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Landscape Ecological Risk Assessment and Impact Factor Analysis of the Qinghai-Tibetan Plateau

Sishi Wang,X. Tan,Fenglei Fan

Published 2022 in Remote Sensing

ABSTRACT

The Qinghai–Tibetan Plateau (QTP) is one of the most vulnerable ecosystems worldwide. Over the last few decades, the QTP has been subjected to increasing external pressures, such as climate change, human activity, and natural hazards. Therefore, ecological risk assessment is vital for the environmental protection and sustainable development of the QTP. A landscape ecological risk (LER) assessment based on landscape disturbance and vulnerability was performed to explore the spatiotemporal characteristics associated with LER in the QTP from 1990 to 2020. Furthermore, the impact of LER was quantitatively evaluated with a boosted regression tree model. Results showed that more than 70% of the locations in the QTP exhibited below-medium LER. The LER for the QTP demonstrated downward trends from 1990 to 2020. The LER presented downward trends during the periods from 1990 to 2001 and from 2012 to 2020 and no significant trend during the period from 2002 to 2011. Additionally, high-LER areas were concentrated in the northwestern QTP, whereas low-LER areas were mainly in the southeastern QTP. The LER displayed clustering characteristics across the QTP. Changes in climate, topographic distribution, and human activity influenced the ecological stability of the study area. Precipitation and temperature had the strongest effects on the LER, followed by elevation and grazing intensity. Lower precipitation and temperatures were associated with higher LER. Our results provide precise and specific support for the environmental protection and ecological management of the QTP and other ecologically fragile areas.

PUBLICATION RECORD

  • Publication year

    2022

  • Venue

    Remote Sensing

  • Publication date

    2022-09-21

  • Fields of study

    Geography, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs14194726
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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