Green vegetation plays a vital role in energy flows and matter cycles in terrestrial ecosystems, and vegetation phenology may not only be influenced by, but also impose active feedback on, climate changes. The phenological events of vegetation such as the start of season (SOS), end of season (EOS), and length of season (LOS) can respond to climate changes and affect gross primary productivity (GPP). Here, we coupled satellite remote sensing imagery with FLUXNET observations to systematically map the shift of SOS, EOS, and LOS in global vegetated area, and explored their response to climate fluctuations and feedback on GPP during the last two decades. The results indicated that 11.5% of the global vegetated area showed a significantly advanced trend in SOS, and that only 5.2% of the area presented significantly delayed EOS during the past two decades, resulting in significantly prolonged LOS in 12.6% of the vegetated area. The climate factors, including seasonal temperature and precipitation, attributed to the shifts in vegetation phenology, but with high spatial and temporal difference. LOS was positively and significantly correlated with GPP in 20.2% of the total area, highlighting that longer LOS is likely to promote vegetation productivity. The feedback on GPP from the shifted vegetation phenology may serve as an adaptation mechanism for terrestrial ecosystems to mitigate global warming through improved carbon uptake from the atmosphere.

Background: There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents’ quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources. Methods: Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city. Results: (1) Pharmacies, clinics and community hospitals show “multicentre + multicircle”, “centre + axis + dot” and “banded” distribution characteristics, respectively, but specialized hospitals and general hospitals present “single core” and “double core” modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive. Conclusions: Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services.

Forests play a vital role in sequestering carbon dioxide from the atmosphere. Vegetation phenology is sensitive to climate changes and natural environments. Exploring the patterns in phenological events of the forests can provide useful insights for understanding the dynamics of vegetation growth and their responses to climate variations. Deciduous forest in North America is an important part of global forests. Here we apply time-series remote sensing imagery to map the critical dates of vegetation phenological events, including the start of season (SOS), end of season (EOS), and growth length (GL) of the deciduous forests in North America during the past two decades. The findings show that the SOS and EOS present considerable spatial and temporal variations. Earlier SOS, delayed EOS, and therefore extended GL are detected in a large part of the study area from temporal trend analysis over the years, though the magnitude of the trend varies at different locations. The phenological events are found to correlate to the environmental factors and the impact on the vegetation phenology from the factors is location-dependent. The findings confirm that the phenology of the deciduous forests in North America is updated such as advanced SOS and delayed EOS in the last two decades and the climate variations are likely among the driving forces for the updates. Considering that previous studies warn that shifts in vegetation phenology could reverse the role of forests as net emitters or net sinks, we suggest that forest management should be strengthened to forests that experience significant changes in the phenological events.

Rapid urbanization has threatened sustainable urban development in many cities across the globe, causing green space loss and vegetation cover degradation which reduce carbon sequestration. Optimal land management practices (LMPs) in an urban context are known as ways capable of promoting urban vegetation growth and contributing to carbon sequestration. Due to variations of physical, biological, and social structures in urban areas, policymakers often lack relevant information to decide and implement site-specific LMPs. Here we try to extract the areas in need of the optimal LMPs, identify location-dependent optimal LMPs, and assess how much more carbon can be captured by applying a combination of segmenting homogeneous urban environments and neighborhood-based analysis. As one of the most developed cities in China, the greater Guangzhou area (GGA) was selected as a case study. We found that the carbon uptake from the urban vegetation in GGA could be improved on average by 185 gC m⁻² yr⁻¹ in flux (or 1.3 TgC yr⁻¹ in total) with optimal LMPs, equivalent to a ~30% increase considering the current level of 662 gC m⁻² yr⁻¹ in flux (4.4 TgC yr⁻¹ in total). The carbon uptake potential was found to differ considerably across locations and among different ecosystem types, highlighting spatially varied priorities for implementing optimal LMPs over the space. This study reveals the usefulness of the model in assessing carbon uptake potential from optimal LMPs and emphasizes that future urban planning may consider the importance of optimal LMPs in enhancing vegetation carbon uptake in urban planning.

A major source of carbon dioxide emissions (CO₂) arises from the household sector. Recent studies have reported increasing household CO₂ emissions (HCO₂) in many countries. Cities represent a key administrative level in China and can be managed to mitigate HCO₂ if spatial and temporal variations in HCO₂ are understood at fine scales. Here, we applied panel data analysis to map HCO₂ at a pixel scale of 1 km in China using remotely sensed time series nighttime light data, grid population density data, and provincial energy consumption statistics from 2000 to 2020. Spatial and temporal variations in HCO₂ were observed with four growth modes, including high growth (HG), low growth (LG), negative growth (NG), and high negative growth (HNG), for different periods, i.e., 2000–2010, 2010–2020, and 2000–2020. We proposed a local scale analysis of HCO₂ growth patterns within a global context to assess the contribution roles of 372 municipal cities to the changes in the national total HCO₂ (T-HCO₂). The results indicated that T-HCO₂ has tripled in the last two decades, but the roles of the contribution to the increase varied among cities. The local scale analysis revealed that more cities contributed to the rise in T-HCO₂ through HG and LG than those that suppressed it through NG and HNG. The majority of the cities displayed contributions to the rise in T-HCO₂ through two or more of the growth modes, confirming a significant variation in HCO₂ across locations, even within a city. This study provides a new approach to understanding the roles cities play in the long-term dynamics of T-HCO₂. We recommend increased efforts to encourage HCO₂ mitigation in cities that have contributed to the rise in T-HCO₂ to help neutralize carbon emissions at the national level.

Digital Elevation Model (DEM)-based mountain vertex extraction is one of the most useful DEM applications, providing important information to properly characterize topographic features. Current vertex-extraction techniques have considerable limitations, such as yielding low-accuracy results and generating false mountain vertices. To overcome these limitations, a new approach is proposed that combines Hotspot Analysis Clustering and the Improved Eight-Connected Extraction algorithms that would quickly and accurately provide the location and elevation of mountain vertices. The use of the elevation-based Hotspot Analysis Clustering Algorithm allows the fast partitioning of the mountain vertex area, which significantly reduces data and considerably improves the efficiency of mountain vertex extraction. The algorithm also minimizes false mountain vertices, which can be problematic in valleys, ridges, and other rugged terrains. The Eight-Connected Extraction Algorithm also hastens the precise determination of vertex location and elevation, providing a better balance between accuracy and efficiency in vertex extraction. The proposed approach was used and tested on seven different datasets and was compared against traditional vertex extraction methods. The results of the quantitative evaluation show that the proposed approach yielded higher efficiency, considerably minimized the occurrence of invalid points, and generated higher vertex extraction accuracy compared to other traditional methods.