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QuickBird image-based estimation of tree stand density using local maxima filtering method: A case study in a Beijing

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Estimating tree stand density using QuickBird imagery and spectral analysis proved effective. Different forest types, like coniferous and broadleaf, require specific Normalized Difference Vegetation Index (NDVI) thresholds for accurate density mapping.

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Area of Science:

  • Forestry
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Tree stand density is crucial for understanding forest growth and structure.
  • Accurate estimation of forest stand density is vital for sustainable forest management.
  • High-resolution imagery offers potential for detailed forest parameter assessment.

Purpose of the Study:

  • To develop and validate a method for estimating tree stand density using QuickBird imagery.
  • To compare the effectiveness of spectral analysis techniques for different forest types.
  • To create a stand density map for Jiufeng National Forest Park.

Main Methods:

  • Extracted the number of spectral local maxima points (NSLMP) using spectral maximum filtering on QuickBird imagery.
  • Employed regression analysis to model the relationship between NSLMP and ground-truthed stand density.
  • Tested various combinations of Normalized Difference Vegetation Index (NDVI) thresholds and window sizes for optimal model performance.

Main Results:

  • The optimal model for coniferous forests used a 3x3 window and NDVI >= 0.3 (R2=0.79, RMSE=12.60).
  • For broadleaf forests, the best model utilized a 3x3 window and NDVI >= 0.1 (R2=0.44, RMSE=9.02).
  • A combined model for unclassified forests achieved R2=0.70 and RMSE=11.20 with a 3x3 window and NDVI >= 0.3.

Conclusions:

  • The study successfully estimated tree stand density using high-resolution imagery and spectral analysis.
  • Different forest types necessitate tailored strategies for optimal stand density extraction.
  • The integration of remote sensing with ground measurements provides a robust approach for forest stand density mapping.