Accurately quantifying the variation of urban green space is the prerequisite for fully understanding its ecosystem services. However, knowledge about the spatiotemporal dynamics of urban green space is still insufficient due to multiple challenges that remain in mapping green spaces within heterogeneous urban environments. This paper uses the city of Hangzhou to demonstrate an analysis methodology that integrates sub-pixel mapping technology and landscape analysis to fully investigate the spatiotemporal pattern and variation of hierarchical urban green space patches. Firstly, multiple endmember spectral mixture analysis was applied to time series Landsat data to derive green space coverage at the sub-pixel level. Landscape metric analysis was then employed to characterize the variation pattern of urban green space patches. Results indicate that Hangzhou has experienced a significant loss of urban greenness, producing a more fragmented and isolated vegetation landscape. Additionally, a remarkable amelioration of urban greenness occurred in the city core from 2002 to 2013, characterized by the significant increase of small-sized green space patches. The green space network has been formed as a consequence of new urban greening strategies in Hangzhou. These strategies have greatly fragmented the built-up areas and enriched the diversity of the urban landscape. Gradient analysis further revealed a distinct pattern of urban green space landscape variation in the process of urbanization. By integrating both sub-pixel mapping technology and landscape analysis, our approach revealed the subtle variation of urban green space patches which are otherwise easy to overlook. Findings from this study will help us to refine our understanding of the evolution of heterogeneous urban environments.

Multiple policy projects have changed land use and land cover (LULC) in China’s rural regions over the past years, resulting in two types of rural settlements: new-fashioned and old-fashioned. Precise extraction of and discrimination between these two settlement types are vital for sustainable land use development. It is difficult to identify these two types via remote sensing images due to their similarities in spectrum, texture, and geometry. This study attempts to discriminate different types of rural settlements by using a spatial contextual information extraction method based on Gaofen 2 (GF-2) images, which integrate hierarchical multi-scale segmentation and landscape analysis. A preliminary LULC map was derived by using only traditional spectral and geometrical features from a finer scale. Subsequently, a vertical connection was built between superobjects and subobjects, and landscape metrics were computed. The vertical connection was used for assigning landscape contextual information to subobjects. Finally, a classification phase was conducted, in which only multi-scale contextual information was adopted, to discriminate between new-fashioned and old-fashioned rural settlements. Compared with previous studies on multi-scale contextual information, this paper employs landscape metrics to quantify contextual characteristics, rather than traditional spectral, textural, and topological relationship information, from superobjects. Our findings indicate that this approach effectively identified and discriminated two types of rural settlements, with accuracies over 80% for both producers and users. A comparison with a conventional top-down hierarchical classification scheme showed that this novel approach improved accuracy, precision, and recall. Our results confirm that multi-scale contextual information with landscape metrics provides valuable spatial information for classification, and indicates the practicability, applicability, and effectiveness of this synthesized approach in distinguishing different types of rural settlements.

Temperature and precipitation are the primary regulators of vegetation phenology in temperate zones. However, the relative contributions of each factor and their underlying combined effect on vegetation phenology are much less clear, especially for the grassland of the Tibetan Plateau To quantify the contribution of each factor and the potential interactions, we conducted redundancy analysis for grasslands spring phenology on the Tibetan Plateau during 2000–2017. Generally, the individual contribution of temperature and precipitation to grasslands spring phenology (the start of growing season (SOS)) was lower, despite a higher correlation coefficient, which further implied that these factors interact to affect the SOS. The contributions of temperature and precipitation to the grasslands spring phenology varied across space on the Tibetan Plateau, and these spatial heterogeneities can be mainly explained by the spatial gradient of long-term average precipitation during spring over 2000–2017. Specifically, the SOS for meadow was dominated by the mean temperature in spring (T_spring) in the eastern wetter ecoregion, with an individual contribution of 24.16% (p < 0.05), while it was strongly negatively correlated with the accumulated precipitation in spring (P_spring) in the western drier ecoregion. Spatially, a 10 mm increase in long-term average precipitation in spring resulted in an increase in the contribution of T_spring of 2.0% (p < 0.1) for meadow, while it caused a decrease in the contribution of P_spring of −0.3% (p < 0.05). Similarly, a higher contribution of P_spring for steppe was found in drier ecoregions. A spatial decrease in precipitation of 10 mm increased the contribution of P_spring of 1.4% (p < 0.05). Considering these impacts of precipitation on the relative contribution of warming and precipitation to the SOS, projected climate change would have a stronger impact on advancing SOS in a relatively moist environment compared to that of drier areas. Hence, these quantitative interactions and contributions must be included in current ecosystem models, mostly driven by indicators with the direct and the overall effect in response to projected climate warming.

Land acquisition and resettlement (LAR) is an important step in urban development. As one of the ‘externalities of development’, LAR conflicts have affected social stability and development in rural areas of China. With social conflict research shifting from value identity to resource allocation, few studies have examined the relationship between the spatial injustice of urban public resources and LAR conflict. To mitigate this research gap and formulate effective policies, this study aims to reinterpret the obstacles of LAR conflicts from the perspective of the spatial injustice of urban public facilities allocation in Hangzhou City by examining 195 administrative litigation cases. Spatial accessibility was used for estimating the spatial justice of urban public resources allocation. A classification and regression tree (CART) model was applied to identify the advantage and disadvantage factors behind LAR conflict, and explored the logical and structural relationships among these factors. Results showed that a spatial mismatch between the spatial behavior preferences of human activity and the spatial injustice of urban public resources allocation had significantly accelerated LAR conflicts. When the spatial behavior preferences of human activity and spatial distribution of urban public resources correspond to each other pre- and after LAR, basic rights to social space are safeguarded and various groups can equitably share spatial resources. There are no conflicts. Conversely, respondents expressed a high level of dissatisfaction in comparison to their pre-LAR conditions, and LAR conflict undeniably occurs. This approach also proposes some good LAR policies by regulating the spatial injustice of urban public resources allocation associated with LAR with the aim of long-term urban sustainable development for Hangzhou.

Accompanied by China’s rapid economic growth, significant urban greening has occurred in Chinese cities, in particular in the urban core areas. In contrast, rapid urbanization and economic growth also led to a high probability of vegetation degradation in urban fringe regions. However, these significant spatial differences in urban greenness associated with economic growth in Chinese cities are not well understood. This study explored the spatiotemporal characteristics of the nighttime light (NTL) and annual maximum enhanced vegetation index (EVI_max) in urban areas from 2001 to 2020. A strong decoupling status between economic growth and urban greenness on the national scale was found. Overall, 49.15% of urban areas showed a decoupling status. Spatially, this percentage of urban areas with a decoupling status would significantly decrease when the long-term average NTL surpasses 51. Moreover, this significant threshold of decoupling status was found in 189 cities out of 344 (54.65%) in China. This threshold in each city showed significant spatial heterogeneity but can mostly be attributed to the gradient in the long-term average precipitation (P_mean) of each city during the period of 2001–2020. Specifically, a spatial increase in P_mean of 100 mm responded to a decrease in the threshold of 0.4 DN (p < 0.01). In contrast, there was no significant correlation between the threshold and the economic growth status of each city. Our results provide valuable insights for coordinating the development of urban greening and economic growth.