Boyu Liu

Understanding the driving mechanisms behind ecosystem service (ES) trade-offs and synergies is crucial for sustainable ecosystem management. This study integrates the Social-Ecological System Framework (SESF) with path analysis to explore the relationships among crop production (CP), water retention (WR), and soil conservation (SC) in Shanxi Province, China. We identified six key driving factors—annual mean temperature (Tem), total annual precipitation (Pre), Net Primary Productivity (NPP), per capita GDP (GDP), agricultural, forestry, and water fiscal expenditure (Exp), and urban and rural per capita disposable income (Inc)—to analyze their direct and indirect influences on ES interactions over the years 2000, 2010, and 2020. Results show that CP and WR exhibit a persistent trade-off, while CP and SC maintain a synergistic relationship. WR and SC display a trade-off in static time points but shift towards synergy in long-term changes. Path analysis confirms that natural factors (Tem, Pre, and NPP) dominate short-term ES dynamics, whereas socio-economic variables (GDP, Exp, and Inc) play a greater role in long-term ES changes. Mediation analysis reveals that NPP partially mediates climate effects on ESs, while Inc mediates the influence of GDP on ESs. However, Exp does not significantly affect ESs through Inc, likely due to policy implementation delays and regional economic priorities. Findings suggest that spatially targeted policies should be implemented to optimize ES relationships. Long-term ES governance should integrate ecological restoration strategies with socio-economic incentives to enhance ES sustainability.

Inverting land cover reflectance or derived indices from low-spatial-resolution images to refine the spatial resolution of this data is cost-effective for land surface monitoring applications that face technical or budget limitations. Based on the linear spectral mixing model, many approaches have successfully unmixed coarse mixed pixels using high-spatial-resolution land cover maps in the past decades. However, in some cases, the solutions of linear systems composed of several mixed pixels may not be acquired due to the underdetermined problem. This study presents the causes of this problem and proposes an iterative approximation strategy to address it. An elastic-window-based algorithm was developed, where the initial size of the window was calculated based on the land cover of the mixed pixel. Mixed pixels of neighborhoods with similar land covers were then selected to form the unmixing linear system, which was examined through a simulation test to ensure it was not underdetermined. Otherwise, the window would expand to include more adjacent pixels. This process was repeated until a successful solution was obtained. A statistical analysis of sixty land cover maps from around the globe shows that the underdetermined problem exists at a low level but becomes more serious with an increase in mixed scale. The results demonstrate that the proposed algorithm effectively prevents the underdetermined problem for mixed pixels of different scales and can be integrated into the coarse NDVI downscaling procedure to refine spatial resolution. This study provides a reference for estimating underdetermined mixed pixels and benefits applications that require dealing with the inversion of land cover values directly.

Understanding the coordination relationship between ecosystem service (ES) supply and demand and elucidating the impact of driving factors is critical for regional land use planning and ecological sustainability. We use a large watershed area as a case to map and analyze ES supply, demand and the coordination relationship, and identify the associated socio-ecological driving variables. This study assessed the supply and demand of five ESs (crop production, water retention, soil conservation, carbon sequestration, and outdoor recreation) in 2000 and 2020, and evaluated the coordination between them employing the coupling coordination degree model (CCDM). Additionally, we utilized the geo-detector model (GDM) to identify driving determinants and their interactive effects on the spatial pattern of the coupling coordination degree (CCD) between ES supply and demand. The results showed that mountainous regions with abundant forest coverage were high-value areas for ES supply, while the ESs were predominantly required in city center areas within each basin area. From 2000 to 2020, there was a slight decline in ES supply and a significant increase in ES demand. Counties were grouped into four coordination zones in the study area: extreme incoordination, moderate incoordination, reluctant coordination, and moderate coordination. The number of counties with extreme incoordination linked to regions with a mountain ecosystem is increasing, where the ES supply is much greater than the demand. The moderate incoordination counties dominated by a cropland ecosystem exhibited slightly higher levels of ES supply than demand. The moderate and reluctant coordination were linked to counties with distinct ecological characteristics. Construction land played a major role in the characteristics of the CCD, followed by grassland. The interaction between construction land and all other factors significantly increased the influence on the CCD. These findings offered valuable insights for land managers to identify areas characterized by incoordination between ES supply and demand and understand associated factors to develop optimal ES management strategies.

Conflicts between ecological conservation and socio-economic development persisted over many decades in the Beijing–Tianjin–Hebei urban agglomeration (BTH). Ecosystem services were affected drastically by rapid urbanization and ecological restoration programs in the BTH since 2000. This study aims to identify the spatial patterns of the four types of ecosystem services (net primary productivity (NPP), crop production, water retention, and soil conservation) in 2000 and 2010, and to make clear the impacts of urbanization and associated factors on the spatial patterns of ecosystem services. Based on the quantification of ecosystem services, we assessed the spatial patterns and changes, and identified the relationships between the type diversity of ecosystem services and land-use change. We also analyzed the effect of the spatial differentiation of influencing factors on ecosystem services, using the geographical detector model. The results showed that the average value of crop production increased substantially between 2000 and 2010, whereas the net primary productivity decreased significantly, and the water retention and soil conservation decreased slightly. The ecosystem services exhibited a spatial similar to that of influencing factors, and the combination of any two factors strengthened the spatial effect more than a single factor. The geomorphic factors (elevation and slope) were found to control the distribution of NPP, water retention, and soil conservation. The population density was responsible for crop production. We also found that the urbanization rate plays a major indirect role in crop production and water retention when interacting with population density and slope, respectively. The normalized difference vegetation index (NDVI) indirectly influences the spatial distribution of NPP when interacting with geomorphic factors. These findings highlight the need to promote new strategies of land-use management in the BTH. On the one hand, it is necessary to carefully select where new urban land should be located in order to relieve the pressure on ecosystem services in dense urban areas. On the other hand, the maintenance of ecological restoration programs is needed for improving vegetation coverage in the ecological functional zones in the medium and long term.

Spectral and NDVI values have been used to calculate the change magnitudes of land cover, but may result in many pseudo-changes because of inter-class variance. Recently, the shape information of spectral or NDVI curves such as direction, angle, gradient, or other mathematical indicators have been used to improve the accuracy of land cover change detection. However, these measurements, in terms of the single shape features, can hardly capture the complete trends of curves affected by the unsynchronized phenology. Therefore, the calculated change magnitudes are indistinct such that changes and no-changes have a low contrast. This problem has prevented traditional change detection methods from achieving a higher accuracy using bi-temporal images or NDVI time series. In this paper, a multiple shape parameters-based change detection method is proposed by combining the spectral correlation operator and the shape features of NDVI temporal curves (phase angle cumulant, baseline cumulant, relative cumulation rate, and zero-crossing rate). The change magnitude is derived by integrating all the inter-annual differences of these shape parameters. The change regions are discriminated by an automated threshold selection method known as histogram concavity analysis. The results showed that the mean differences in the change magnitudes of the proposed method between 2100 changed and 2523 unchanged pixels was 32%, the overall accuracy was approximately 88%, and the kappa coefficient was 0.76. A comparative analysis was conducted with bi-temporal image-based methods and NDVI time series-based methods, and we demonstrate that the proposed method is more effective and robust than traditional methods in achieving high-contrast change magnitudes and accuracy.