贾路,于坤霞,李占斌,等. 长江经济带景观格局对生态系统服务价值影响的概率评估[J]. 农业工程学报,2023,39(15):217-227. DOI: 10.11975/j.issn.1002-6819.202304177
    引用本文: 贾路,于坤霞,李占斌,等. 长江经济带景观格局对生态系统服务价值影响的概率评估[J]. 农业工程学报,2023,39(15):217-227. DOI: 10.11975/j.issn.1002-6819.202304177
    JIA Lu, YU Kunxia, LI Zhanbin, et al. Probability evaluation of the impact of landscape pattern on ecosystem service value in the Yangtze River Economic Belt[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(15): 217-227. DOI: 10.11975/j.issn.1002-6819.202304177
    Citation: JIA Lu, YU Kunxia, LI Zhanbin, et al. Probability evaluation of the impact of landscape pattern on ecosystem service value in the Yangtze River Economic Belt[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(15): 217-227. DOI: 10.11975/j.issn.1002-6819.202304177

    长江经济带景观格局对生态系统服务价值影响的概率评估

    Probability evaluation of the impact of landscape pattern on ecosystem service value in the Yangtze River Economic Belt

    • 摘要: 经济的快速发展改变了区域景观格局,定量评价景观格局变化对生态系统服务价值的影响是实现生态文明建设和高质量发展的重要途径。该研究以长江经济带为研究对象,使用1980—2020年8期土地利用数据,在渔网分析的基础上,采用线性回归和相关性检验方法分析了4种景观格局指数和生态系统服务总价值的时空演化特征与线性响应,最后通过构建非线性概率评估框架,定量评估了生态系统服务总价值对景观格局指数变化的敏感性。结果表明:1)1980—2020年间,耕地、林地和草地是长江经济带的主要土地利用类型,耕地和林地面积逐年减少,其他土地利用面积均有所增加。2)四川省南部、云南省东部、贵州省西部和重庆市等地区多年平均景观分离度指数较大,景观破碎化严重。长江经济带43.97%地区景观分离度指数和64.28%的区域的香农多样性指数显著增加,大部分地区景观呈现均衡化和多样化发展,城市化会降低生态系统服务总价值。3)景观分离度和香农多样性指数的增加可能加剧景观破碎化,导致生态系统服务价值的降低。4)根据概率评估框架,较高的生态系统服务总价值对景观分离度指数或香农多样性指数比较敏感,林地和草地是影响长江经济带生态系统服务总价值变化的主导景观。研究成果有助于掌握长江经济带景观格局和生态系统服务的变化特征,从而进一步为长江经济带生态保护与高质量发展提供科学依据。

       

      Abstract: The rapid development of the economy has changed the regional landscape pattern. It is a high demand to quantitatively evaluate the impact of landscape pattern changes on the value of ecosystem services for ecological civilization construction and high-quality development. In this study, the research object was taken as the Yangtze River Economic Belt. Fishing net analysis was also made using land use data from eight periods from 1980 to 2020. The linear regression and correlation tests were carried out to clarify the spatiotemporal evolution and linear responses of four landscape pattern indexes and ecosystem service total value. Finally, a nonlinear probability evaluation framework was constructed to quantitatively evaluate the sensitivity of ecosystem service total value to changes in landscape pattern indexes. The results indicated that: 1) cropland, forestland, and grassland were the main types of land use in the study area. The area of cropland and forestland decreased each year, whereas, there was an increase in the area of other types of land use. Rapid economic development and urbanization were the main reasons for the significant increase in the construction land. 2) There was a relatively high index on the multi-year average landscape separation in southern Sichuan Province, eastern Yunnan Province, western Guizhou Province, and Chongqing City, resulting in severe landscape fragmentation, whereas, the distribution of patches was relatively concentrated in eastern Sichuan Province, southern Yunnan Province, western Hubei Province, western Hunan Province, northern Anhui Province, eastern Jiangsu Province, and southern Zhejiang Province. There was a significant increase in the indexes of landscape division and Shannon’s diversity in 43.97% and 64.28% of the study area, indicating the balanced and diversified landscape development. Urbanization was attributed to reducing the ecosystem service total value. 3) The increasing landscape separation index and Shannon’s diversity index were selected to represent the serious landscape fragmentation, leading to a decrease in the ecosystem service total value. It was very necessary to explore the response relationship between different landscape pattern indexes and ecosystem service total value, rather than the simple causal or linear relationship. 4) Four distribution functions were utilized to better fit the landscape pattern index and variations in the ecosystem service total value of each grid. Furthermore, the higher ecosystem service total value was more sensitive to the landscape division index and Shannon’s diversity index, according to the probability evaluation framework. The forestland and grassland were the dominant landscapes that affected the increase in the ecosystem service total value. The probability evaluation framework can be expected to quantitatively describe the nonlinear response relationship between the various ecosystem service total value and landscape pattern index. Landscape optimization should be carried out to promote protection and high-quality development. The finding can provide a solid scientific basis to grasp the variation trends of the landscape pattern and ecosystem services value, particularly for the ecological protection and high-quality warfare in the Yangtze River Economic Belt.

       

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