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Establishing an urban ecological network is a means to improve the ecological environment and enhance sustainable urban development amidst rapid urbanization. Based on the demand of eco-city, morphological spatial pattern analysis (MSPA), ecological regulation services value (ERSV), and landscape connectivity have been combined to identify ecological resources with high connectivity and ecological benefits in this study. Using the Minimum Cumulative Resistance (MCR) model, ecological corridors have been constructed and ecological critical nodes have been identified. In addition, the urban ecological network has then been optimized from the overall layout and local key nodes based on the circuit theory to balance the ecological livability and species diversity in the eco-city. The main conclusions could be demonstrated as follows: (1) 76.45% of the high ecological regulation service value areas have been identified as core areas. (2) The distribution of ecological corridors has been found uneven with significant disparities (3) The connectivity indices, especially the line point rate (β) index, have increased by 21.66%, while the robustness has increased by 75.44%. Overall, the combined methods have been used to establish and optimize the ecological network within the limited space to balance the ecological livability and biodiversity. The results of this study could provide a scientific reference for the optimization of urban ecological space and promote sustainable development in the eco-city.

High-precision landcover classification is a fundamental prerequisite for resource and environmental monitoring and land-use status surveys. Imbued with intricate spatial information and texture features, very high spatial resolution remote sensing images accentuate the divergence between features within the same category, thereby amplifying the complexity of landcover classification. Consequently, semantic segmentation models leveraging deep backbone networks have emerged as stalwarts in landcover classification tasks owing to their adeptness in feature representation. However, the classification efficacy of a solitary backbone network model fluctuates across diverse scenarios and datasets, posing a persistent challenge in the construction or selection of an appropriate backbone network for distinct classification tasks. To elevate the classification performance and bolster the generalization of semantic segmentation models, we propose a novel semantic segmentation network architecture, named the aggregated backbone network (ABNet), for the meticulous landcover classification. ABNet aggregates three prevailing backbone networks (ResNet, HRNet, and VoVNet), distinguished by significant structural disparities, using a same-stage fusion approach. Subsequently, it amalgamates these networks with the Deeplabv3+ head after integrating the convolutional block attention mechanism (CBAM). Notably, this amalgamation harmonizes distinct scale features extracted by the three backbone networks, thus enriching the model’s spatial contextual comprehension and expanding its receptive field, thereby facilitating more effective semantic feature extraction across different stages. The convolutional block attention mechanism primarily orchestrates channel adjustments and curtails redundant information within the aggregated feature layers. Ablation experiments demonstrate an enhancement of no less than 3% in the mean intersection over union (mIoU) of ABNet on both the LoveDA and GID15 datasets when compared with a single backbone network model. Furthermore, in contrast to seven classical or state-of-the-art models (UNet, FPN, PSPNet, DANet, CBNet, CCNet, and UPerNet), ABNet evinces excellent segmentation performance across the aforementioned datasets, underscoring the efficiency and robust generalization capabilities of the proposed approach.

Exploring the spatial heterogeneity of urban multi-dimensional networks and influencing factors are of great significance for the integrated development of metropolitan circle. This study took Hangzhou metropolitan circle as an example, using multi-source geospatial big data to obtain urban population, transportation, goods, capital, and information flow information among sub-cities. Then, spatial visualization analysis, social network analysis, and geographical detector were applied to analyze the differences in spatial structure of multiple urban networks and influencing factors in Hangzhou metropolitan circle, respectively. The results showed that (1) the network connections of population, traffic, goods, and capital flows transcended geographical proximity except that of information flow, and population and traffic flow networks were found to be more flattened in Hangzhou metropolitan circle than in other urban networks; (2) the comprehensive urban network of Hangzhou metropolitan circle was imbalanced across sub-cities, presenting hierarchical and unipolar characteristics; and (3) the influence of traffic distance on the network spatial structure of Hangzhou metropolitan was stronger than the geographical distance, and the interactions between traffic distance and socioeconomic factors would further enhance the regional differentiation of the network spatial structure. This study could provide scientific reference for constructing a coordinated and integrated development pattern in a metropolitan circle.

Due to the growing demand for land resources, coastal reclamation has become a global land use practice. Although the reclamation of coastal wetlands brings substantial economic benefits, it can lead to a series of adverse ecological impacts. Considerably, economic and ecological trade-offs of reclamation activities must be analyzed to make targeted land use policy decisions. This study focuses on land reclamation on the east coast of China, taking Hangzhou Bay as a case study. Remote sensing techniques were used to detect the coastline and landscape changes in the reclaimed areas from 1985 to 2015. Subsequently, field surveys and empirical parameters were employed to evaluate the impact on coastal ecosystem services. Finally, the monetary analysis was used to evaluate the trade-offs between ecological loss and economic gain due to land reclamation. The results showed that a total of 75134.3 ha of coastal wetlands (accounting for 8.58% of the total land area) were reclaimed from 1985 to 2015 in the Hangzhou Bay. A substantial decline in ecosystem service value (ESV) was observed with a drop from 866,400 Yuan ha−1 in 1985 to 285,800 Yuan ha−1 in 2015 due to the conversion of natural to artificial landscape. The total ESV loss exceeded the economic benefits throughout the study period, thus the economic benefits failed to compensate for the ESV loss. This trade-off relationship resulted from reclamation expansion on short-term economic benefits at the expense of long-term ecological value. According to these findings, we propose three main ways to achieve a “win–win” situation between gains and loss of reclamation for sustainability.

Administering an urban boundary (UB) is increasingly important for curbing disorderly urban land expansion. The traditionally manual digitalization is time-consuming, and it is difficult to connect UB in the urban fringe due to the fragmented urban pattern in daytime data. Nighttime light (NTL) data is a powerful tool used to map the urban extent, but both the blooming effect and the coarse spatial resolution make the urban product unable to meet the requirements of high-precision urban study. In this study, precise UB is extracted by a practical and effective method using NTL data and Landsat 8 data. Hangzhou, a megacity experiencing rapid urban sprawl, was selected to test the proposed method. Firstly, the rough UB was identified by the search mode of the concentric zones model (CZM) and the variance-based approach. Secondly, a buffer area was constructed to encompass the precise UB that is near the rough UB within a certain distance. Finally, the edge detection method was adopted to obtain the precise UB with a spatial resolution of 30 m. The experimental results show that a good performance was achieved and that it solved the largest disadvantage of the NTL data-blooming effect. The findings indicated that cities with a similar level of socio-economic status can be processed together when applied to larger-scale applications.

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.