Predicting Carbon Accumulation in Temperate Forests of Ontario, Canada Using a LiDAR-Initialized Growth-and-Yield Model

Paulina T Marczak,Paul M Treitz,Karin Y Van Ewijk,Donald C.E Robinson,Neal A Scott

doi:10.3390/rs12010201

Abstract

Climate warming has led to an urgent need for improved estimates of carbon accumulation in uneven-aged, mixed temperate forests, where high uncertainty remains. We investigated the feasibility of using LiDAR-derived forest attributes to initialize a growth and yield (G&Y) model in complex stands at the Petawawa Research Forest (PRF) in eastern Ontario, Canada; i.e., can G&Y models based on LiDAR provide accurate predictions of aboveground carbon accumulation in complex forests compared to traditional inventory-based estimates? Applying a local G&Y model, we forecasted aboveground carbon stock (tons/ha) and accumulation (tons/ha/yr) using recurring plot measurements from 2012–2016, FVS1. We applied statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist). These data, along with measured species abundance, were used to initialize a second model (FVS2). A third model was tested using LiDAR-initialized tree lists and photo-interpreted estimates of species abundance (i.e., FVS3). The carbon stock projections for 2016 from the inventory-based G&Y model) were equivalent to validation carbon stocks measured in 2016 at all size-class levels (p < 0.05), while LiDAR-based G&Y models were not. None of the models were equivalent to validation data for accumulation (p > 0.05). At the plot level, LiDAR-based predictions of carbon accumulation over a nine-year period did not differ when using either inventory or photo-interpreted species (p < 0.05). Using a constant mortality rate, we also found statistical equivalency of inventory and photo-interpreted accumulation models for all size classes ≥17 cm. These results suggest that more precise information is needed on tree characteristics than we could derive from LiDAR, but that plot-level species information is not as critical for predictions of carbon accumulation in mixed-species forests. Further work is needed on the use of LiDAR to quantify stand properties before this technique can be used to replace recurring plot measurements to quantify carbon accumulation.

Highlights

Forests play a key role in the global carbon cycle, storing 80% of all terrestrial aboveground carbon [1] and contribute substantially to the total annual global terrestrial carbon sink (3.0 ± 0.8 GtC/yr) [2] with recent estimates placing the net global forest sink at 1.1 ± 0.8 GtC/yr [3]
Equivalence testing indicated that neither FVS2- nor FVS3-based carbon accumulation or carbon stocks at any size class and at the plot level were equivalent to inventory-initialized estimates at a four and nine-year projection, emphasizing the difficulty in parameterizing models for complex temperate forests with moderate-to-poor SDD/SD and BA_dist predictions
In the context of growth and yield (G&Y) modelling, LiDAR-derived tree lists for initializing G&Y models of direct carbon stock and accumulation have not been tested, which could lead to new insights for climate change mitigation in forest management practices

Summary

Introduction

Forests play a key role in the global carbon cycle, storing 80% of all terrestrial aboveground carbon [1] and contribute substantially to the total annual global terrestrial carbon sink (3.0 ± 0.8 GtC/yr) [2] with recent estimates placing the net global forest sink at 1.1 ± 0.8 GtC/yr [3]. To account for the impact of SFM practices on rates of carbon storage over large areas, spatially comprehensive techniques are needed to quantify carbon stocks (tons/ha) and predict carbon stock changes (tons/ha/yr). This would allow forest managers to (i) make more informed management decisions and; (ii) enhance the use of forests to limit the rate of climate warming [5]. Plot-scale forest inventories often have limited point sampling, high cost, and limited personnel [5,7] These G&Y models may require inputs of tree-level diameter-at-breast height (DBH), species, site quality, and other attributes to project future carbon accumulation [8]. Comprehensive estimates of biomass and growth would support SFM, especially in complex forests with multiple tree ages (i.e., size classes) and species [9]

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