Effects of Different Urbanization Levels on Land Surface Temperature Change: Taking Tokyo and Shanghai for Example

Achieving balance between socioeconomic development and ecosystem conservation via policy adjustments in Guangdong Province of southeastern China

Rapid urbanization improves socioeconomic development but challenges ecosystem sustainability. Meanwhile, the gradient responses of ecosystem services (ESs) to landscape structures and associated regime shifts of the agriculture-ecosystem-economy nexus (AEEN) have not been sufficiently addressed, preventing an effective balance between socioeconomic prosperity and ecosystem conservation. To bridge this knowledge gap, this study selected the Guangdong Province of southeastern China to explore landscape dynamics from 1985 to 2020 and their spatially heterogeneous impacts on ESs and the AEEN, based on Integrated Valuation of Ecosystem Services and Trade-offs approach and other biophysical models as well as statistical records about socioeconomic factors. AEEN elements, including ESs, responded directly to policy adjustments in terms of ecosystem restoration and landscape management and presented remarkable regime shifts (i.e., phase changes) and spatial heterogeneity. Aggressive agricultural reclamation before 1999 increased crop productivity but caused vegetation degradation and biomass decline. Accelerated urban expansion and ecosystem restoration efforts have improved economic and ecological benefits but have substantially reduced crop productivity and threatened food security. However, timely policy adjustments since 2009 reversed the declining trend and maintained the grain supply. Landscape composition presented patterns of gradual decline along the urban-rural gradient, which in turn determined ES gradient patterns. For instance, water yield and nitrogen export positively correlated with each other (p < 0.0001) but negatively correlated with other ESs. Our study enriches the understandings of social-ecological systems' response to man-made interventions from AEEN perspective allowing for spatial variabilities and regime shifts, which support policy formulation for coordinating ecological and economic benefits.

Investigating the impacts of COVID-19 lockdown on air quality, surface Urban Heat Island, air temperature and lighting energy consumption in City of Melbourne

The COVID-19 pandemic has threatened city economies and residents' public health and quality of life. Similar to most cities, Melbourne imposed extreme preventive lockdown measures to address this situation. It would be reasonable to assume that during the two phases of lockdowns, in autumn (March) and winter (June to August) 2020, air quality parameters, air temperature, ^{Surface Urban Heat Island} (SUHI), and lighting energy consumption most likely increased. As such, to test this assumption, Sentinel 5, ERA-5 LAND, Sentinel 1 and 2, NASA SRTM, MODIS Aqua and Terra, and VIIRS satellite imageries are utilized to investigate the alterations of NO₂, SO₂, CO, ^{UV Aerosol Index} (UAI), air temperature, SUHI, and lighting energy consumption factors in the City of Melbourne. Furthermore, satellite imageries of SentiThe results indicate that the change rates of NO₂ (1.17 mol/m²) and CO (1.64 mol/m²) factors were positive. Further, the nighttime SUHI values increased by approximately 0.417 °C during the winter phase of the lockdown, while during the summer phase of the lockdown, the largest negative change rate was in NO₂ (−100.40 mol/m²). By contrast, the largest positive change rate was in SO₂ and SUHI at night. The SO₂ values increased from very low to 330 μm mol/m^2, and the SUHI nighttime values increased by approximately 4.8 °C. From the spatial point of view, this study also shows how the effects on such parameters shifted based on the urban form and land types across the City of Melbourne by using satellite data as a significant resource to analyze the spatial coverage of these factors. The findings of this study demonstrate how air quality factors, SUHI, air temperature, and lighting energy consumption changed from pre-lockdown (2019) to lockdown (2020), offering valuable insights regarding practices for managing SUHI, lighting energy consumption, and air pollution.

Impact of urban land use and land cover change on urban heat island and urban thermal comfort level: a case study of Addis Ababa City, Ethiopia

The increase in the urban heat island is caused by the replacement of vegetation cover by impervious surfaces. As the population of Addis Ababa City has increased dramatically, the vegetation cover and other land cover classes have been converted into built-up areas. This study attempted to examine the relationship between urban heat islands and urban thermal comfort (UTCL) and land use and land cover (LULC) change using geospatial technologies in Addis Ababa City, Ethiopia. Landsat TM 1991, Landsat ETM + 2005, and Landsat OLI/TIRS 2021 data were used in this study. During the study period, LULC change, land surface temperature (LST), and urban heat island were calculated using the multispectral and thermal infrared bands (1991-2021). Results revealed that the built-up area in 1991 was 96.6 km² (18.3%), and increased to 165.4 km² (31.4%) and 277.2 km² (52.6%) by 2005 and 2021, respectively. In contrast, agriculture and vegetation land cover classes were declined by 66.8 km² and 25.7 km², respectively between 1991 and 2021. Rapid conversion of LULC change increases the mean LST of Addis Ababa City by 8.3 °C over the last three decades. According to the results, a high LST was recorded over built-up regions and areas with little vegetative cover. Furthermore, the central areas of the study area suffered a greater UHI effect than the surrounding areas. The results of the urban thermal field variance index (UTFVI) revealed that the UHI varies greatly across the city. Strong, stronger, and strongest urban heat islands dominated the central, southwestern, and southeastern suburbans of the study area, respectively. The excellent comfort level has declined from 16.3 km² (3.1%) in 1991 to 12.1 km² (2.3%) in 2021. The study proposed that local community awareness needs to be raised for environmental conservation through the establishment of urban green spaces that reduce UHI and increase comfort in Addis Ababa City.

Combined Effects of Artificial Surface and Urban Blue-Green Space on Land Surface Temperature in 28 Major Cities in China

The two most common land cover types in urban areas, artificial surface (AS) and urban blue-green space (UBGS), interact with land surface temperature (LST) and exhibit competitive effects, namely, heating and cooling effects. Understanding the variation of these effects along the AS ratio gradient is highly important for the healthy development of cities. In this study, we aimed to find the critical point of the joint competitive effects of UBGS and AS on LST, and to explore the variability in different climate zones and cities at different development levels. An urban land cover map and LST distribution map were produced using Sentinel-2 images and Landsat-8 LST data, respectively, covering 28 major cities in China. On this basis, the characteristics of water, vegetation, and LST in these cities were analyzed. Moreover, the UBGS (water or vegetation)–AS–LST relationship of each city was quantitatively explored. The results showed that UBGS and AS have a competitive relationship and jointly affect LST; this competition has a critical point (threshold). When the proportion of UBGS exceeds this value, UBGS replaces AS as the dominant variable for LST, bringing about a cooling effect. In contrast, when AS dominates LST, it causes a warming effect. The critical points between AS and water and between AS and vegetation in 28 major cities in China were 80% and 70%, respectively. The critical point showed an obvious zonal difference. Compared with cities in subtropical and temperate climate regions, the critical point of arid cities is higher, and UBGS exhibited better performance at alleviating the urban thermal environment. The critical point of cities with higher development levels is lower than that of cities with lower development levels. Even areas with relatively low AS coverage are prone to high temperatures, and more attention should be paid to improving the coverage of UBGS. Our research results provide a reference for the more reasonable handling of the relationship between urban construction, landscape layout, and temperature control.

Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing’s impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing’s sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997–2002, 2002–2007, and 2013–2017, and only shrank in 2007–2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions.

Evaluation of the Built-Up Area Dynamics in the First Ring of Cluj-Napoca Metropolitan Area, Romania by Semi-Automatic GIS Analysis of Landsat Satellite Images

The accentuated dynamics of the real estate markets of the last 20 years, determined that a large part of the territories in the immediate vicinity of the big urban centers, to change their category of land use, in an accelerated rhythm. Most of the time, the land use changes according to the market requirements, the predominantly agricultural lands being occupied by constructions with residential or industrial functions. Identifying these changes is a difficult task due to the heterogeneity of spatial databases that come from different real estate development projects, so determining and implementing new methods to track land changes are currently highly required. This paper presents a methodologically innovative index-based approach for the rapid mapping of built-up areas, using Landsat-5, Landsat-7, and Landsat-8 satellite imagery. The approach described in this study differs from other conventional methods by the way the analysis was performed and also by the thematic indices used in the processes of built-up area delineation. The method, structured in a complex model, based on Remote Sensing and GIS techniques, can be divided into three distinct phases. The first stage is related to the pre-processing of the remote sensing data. The second stage involves the calculation of the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BI) correlated with the extraction of all areas not covered by vegetation; respectively, the elimination from the result of all areas covered by water, bare land, or uncultivated arable land. The result of this stage is represented by a distinct thematic layer that contains only built-up areas and other associated territories. The last step of the model is represented by the validation of the results, which was performed based on statistical methods and also by direct comparison with field reality, obtaining a validation coefficient which is generally above 85% for any of the methods used. The validation process shows us that by applying this method, the fast mapping of the built-up areas is significantly enhanced and the model is suitable to be implemented on a larger scale in any practical and theoretical application that aims at the rapid mapping of the built-up areas and their evolutionary modeling.