Area-based estimation of growing stock volume in Scots pine stands using ALS and airborne image-based point clouds

Abstract

Recent research has shown that image-derived point clouds (IPCs) are a highly competitive alternative to airborne laser scanning (ALS) data in the context of selected forest inventory activities. However, there is still a need for investigating different kinds of aerial images used for point cloud generation. This study compares the effectiveness of IPCs derived from true colour (RGB) and colour infrared (CIR) aerial images with ALS data for growing stock volume estimation of single canopy layer Scots pine stands. A multiple linear regression method was used to create predictive models. All models predicted growing stock volume with low root mean square errors -ALS: 15.2%, IPC-CIR: 17.0% and IPC-RGB: 17.5%. The following variables for each data type were found to be the most robust: ALSmean height of points, percentage of all returns above mean height of points, interquartile range of point heights; IPC-CIRmean height of points, percentage of all returns above mode height of points, canopy relief ratio; IPC-RGBmean height of points and canopy relief ratio. Our results show that for single canopy layer Scots pine dominated stands it is possible to predict growing stock volume using IPCs with a comparable accuracy as using ALS data. The comparable performance of IPC-RGB and IPC-CIR based models suggests that a mixed usage of RGB and CIR data in retrospective studies could be possible.