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This paper examines long-term settlement expansion in western Hunan region of China and presents a case study of socio-cultural landscape transformation. We extract historical settlements from historical documents in the area's representative counties and outline their spatial distributions during the eighteenth century. Following these distributions, we then examine the influencing factors behind the expansion. We found that when compared with older settlements, new settlements were concentrated primarily on sloping, high-elevation fields rather than more level, lower-elevation fields. Multiple factors affected this settlement expansion. Topography strongly influenced settlement distribution, while policy shifts strongly influenced settlement expansion. Among these was a policy shift from prohibition to encouragement. Other contributing factors to settlement expansion included population growth, immigration, the introduction of new crops, improved farming techniques, and farmland water conservation facilities, which likely benefited from temperature rises. Furthermore, a portion of the indigenous people gradually transitioned into permanent agriculture from slash-and-burn farming, possibly resulting in increased settlement activities. Overall, this study makes important contributions to understanding human impacts on mountainous environments of the past, highlighting patterns of historical-geographical continuity and change over time. Future investigations on historical settlements in a typical region would improve knowledge of these human-nature interactions over the long term. • Maps settlement in western Hunan mountainous areas during the eighteenth century. • Showing a trend of new settlements expanding to higher elevations and steeper slopes. • Describing Anthropogenic land cover change associated with settlement expansions. • Revealing historical landscape changes in conjunction with settlement expansions. • Examining the influencing factors behind the settlement distribution in space. [ABSTRACT FROM AUTHOR]

Intensive anthropogenic activities have led to drastic changes in land use/land cover (LULC) and impacted the carbon storage in high-groundwater coal basins. In this paper, we conduct a case study on the Yanzhou Coalfield in Shandong Province of China. We further classify waterbodies by using the Google Earth Engine (GEE) to better investigate the process of LULC transformation and the forces driving it in four periods from 1985 to 2020 (i.e., 1985–1995, 1995–2005, 2005–2015, and 2015–2020). We modeled the spatiotemporal dynamics of carbon storage by using InVEST based on the transformation in LULC and its drivers, including mining (M), reclamation (R), urbanization and village relocation (U), and ecological restoration (E). The results indicate that carbon storage had depleted by 19.69 % (321099.06 Mg) owing to intensive transformations in LULC. The area of cropland shrank with the expansion of built-up land and waterbodies, and 56.31 % of the study area underwent transitions in land use in the study period. U was the primary driver of carbon loss while E was the leading driver of carbon gain. While the direct impact of M on carbon loss accounted for only 5.23 % of the total, it affected urbanization and led to village relocation. R led to the recovery of cropland and the reclamation of water for aquaculture, which in turn improved the efficiency of land use. However, it contributed only 2.09 % to the total increase in carbon storage. Numerous complicated and intertwined processes (211) drove the changes in carbon storage in the study area. The work here provides valuable information for decision-makers as well as people involved in reclamation and ecological restoration to better understand the link between carbon storage and the forces influencing it. The results can be used to integrate the goals of carbon sequestration into measures for land management. [Display omitted] • The carbon storage impacted by LULC driving processes in high groundwater coal basins was investigated. • 56.31 % of the study area has undergone land use transition during 1985–2020. • The carbon storage decreased by19.69 % during the study years. • Urbanization and village relocation was the primary driver of carbon loss, ecological restoration was the leading driver of carbon gain. [ABSTRACT FROM AUTHOR]

China has begun a rapid move towards Sponge City implementation, which will include widespread installation of various Low Impact Development (LID) stormwater infrastructure. Ambitious goals have been set for all Sponge Cities, including a target capture rate of 80–85% of annual precipitation in Beijing. However, there has so far been limited investigation into whether these goals are obtainable at the catchment scale given the physical restrictions of the existing urban landscape. This paper demonstrates the use of long-term continuous hydrologic modelling to evaluate the potential of meeting rainfall capture goals over a long time period for a case study area in Beijing. Detailed land cover classification derived from satellite imagery aided in the parameterization of a SWMM (Stormwater Management Model) model representing a 133 km2 urban study area. The calibrated and validated model demonstrated satisfactory agreement between computed and observed runoff for a variety of precipitation events ranging from 10 to 207 mm. A synthetic high resolution rainfall time series was generated from historic daily observations then used as input to run the model continuously for a 35 year period. Feasible LID model scenarios based on varying degrees of implementation of rain gardens, permeable pavements and green roofs were compared in terms of their potential volume capture of annual rainfall. Modelling results demonstrated that the conversion of 30% of roof area to green roof, 10% of green areas to rain gardens and 35% of paved areas to permeable pavement could increase the baseline volume capture ratio from 59.9% to 82.2%, indicating that Sponge City goals can be met within the case study area with realistic levels of LID implementation. This study provides a decision-making basis for future planning in the study area. • Satellite imagery based hydrologic model shows good agreement with observed flows. • Continuous modelling approach suggested for evaluation of Volume Capture Ratio. • >80% of annual rainfall captured with moderate catchment wide implementation of LID. [ABSTRACT FROM AUTHOR]

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]

As the most biodiversity-rich part of the protected areas system, habitats within the pilot national parks have long been threatened by drastic human-induced land use and land cover changes. The growing concern about habitat loss has spurred China's national park project to shift from pilot to construction phase with the official establishment of China's first group of national parks (CFGNPs) in October 2021. But far too little attention has been paid to the synergistic work concerning the habitat quality (HQ) dynamics of all five national parks. Here, the InVEST model, combined with a satellite-derived land use and land cover product and a hot spot analysis (HSA) method, was used to investigate the HQ dynamics at the park- and pixel-scale within the CFGNPs. Our results demonstrate that the past ecological conservation practices within national parks have been unpromising, especially in Giant Panda National Park, Northeast China Tiger and Leopard National Park (NCTL), and Wuyi Mountain National Park (WYM), where HQ as a whole showed a significant decline. Furthermore, more than half of Hainan Tropical Rainforest National Park (87.2%), WYM (77.4%), and NCTL (52.9%) showed significant HQ degradation from 1980 to 2019. Besides, increasing trends in the area shares of HQ degraded pixels were observed in all five national parks from 1980–1999 to 2000–2019. The HSA implied that the hot spots of high HQ degradation rates tend to occur in areas closer to urban settlements or on the edge of national parks, where human activities are intensive. Despite these disappointing findings, we highlighted from the observed local successes and the HQ plateau that the construction of CFGNPs is expected to reverse the deteriorating HQ trends. Thus, we concluded our paper by proposing an HSA-based regulatory zoning scheme that includes five subzones to guide the future construction of China's national park system. [Display omitted] • The first known investigation to reveal the habitat quality trends for China's first group of national parks was presented. • The past ecological conservation practices within national parks have been unpromising over the past four decades. • The area shares of habitat quality degraded pixels show an increasing trend in all five national parks from 1980 to 2019. • We developed a regulatory zoning scheme for habitat protection or ecosystem restoration within these national parks. [ABSTRACT FROM AUTHOR]

Maintaining ecosystem services (ESs) and reducing ecosystem degradation are important goals for achieving sustainable development. However, under the influence of various anthropogenic factors, the total ecosystem service value (ESV) of China continues to decline, and the detailed processes involved in this decline are unclear. In this paper, a new long-term annual land cover dataset (the Climate Change Initiative Land Cover or CCI-LC dataset) with a spatial resolution of 300 m was employed to estimate the ESV of China, and Bayesian spatiotemporal hierarchy models were built to examine the detailed patterns and anthropogenic driving factors. From 1992 to 2018, the total ESV of China fluctuated and decreased from 3265.3 to 3253.29 billion US$ at an average rate of 0.55 billion US$ per year. Furthermore, the model revealed the spatiotemporal variations in the ESV pattern, and simultaneously detected the influences of 9 variables related to economic factors, population, infrastructure, energy, agriculture and ecological restoration, providing a convenient and effective method for ESV spatiotemporal analysis. The results enrich our understanding of the detailed spatiotemporal variation and anthropogenic driving factors underlying the declining ESV in China. These findings have substantial guiding implications for adjusting ecological regulation policies. • The annual ESVs of China from 1992 to 2018 was estimated. • China's total ESV showed a trend of decline and fluctuation. • The Bayesian spatiotemporal hierarchy model was constructed for ESV analysis. • The hot and cold provinces of ESV spatial distribution were identified. • The effects of 9 variables from anthropogenic driving factors on ESV were examined. [ABSTRACT FROM AUTHOR]

Maintaining ecosystem services (ESs) and reducing ecosystem degradation are important goals for achieving sustainable development. However, under the influence of various anthropogenic factors, the total ecosystem service value (ESV) of China continues to decline, and the detailed processes involved in this decline are unclear. In this paper, a new long-term annual land cover dataset (the Climate Change Initiative Land Cover or CCI-LC dataset) with a spatial resolution of 300 m was employed to estimate the ESV of China, and Bayesian spatiotemporal hierarchy models were built to examine the detailed patterns and anthropogenic driving factors. From 1992 to 2018, the total ESV of China fluctuated and decreased from 3265.3 to 3253.29 billion US$ at an average rate of 0.55 billion US$ per year. Furthermore, the model revealed the spatiotemporal variations in the ESV pattern, and simultaneously detected the influences of 9 variables related to economic factors, population, infrastructure, energy, agriculture and ecological restoration, providing a convenient and effective method for ESV spatiotemporal analysis. The results enrich our understanding of the detailed spatiotemporal variation and anthropogenic driving factors underlying the declining ESV in China. These findings have substantial guiding implications for adjusting ecological regulation policies. • The annual ESVs of China from 1992 to 2018 was estimated. • China's total ESV showed a trend of decline and fluctuation. • The Bayesian spatiotemporal hierarchy model was constructed for ESV analysis. • The hot and cold provinces of ESV spatial distribution were identified. • The effects of 9 variables from anthropogenic driving factors on ESV were examined. [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of the spatiotemporal changes in drought is very important for the reduction in the social losses caused by drought. The Optimized Meteorological Drought Index (OMDI), originally established in southwestern China, showed great potential for drought monitoring over large regions on a large scale. However, the applicability of the index requires further evaluation, especially when used throughout China, which has a different agricultural divisions, variable climatic conditions, complex terrain and diverse land cover. In addition, the OMDI model relies on training data to construct local parameters for the model. On a large scale, it is of great significance to use multisource remote sensing data sets to construct OMDI model parameters. In this paper, the constrained optimization method was used to establish weights for the MODIS-derived Vegetation Conditional Index (VCI), TRMM-derived Precipitation Condition Index (PCI), and GLDAS-derived Soil Moisture Condition Index (SMCI) and calculate the OMDI based on the Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and weather stations. The accuracy of the OMDI model was evaluated by using the correlation coefficient. Moreover, the spatiotemporal changes in drought were also analyzed through trend analysis, Mann-Kendall (MK) statistics and the Hurst index on the monthly and annual scales. The results showed that (1) the highest positive correlation between the OMDI and the SPI was SPI-1, which was higher than that for any other month interval, such as 3 months, 6 months, 9 months and 12 months of the SPI. The results indicated that the OMDI was suitable to monitor meteorological drought. (2) In the nine agricultural subareas in China, the degree of drought in the Yangtze River (DYR) area had the most severe evolution and change frequency. This region was very sensitive to drought in the past two decades. (3) The area with OMDI variation coefficient less than 0.1 accounted for 94%, indicating that the degree of drought fluctuates little; The linear tendency rate is 0.0004, and the area greater than 0 reaches 66.44%, indicating that the drought is developing in a lightning trend. (4) The Hurst index value is mostly higher than 0.5 (the area ratio is 56.31%), and the area of "Positive-Consistent" and "Negative- Opposite" accounted for 54.02%, indicating that more than half of China's area drought changes will show a trend of mitigation in the future. [Display omitted] • OMDI model was improved based on multi-source remote sensing data sets. •OMDI model was improved based on constrained optimization theory. •Drought dynamics was monitored on large scale based on remote sensing technology. •Drought conditions in China were valuated both on month scale and annual scale. •Change trend in the future was analyzed using Linear trend and OMDI Hurst index. [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]