Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion

Michael Alonzo,Douglas Morton,Hans-Erik Andersen,Bruce Cook

doi:10.3390/f9030119

Abstract

The vast extent and inaccessibility of boreal forest ecosystems are barriers to routine monitoring of forest structure and composition. In this research, we bridge the scale gap between intensive but sparse plot measurements and extensive remote sensing studies by collecting forest inventory variables at the plot scale using an unmanned aerial vehicle (UAV) and a structure from motion (SfM) approach. At 20 Forest Inventory and Analysis (FIA) subplots in interior Alaska, we acquired overlapping imagery and generated dense, 3D, RGB (red, green, blue) point clouds. We used these data to model forest type at the individual crown scale as well as subplot-scale tree density (TD), basal area (BA), and aboveground biomass (AGB). We achieved 85% cross-validation accuracy for five species at the crown level. Classification accuracy was maximized using three variables representing crown height, form, and color. Consistent with previous UAV-based studies, SfM point cloud data generated robust models of TD (r2 = 0.91), BA (r2 = 0.79), and AGB (r2 = 0.92), using a mix of plot- and crown-scale information. Precise estimation of TD required either segment counts or species information to differentiate black spruce from mixed white spruce plots. The accuracy of species-specific estimates of TD, BA, and AGB at the plot scale was somewhat variable, ranging from accurate estimates of black spruce TD (+/−1%) and aspen BA (−2%) to misallocation of aspen AGB (+118%) and white spruce AGB (−50%). These results convey the potential utility of SfM data for forest type discrimination in FIA plots and the remaining challenges to develop classification approaches for species-specific estimates at the plot scale that are more robust to segmentation error.

Highlights

Boreal forests store approximately 27% of global aboveground biomass [1] and contain 50% of carbon stored in organic soils [2]
Except for BZ7, digital terrain model (DTM) generated from the structure from motion (SfM) point cloud and GPS elevations had a mean absolute error of
Our study builds on existing research by demonstrating the potential for classifying boreal forest types at the individual crown level

Summary

Introduction

Boreal forests store approximately 27% of global aboveground biomass [1] and contain 50% of carbon stored in organic soils [2]. The stability of these carbon pools is uncertain, as boreal forests and other high-latitude ecosystems have warmed twice as fast as temperate and tropical regions [3]. Extensive, and spatially-explicit measurements of boreal forest structure and composition are critical to understanding the local, regional, and global consequences of ecosystem responses to climate warming. Remote sensing approaches to characterize forest composition [6], structure [7], and productivity [8] sample larger boreal forest

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