The population aggregation and built-up area expansion caused by urbanization can have significant impacts on the supply and distribution of crucial ecosystem services. The correlation between urbanization and ecosystem services has been well-studied, but additional research is needed to better understand the spatiotemporal interactions between ecosystem services and urbanization processes in highly urbanized areas as well as surrounding rural areas. In this paper, the relationships of urbanization with natural habitat and three key regulating ecosystem services—water retention, soil conservation, and carbon sequestration, were quantified and mapped for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a rapidly developing urban agglomeration of over 70 million people, for the period of 2000–2018. Our results showed that urbanization caused a general decline in ecosystem services, and urbanization and ecosystem services exhibited a negative spatial correlation. However, this relationship varied along urban-rural gradients and weak decoupling was the overall trend during the course of the study period, indicating a greater need for the protection and improvement of ecosystem services. Our results provide instructive insights for new urbanization planning to maintain regional ecosystem services and sustainable development in the GBA and other large, rapidly urbanized agglomerations.

The extraction of water distribution is extremely useful in research and planning activities, including those associated with water resources, environments, disasters, local climates, and other factors. Remote-sensing images with moderate resolution have been the main data source due to the vast distribution of water and the high cost, access difficulty, and massive size of high-resolution images. Although some water indices and methods for water extraction have been proposed, there is still a lack of these resources to easily, accurately, efficiently, and automatically extract water. This paper focused on some improvements that mainly used the most traditional but also the newest Operational Land Imager (OLI) images in Landsat 8. This study first analysed the variation features of previous water indices. Secondly, taking the city of Beijing and its surrounding area as the experimental site, a spectral curve analysis was performed and a new water index was proposed. This index was compared to three typical indices. Thirdly, a new approach was proposed to accurately and easily extract water. It included four major steps: background partitioning, thresholding and preliminary segmentation, noise removal by patch size, and local region growth. Next, the stricter and more effective stratified random sampling method was used to test the accuracy. Then, we tested the generality of the proposed water index and extraction method using nine typical test sites from around the world and tried to simplify the workflow. Finally, this paper discusses threshold optimization issues, such as automatic selection and reduction of the number of thresholds. The results show that the normalized water index (NDWI), modified normalized water index (MNDWI), and normalized difference built-up index (NDBI) may fail in some situations due to the complex spectrum of the impervious surface class. Some shadow pixels were impossible to remove using only spectral analysis because both the digital number (DN) trends and values were similar to those of water. The proposed water index was easy and simple, but it corresponded better to water bodies. Additionally, it was more accurate and universal and showed greater potential for extracting water. This method relatively accurately and completely extracted various water bodies from plain city, plain country, and natural mountainous regions in many typical climate zones, eliminating interference caused by dark impervious surfaces, plants, sand, suspended sediments, snow, ice, bedrock, reservoir drawdown areas, shadows from mountains and buildings, mixed pixels, etc. The mean kappa coefficients were 0.988, 0.982, and 0.984 in plain city, plain country, and natural mountainous regions, respectively. This paper suggests that thresholds can be automatically determined by comparing the accuracy changes of different thresholds according to preselected sample and test points. Furthermore, the combined use of the maximum class square error method (also known as the Ostu algorithm) and the adaptive thresholding method exhibits great potential for automatic determination of thresholds in regions without many noises with higher water index values. In addition, water bodies could also be accurately extracted by setting these thresholds to fixed values based on the results at more test sites.

The world is faced with significant climate change, rapid urbanization, massive energy consumption, and tremendous pressure to reduce greenhouse gases. Building heating and cooling is one primary source of energy consumption and anthropogenic carbon dioxide emissions. First, this review presents previous studies that estimate the specific amount of climate change impact on building heating and cooling energy consumption, using the statistical method, physical model method, comprehensive assessment model method, and the combination method of statistical and physical model methods. Then, because the heating and cooling degree days indices can simply and reliably indicate the effects of climate on building heating and cooling energy consumption, previous studies were reviewed from the aspects of heating and cooling degree days indices, regional spatial-temporal variations in degree days and related indices, influencing factors of the spatial distributions of degree days, and the impacts of urbanization on degree days. Finally, several potential key issues or research directions were presented according to the research gaps or fields that need to be studied further in the future, such as developing methods to simply and accurately estimate the specified amounts of climate change impact on building cooling and heating energy consumption; using more effective methods to analyze the daytime, nighttime, and all-day spatial-temporal changes in different seasons in the past and future under various environment contexts by considering not only the air temperature but also the relative humidity, solar radiation, population, etc., and further exploring the corresponding more kinds of driving forces, including the various remotely sensed indices, albedo, nighttime light intensity, etc.; estimating the daytime, nighttime, and all-day impacts of urbanization on heating degree days (HDDs), cooling degree days (CDDs), and their sum (HDDs + CDDs) for vast cities in different environmental contexts at the station site, city, regional and global scales; producing and sharing of the related datasets; and analyzing the subsequent effects induced by climate change on the energy consumption for building heating and cooling, etc.

Shortfalls and mismatches between the supply and demand of ecosystem services (ES) can be detrimental to human wellbeing. Studies focused on these problems have increased in recent decades, but few have applied land use optimization to reduce such spatial mismatches. This study developed a methodology to identify ES mismatches and then use these mismatches as objectives for land use optimization. The methodology was applied to the Guangdong-Hong Kong-Macao “Greater Bay Area” (GBA), a megacity of over 70 million people and one of the world’s largest urban agglomerations. Considering the demand for a healthy and secure living environment among city-dwellers, we focused on three ES: heat mitigation, flood mitigation, and recreational services. The results showed large spatial heterogeneity in supply and demand for these three ES. However, compared to current conditions in the GBA, our model showed that optimized land use allocation could better match the supply and demand for heat mitigation (number of beneficiaries increased by 15%), flood mitigation (amount of population exposed to flood damage decreased by 37%), and recreation (number of beneficiaries increased by 14%). By integrating land use allocation and spatial mismatch analysis, this methodology provides a feasible way to align ES supply and demand to advance urban and regional sustainability.