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The Sustainable Development Goals (SDGs) are essential measure for preserving the balance between human well-being and natural ecosystems. The benefit of preserving ecosystems health play a crucial role in promoting the SDGs by providing stable ecosystem services (ESs). However, the ecological health of mountainous cities is vulnerable, with relative low ecological resilience. To investigate the conflict between ecosystems and sustainable development, this study takes the Chengdu-Chongqing Urban Agglomeration as the study area. The major tasks and results in this study include: (1) using the entropy weighting method and the InVEST model, we combined remote sensing, geographic, and statistical data to quantify three types of SDGs (economic, social, environmental) and four ESs (water yield, soil conservation, habitat quality, carbon storage), and establish a localized sustainable development assessment framework that is applicable to the Chengdu-Chongqing Urban Agglomeration. The results show that from 2014 to 2020, the three types of SDGs exhibited an overall upward trend, with the lowest values occurring in 2016. The gap between different counties has narrowed, but significant regional differences still remain, indicating an unbalanced development status quo. Among the 142 counties, water yield and soil conservation values show a consistent downward trend but occupies significant interannual variations, while habitat quality and carbon storage values increases consistently each year. (2) using Spearman's nonparametric correlation analysis and multiscale geographically weighted regression model to explore the temporal variation and spatial heterogeneity of correlations between county ESs and SDGs. The results showed significant heterogeneity in the spatial trade-offs and synergies between ESs and SDGs, with two pairs of synergies weakening, seven pairs of trade-offs increasing, and the strongest negative correlation between Economic Sustainable Development Goals and habitat quality. (3) we applied the self-organizing mapping neural networks to analyze the spatial clustering characteristics of ESs-SDGs. Based on the spatial clustering effects, we divides the Chengdu-Chongqing Urban Agglomeration into four zones, and different zones have different levels of ESs and SDGs. The targeted strategies should be adopted according to local conditions. This work is of great practical importance in maintaining the stability and sustainable development of the Chengdu-Chongqing Urban Agglomeration ecosystem and provides a scientific reference for the optimal regulation of mountainous cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Vegetation is an essential component of terrestrial ecosystems, influenced by climate change and human activities. Quantifying the relative contributions of climate change and human activities to vegetation dynamics is crucial for addressing global climate change. Sichuan Province is one of the essential ecological functional areas in the upper reaches of the Yangtze River, and its vegetation change is of great significance to the environmental function and ecological security of the Yangtze River Basin and southwest China. In this paper, the modified Carnegie-Ames-Stanford Approach(CASA) model was used to estimate the monthly NPP (Net Primary Productivity) of vegetation in Sichuan Province from 2000 to 2018, and the univariate linear regression analysis was used to analyze the temporal and spatial variation of vegetation NPP in Sichuan Province from 2000 to 2018. In addition, taking vegetation NPP as an index, Pearson correlation analysis, partial correlation analysis, and second-order partial correlation analysis were carried out to quantitatively analyze the contribution of climate change and human activities to vegetation NPP. Finally, the Hurst index and nonparametric Man-Kendall significance test were used to predict the future change trend of vegetation NPP in Sichuan Province. The results show that (1) from 2000 to 2018, the NPP of vegetation in Sichuan Province has a significant increasing trend (Slope = 6.09gC·m -2 ·a -1 ), with a multi-year average of 438.72 gC·m -2 ·a -1 , showing a trend of low in the east and high in the middle. The response of vegetation NPP to altitude is different at different elevations; (2) the contribution rates of climate change and human activities to vegetation NPP change are 4.12gC·m -2 ·a -1 and 1.97gC·m -2 ·a -1 , respectively. In contrast, the impact of human activities on NPP is more significant than climate change. Human activities are the main factors affecting vegetation restoration and degradation in Sichuan Province. However, the positive contribution to NPP change is less than climate change; (3) the future vegetation NPP change trend in Sichuan Province is mainly rising, and the same direction change trend is much larger than the reverse change trend. The areas with an increasing trend in the future account for 89.187% of the total area. This research helps understand the impact of climate change and human activities on vegetation change in Sichuan Province. It offers scientific bases for vegetation restoration and ecosystem management in Sichuan and the surrounding areas., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The Yellow River Basin in China has the world's most serious soil erosion problem. The Yellow River Basin in Sichuan Province (YRS), as the upper reaches of the Yellow River, and its water conservation (WC) capacity greatly affects the ecological environment of the downstream basin. In recent years, YRS has received more and more attention, and numerous policies have been developed to improve local WC. However, there is a vacancy in the long-term research of WC in the YRS due to the lack of in-situ data. This study quantitatively evaluated the WC of YRS from 2001 to 2020 through Google Earth Engine (GEE) and analyzed the spatio-temporal variations of WC and land cover (LC). CA-Markov predicted the LC and WC in 2025 under three scenarios to assess the contribution of different scenarios to WC. The WC in YRS fluctuated from 1.93 to 6.77 billion m 3 . The climate is the dominant factor of WC change, but the effect of LC on WC is also evident. The WC capacity increases with vegetation coverage and height. The WC capacity of forests per km 2 exceeds 600 mm, while that of grasslands is about 250 mm, and barren can cause around 300 mm of WC loss. In 2025, the WC in YRS may exceed 7.5 billion m 3 , but the past ecological management mode should be transformed. Improving the quality of land use and converting grasslands to forests is better than reducing cropland to improve WC., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

The Himalayan volcanic earthquake zone has significantly impacted China's Sichuan-Tibet region. Many barriers have formed as a result of the earthquake and secondary disasters, such as landslides, which have blocked the river. The breach of the barrier lake seriously threatens the lives and property safety of downstream personnel. There has been little research on the surrounding ecology for the later treatment of the barrier lake. This paper aims to scientifically predict the risk of dam break in a barrier lake as well as to explore its impact on the ecological environment and put forward controllable measures. Based on four major barrier lake events in the Sichuan-Tibet area, Diexihaizi, Tangjiashan barrier lake, and so on, we extract water bodies from remote sensing images and use the HEC-RAS (Hydrologic Engineering Center of River Analysis System) model to investigate whether there is a dam break risk and the route of the dam break is predictable. Simultaneously, from 1990 to 2020, the smallest administrative region is located. The InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model is utilized to evaluate and analyze its habitat and create an evaluation based on flood inundation data. The results suggest that a stable barrier lake (such as Diexihaizi) has a sound effect on the habitat quality index following engineering treatment. The development of the barrier lake has altered the types of neighboring lands used and the natural patterns of the region's landscape. The habitat quality index will marginally deteriorate within a 1-km radius of the barrier lake. However, the quality of habitats in the area ranging within 3 km and 5 km has improved. It is necessary to discharge and strengthen the barrier lake artificially. Human-controlled regions, according to studies, will recover higher habitat quality index values than other locations. Whether the barrier lake has a positive impact on the surrounding area, on the other hand, is primarily dependent on the original ecology. The development of barrier lakes is damaging and unprofitable in Tibet, where the actual ecology is better in the short term. Still, in Sichuan Province, where the habitat quality is relatively low, the appearance of dammed lakes has played a role in correcting the ecology., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Panzhihua City, a typical eco-fragile region for agro-sylvo-pastoral industry in China, is located in the dry-hot valley of the Jinsha River, characterized by its big landform undulation, great elevation difference, uneven hydrothermal conditions, and complex geological structure. As a crucial ecological barrier in upper reaches of the Yangtze River, this area is abundant in water resources and mineral resources, such as vanadium and titanium. However, due to its over-development for nonnatural urban economy in the mining industry, agriculture, and animal husbandry, ecological problems are getting worse. Such problems as soil erosion and groundwater pollution have led obvious ecological degeneration in Panzhihua city. Therefore, for protecting the eco-environment and planning construction, it is significant to scientifically recognize that how eco-environment changes based on spatial-temporal, and how the driving mechanism affects Panzhihua city. Nowadays, there are some theories and methods that study eco-environmental protection and city construction in Panzhihua, but they are not comprehensive enough to study its spatial-temporal evolution and driving-force system. This study takes Panzhihua City as the research area of which evaluation factors, for example, topography, soil, vegetation, and meteorological factors, are chosen to construct an evaluation system suitable for the ecological environment vulnerability of Panzhihua City. These factors are selected in three aspects, which are ecological sensitivity, ecological recovery, and ecological pressure from 2005 to 2015 in this area. Then, spatial principal component analysis method, CA-Markov model, and dynamic degree model are applied to analyze the spatial-temporal evolution for ecological vulnerability based on three periods from 2005 to 2015 in Panzhihua City. Besides, GeoDetector is used to quantitatively analyze how spatial-temporal disparities change and what drives them to change. The results show that (1) during these 10 years, the overall ecological fragility of Panzhihua City is steadily increasing from northwest to southeast. The overall ecological quality is moderate, and regional differences are obvious. Places of moderate vulnerability or above are distributed in central and eastern regions of frequent human activities; places of mild vulnerability or below are distributed in the regions of Yanbian County and Miyi County. (2) The comparison of the changing rates based on vulnerability levels is severe > potential > moderate > mild > slight. The overall vulnerability changes within a small trend, showing a balanced two-way transition state between adjacent vulnerability levels. The comprehensive index for overall ecological vulnerability decreases period by period. (3) The interactions between each two factors toward spatial differentiation and explanatory power by ecological vulnerability show a two-factor-enhanced relation, indicating that multiple factors form the ecological vulnerability of Panzhihua City.