Quantitative evaluation of ecological cumulative effect in mining area using a pixel-based time series model of ecosystem service value

Li, Jing; Liang, Junling; Wu, Yonghui; Yin, Shouqiang; Yang, Zhen; Hu, Zhenqi

doi:10.1016/j.ecolind.2020.106873

Cited by 37 publications

(12 citation statements)

References 58 publications

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“…Secondly, the ecosystem impact assessment of IML change focused on the ecosystem structure, ecosystem quality, and ecosystem service (Figure 2). Specifically, based on the common landscape index, NDVI trends, and ESV changes [19,39], comprehensive impacts of IML change on the ecosystem were identified by comparing the IML area with its 10 km buffer zone. We briefly describe each of the methods in the following sections.…”

Section: Methodsmentioning

confidence: 99%

“…From 2005-2020, the ecological service value (ESV) of the surrounding areas of the Yanzhou coal mine in Shandong Province exhibited a decreasing trend. Therefore, a set of research schemes was proposed that comprehensively examine the supply, trade-off, and demand of ecosystem services [19]. In addition, the qualitative relationship between vegetation diversity and ecosystem factors were analyzed in the Antaibao mining area in China, based on a regional scale [17].…”

Section: Introductionmentioning

confidence: 99%

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Complex Ecosystem Impact of Rapid Expansion of Industrial and Mining Land on the Tibetan Plateau

et al. 2022

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The ecological security of the Tibetan Plateau is vital for sustainable development. In recent years, biodiversity loss and ecosystem degradation caused by industrial and mining activities have attracted wide attention. However, a synthesis assessment of the impacts of industrial and mining land (IML) on the ecosystem is currently lacking. In this study, based on the land cover data and normalized differential vegetation index, we used the landscape ecological index, Theil-Sen trend analysis and equivalent value factors method to evaluate the change in IML and its ecosystem impact on the TP. The results demonstrated that the area of IML expanded by 3.3 times (228.56%) during 1990–2020, and reached 968.95 km² in 2020. Within this area, the newly added, stable, and reduced areas were 842.71, 126.26, and 168.65 km², respectively. Simultaneously, IML expansion made the landscape more fragmented during 1990–2020. The number of patches, splitting index, and landscape shape index in 2020 increased by 3.59-, 2.70-, and 1.90-fold compared to those in 1990, respectively. Furthermore, the difference in the vegetation change between the IML and its 10 km buffer zone was significant. About 77.34% of the vegetation in the IML area showed a trend of decrease, while about 76.51% of the vegetation in the buffer zone of IML showed a trend of increase. In addition, the expansion of IML also reduced the total ecological services value by USD 6969.31 million (0.66%) from 1990 to 2020. However, the lowered value was USD 8649.50 million (0.82%) in the newly added IML. This study highlights the rapid expansion of IML and reveals the ecosystem structure, ecosystem quality, and ecosystem service impact on the TP, which helps guide ecosystem protection and the sustainable development of mining.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Complex Ecosystem Impact of Rapid Expansion of Industrial and Mining Land on the Tibetan Plateau

et al. 2022

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“…MNDWI can quickly extract water body information [ 63 ]. Li et al corrected the equivalent factor pixel by pixel through RSEI and MNDWI, which can effectively display the temporal and spatial changes of ESVs in each pixel [ 64 ]. In order to better distinguish the ecological status between pixels, this paper introduced RSEI and MNDWI to construct

.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Variation and Driving Forces Analysis of Eco-System Service Values: A Case Study of Sichuan Province, China

Shao

Xian

2022

IJERPH

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Sichuan Province is an important ecological barrier in the upper reaches of the Yangtze River. Therefore, it is critical to investigate the temporal and spatial changes, as well as the driving factors, of ecosystem service values (ESVs) in Sichuan Province. This paper used land use data from 2000, 2005, 2010, 2015, and 2020 to quantify the spatiotemporal changes in the ESVs in Sichuan Province. Correlation coefficients and bivariate spatial autocorrelation methods were used to analyze the trade-offs and synergies of ESVs in the city (autonomous prefecture) and grid scales. At the same time, we used a Geographical Detector model (GDM) to explore the synergies between nine factors and ESVs. The results revealed that: (1) In Sichuan Province, the ESVs increased by 0.77% from 729.26 × 109 CNY in 2000 to 741.69 × 109 CNY in 2020 (unit: CNY = Chinese Yuan). Furthermore, ecosystem services had a dynamic degree of 0.13%. Among them, the ESVs of forestland were the highest, accounting for about 60.59% of the total value. Among the individual ecosystem services, only food production, environmental purification, and soil conservation decreased in value, while the values of other ecosystem services increased. (2) The ESVs increased with elevation, showing a spatial distribution pattern of first rising and then decreasing. The high-value areas of ESVs per unit area were primarily distributed in the forestland of the transition area between the basin and plateau; The low-value areas were distributed in the northwest, or the urban areas with frequent human activities in the Sichuan Basin. (3) The tradeoffs and synergies between multi-scale ecosystems showed that ecosystem services were synergies-dominated. As the scale of research increased, the tradeoffs between ecosystems gradually transformed into synergies. (4) The main driving factors for the spatial differentiation of ESVs in Sichuan Province were average annual precipitation, average annual temperature, and gross domestic product (GDP); the interaction between normalized difference vegetation index (NDVI) and GDP had the strongest driving effect on ESVs, generally up to 30%. As a result, the distribution of ESVs in Sichuan Province was influenced by both the natural environment and the social economy. The present study not only identified the temporal and spatial variation characteristics and driving factors of ESVs in Sichuan Province, but also provided a reference for the establishment of land use planning and ecological environmental protection mechanisms in this region.

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“…Different disturbance impacts on ESs (individual ESs and ES groups) and their interconversion results should be accounted for in ES sustainability management of mining areas under global climate change [97]. Thus, it is important to determine the spatiotemporal scales and threshold value of disturbance [48,98], if the spatial extent and time length of ESs are considered as the key features for determining the disturbance threshold, which favors regulating the effect of disturbance on multi-group ESs and understanding the spatiotemporal variability [99,100].…”

Section: Recommendations For How To Cope With the Twofold Pressure Of...mentioning

confidence: 99%

Spatiotemporal Variability of Human Disturbance Impacts on Ecosystem Services in Mining Areas

Liu

et al. 2022

Sustainability

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Human activities pose significant impacts on ecosystem services (ESs) in mining areas, which will continually increase over time and space. However, the mechanism of ES change on spatiotemporal scales post-disturbance remains unclear, especially in the context of global climate change. Here, we conducted a global literature review on the impact of two of the most frequent disturbance factors (mining and restoration) on 27 different ESs, intending to synthesize the impacts of human disturbance on ESs in mining areas via a meta-analysis, and analyze the spatiotemporal variability of ESs after disturbance. We screened 3204 disturbance studies published on the Web of Science between 1950 and 2020 and reviewed 340 in detail. The results of independence test showed that human disturbance had a significant impact on ESs in the mining areas (p < 0.001). The impacts (positive and/or negative) caused by mining and restoration differed considerably among ESs (even on the same ESs). Additionally, spatiotemporal scales of human disturbance were significantly related to spatiotemporal scales of ES change (p < 0.001). We found that the positive and negative impacts of disturbances on ESs may be interconversion under specific spatiotemporal conditions. This seems to be associated with spatiotemporal variability, such as the temporal lag, spatial spillover, and cumulative spatiotemporal effects. Climate changes can lead to further spatiotemporal variability, which highlights the importance of understanding the changes in ESs post-disturbance on spatiotemporal scales. Our research presents recommendations for coping with the twofold pressure of climate change and spatiotemporal variability, to understand how ESs respond to human disturbance at spatiotemporal scales in the future, and manage disturbances to promote sustainable development in mining areas.

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Quantitative evaluation of ecological cumulative effect in mining area using a pixel-based time series model of ecosystem service value

Cited by 37 publications

References 58 publications

Complex Ecosystem Impact of Rapid Expansion of Industrial and Mining Land on the Tibetan Plateau

Complex Ecosystem Impact of Rapid Expansion of Industrial and Mining Land on the Tibetan Plateau

Spatiotemporal Variation and Driving Forces Analysis of Eco-System Service Values: A Case Study of Sichuan Province, China

Spatiotemporal Variability of Human Disturbance Impacts on Ecosystem Services in Mining Areas

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