Estimating the Average Age and Height of the Trees using SPOT-5 Panchromatic and Multi-spectral Image Fusion

Sustainable management of the forests requires satellite data at a large scale. This research aims to exploit pixel-based image fusion methods including principal component analysis (PCA) transformation, wavelet transformation, PCA/Wavelet transformation to improve the estimation accuracy of the mean height and age of a Pinus radiata plantation using SPOT-5 panchromatic and multi-spectral images at segment level. Therefore, the average height and age of the trees is measured within 61 plots in a Pinus radiata plantation in NSW, Australia. After applying pre-processing on the images, the spectral information including reflectance and vegetation indices along with textural information derived from gray level co-occurrence matrix for four window sizes and orientations are extracted from multispectral and panchromatic images, respectively. The same information is extracted from the fused images. It is shown that the textural information derived from the fused images performs more efficient than the textural information derived from panchromatic images to estimate average height and age. The results indicate that the models derived from PCA/Wavelet-based fused images with estimation error of 16% and 11% for age and height, respectively, perform better than the models derived from the data extracted from the images fused by the other fusion methods.