Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment

Forests

Joanne C. White¹  Christoph Stepper²  Piotr Tompalski³  Nicholas C. Coops³  Michael A. Wulder¹ 

[1] Canadian Forest Service, Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada; E-Mail:;Bavarian State Institute of Forestry (LWF), Department of Information Technology, Research Group: Remote Sensing, Hans-Carl-von-Carlowitz-Platz 1, Freising D-85354, Germany; E-Mail:;Department of Forest Resources Management, Forest Science Centre, 2424 Main Mall, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; E-Mails:

关键词: digital aerial images;    image matching;    airborne laser scanning;    forest inventory;    photogrammetry;    lidar;   

DOI  :  10.3390/f6103704

来源: mdpi

【 摘 要 】

Digital aerial photogrammetry (DAP) is emerging as an alternate data source to airborne laser scanning (ALS) data for three-dimensional characterization of forest structure. In this study we compare point cloud metrics and plot-level model estimates derived from ALS data and an image-based point cloud generated using semi-global matching (SGM) for a complex, coastal forest in western Canada. Plot-level estimates of Lorey’s mean height (H), basal area (G), and gross volume (V) were modelled using an area-based approach. Metrics and model outcomes were evaluated across a series of strata defined by slope and canopy cover, as well as by image acquisition date. We found statistically significant differences between ALS and SGM metrics for all strata for five of the eight metrics we used for model development. We also found that the similarity between metrics from the two data sources generally increased with increasing canopy cover, particularly for upper canopy metrics, whereas trends across slope classes were less consistent. Model outcomes from ALS and SGM were comparable. We found the greatest difference in model outcomes was for H (ΔRMSE% = 5.04%). By comparison, ΔRMSE% was 2.33% for G and 3.63% for V. We did not discern any corresponding trends in model outcomes across slope and canopy cover strata, or associated with different image acquisition dates.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.