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Spatio-Temporal Evolution of Ecological Environment Quality Based on High-Quality Time-Series Data Reconstruction: A Case Study in the Sanjiangyuan Nature Reserve of China

Author

Listed:
  • Xingzhu Xiao

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

  • Yanxi Chen

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

  • Yongle Zhang

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

  • Min Huang

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

  • Hao Li

    (College of Resources, Sichuan Agricultural University, Chengdu 611130, China)

Abstract

The Sanjiangyuan Nature Reserve of China (SNRC) is recognized as one of the most fragile and sensitive terrestrial ecosystems in China, posing challenges for obtaining reliable and complete Moderate Resolution Imaging Spectro Radiometer (MODIS) data for ecological environment quality (EEQ) monitoring due to adverse factors like clouds and snow. In this study, a complete high-quality framework for MODIS time-series data reconstruction was constructed utilizing the Google Earth Engine (GEE) cloud platform. The reconstructed images were used to compute the Remote Sensing based Ecological Index (RSEI) on a monthly scale in the SNRC from 2001 to 2020. The results were as follows: The EEQ of the study area exhibited a “first fluctuating decline, then significant improvement” trend, with the RSEI values increasing at a rate of 0.84%/a. The spatial pattern of the EEQ displayed significant spatial heterogeneity, characterized by a “low in the west and high in the east” distribution. The spatial distribution pattern of the RSEI exhibited significant clustering characteristics. From 2001 to 2020, the proportion of “high–high” clustering areas exceeded 35%, and the proportion of “low–low” clustering areas exceeded 30%. Poor ecological conditions are mainly associated with population agglomerations, cultivated land, unutilized land, and bare ground, while grasslands and forests have higher RSEI values. The result of the trend analysis revealed a significant trend in RSEI change, with 62.96% of the area significantly improved and 6.31% significantly degraded. The Hurst Index (HI) results indicated that the future trend of the RSEI is predominantly anti-persistence. The proportion of areas where the EEQ is expected to continue improving in the future is 33.74%, whereas 21.21% of the area is forecasted to transition from improvement to degradation. The results showed that the high-quality framework for MODIS time-series data reconstruction enables the effective continuous monitoring of EEQ over long periods and large areas, providing robust scientific support for long time-series data reconstruction research.

Suggested Citation

  • Xingzhu Xiao & Yanxi Chen & Yongle Zhang & Min Huang & Hao Li, 2024. "Spatio-Temporal Evolution of Ecological Environment Quality Based on High-Quality Time-Series Data Reconstruction: A Case Study in the Sanjiangyuan Nature Reserve of China," Sustainability, MDPI, vol. 16(14), pages 1-27, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6231-:d:1439756
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    References listed on IDEAS

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    1. Sheng Yue & ChunYuan Wang, 2004. "The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(3), pages 201-218, June.
    2. Qiang Liu & Feihong Yu & Xingmin Mu, 2022. "Evaluation of the Ecological Environment Quality of the Kuye River Source Basin Using the Remote Sensing Ecological Index," IJERPH, MDPI, vol. 19(19), pages 1-21, September.
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    1. Jing Liu & Chonghao Liu & Jianan Zhao & Xiangying Jia, 2024. "Comparative Analysis on Policy Frameworks of High-Altitude Mineral Resource Management: Implications for Sustainable Development Goals (SDGs)," Sustainability, MDPI, vol. 16(23), pages 1-27, November.

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