, gardens) and blue (rivers and canals, lakes and ponds) features within cities, and derived aggregate cooling factors for urban areas in each City Region, applying additional cooling factors to buffer zones around larger GBI features. We collated gridded meteorology data to assess the number of hot-days exceeding 28 °C Wet Bulb Globe Temperature in each City Region over the period 2008–2017, and applied response functions to evaluate loss of worker productivity for ten economic sectors. For the GBI features (aggregated adjacent features >200 m2), gardens make up the biggest component (26% of urban extent) closely followed by grassland and parks (24%), with woodland at 6%. The aggregate cooling factor of GBI ranged from 0.64 – 0.89 °C across the eleven City Regions. The economic benefit of cooling was greatest for London, due to its greater exposure to hot days, and its greater contribution to the economy than other City Regions. In the hottest year of 2015, the cooling benefit in London was £ 13.97 m. The cooling benefit varied considerably from one year to the next, depending on meteorology, and will increase under climate change.