Search Results (418)

The process of urbanization has intensified the urban heat environment, with the degradation of thermal conditions closely linked to the morphological characteristics of different functional zones. This study delineated urban functional areas using a multivariate dataset and investigated the seasonal and threshold effects of landscape and architectural features on land surface temperature (LST) through boosted regression tree (BRT) modeling and Spearman correlation analysis. The key findings are as follows: (1) LST exhibits significant seasonal variation, with the strongest urban heat island effect occurring in summer, particularly within industry, business, and public service zones; residence zones experience the greatest temperature fluctuations, with a seasonal difference of 24.71 °C between spring and summer and a peak temperature of 50.18 °C in summer. (2) Fractional vegetation cover (FVC) consistently demonstrates the most pronounced cooling effect across all zones and seasons. Landscape indicators generally dominate the regulation of LST, with their relative contribution exceeding 45% in green land zones. (3) Population density (PD) exerts a significant, seasonally dependent dual effect on LST, where strategic population distribution can effectively mitigate extreme heat events. (4) Mean building height (MBH) plays a vital role in temperature regulation, showing a marked cooling influence particularly in residence and business zones. Both the perimeter-to-area ratio (LSI) and frontal area index (FAI) exhibit distinct seasonal variations in their impacts on LST. (5) This study establishes specific indicator thresholds to optimize thermal comfort across five functional zones; for instance, FVC should exceed 13% in spring and 31.6% in summer in residence zones to enhance comfort, while maintaining MBH above 24 m further aids temperature regulation. These findings offer a scientific foundation for mitigating urban heat waves and advancing sustainable urban development. Full article

Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy. Full article

This study examines the impact of vegetation structure on bird species richness and diversity across four urban parks in Chaoyang District, Beijing. Throughout the year, using the Point Count Method (PCM), a total of 68 bird species and 4279 individual observations were recorded, with surveys conducted across all four seasons to capture seasonal variations. The parks with more complex vegetation, such as those with a higher tree canopy cover of species like poplars, ginkgo, and Chinese pines, exhibited higher bird species richness. For example, Olympic Forest Park, with its dense vegetation structure, hosted 42 species, whereas parks with less diverse vegetation supported fewer species. An analysis using PERMANOVA revealed that bird communities in the four parks were significantly different from each other (F = 2.76, p = 0.04075), and every comparison between parks showed significant differences as well (p < 0.001). Variations in the arrangement and level of disturbance within different plant communities likely cause such differences. Principal component analysis (PCA) identified tree canopy cover and shrub density as key drivers of bird diversity. These findings underscore the importance of preserving urban green spaces, particularly those with a diverse range of native tree species, to conserve biodiversity and mitigate the adverse effects of urbanisation. Effective vegetation management strategies can enhance avian habitats and provide ecological and cultural benefits in urban environments. Full article

Urban residential design influences local microclimates and human thermal comfort. This study combines empirical microclimate data with remotely sensed data on tree canopy cover, housing lot size, surface permeability, and roof colour to examine thermal differences between three newly built and three established residential suburbs in Western Sydney, Australia. Established areas featured larger housing lots and mature street trees, while newly developed suburbs had smaller lots and limited vegetation cover. Microclimate data were collected during summer 2021 under both heatwave and non-heatwave conditions in full sun, measuring air temperature, relative humidity, wind speed, and wet-bulb globe temperature (WBGT) as an index of heat stress. Daily maximum air temperatures reached 42.7 °C in new suburbs, compared to 39.3 °C in established ones (p < 0.001). WBGT levels during heatwaves were in the “extreme caution” category in new suburbs, while remaining in the “caution” range in established ones. These findings highlight the benefits of larger green spaces, permeable surfaces, and lighter roof colours in the context of urban heat exposure. Maintaining mature trees and avoiding dark roofs can significantly reduce summer heat and improve outdoor thermal comfort across a range of conditions. Results of this work can inform bottom-up approaches to climate-responsive urban design where informed homeowners can influence development outcomes. Full article

Increasing soil carbon storage is an important strategy for achieving sustainable development. Enhancing soil carbon sequestration capacity can effectively reduce the concentration of atmospheric carbon dioxide, which not only contributes to the carbon neutrality goal but also helps maintain ecosystem stability. Based on 146 soil samples collected at plot locations selected across Beijing, we examined relationships between soil organic carbon (SOC) and key characteristics of urban forests, including their spatial structure and species complexity. The results showed that SOC in the topsoil with a depth of 20 cm was highest over forested plots (6.384 g/kg–20.349 g/kg) and lowest in soils without any vegetation cover (5.586 g/kg–6.783 g/kg). The plots with herbaceous/shrub vegetation but no tree cover had SOC values in between (5.586 g/kg–15.162 g/kg). The plot data revealed that SOC was better correlated with the physical structure than the species diversity of Beijing’s urban trees. The correlation coefficients (r) between SOC and five physical structure indicators, including average diameter at breast height (DBH), average tree height, basal area density, and the diversity of DBH and tree height, ranged from 0.32 to 0.52, whereas the r values for four species diversity indicators ranged from 0.10 to 0.25, two of which were not statistically different from 0. Stepwise linear regression analyses revealed that the species diversity indicators were not very sensitive to SOC variations among a large portion of the plots and were about half as effective as the physical structure indicators for explaining the total variance of SOC. These results suggest that urban planning and greenspace management policies could be tailored to maximize the carbon co-benefits of urban land. Specifically, trees should be planted in urban areas wherever possible, preferably as densely as what can be allowed given other urban planning considerations. Protection of large, old trees should be encouraged, as these trees will continue to sequester and store large quantities of carbon in above- and belowground biomass as well as in soil. Such policies will enhance the contribution of urban land, especially urban forests and other greenspaces, to nature-based solutions (NBS) to climate change. Full article

Miami, Florida, renowned for its cultural richness and coastal beauty, also faces the concerning challenges created by urban heat islands (UHIs). As one of the hottest cities of the United States, Miami is facing escalating temperatures and threatening heat-related vulnerabilities due to urbanization and climate change. Our study addresses the critical issue of mapping and investigating UHIs in complex urban settings. This study leveraged Planet satellite data and Landsat data to conceptualize and develop appropriate mitigation strategies for UHIs in Miami. Utilizing the Planet satellite imagery and Landsat data, we conducted a combined study of land cover and land surface temperature variations within the city. This approach fuses remotely sensed data to identify the UHI hotspots. This study aims for dynamic approaches for UHI mitigation. This includes studying the status of green spaces present in the city, possible expansion of urban green spaces, the propagation of cool roof initiatives, and exploring the recent climatic trend of the city. The research revealed that built-up areas consistently showed higher land surface temperatures while zones with dense vegetation have lower surface temperatures, supporting the role of urban green spaces in surface temperature reduction. This research can also set a robust model for addressing UHIs in other cities facing rapid urbanization and experiencing mounting temperatures each passing year by helping in assessing LST, land cover, and related spectral indices as well. Full article

Land subsidence significantly threatens vulnerable coastal environments. This study aims to explore how Semarang’s government, local communities, and researchers address land subsidence and its role in exacerbating flood risk, against the backdrop of ongoing efforts within flood risk governance. Employing an integrated mixed-methods approach, the research combined quantitative geospatial analysis (InSAR and land cover change detection) with qualitative socio-political and governance analysis (interviews, FGDs, field observations). Findings show high subsidence rates in Semarang. Line of sight displacement measurements revealed a continuous downward trend from late 2014 to mid-2023, with rates varying from −8.8 to −10.1 cm/year in Karangroto and Sembungharjo. Built-up areas concurrently expanded from 21,512 hectares in 2017 to 23,755 hectares in 2023, largely displacing cropland and tree cover. Groundwater extraction was identified as the dominant driver, alongside urbanization and geological factors. A critical disconnect emerged: community views focused on flooding, often overlooking subsidence’s fundamental role as an exacerbating factor. The study concluded that multi-level collaboration, improved risk communication, and sustainable land management are critical for enhancing urban coastal resilience against dual threats of subsidence and flooding. These insights offer guidance for similar rapidly developing coastal cities. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is difficult due to (1) the coarse scale of common remote sensing data, which do not observe the key environments beneath urban tree canopies, and, (2) conversely, the immense labor of intense, location-specific, ground-based survey campaigns. This work tested whether remotely sensed urban heat merged with land cover heterogeneity and shade/sun fractions, if combined at a sufficiently fine scale so as to be linearly additive, would enable simple and accurate statistical modeling of street-scale urban air temperatures with minimal empirical fitting. We used ground-based thermography of a sample of 12 residential streetscapes in Portland, Oregon, to characterize the land surface temperatures (LST_g) of eleven common urban surface cover types when sun-exposed and in shade. Surfaces were cooler in shade than sun, but with surface-specific differences not explained by greenery nor (im)perviousness. Also, surfaces on streetscapes with more canopy cover, even when sun-exposed at midday, remained significantly cooler than comparable sun-exposed surfaces on streets with less canopy cover, indicating the key significance of partial diurnal shading, not typically accounted for in urban thermal statistical models. We used high-resolution orthoimagery to quantify the area of each surface cover type within each streetscape and computed an area-weighted average surface temperature (T_s), accounting for sun/shade heterogeneity. The data revealed a significant, nearly 1:1 relationship between calculated T_s values and sun-shielded air temperatures (T_a). In contrast, relationships of T_a to tree coverage, impervious area, or the LST_g of dominant surface cover types were all statistically insignificant. These results suggest that statistical models may more reliably bridge the gap between remote sensing urban surface temperatures and reliable predictions of street-scale air temperatures if (1) analysis is at a sufficiently high resolution (e.g., <10 m) to avoid some of the known scale-dependence of urban thermal environments and enable simple weighted linear models, and (2) distinctions between thermal contributions of sunlit and shaded surfaces are included along with the influence of diurnal shading. Such models may provide effective and low-cost predictions of local UHIs and help inform effective street-level approaches to mitigating urban heat. Full article

The pre-training and fine-tuning paradigm has significantly advanced satellite remote sensing applications. However, its potential remains largely underexplored for airborne laser scanning (ALS), a key technology in domains such as forest management and urban planning. In this study, we address this gap by constructing a large-scale ALS point cloud dataset and evaluating its effectiveness in downstream applications. We first propose a simple, generalizable framework for dataset construction, designed to maximize land cover and terrain diversity while allowing flexible control over dataset size. We instantiate this framework using ALS, land cover, and terrain data collected across the contiguous United States, resulting in a dataset geographically covering 17,000 + ${\mathrm{km}}^2$ (184 billion points) with diverse land cover and terrain types included. As a baseline self-supervised learning model, we adopt BEV-MAE, a state-of-the-art masked autoencoder for 3D outdoor point clouds, and pre-train it on the constructed dataset. The resulting models are fine-tuned for several downstream tasks, including tree species classification, terrain scene recognition, and point cloud semantic segmentation. Our results show that pre-trained models consistently outperform their counterparts trained from scratch across all downstream tasks, demonstrating the strong transferability of the learned representations. Additionally, we find that scaling the dataset using the proposed framework leads to consistent performance improvements, whereas datasets constructed via random sampling fail to achieve comparable gains. Full article

A thorough understanding of the consequences of urbanization can be significantly advanced by examining urban environmental dynamics at high spatial and temporal resolutions. This study evaluates eco-environmental quality and investigates the underlying drivers of urban greening within the functional units of Haikou, a tropical coastal city located on Hainan Island, China, using advanced techniques from Landsat and Google Earth imagery. Ecological index and land use change analyses were conducted using Landsat 5 (TM) imagery for 2010 and Landsat 8 (OLI) imagery for 2020. In addition, Google Earth imagery was used to interpret the driving factors influencing urban functional units (UFUs) in 2010 and 2020. Spatial and temporal environmental changes were quantitatively assessed. Multi-spectral Landsat 8 data at a 30 m resolution were used to construct a remote sensing ecological index (RSEI) to assess Haikou’s ecological condition. Land use impacts on eco-environmental quality were evaluated through RSEI values from 2010 to 2020, showing that eco-environmental quality improved over time, revealing a gradual improvement over time. Land use across 190 UFUs from 2010 to 2020 was categorized into five types: trees and shrubs, herbs, built-up areas, sandy lands, and water bodies. The primary drivers of greening percentage in each UFU were identified as housing prices, maintenance duration, and construction age. The most significant changes in land cover type were observed in the herb areas. Similarly, maintenance duration emerged as the most influential factor driving changes in urban green space (UGS). In conclusion, this study offers valuable insights for future urban planning and improvements in eco-environmental quality in Haikou, Hainan Island, China. Full article

This research assesses the involvement of green urban spaces in creating social interaction among the residents of a neighborhood. It emphasizes the significance of urban parks, particularly in the context of Saudi Arabia’s New Vision 2030, and showcases the proactive approach of Jubail Industrial City in planning and distributing parks. The study delves into the legibility of parks, exploring factors that impact user experiences, including accessibility and amenities. It highlights how park design can influence social interactions. Furthermore, the research underscores the importance of social interaction within neighborhood parks, especially among diverse cultural and age groups. The results prove to be a significant output for future use in enhancing the quality of green spaces and providing efficient means of social interaction among people. The study’s findings, such as increased social interaction with diverse amenities and improved safety perceptions, contribute to sustainable urban planning by fostering social cohesion, enhancing ecological benefits through tree cover, and building community resilience, aligning with Vision 2030’s sustainability goals. Recommendations are provided to improve the park user experience and promote increased utilization of neighborhood parks. Full article

Urban trees reduce particulate matter (PM) concentrations through dry deposition, interception, and modifying wind patterns, improving air quality and saving public health expenses in urban planning. The main objective of this article is to present an analysis of the influence of urban trees on PM₁₀ and PM_2.5 concentrations in a high-altitude Latin American megacity (Bogotá, Colombia) using UFORE-D modeling. Six PM monitoring stations distributed throughout the megacity were used. Hourly climatic and PM data were collected for seven years, along with dendrometric and cartographic analyses within 200 m of the monitoring stations. Land cover was quantified using satellite imagery (Landsat 8) in order to perform a spatial analysis. The results showed that the UFORE-D model effectively quantified urban forest canopy area (CA) impact on PM₁₀ and PM_2.5 removal, showing strong correlations (R² = 0.987 and 0.918). PM removal increased with both CA and ambient pollutant concentrations, with CA exhibiting greater influence. Sensitivity analysis highlighted enhanced air quality with increased leaf area index (LAI: 2–4 m²/m²), particularly at higher wind speeds. PM₁₀ removal (1.05 ± 0.01%) per unit CA exceeded PM_2.5 (0.71 ± 0.09%), potentially due to resuspension modeling. Model validation confirmed reliability across urban settings, emphasizing its utility in urban planning. Scenario analysis (E1–E4, CA: 8.30–95.4%) demonstrated a consistent positive correlation between CA and PM removal, with diminishing returns at extreme CA levels. Urban spatial constraints suggested integrated green infrastructure solutions. Although increased CA improved PM removal rates, the absolute reduction of pollutants remained limited, suggesting comprehensive emission monitoring. Full article

Extreme weather events and rising temperatures pose significant risks, not only in urban areas but also in metropolitan forests, that affect the well-being of the people who visit them. City forests are considered one of the best bets for mitigating high temperatures within civic areas. Such areas modulate microclimates in contemporary cities, offering environmental, social, and economic advantages. Therefore, comprehending the intricate relationships between municipal forests and the climatic changes of various destinations is crucial for attaining healthier and more sustainable city environments for people. In this research, the thermal comfort index (Modified Temperature–Humidity Index (MTHI)) has been analysed using Landsat images of six urban forests in London during July 2022, when the area first experienced record-breaking temperatures of over 40 °C. Our results show a significant growth in the MTHI that goes from 2.5 (slightly hot) under normal conditions to 3.4 (hot) during the heat wave period. This situation intensifies the environmental discomfort for visitors and highlights the necessity to enhance their adaptability to future temperature increases. In turn, it was found that the places most affected by heat waves are those that have grass cover or that have small associated buildings. Conversely, forested regions or those with lakes and/or ponds exhibit lower temperatures, which results in enhanced resilience. These findings are noteworthy in their concentration on one of the UK’s most severe heat waves and illustrate the efficacy of integrating spectral measurements with statistical analyses to formulate customized regional initiatives. Therefore, the results reported will allow the implementation of new planning and adaptation policies such as incorporating thermal comfort into planning processes, improving green and blue amenities, increasing tree densities that are resilient to rising temperatures, and increasing environmental comfort conditions in metropolitan forests. Finally, the applicability of this approach in similar urban contexts is highlighted. Full article

In rapidly urbanizing regions, encroachment on native green spaces has exacerbated ecological issues such as urban heat islands and flooding. Accurate mapping of tree species distribution is therefore vital for sustainable urban management. However, the high heterogeneity of urban landscapes, resulting from the coexistence of diverse land covers, built infrastructure, and anthropogenic activities, often leads to reduced robustness and transferability of remote sensing classification methods across different images and regions. In this study, we used very high–resolution Pléiades imagery and field-verified samples of eight common urban trees and background land covers. By employing transfer learning with advanced segmentation networks, we evaluated each model’s accuracy, robustness, and efficiency. The best-performing network delivered markedly superior classification consistency and required substantially less training time than a model trained from scratch. These findings offer concise, practical guidance for selecting and deploying deep learning methods in urban tree species mapping, supporting improved ecological monitoring and planning. Full article