Urban Land Surface Research Articles

Effective Screening and Texture Segmentation of Green Vegetation Cover Based on UAV Images

As an important part of the ecosystem, green vegetation coverage is crucial to people’s sensory and mental health. Using reliable data sets to classify and identify the green vegetation cover on the land surface and explore its spatial distribution law can provide important reference for the work of regional ecosystem managers and urban planners. The optimization of effective screening methods for green vegetation coverage areas is an important requirement to measure the surface vegetation status. UAV aerial images feature high definition, large scale, small area and high up-to-dateness. However, at present, there are few studies based on the reliable UAV aerial image system to identify green vegetation cover and further explore its spatial changes. In this study, 701 residential neighborhoods in Beijing were taken as the research objects, and the green vegetation of 7,695 sample points was identified by UAV. The green vegetation coverage was measured, and the spatial distribution pattern of green vegetation in different land surface areas was quantitatively compared. The results show that the image processing method proposed in this paper can effectively detect the boundary of green vegetation cover area from UAV aerial images, the correlation of texture segmentation is good, and the segmentation performance is better than other methods. The distribution of green vegetation cover in the research target area is uneven, with 63.79% of the research area having relatively low (Level 2) and medium (Level 3) green vegetation coverage, which indicates that the green vegetation coverage area in the research area is insufficient to meet the needs of regional ecosystem development. The characteristics of green vegetation cover in 16 districts in the study area are different, showing different spatial distribution patterns; except Xicheng District, there are 211 points without landscape in the area covered by green vegetation in 15 districts. The results can provide support for urban land surface planning and management.

Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India

Planning for a sustainable future involves understanding the past and present problems associated with urban centers. Rapid urbanization has caused significant adverse impacts on the environment and natural resources. In cities, one such impact is the unsettling urban growth, resulting in the urban heat island (UHI) effect, which causes considerable positive feedback in the climate system. It can be assessed by investigating the relationships between urban Land Use/Land Cover (LULC) changes and changes in land surface temperature. This study links the urban transformations in Bangalore, India, between 2001 and 2021, with the city’s changing average land surface temperatures. LULC classification was performed on Landsat satellite images for the years 2001, 2011, and 2021, using the support vector machine (SVM) classification algorithm. LULC change analysis revealed an increase in the built-up area coinciding with a decreasing trend of water bodies, vegetation, and the area under the others (wasteland/open land/barren land) category. The results show that built-up increased from 462.49 km2 to 867.73 km2, vegetation decreased from 799.4 km2 to 485.72 km2, and waterbody declined from 34.28 km2 to 24.69 km2 in 20 years. The impact of urbanization was evident in Bangalore’s land temperature changes between 2001 and 2021, showing the average temperature increased by 0.34 °C per year between the highest UHI events, contrary to 0.14 °C per year in non-urbanized areas. It is hoped that the results of this study can help the urban planners of Bangalore city identify critical areas where improvement in urban dwelling could be planned sustainably according to the global smart cities concept, an offshoot concept of the Sustainable Development Goal (SDG)-11.

Recognizing surface urban heat ‘island’ effect and its urbanization association in terms of intensity, footprint, and capacity: A case study with multi-dimensional analysis in Northern China

Current research on the surface urban heat island (SUHI) effect rarely address the discussion of what exactly is the ‘island’ of urban surface thermal environment. Given this, this study attempted to ‘islanding’ the spatial morphology of the SUHI effect for 13 different case cities in Northern China. A Gaussian surface model was applied to depict the remote sensed thermal characteristics of the case cities along the urban-rural gradient. Multi-dimensional indicators including SUHI intensity, footprint, and capacity were derived to quantify the seasonal SUHI characteristics, representing the maximum intensity, impact range, and accumulated volume of SUHI effect, respectively. Based on that, the seasonal SUHI characteristics of the case cities thus were analyzed during a long-term period from 2000 to 2015. Thereafter the variations of the SUHI effect and its possible associations with several representative urbanization factors were examined with the panel, cross-sectional, and time-series regression analyses separately. In this study, multi-indicator SUHI analysis revealed the generally prevalent and increasing seasonal SUHI effects in most cases, while significant urban cold island phenomena were also documented in part during the cold season. Urbanization processes such as booming population, land transformation, and economic development performed as the key contributors to the thermal variations in most cases. However, the case cities do not only suffer from their own unique thermal risks but are also subjected to varying influencers. In summary, this study highlights the multi-dimensional and heterogeneous characteristics of the SUHI effect and its urbanization association. That is, multi-indexing analysis of surface urban heat ‘island’ provides more robust and comprehensive characterization of urban land surface thermal environment, and multi-dimensional regression analysis contributes to reexamining the complicated nexus between the SUHI effect and urbanization process in the context of significant spatiotemporal heterogeneity. We believe that our work can provide relevant scholars with meaningful inspiration for future exploration of the SUHI effect.

Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria

This study develops a general method to evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. The method is based on the understanding that long-term annual ULST is controlled by three factors: (1) localized urbanization, (2) global climate change, and (3) interannual climate variation. Then the method removes the interannual climate fluctuations on long-term observed LST time series via linear regression and separates the contributions of urbanization and climate change to the impacts on long-term ULST via urban–rural comparison. The method is applied to Lagos, a fast-growing metropolis in the tropical West Africa, as an example for reference. Combined time-series daily daytime and nighttime MODIS Land Surface Temperature (LST) data over the years of 2003–2021 are used as the representation of land surface temperature. To avoid the potentioal interannual data biase due to uneven availability of data in the rainy seasons over years, only MODIS LST data from dry seasons are used in the study. The results are summarized as follows for Lagos: (1) long-term annual ULST is confirmed to be controlled by the three factors; (2) the proposed method can separate the contribution of the three factors to the ULST; (2) both localized urbanization and global warming are verified to contribute to the ULST increase with positive trends; (3) daytime ULST increased the most in the afternoon time at a mean rate of 1.429 °C per decade, with 0.985 °C (10 year)−1 contributed by urbanization and 0.444 °C (10 year)−1 contributed by climate warming; (4) nighttime ULST in Lagos increased the most after midnight at a rate of 0.563 °C (10 year)−1, with 0.56 °C (10 year)−1 contributed by urbanization and 0.003 °C (10 year)−1 contributed by climate warming; and (5) urbanization is generally responsible for around 60.97% of the urban warming in Lagos. Therefore, the increasing urbaniztion-induced urban heat island effect is the major cause for more heat-related health risks and climate extremes that many urban residents are suffering. The results of this study are of useful reference for both urbanization and climate change related issues in the geo-science field.

Impacts of land-surface forcing on local meteorology and ozone concentrations in a heavily industrialized coastal urban area

In this study, the Weather Research and Forecasting/Community Multiscale Air Quality (WRF-CMAQ) model was used to investigate the interaction of urban land-surface forcing with local circulations and the impact on boundary layer ozone concentrations in southern Taiwan at an urban-scale resolution. Two simulations were performed with the same emissions but different land cover designations. URBAN was the baseline simulation representing the current urbanized condition, while NO-URBAN replaced all urban grid cells with cropland. The interaction of the sea-breeze with the urban-heat-island (UHI) convergent flow during the daytime in URBAN transports near-surface O3 precursors to the upper planetary boundary layer (PBL). When the UHI convergent flow stalls over the city center, a circulation flow is formed and traps the pollutants at an elevated height, increasing the reaction rate of hydroxyl radical with volatile organic compounds by 2.0–4.0 ppbv h−1 at 1000–1500 m. At nighttime, the deeper boundary layer of URBAN diluted the NOx mixing ratio by ~17 ppbv and weakened the titration effect, contributing to higher O3 by +15 ppbv in the urban area. However, once the daytime vertical mixing diminished, the O3 aloft also diffused downward to the surface level and further degraded the nighttime air quality.

Quantitative evaluation of impacts of the steadiness and duration of urban surface wind patterns on air quality

The complexity and heterogeneity of urban land surfaces result in inconsistencies in near-surface winds, which in turn influence the diffusion and dispersion of air pollutants. In this study, we classified urban surface wind fields, quantified their steadiness, duration, and influence on air quality using hourly wind observations from 50 meteorological stations, as well as hourly PM2.5 and NO2 concentrations from 18 monitoring stations during 2017–2018 in Shenzhen, a mega city in southern China. We found that the K-means clustering technique was reliable for distinguishing surface wind patterns within the city. Urban surface-wind patterns greatly affected pollutant concentrations. When dominated by calm, northerly wind, high PM2.5/NO2 concentration episodes occurred more frequently than those during other surface wind patterns. The urban surface transport index (USTI) was used to quantify the steadiness of surface wind classes. High pollutant concentrations were present during both high wind speed periods with a large USTI, indicating external pollutant transport, and during low wind speed periods with a small USTI, indicating pollutant accumulation. The threshold durations for surface wind fields (TDSWF) was proposed to quantify the impacts of surface wind persistence on air quality. We found that poor air quality occurred during the first several hours of a dominant wind pattern, indicating that transitions between wind patterns should be a particular focus when assessing air-quality deterioration. USTI and TDSWF are potentially applicable to other urban areas, owing to their clear definitions and simple calculation. In combination with wind speeds, these indices are likely to improve air quality forecasting and strategic decisions on air pollution emergencies, based on long time series of multiple wind and pollutant concentration observations.

A Procedural Modeling Approach for Ecosystem Services and Geodesign Visualization in Old Town Pocatello, Idaho

City population has been growing rapidly worldwide due to urban expansion, which can bring negative impacts on local ecosystem services (ES). Efficient tools for urban design and visualization are essential for city planners and stakeholders to better understand the valuation impact of plans for future sustainable development. Current urban design methods are mainly based on a 2D perspective and lack vertical visualization. Although conventional 3D modeling was introduced to address these limitations, it still has some challenges, such as requiring powerful computing resources and specialized training. Procedural 3D modeling is a grammar-based set of rules that can effectively generate 3D models and enhance spatial visualization when compared with conventional 2D or 3D methods. This paper describes a framework for developing a geodesign tool in Old Town Pocatello, Idaho, USA using procedural modeling to improve planning and visualization for urban design, including (1) Geospatial data preparation in ArcGIS, (2) 3D cityscape model generation in CityEngine, and (3) interactive visualization applications for multiple platforms developed with the Unity game engine. Pocatello is a mid-sized city in southeast Idaho that faces several challenges towards integrating ecosystem services in urban design. As a case study in ecosystem service modelling, we proposed a green scenario for Old Town to demonstrate a tool where permeable surfaces were increased from 37% to 45% to help mitigate urban land surface temperature and improve stormwater management. This geodesign tool offers city planners and stakeholders an opportunity to visualize and analyze block-level scenarios in real time. The interactive applications can encourage public participation in the design process. More ES measurements can be implemented into this tool in the future. The techniques of 3D procedural modeling and ES modeling in this study are also applicable to other small to mid-sized cities worldwide.

Fractal and multifractal spatiotemporal patterns of land surface temperatures in a coastal city

ABSTRACT In order to discover the fractal and multifractal nature of land surface temperature (LST) spatiotemporal patterns, this study computed the LST in thecoastal city, Xiamen, southeastern China using Landsat TM/OLI/TIRS images from 1994 to 2015, and introduced two-dimensional box-counting and two-dimensional multifractal box-counting methods to quantitatively characterize the LST pattern. Results suggest that increasing the binarization threshold decreased the box-counting dimensions with values from 1.86 to 1.65 in 1994, 1.67 in 2000, 1.56 in 2004, 1.57 in 2010, and 1.56 in 2015, respectively. The two-dimensional multifractal approach employs a set of intertwined nonfractal subsets over different spatial scales to describe the LST patterns. LST pattern possesses a increased multifractality and the similar multifractal spectra of left-hook shapes throughout the study period, suggesting the increasing trends of the spatial heterogeneity in LST distribution. The probability of a given pixel having a high LST value is consistently high in the study area, as indicated by the positive ratio between the regions in which the probability measure appears most concentrated and those in which it is most sparse. Rapid urbanization and the large-scale urban land surface changes in the coastal city determine the variation in the multifractal parameters.

Feasibility of afforestation as an equitable nature-based solution in urban areas

Although nature-based solutions for urban heat mitigation have gained momentum, it is important to quantitatively assess the feasibility of such strategies to utilize space efficiently and prioritize lower-income communities, who have fewer options for climate change adaptation. Here we combine data from US census estimates, satellites, and satellite-derived products to develop a framework to target potentially suitable areas for urban afforestation to mitigate urban heat and minimize tree cover disparity. We test this framework for California and show that space exists for an additional 36 million (1.28 million acres of) trees in the state's urban areas. This would reduce the average urban land surface temperature by 1.8°C and provide multiple co-benefits totaling $1.1 billion annually, including reduction in heat-related medical visits (almost 4000 over 10 years) and 4.5 million metric tons of annual CO2 sequestration. Because funding is limited, we provide suitability scores for urban afforestation at the census block group (CBG) scale based on multiple considerations. In California, afforestation in CBGs with positive suitability scores will lead to $712 million of net annual benefits (against an annual investment of $467 million) and will serve 89% of the ≈9 million urban residents in the lowest income quartile for their cities. This method can guide equitable urban afforestation efforts and be scaled to other North American cities.

Synchronization, Decoupling, and Regime Shift of Urban Thermal Conditions in Xi’an, an Ancient City in China under Rapid Expansion

Urbanization has profound impacts on economic development and environmental quality. Some of the serious consequences of urbanization are the changes in the thermal environment, which directly affect the greater environment and quality of life. Although many studies have been performed on urban heat islands, few have specifically examined the thermal evolution of rapidly expanding ancient cities and the impacts of urbanization on the thermal environments of important heritage sites. In this study, we analyzed the temporal and spatial patterns of the thermal environment quantified as the surface urban heat island (SUHI) and land surface temperature (LST) values from 2000 to 2018 in Xi’an, an ancient city with rich cultural heritage in China. Specifically, we analyzed the temporal evolution of the thermal environments of the functional zones and heritage sites and explore their coupling relationships with the overall temperature of the study area using a statistical analysis approach. Furthermore, we revealed time-sensitive changes in temperature regimes using the newly proposed double temperature curve approach (DTCA). The results showed that the heat island phenomenon has been intensifying in Xi’an, as evidenced by the summer daytime mean SUHI values being greater than 7 °C continuously since 2010 and the increased frequency of high-intensity SUHI effects. Extreme heat conditions were more frequent in the old urban area (built-up and in existence before 2000) than in the new urban area, while SUHI values in the new area deteriorated more rapidly. The changes in temperature in the functional zones were strongly synchronized with the overall temperature changes in Xi’an, and the temperature differences increased linearly with the overall temperature. The LST values in the four major historical heritage sites investigated in this study were 2–8 °C higher than the background temperature and were decoupled from background temperature changes. From the DTCA, we found the time periods of the thermal environment regime changes for each functional zone or heritage site, which were largely the result of policy guidance. Regional synchronization, site decoupling, and regime shifts in LST suggest opportunities for regional planning and urban landscape optimization to reduce adverse effects of urbanization on the urban environment, particularly in cities with rich historical heritage sites.

Quantitative assessment of the relationship between land use/land cover (LULC), topographic elevation and land surface temperature (LST) in Ilorin, Nigeria

The urbanization of landscapes and the increase in impervious land cover materials is known to cause significant changes in the landscape's thermal properties, typically leading to urban heat island (UHI). Although previous studies have investigated the impacts of land cover land use (LULC) and other factors on the urban land surface temperature (LST), the results of such studies are mixed. For instance, it is not yet clear what factors affect the spatial and temporal variation of the relationship between LULC and the LST, as well as the exact trajectory through which identified factors affect the thermal character of the urban landscape. In the current study, we examined the relationship between LULC, elevation and LST in Ilorin from the period of 2002–2020. The overriding aim was to understand the degree of association between LULC, elevation and LST, and the drivers of the spatial and temporal variation in the observed relationship. LST and NDVI were derived from Landsat data products. LST was derived using a mono-window algorithm and NDVI was used as proxy for LULC. The spatial pattern of LULC was analyzed using Moran's I spatial autocorrelation statistics. To investigate the relationship between LULC, elevation and LST, we adopted both ordinary least square (OLS) regression models and the geographically weighted regression (GWR) method to reveal both the linear and the geographically varying relationship between the LULC, elevation and LST. The results of the study show that the LULC pattern of Ilorin has been significantly altered during the 2000 to 2020 period. The urban proportion of the landscape has shown a significant increase, rising by more than 11% relative to 2002 figures, and the vegetation proportion, especially the forest component, has diminished within same temporal extent. LST values varied in both space and time, with high temperature clusters noticeable in the built-up areas and decreasing towards the urban periphery. The result of the autocorrelation analysis using Moran's I index reveals a significant clustering of LST in all the epochs investigated. Across the study area, minimum and maximum LST values of 0 °C and 41 °C were recorded in 2002 and 2020 respectively. Statistically significant relationships were observed between LULC, elevation and LST. However, the relationship between elevation and LST was very weak. Temporally, the strength of the relationship between the variables (as indicated in the variables' coefficient) as well as the overall model predictive performance (indicated by in the R2) fluctuated over the years. Spatially, the strength of relationship between LST and LULC and elevation varied significantly. LULC explained approximately between 26% and 64% of total LST variation in the city between 2002 and 2020. The study findings are relevant for efforts geared towards alleviating the degree of UHI or its impacts, generally, and in the city of Ilorin, in particular. An understanding of the spatial distribution of LULC and their impacts on the LST can be helpful in identifying areas needing attention. The observed relationship between relevant LULC classes can be incorporated into urban planning strategies to ensure a sustainable city development.

Localized Downscaling of Urban Land Surface Temperature—A Case Study in Beijing, China

High-resolution land surface temperature (LST) data are essential for fine-scale urban thermal environment studies. Urban LST downscaling studies mostly remain focused on only two-dimensional (2-D) data, and neglect the impact of three-dimensional (3-D) surface structure on LST. In addition, the choice of window size is also important for LST downscaling over heterogeneous surfaces. In this study, we downscaled Landsat-LST using localized and stepwise approaches in a random forest model (RF). In addition, both 2- and 3-D building morphologies were included. Our results show that: (1) The performances of a local moving window and stepwise downscaling are dependent on the extent of surface heterogeneity. For mixed surfaces, a localized window performed better than the global window, and a stepwise approach performed better than a single-step approach. However, for monotonous surfaces (e.g., urban impervious surfaces), the global window performed better than a localized window; (2) That multi-scale geographically weighted regression (MGWR) could provide a possibility for selection of the optimal moving window. 7 × 7 windows derived from MGWR by the minimum bandwidth of predictors, performed better than other windows (3 × 3, 5 × 5, and 11 × 11) in the Beijing area; (3) That the morphology of buildings has a non-negligible impact and scaling effect on urban LST. When building morphologies were included in downscaling, the performance of the RF model improved. Furthermore, the importance of the sky view factor, building height, and building density was greater at a higher resolution than at a lower resolution.

Relationship between urban landscape structure and land surface temperature: Spatial hierarchy and interaction effects

Urban landscape structure has profound impacts on the urban thermal environment (UTE), however, the spatial hierarchy and interaction effects of the relationship between them are limitedly explored. Taking Xi'an Metropolitan area as the case, this study applies satellite imageries, local contour tree algorithm, and XGBoost model to identify the spatial hierarchy of UTE represented by nested heat centers, and then elaborate the relationship between landscape structure (landscape metrics) and land surface temperature (LST) across the spatial levels of UTE. We identify 21 main urban heat island (UHI) areas and their UTE's hierarchy, among which the Xi'an main urban district has the most complex structure (six levels) and is the core area. Then, in the core area, we found that the discussed relationship varies across the levels of UTE. Specifically, the importance of impervious surface increases from the local heat center with the lowest level (40.7%) to the entire UHI area with the highest level (62.0%), whereas that of urban green space has opposite trend (from 41.4% to 25.6%). Besides, landscape metrics within specific range of values show significant interaction effects on LST. Our results imply that differential landscape optimisation criteria should be considered in the different spatial levels of UTE.

Impact of surface characteristics and their adjacency effects on urban land surface temperature in different seasonal conditions and latitudes

Urban land surface temperature (ULST) is a key variable for environmental applications and widely used in a variety of fields. However, retrieving ULST with high spatial and temporal resolution is still a challenging task since its a function of surface characteristics and geographical, climatic, and seasonal conditions. Assessing the type and intensity of the impact of surface characteristics effect on ULST is one of great importance way in improving urban environmental conditions. Hence, the purpose of this study is to evaluate and model the impact of surface characteristics and their adjacency effects on ULST in different seasonal conditions and latitudes. First, ULST and surface characteristic maps were prepared for six European cities in different seasonal conditions. Then, adjacency effects maps of these surface characteristicswere prepared using the proposed inverse distance weighted kernel. Finally, the impacts of surface characteristics and their adjacency effects on ULST were evaluated and compared in different seasonal conditions and latitudes. The mean absolute r between biophysical (topographical) characteristics and ULST in warm and cold seasons were 0.52 (0.22) and 0.25 (0.12), respectively. In the warm season, the mean increase in absolute r between the surface biophysical and topographical characteristics and ULST due to the adjacency effects were 0.10 and 0.04, respectively. For the cold season, these values were 0.03 and 0.03, respectively. By increasing latitude, the impact of surface biophysical characteristics on ULST increased, and the impact of topographical characteristics decreased. ULST modeling accuracy increases by consideration of the adjacency effects. ULST was affected more by the adjacency effects of surface biophysical characteristics than the adjacency effects of surface topographical characteristics. The results of this study showed that in evaluating the impact of surface characteristics on ULST, their adjacency effects and seasonal and geographical conditions should be taken into account.

Determining the Influence of Long Term Urban Growth on Surface Urban Heat Islands Using Local Climate Zones and Intensity Analysis Techniques

Urban growth, typified by conversion from natural to built-up impervious surfaces, is known to cause warming and associated adverse impacts. Local climate zones present a standardized technique for evaluating the implications of urban land use and surface changes on temperatures of the overlying atmosphere. In this study, long term changes in local climate zones of the Bulawayo metropolitan city were used to assess the influence of the city’s growth on its thermal characteristics. The zones were mapped using the World Urban Database and Access Portal Tool (WUDAPT) procedure while Landsat data were used to determine temporal changes. Data were divided into 1990 to 2005 and 2005 to 2020 temporal splits and intensity analysis used to characterize transformation patterns at each interval. Results indicated that growth of the built local climate zones (LCZ) in Bulawayo was faster in the 1990 to 2005 interval than the 2005 to 2020. Transition level intensity analysis showed that growth of built local climate zones was more prevalent in areas with water, low plants and dense forest LCZ in both intervals. There was a westward growth of light weight low rise built LCZ category than eastern direction, which could be attributed to high land value in the latter. Low plants land cover type experienced a large expansion of light weight low rise buildings than the compact low rise, water, and open low-rise areas. The reduction of dense forest was mainly linked to active expansion of low plants in the 2005 to 2020 interval, symbolizing increased deforestation and vegetation clearance. In Bulawayo’s growth, areas where built-up LCZs invade vegetation and wetlands have increased anthropogenic warming (i.e., Surface Urban Heat Island intensities) in the city. This study demonstrates the value of LCZs in among others creating a global urban land use land cover database and assessing the influence of urban growth pattern on urban thermal characteristics.

Carbon sequestration potential of street tree plantings in Helsinki

Abstract. Cities have become increasingly interested in reducing their greenhouse gas emissions and increasing carbon sequestration and storage in urban vegetation and soil as part of their climate mitigation actions. However, most of our knowledge of the biogenic carbon cycle is based on data and models from forested ecosystems, despite urban nature and microclimates differing greatly from those in natural or forested ecosystems. There is a need for modelling tools that can correctly consider temporal variations in the urban carbon cycle and take specific urban conditions into account. The main aims of our study were to (1) examine the carbon sequestration potential of two commonly used street tree species (Tilia × vulgaris and Alnus glutinosa) growing in three different growing media by taking into account the complexity of urban conditions and (2) evaluate the urban land surface model SUEWS (Surface Urban Energy and Water Balance Scheme) and the soil carbon model Yasso15 in simulating the carbon sequestration of these street tree plantings at temporal scales (diurnal, monthly, and annual). SUEWS provides data on the urban microclimate and on street tree photosynthesis and respiration, whereas soil carbon storage is estimated with Yasso. These models were used to study the urban carbon cycle throughout the expected lifespan of street trees (2002–2031). Within this period, model performances were evaluated against transpiration estimated from sap flow, soil carbon content, and soil moisture measurements from two street tree sites located in Helsinki, Finland. The models were able to capture the variability in the urban carbon cycle and transpiration due to changes in environmental conditions, soil type, and tree species. Carbon sequestration potential was estimated for an average street tree and for the average of the diverse soils present in the study area. Over the study period, soil respiration dominated carbon exchange over carbon sequestration due to the high initial carbon loss from the soil after street construction. However, the street tree plantings turned into a modest sink of carbon from the atmosphere on an annual scale, as tree and soil respiration approximately balanced the photosynthesis. The compensation point when street tree plantings turned from an annual source into a sink was reached more rapidly – after 12 years – by Alnus trees, while this point was reached by Tilia trees after 14 years. However, these moments naturally vary from site to site depending on the growing media, planting density, tree species, and climate. Overall, the results indicate the importance of soil in urban carbon sequestration estimations.

Quantification of landscape metrics effects on downscaled urban land surface temperature accuracy of satellite imagery

Improving the spatial resolution of Land Surface Temperature (LST) obtained from satellite images is of great importance in various applications, especially in urban areas. Aiming to bridge the gap in the research background, this study attempted to quantify the effect of landscape metrics on the accuracy of urban LST downscaling. The research data included a collection of satellite images from Landsat and Terra satellites and auxiliary data of nine European cities, namely Lisbon, Madrid, Bucharest, Vienna, Prague, Paris, Copenhagen, Stockholm, and Helsinki. In the first stage, surface biophysical, topographic, and thematic property maps affecting the spatial distribution of LST were prepared. Secondly, the effect of the endogenous (influence of intra-pixel properties) and exogenous (influence of landscape metrics) variables of each surface property on LST was evaluated in warm and cold seasonal conditions. Finally, the effect of landscape metrics on the accuracy of LST downscaling from 960 m to 30 m was quantified. The results showed that the mean absolute correlation coefficient (r) values between the endogenous (exogenous) effects of surface biophysical, topographic, and thematic properties and LST in the cold season were 0.25 (0.12), 0.19 (0.06), and 0.09 (0.04), respectively. For the warm season, these values were 0.55 (0.24), 0.11 (0.03), and 0.23 (0.09), respectively. The mean r (root-mean-square error (RMSE)) between the actual LST and downscaled LST (DLST) obtained by considering only the endogenous effects in the cold and warm seasons were 0.79 (0.66) and 0.87 (1.38 °C), respectively. Considering the simultaneous effect of endogenous and exogenous variables in LST downscaling, these values reached 0.83 (0.47) and 0.91 (0.99 °C), respectively. The greatest effect of landscape metrics on the accuracy of LST downscaling was associated with built-up lands. Accounting for landscape metrics in the downscaling process significantly increases the accuracy of downscaled LST.

Spatiotemporal Evolution and Relationship between Night Time Light and Land Surface Temperature: A Case Study of Beijing, China

Rapid urbanization has triggered significant changes in urban land surface temperature (LST), which in turn affects urban ecosystems and the health of residents. Therefore, exploring the interrelationship between urban development and LST can help optimize the urban thermal environment and promote sustainable development. Based on remote sensing data from 2004–2019 within the sixth ring road of Beijing, this study investigates the spatiotemporal coupling law of night time light (NTL) and LST using an overall coupling model and analyzes the degree of coordination between them using a coordination model. The spatial response law between them was also analyzed using standard deviation ellipses and bivariate spatial autocorrelation. The results show that, from the perspective of spatiotemporal evolution, the spatial distributions of NTL and LST within the sixth ring road of Beijing were closely related from 2004 to 2019, although the overall coupling of NTL and LST was initially decreased and then continuously increased. From the perspective of coordination types, the main types of coordination between NTL and LST deteriorated over time. The increase in LST lagged behind NTL from 2004 to 2009 (heating hysteresis type), while LST increased ahead of NTL from 2014 to 2019 (heating advance type). This suggests that urban development became less efficient, while LST increases became more significant. In terms of correlation, NTL and LST showed significant positive correlation and spatial positive correlation; the correlation coefficient first decreased significantly and then continued to increase. From 2004 to 2009, the temperature increase caused by urbanization was suppressed due to the 2008 Beijing Olympics and related ecological protection policies, resulting in a significant decrease in the correlation coefficient between NTL and LST. From 2009 to 2019, short-term measures taken by Beijing during the Olympic Games were no longer effective, and the opposition between urban development and related policies made the policies increasingly less effective, thereby increasing the correlation coefficient between NTL and LST, and the increase in LST was more significant. This will greatly affect the urban ecological environment and residents’ health and make the previous government investment to suppress the temperature increase all in vain. This study can provide theoretical and practical support for the development of thermal environment optimization schemes and LST mitigation strategies in Beijing and other cities.

Identifying the Driving Factors of Urban Land Surface Temperature

Land surface temperature (LST) has a profound impact on urban climate and ecology, and is widely used to quantify surface urban heat islands. The spatial heterogeneity of LST is affected by natural and human factors, with seasonal differences. This study selected Dongguan, a rapidly urbanizing city in China, as an example to analyze the relationship between the spatial heterogeneity of LST in different seasons and influencing factors in six dimensions. Multi-source spatial data were combined, including Landsat images, meteorological data, digital elevation models, National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite nighttime light, and points of interest. The results show that spatial patterns of LST across different seasons were consistent, although there were local differences. Based on the GeoDetector model, the result indicated differences between separate effects and interactive effects, and identified the high temperature risk areas.

Floating Solar Park Impacts Urban Land Surface Temperature Distribution Pattern

In recent years, the global photovoltaic industry has developed rapidly. It is significant for evaluating the impact of large-scale solar parks on the environment for the sustainable development of the photovoltaic industry. At present, researchers have paid attention to changes in the local thermal environment caused by solar parks. As a new type of solar park, the influence of floating solar parks on urban land surface temperature distribution patterns is still unclear. In this article, we (1) take the floating solar parks in Huainan City, China, as the study area; (2) calculate the land surface temperature (LST) of the study area and its adjacent areas by using Landsat 8 remote sensing data and the radiation transfer equation method; and (3) judge the influence of solar parks on the distribution pattern of LST. On this basis, we analyzed the influence range and seasonal differences of floating solar parks on LST. Our results revealed that, first, the floating solar park has a warming effect in the summer and winter, and the warming area is concentrated mainly within 200 m of the photovoltaic panels' coverage area. Second, during the construction phase and after the completion of the floating solar parks, the average monthly LST of the solar panels is generally higher than the water, and the average annual temperature increase in the above two stages is 3.26°C and 4.50°C, respectively. The change of floating solar parks on urban LST distribution patterns may impact the local ecosystem. In the future, it is necessary to consider the impact of floating solar parks on local LST during the construction of floating solar parks.