Effect of ground surface interpolation methods on the accuracy of forest attribute modelling using unmanned aerial systems-based digital aerial photogrammetry

编号 030021701

推送时间 20191216

研究领域 森林经理 

年份 2019 

类型 期刊 

语种 英语

标题 Effect of ground surface interpolation methods on the accuracy of forest attribute modelling using unmanned aerial systems-based digital aerial photogrammetry

来源期刊 INTERNATIONAL JOURNAL OF REMOTE SENSING

期 第217期

发表时间 20191125

关键词 INDIVIDUAL TREE DETECTION;  MOUNTAIN PINE-BEETLE;  LASER SCANNER DATA;  LIDAR DATA;  DTM INTERPOLATION;  BIOMASS EQUATIONS;  BRITISH-COLUMBIA;  STEM VOLUME;  UAV-LIDAR;  DENSITY; 

摘要 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%).

服务人员 付贺龙

PDF文件 浏览全文