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[Object-oriented stand type classification based on the combination of multi-source remote sen-sing data].

Xue Gang Mao1, Jing Yu Wei1

  • 1School of Forestry, Northeast Forestry University, Harbin 150040, China.

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|April 26, 2018
PubMed
Summary
This summary is machine-generated.

Combining Radarsat-2 and QuickBird remote sensing data improves object-based forest type classification. Optimal segmentation at scale 100 achieved 86% accuracy for identifying Chinese fir, Masson pine, and broad-leaved forests.

Keywords:
QuickBirdRadarsatobject-basedsupport vector machinesynthetic aperture radar

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Area of Science:

  • Forestry
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Accurate forest type recognition is crucial for effective forest resource monitoring.
  • Multi-source remote sensing data integration offers potential for enhanced classification accuracy.

Purpose of the Study:

  • To evaluate the effectiveness of combining Radarsat-2 and QuickBird remote sensing data for object-based forest type classification.
  • To determine the optimal segmentation scheme and scale for multi-source data integration.
  • To assess classification accuracy using various feature combinations.

Main Methods:

  • Object-based image analysis using three segmentation schemes: QuickBird only, Radarsat-2 only, and combined data.
  • Evaluation of segmentation scale parameters (25-250) using modified Euclidean distance 3 index.
  • Support Vector Machine (SVM) classification with Radial Basis Function (RBF) kernel using topographic, height, spectral, and common features.

Main Results:

  • The combination of Radarsat-2 and QuickBird data yielded superior object-based forest type classification compared to single-source data.
  • Optimal segmentation scale for combined data was identified as 100.
  • Highest classification accuracy (Overall Accuracy=86%, Kappa=0.86) was achieved using all extracted features at the optimal scale.

Conclusions:

  • Integrating Radarsat-2 and QuickBird data significantly enhances object-based forest type classification accuracy.
  • The study provides a valuable reference for forest type recognition using multi-source remote sensing data.
  • Findings have practical implications for forest resource investigation and monitoring.