Effect of ground surface interpolation methods on the accuracy of forest attribute modelling using unmanned aerial systems-based digital aerial photogrammetry. Issue 9 (2nd May 2020)

• Record Type: Journal Article
• Authors: Graham, Alexander N. V.; Coops, Nicholas C.; Tompalski, Piotr; Plowright, Andrew; Wilcox, Mike
• Is Part Of: International journal of remote sensing. Volume 41:Issue 9(2020)
• Page Start: 3287
• Page End: 3306
• Publication Date: 2020-05-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1694722
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Unmanned aerial systems digital aerial photogrammetry (UAS-DAP) is an emerging technology that has the capacity to generate dense three-dimensional point clouds similar to airborne laser scanning (ALS). Over forested areas, these point clouds can be used to model forest attributes using the area-based approach (ABA). However, with DAP point clouds, canopy occlusion contributes to larger gaps in terrain registration from UAS-DAP compared to ALS point-clouds. Few studies have investigated the terrain modelling and forest inventory capacity of UAS-DAP over complex coniferous forests. In this study, we applied common terrain surface-interpolation routines using an established set of optimal UAS-DAP ground points and analysed how these routines influenced the prediction accuracy of forest stand attributes. Interpolation routines included inverse-distance weighted (IDW), natural neighbour (NATN), triangulated irregular network (TIN), and spline with tension (SPLT). The forest attributes of interest included mean tree height ( H mean ), Lorey's height ( H Lorey ) and stem volume per hectare ( V stem ). Models were developed using metrics calculated from the vertical distribution of the UAS-DAP point cloud normalized by the different UAS-DAP terrain surfaces in addition to a reference surface generated from commercially provided ALS ground points. Results showed no significant difference between predictions derived from different terrain surfaces for all three dependent variables; however, the IDW method produced a distribution of wall-to-wall predictions most similar to those from the ALS-DEM. The best performing forest attribute models for H mean, H Lorey and V stem yielded mean RMSE values of 1.19 m (7.29%), 0.92 m (5.04%) and 54.55 m 3 ha −1 (26.66%) respectively across the four UAS-DAP terrain surfaces generated. Model performance was higher yet comparable when using the ALS-DEM for point cloud height normalization with RMSE values of 0.73 m (4.43%), 0.59 m (3.24%) and 37.31 m 3 ha −1 (18.24%).