Background: Rural settlements are undergoing significant changes under the rapid urbanisation, and understanding their evolution characteristics and surrounding land use will provide a basis for land spatial planning. This study takes Pingnan County, Fujian Province, China as study area, reveals the characteristics of spatial–temporal evolution and surrounding land use transition of settlements during 1985–2020 through landscape metrics, spatial “hot spot” analysis, scale classification statistics, rank-size model, Gini index, land use transition matrix. Results: The results show that: (1) Concerning the size and morphological characteristics, the settlements have witnessed a considerable increase in number and scale while remaining stable in shape. (2) Regarding spatial distribution characteristics, the settlements became more evenly spread, forming three main hotspot clusters. (3) Concerning scale structure characteristics, there are significant differences in scale, growth rates, and polarisation of settlements; the polarisation of large settlements shifted from a marked divergence before 2010 to a more balanced trend after 2010. (4) The land use transition around settlements differed in buffer zones and periods. During 1985–2010, settlement expansion heavily depended on cropland, depleting nearby resources, with an increase of woodland and grassland. During 2010–2020, expansion integrated cropland, woodland, and grassland, with cropland growth mainly encroaching on woodland and grassland. Conclusions: The study's findings are significant for optimising rural settlement structure in mountains and promoting sustainable land resource use.

Amid global industrialization and urbanization, mountainous rural settlements, especially those in metropolitan fringe area, are experiencing significant spatial changes in location and scale. This study takes Pingnan County, Fujian Province, China, as an example. Utilizing land use data and employing methods including standard deviation ellipse, average nearest neighbor index, kernel density estimation, spatial hotspot detection, binary logistic regression model, and Geodetector, this study aims to scientifically identify the spatial pattern characteristics and influencing factors of its settlements. The results show that: (1) The spatial distribution of settlements in Pingnan County is biased toward the southern part of the county; the center of settlement's spatial distribution is located south of the junction of Gufeng Town and Pingcheng Town; the spatial distribution trend of settlements is north-east-southwest. Settlements are generally aggregated, and the aggregation degree of Gufeng Town is obviously lower than that of other towns. (2) The density distribution of settlements presents a “core-periphery” structure and a “north-south linear” structure in space; the spatial pattern characteristics show high-density, large patches in Gufeng Town, high-density, small patches in Changqiao Town, Tangkou Town and Gantang Town, and medium-density or low-density, small patches in other towns. (3) Settlement location is mainly affected by the elevation, distance to cultivated land, and distance to main roads, while settlement scale is mainly affected by slope, relief degree of land surface, and distance to urban centers. The interaction between these factors exhibits enhancement effects, with natural terrain and location conditions exerting the most prominent influence. These findings underscore the strong constraints posed by natural topography on mountainous rural settlements in metropolitan fringe areas, coupled with a more pronounced influence from socio-economic factors. The study's results hold significant implications for optimizing the layout of such settlements, guiding land spatial planning, and promoting rural revitalization.

West Lake holds a significant position internationally. This article studies the spatial distribution, historical development, functions, and the lake–city spatial relationship of 81 West Lakes (WLs), as well as their landscape characteristics. The results indicate the following: (1) The overall spatial distribution of WLs is related to China’s history of water conservancy development. (2) The evolution of and functional changes in WLs are influenced by multiple factors such as politics, economy, and culture during different historical periods and are directly related to the will of local administrators. (3) The initial functions of WLs can be categorized into three types, primarily related to urban infrastructure. (4) In terms of spatial relationships, there are four types of spatial relationships between WLs and their water sources and three types of spatial relationships between WLs and cities, forming a common pattern of “Mountains/Hills(–Water)–WL(–Water)–Cities(–Water, River, Sea)” or “WL(–Water)–Cities(–Water, River, Sea)”. (5) The scenery of WLs comprises six elements, including natural basements, water conservancy facilities, human settlements, secularization, landscape architecture, and animal and human activities, all imbued with poetic cultural connotations. Furthermore, this study summarizes three causes of WLs’ scenery; excavates the historical wisdom of WLs in terms of a holistic approach, ecological techniques, dynamic management, and landscape esthetics; and subsequently proposes recommendations for lake ecological governance and landscape construction.

Introduction: Crown width (CW) is a significant variable of tree growth, but measuring crown width is laborious and time-consuming. Diameter at breast height (D) is a commonly used growth variable in crown width prediction. Here, a CW-D model was developed to estimate the crown width of larch.Methods: The data of 1,515 larch trees were collected in Guandi mountain, the northern China. We chose linear function, quadratic function, and other form of base functions to develop the CW models, and we introduced non-linear least squares techniques (NLS), non-linear mixed-effect (NLME), and Bayesian method in modeling process. Because the data was from different plot, we added a plot level random effect in NLME method to predict the effect from environment. For equally comparing the Bayesian method with the NLME, we also added the plot level random effect to the Bayesian MCMC procedure. We selected Akaike's information criterion and logarithm likelihood to evaluate NLS and NLME models, and chose deviance information criterion and stationary test to test Bayesian method. These methods had another three same indicators (the determination coefficient, root mean square error, and mean absolute deviation) in model evaluation.Results and discussion: Heteroskedasticity wasn't occurred in this study. The model I.2 (quadratic formula) showed a best fitting effect in each method, and Bayesian method with random effect was slightly superior than other methods. Therefore, the selected final model was quadratic function by Bayesian method with plot level random effect, this combination had the highest prediction accuracy in the larch trees' crown width estimation of Guandi mountain.

In the context of global change, assessing the sustainability of ecological restoration in water-scarce regions presents a critical scientific challenge. The Haihe River Basin (HRB), vital to China’s food and water security, has experienced extensive greening over the past two decades. However, the hydrological cost of this greening remains uncertain. This study leverages multi-source satellite remote sensing data (MODIS, CLCD) from 2001 to 2023 to investigate the hydrological implications of this greening. Our analysis reveals a stark ‘decoupling’: despite significant increases in Gross Primary Production (GPP) (9.45 g C·m⁻²·yr⁻¹, p < 0.01), the basin-wide Water Use Efficiency (WUE) exhibited a gradual yet statistically significant decline (slope = −0.01 g C·m⁻²·mm⁻¹·yr⁻¹, p < 0.01). In contrast, Carbon Use Efficiency (CUE) demonstrated no significant basin-wide trend but exhibited significant spatial decreases in mature forest areas. Spatially, the trends are heterogeneous; while 40.80% of the basin showed improved WUE, a significant decrease was observed in only 2.88% of the area, primarily in high-productivity agricultural zones. This localized decline, however, was substantial enough (with mean rates of decrease exceeding −0.06 g C·m⁻²·mm⁻¹·yr⁻¹) to influence the basin-wide average downward. Attribution analysis identified that climate change, particularly rising temperatures and the associated increase in vapor pressure deficit (VPD), were the dominant drivers of this decline by stimulating evapotranspiration (ET) at a rate faster than GPP enhancement. Collectively, our findings suggest that the observed greening trajectory in the HRB, while increasing carbon uptake, is becoming progressively less water-efficient, indicating a path of hydrological unsustainability. This research highlights the urgent need for hydrologically informed policies in ecological restoration, shifting the focus from simple ‘greening’ towards achieving ‘sustainable and hydrologically sound greening’.