Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests

Abstract

Forest fertilization is common in coastal British Columbia as a means to increase wood production and potentially enhance carbon sequestration. Generally, the effects of fertilization are determined by measuring sample plots pre- and post-treatment, resulting in fertilization effects being determined for a limited portion of the treatment area. Applications of remote sensing-based enhanced forest inventories have allowed for estimations to expand to the wider forested area. However, these applications have not focused on monitoring the effects of silvicultural treatments. The objective of this research was to examine if a multi-temporal application of the LiDAR area-based method can be used to detect the fertilization effects on volume, biomass, and height in a second-growth Douglas-fir (Pseudotsuga menziesii) stand. The study area on Vancouver Island was fertilized in January 2007, and sample plots were established in 2011. LiDAR acquisitions were made in 2004, prior to fertilization, and in 2008, 2011, and 2016, covering both treated and untreated areas. A total of 29 paired LiDAR blocks, comprised of four 20 m resolution raster cells, were selected on either side of the fertilization boundary for analysis of the effects across several different stand types differing in the percentage of Douglas-fir, site index, and age. Random forest (RF) plot-level models were developed to estimate total stem volume and total stem biomass for each year of LiDAR acquisition using an area-based approach. Plot level results showed an increase in stem volume by 13% fertilized over control from 2005 to 2011, which was similar to a 14% increase in above-ground carbon stocks estimated using a tree-ring stand reconstruction approach. Plot-level RF models showed R2 values of 0.86 (volume) and 0.92 (biomass) with relative cross-validated root mean square errors of 12.5% (volume) and 11.9% (biomass). For both the sample plots and LiDAR blocks, statistical results indicated no significant differences in volume or biomass between treatments. However, significant differences in height increments were detected between treatments in LiDAR blocks. The results from this research highlight the promising potential for the use of enhanced forest inventory methods to rapidly expand the assessment of treatment effects beyond sample plots to the stand, block, or landscape level.

Combining Area-Based and Individual Tree Metrics for Improving Merchantable and Non-Merchantable Wood Volume Estimates in Coastal Douglas-Fir Forests

“…A series of LiDAR metrics more common with traditional AB approaches was also generated using the same 20 m rasterized grid, similar in size to sample plots (0.04 ha). These predictor metrics included a combination of height, density, canopy cover, and statistical metrics similar to those produced and used by Kelley et al [28]. In total, 34 AB metrics were created using the lidR package in R [49] to be included with the six IT metrics (Table 3).…”

“…The forty combined metrics for each of the 38 sample plots were used in a Boruta feature selection routine to select a subset of training metrics for merchantable and non-merchantable volume models. Boruta feature selection is a method popularly used for models with many predictor metrics and, more recently, in implementations of LiDAR EFI models [28,37]. First, the Boruta method adds copies of the predictor metrics that are randomly permuted noise metrics [53].…”

“…The AB approach creates a generalized statistical model that accounts for the entire forest canopy profile within a rasterized grid cell. LiDAR data and a network of training ground plots are used to develop a statistical model using either parametric [23][24][25] or non-parametric [26][27][28] methods. Once a plot level model is developed, it can then be applied to the larger area of interest, provided that it has similar characteristics as the training plots, by using a rasterized grid of LiDAR metrics similar to those used in the model development [29].…”

Above-Ground Forest Biomass Estimation using Multispectral LiDAR Data in a Multilayered Coniferous Forest

“…However, the single-tree approach requires high pulse densities and its effectiveness is closely related to the accuracy of tree detection and of the crown delineation (Georgopoulos et al, 2021). Biomass estimates at the plot level, on the other hand, have been widely used in different biomes (Kelley, Trofymow, Metsaranta, Filipescu & Bone, 2021;Naesset, 2004;Silva Carlos Alberto et al, 2018) and include the integration of height-derived and other LiDAR-related metrics to predict forest characteristics. It should be noted that the plot-level approach provides more accurate AGB and carbon estimates compared to the tree-level approach, mostly due to the poor performance of the segmentation algorithms used to identify the understorey trees (Coomes et al, 2017).…”

Correction: Kelley et al. Use of Multi-Temporal LiDAR to Quantify Fertilization Effects on Stand Volume and Biomass in Late-Rotation Coastal Douglas-Fir Forests. Forests 2021, 12, 517

“…The authors wish to make the following corrections to their paper [1]. We realized that one of our earlier analyses had used a date range off by one year.…”