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Land cover change (LCC) is both a consequence and a cause of global environmental change. This paper attempts to construct a framework to reveal the driving mechanism and ecological effects of different ecological factors under LCC and to explore the ecological characteristics of future LCC. A rule-mining framework based on a land expansion analysis strategy (LEAS) in the patch-generating land use simulation (PLUS) model was used to analyze the drivers of LCC. Neighborhood analysis and ecological effect index were used to investigate multiple ecological effects of LCC. Remote sensing-based ecological indices (RSEI) and the PLUS and stepwise regression model were introduced to explore and predict the integrated ecological effect of LCC. Focusing on the Weihe River basin, study's main drivers of LCC were precipitation, temperature, elevation, population, water table depth, proximity to governments and motorways, GDP, and topsoil organic carbon were the main drivers of LCC. Change directionality were similar for the effects of greenness and biomass formation but opposite for summertime and wintertime temperature. In addition, the conversion of land cover types to cropland had the most significant integrated ecological effect, followed by forest, grassland-shrubland, and other types. The RSEI is predicted to rise to 0.77 in 2030, and the areas where the ecological quality grade will improve and decrease are concentrated on the east and west sides of Ziwuling Mountain, respectively. The findings of this study have practical significance for land management and ecological protection. • The PLUS Model is used to explore driving mechanism of land cover change (LCC). • Ecological effects of LCC are analyzed from multiple and integrated perspectives. • The integrated ecological effect of land cover types converted to cropland is the most significant. • The PLUS model with stepwise regression predicts a decreasing trend of RSEI in 2030. [ABSTRACT FROM AUTHOR]

Integration of mining brownfields into green infrastructure(GI) planning has been widely recognized as a sustainable developmentstrategy. The key is to locate the spatial priority areas for brownfieldrestoration that would provide the greatest enhancement of themultifunctionality of the GI network. At the regional scale, a wellconnectedGI network can help maximize the benefits that it generates andachieve greater multifunctionality. In this paper, an approach forassessing the restoration priority of brownfields is proposed from the perspective of enhancing the structural connectivity of GI network. Itcan also be used as an assistance tool for planning GI networks at theregional level. The Least Cost Path (LCP) model is adopted for mapping corridors among GI patches on the basis of a resistance map composed by considering the naturalness of land covers, the riparian corridor, and disturbance of roads. Taking Fenhe River Basin (in China) as an example, three factors are jointly considered in the priority assessment of brownfields restoration: their potential contribution as corridors, their importance as stepping stones, and their resistance values. Then, we divided the brownfields into groups indicating the level of priority for restoration. The results show that 67% of the brownfields are of high or very high priority for restoration (mainly located in the north), and less than 10% area is recognized as very low or low priority. Our proposed approach could be a valuable tool for providing a macro-scale assessment of restoration priorities of brownfields for enhancing the GI network connectivity, and giving instructions for on-site redevelopment. Image 1 • Brownfields can be restored as potential elements of Green Infrastructure (GI). • The lease cost path model is adopted for simulating the corridors of GI networks. • The restoration priority of brownfields is related to their contribution to GI connectivity. • Post-mining brownfields are evaluated acting as potential stepping stones and corridors. • The proposed method can help planners to develop GI networks at a regional level. [ABSTRACT FROM AUTHOR]