The Effects of GLCM parameters on LAI estimation using texture values from Quickbird Satellite Imagery

Abstract

When the leaf area index (LAI) of a forest reaches 3, the problem of spectrum saturation becomes the main limitation to improving the accuracy of the LAI estimate. A sensitivity analysis of the Grey Level Co-occurrence Matrix (GLCM) parameters which can be applied to satellite image processing and analysis showed that the most important parameters included orientation, displacement and moving window size. We calculated the values of Angular Second Moment (ASM), Entropy (ENT), Correlation (COR), Contrast (CON), Dissimilarity (DIS) and Homogeneity (HOM) from Quickbird panchromatic imagery using a GLCM method. Four orientations, seven displacements and seven window sizes were considered. An orientation of 90° was best for estimating the LAI of black locust forest, regardless of moving window size, displacement and texture parameters. Displacements of 3 pixels appeared to be best. The orientation and window size had only a little influence on these settings. The highest adjusted r² values were obtained using a 3 × 3 moving window size for ASM and ENT. The tendency of CON, COR, DIS and HOM to vary with window size was significantly affected by orientation. This study can help with parameter selection when texture features from high resolution imagery are used to estimate broad-leaved forest structure information.

Figure 1

The effect of the orientation parameter on the values of adjusted r² for different texture features calculated using a 3 × 3 moving window size and 3, 5, 7, 9, 11, 13 and 15 pixels (ASM, CON, COR, DIS, ENT, HOM and VAR are abbreviations for Angular Second Moment, Contrast, Correlation, Dissimilarity, Entropy, Homogeneity and Variance, respectively).

Figure 2

The effect of the orientation parameter on the values of adjusted r² for different texture features calculated using 3 pixel displacement and 3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, 13 × 13, and 15 × 15 moving window sizes.

Figure 3

The effect of the displacement parameter on the values of adjusted r² for different texture features when the orientation was set to 0° and the window size was set to 3 × 3 pixels (a); when the orientation was set to 0° and the window size was set to 15 × 15 pixels (b); when the orientation was set to 90° and the window size was set to 3 × 3 pixels (c); when the orientation was set to 45° and the window size was set to 3 × 3 pixels (d).

Figure 4

The effect of the displacement parameter on the values of adjusted r² for different texture features when the orientation was set to 45° and the window size was set to 3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, 13 × 13, and 15 × 15 pixels.

Figure 5

The effect of the window size on the values of adjusted r² for different texture features when the orientation was set to 45° and the displacement was set to 5 pixels (a); when the orientation was set to 135° and the displacement was set to 5 pixels (b); when the orientation was set to 135° and the displacement was set to 15 pixels (c).

Figure 6

The effect of the window size on the values of adjusted r² for ASM.

Figure 7

The effect of the window size on the values of adjusted r² for ENT.

Figure 8

The effect of the window size on the values of adjusted r² for different texture features when the orientation was set to 90°.

Figure 9

A subset of ASM features (which was calculated using a 3 × 3 moving window size, 3 pixel displacement and 135° orientation) and the location of the sample plots in the Loess Plateau region of Yongshou County, Shaanxi Province, China. The figure was created using Arcgis software package (version 10.2, http://www.esrichina.com.cn/softwareproduct/ArcGIS) for Windows.

Figure 10

Subsets of ASM, CON, COR, DIS, ENT, HOM and VAR (which were calculated using a 3 × 3 moving window size, 3 pixel displacement and 135° orientation).