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A UAV-based assessment for alpine meadows micro-patch pattern: Spatial scale thresholds and landscape indices

Jiayuan Yin1, Xiaofeng Liu2, Jianjun Chen1,3

  • 1College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China.

Ecological Applications : a Publication of the Ecological Society of America
|January 14, 2026
PubMed
Summary

This study introduces a scale-adaptive framework for monitoring alpine meadow micro-patches on the Qinghai-Tibet Plateau. It identifies key landscape indices and optimal spatial extents for early detection of ecological degradation.

Keywords:
Qinghai‐Tibet PlateauUAV remote sensingalpine meadow degradationlandscape indices optimizationscale effectspatial extent thresholds

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Area of Science:

  • Ecology
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Alpine meadow micro-patches are crucial indicators of ecological degradation on the Qinghai-Tibet Plateau (QTP).
  • Unmanned aerial vehicle (UAV) remote sensing is effective for micro-patch detection but faces challenges in landscape index interpretation and spatial scale effects.
  • Existing methods struggle with unclear response thresholds and index redundancy, limiting ecological interpretability.

Purpose of the Study:

  • To develop a novel scale-adaptive framework for analyzing alpine meadow micro-patch dynamics.
  • To address methodological challenges in landscape index sensitivity and dimensionality reduction for ecological monitoring.
  • To establish an early-warning diagnostic system for ecological degradation on the QTP.

Main Methods:

  • Integrated spatial extent effect analysis with principal component analysis (PCA)-driven dimensionality reduction.
  • Quantified sensitivity thresholds of 34 landscape indices across 2-50-m spatial extents using spatial autocorrelation-heterogeneity trade-off analysis.
  • Utilized UAV imagery with 0.02-m resolution for micro-patch characterization.

Main Results:

  • Identified six critical indices (e.g., number of patches, mean patch size) sensitive to spatial extent variations.
  • Determined an optimal spatial extent domain of 10-21 m, with 17 m balancing spatial autocorrelation and heterogeneity.
  • PCA reduced dimensionality to three factors, with four key indices (AREA_MN, ENN_MN, PAFRAC, FRAC_MN) selected for an early-warning system.

Conclusions:

  • The developed framework offers a replicable protocol for monitoring micro-patch dynamics in fragile ecosystems.
  • The identified optimal spatial extents and key indices enhance the ecological interpretability of remote sensing data.
  • This approach supports targeted restoration policies for alpine meadows on the QTP and similar regions.