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Estimation of Tree Size Diversity Using Object Oriented Texture Analysis and Aster Imagery.

Ibrahim Ozdemir1, David A Norton2, Ulas Yunus Ozkan3

  • 1Suleyman Demirel University, Faculty of Forestry, 32269, Isparta, Turkey. ibrahim@orman.sdu.edu.tr.

Sensors (Basel, Switzerland)
|November 23, 2016
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Summary
This summary is machine-generated.

Object-based texture analysis of ASTER satellite imagery effectively estimates tree size diversity in Mediterranean forests. The Gini Coefficient showed strong correlations with texture parameters, unlike the Shannon Index.

Keywords:
Tree size diversitybrutian pineimage segmentationremote sensingtexture analysis

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Area of Science:

  • Remote Sensing
  • Forest Ecology
  • Geospatial Analysis

Background:

  • Assessing tree size diversity is crucial for understanding forest structure and health.
  • Traditional field methods for measuring tree size diversity are labor-intensive and time-consuming.
  • Satellite imagery offers a potential alternative for large-scale forest assessment.

Purpose of the Study:

  • To evaluate the utility of object-based texture parameters from ASTER imagery for estimating tree size diversity.
  • To compare the performance of Shannon Index and Gini Coefficient in relation to image texture.
  • To identify the most significant texture parameters for predicting tree size diversity in Mediterranean forests.

Main Methods:

  • Extracted object-based texture parameters using Grey Level Co-occurrence Matrix (GLCM) from 15m ASTER imagery.
  • Calculated tree size diversity using Shannon Index and Gini Coefficient based on tree basal area at the plot level.
  • Analyzed the statistical relationships between texture parameters and diversity indices.

Main Results:

  • The Gini Coefficient demonstrated statistically significant correlations with GLCM texture parameters.
  • GLCM Homogeneity showed the highest correlation (r=0.69) with the Gini Coefficient.
  • The Shannon Index exhibited weak correlations with the derived image texture parameters.

Conclusions:

  • Object-based texture analysis of 15m ASTER imagery shows significant potential for estimating tree size diversity, particularly using the Gini Coefficient.
  • This approach is promising for monitoring heterogeneous Mediterranean forest landscapes.
  • Texture parameters, especially homogeneity, are valuable indicators of tree size distribution.