ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume II-5/W2
16 Oct 2013
 | 16 Oct 2013

Relating WorldView-2 data to pine plantation lidar metrics

J. C. Trinder, A. Shamsoddini, and R. Turner

Keywords: Lidar, Optical, Spectral, Texture, Prediction, Forestry

Abstract. Over last decades, different types of remotely sensed data including lidar, radar and optical data were investigated for forest studies. Undoubtedly, lidar data is one of the promising tools for these purposes; however, the accessibility and cost of this data are the main limitations. In order to overcome these limitations, optical data have been considered for modelling lidar metrics and their use for inferring lidar metrics over areas with no lidar coverage. WorldView-2 (WV-2) data as a high resolution optical data offer 8 bands including four traditional bands, blue, green, red, and infrared, and four new bands including coastal blue, yellow, red edge and a new infrared band whose relationships with lidar metrics were investigated in this study. For this purpose, band reflectance, band ratios, and principal components (PCs) of WV-2 multispectral data along with 23 vegetation indices were extracted. Moreover, the grey level co-occurrence matrix (GLCM) indices of bands, band ratios and PCs were also calculated for different window sizes and orientations. Spectral derivatives and textural attributes of WV-2 were provided for a stepwise multiple-linear regression to model 10 lidar metrics including maximum, mean, variance, 10th, 30th, 60th and 90th height percentiles, standard error of mean, kurtosis and skewness for a Pinus radiata plantation, in NSW, Australia. The results indicated that the textural-based models are significantly more efficient than spectral-based models for predicting lidar metrics. Moreover, the integration of spectral derivatives with textural attributes cannot improve the results derived from textural-based models. The study demonstrates that WV-2 data are efficient for predicting lidar metrics.