Pine plantation structure mapping using WorldView-2 multispectral image

Abstract

Optical images of different spectral and spatial resolutions continue to provide a reliable source of data for estimating forest inventory parameters. WorldView-2 launched in October 2009 is the first commercial optical satellite to provide high spatial resolution images with eight spectral bands, some of which are new and require investigation for estimation of forest structure parameters. In this study, a WorldView-2 multispectral image has been investigated for mapping pine plantation structural parameters including stand volume, basal area, stocking, mean diameter at breast height (mean DBH), and mean height of trees over a Pinus radiata plantation in New South Wales, Australia. Spectral derivatives including reflectance bands, band ratios, principal components (PCs), and several vegetation indices (VIs) were calculated using four typical bands, including blue, green, red, and near-infrared (NIR1), and all eight bands. Moreover, textural information, including 11 grey-level co-occurrence matrix (GLCM) indices, was extracted using four window sizes and orientations. Several models were developed using the extracted attributes separately to compare the efficiency of the models derived from the attributes of four typical bands and eight bands, as well as to compare between the capability of spectral-based and textural-based models for estimating structural parameters. The results showed that models derived from textural attributes of eight spectral bands provide the best estimates compared to those derived from four typical bands and the models derived from spectral derivatives. Moreover, the mean height and mean DBH with 8% and 13.7% error of estimation, respectively, were estimated more accurately than basal area, stand volume, and stocking, where the error of estimation is up to 30%.

Acknowledgements

The authors wish to express their appreciation to DigitalGlobe for providing the WorldView-2 multispectral image. Moreover, lidar, field survey data, and biometric equations utilized in this study were kindly supplied by Dr Christine Stone and Dr Huiquan Bi from the New South Wales Department of Industry and Investment (IINSW) and Forests NSW (FNSW), with partial sponsorship from Forest and Wood Products Australia (FWPA). Also, the authors would like to thank the anonymous reviewers whose comments improved this article significantly.