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

Joanne White,Christoph Stepper,Michael Wulder,Piotr Tompalski,Nicholas Coops

doi:10.3390/f6103704

Abstract

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.

Highlights

Airborne laser scanning (ALS) is widely acknowledged as an important data source for forest inventories [1]
airborne laser scanning (ALS) and semi-global matching (SGM) metrics used for attribute models were compared across the 16 strata defined by ALS-derived slope and canopy cover (Table 5, Figures 3 and 4)
In accordance with the results of other studies reported in the literature, we found that ALS and SGM data were capable of providing comparable results in terms of area-based model outcomes

Summary

Introduction

Airborne laser scanning (ALS) is widely acknowledged as an important data source for forest inventories [1]. The three-dimensional information conferred by ALS data enables the characterization of vertical forest structure, and thereby, the estimation of forest inventory attributes of interest such as height, basal area, and volume, among others [2]. Earlier studies examining the capabilities of DAP in an area-based approach (ABA) to model forest attributes were conducted in highly managed and relatively simple forest environments (i.e., even-aged, single-layer forests) [4,5,6,7]. These studies generally found that DAP outputs could produce area-based predictive models for inventory attributes that had accuracies similar to predictive models generated using

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Conclusion