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

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.

A New Method for Reconstructing Tree-Level Aboveground Carbon Stocks of Eucalyptus Based on TLS Point Clouds

“…The development of aerial and satellite remote sensing has greatly reduced the uncertainty in scaling up plot-level biomass and carbon stock estimates to regional or national estimates [22]. When estimating carbon stocks per tree in a plot based on the establishment of allometric growth equation, a large amount of uncertainty is introduced into the system [18,[43][44][45]. Currently, TLS point cloud reconstruction methods allow for direct determination of tree structure and are important for calibrating airborne and satellite-based remote sensing carbon stock estimates [46].…”

Satellite Image Fusion Airborne LiDAR Point-Clouds-Driven Machine Learning Modeling to Predict the Carbon Stock of Typical Subtropical Plantation in China

“…Remarkable progress has been made in forest stock estimation based on satellite remote sensing, and forest parameters are constructed to estimate stock by extracting spectral and texture features [24]. By developing or improving data preprocessing methods and machine learning methods, people try to integrate multiple data sources to improve the estimation accuracy [25,26]. While machine learning models perform well on specific data sets, their ability to generalize remains a challenge.…”

Stratification of vertical canopy structure to improve estimation of forest inventory attributes using airborne LiDAR data in a large subtropical region of China

“…Airborne laser scanning (ALS, also referred to as light detection and ranging, LiDAR) is an active remote sensing technology that accurately depicts the three-dimensional (3D) structure of forest canopies (Montaghi et al 2013, Bouvier et al 2015. Robust statistical relationships exist between certain descriptive statistics of ALS data (LiDAR-derived metrics) and field-measured attributes (Naesset et al 2004, making it widely used for estimating and mapping forest attributes like tree height (H), diameter at breast height (DBH), basal area (BA), stand volume (VOL), aboveground biomass (AGB), carbon, and leaf area index (LAI) (Nilsson, 1996, Means et al 2000, Lefsky et al 2002, Naesset and Økland 2002, Ioki et al 2010, Tang et al 2015, Marczak et al 2020, Leboeuf et al 2022. Since 2002, ALS has replaced conventional field measurement in Scandinavian countries (Naesset et al 2004) and Canada (White et al 2017) for operational forest resource inventory.…”