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Evaluating UN sustainable development goal (SDG) indicator 15.3.1 and methods for land degradation monitoring in mountainous regions

  • 0State Key Laboratory for Ecological Security of Regions and Cities, Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen City, 361021, China.
Environmental Monitoring and Assessment +

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September 9, 2025

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September 9, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Effective land degradation (LD) monitoring is crucial. This study enhances UN SDG indicator 15.3.1 for mountainous regions by integrating national land cover data and customized matrices, improving accuracy in assessing land degradation.

Area Of Science

  • Environmental Science
  • Remote Sensing
  • Sustainable Development

Background

  • Land degradation (LD) is a significant global environmental issue driven by human activities and climate change.
  • Accurate monitoring of LD is essential for effective land management and achieving Sustainable Development Goals (SDGs).
  • The UN SDG indicator 15.3.1 faces challenges in data consistency and accuracy, particularly in complex mountainous terrains.

Purpose Of The Study

  • To assess land degradation in Southern China's mountainous regions.
  • To improve the effectiveness of UN SDG indicator 15.3.1 for LD assessment in challenging environments.
  • To compare the performance of national versus global land cover datasets and customized transition matrices.

Main Methods

  • Integration of national and global land cover (LC) datasets.
  • Development and application of a customized LC transition matrix tailored to regional ecological policies.
  • Comparison of LD assessment results using different datasets against validated LD findings.

Main Results

  • National LC datasets indicated 12.74% land degradation, significantly lower than the 20.58% from global datasets.
  • Land improvement was assessed as 7.58% higher using national data compared to global data.
  • LD results derived from national datasets and a customized transition matrix achieved 94% overall accuracy, closely matching validated data.

Conclusions

  • National LC datasets and customized transition matrices enhance the accuracy of LD assessments in mountainous regions.
  • Integrating tailored national data improves the reliability of UN SDG indicator 15.3.1 for LD monitoring.
  • This approach offers a more effective method for assessing and reporting land degradation status globally.

Keywords:

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