Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest

Abstract

Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R 2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of −0.17%, −0.12% and −0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume.

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

“…Aerial photography, light detection and ranging (LiDAR) and airborne multispectral, and hyperspectral images had been perceived as potential tools for observing forest areas and for performing broad-scale analysis of forest systems. These methods have the ability to quantify the composition and structure of the forest at different temporal and geographical scales with the support of various statistical methods, and therefore can supplement forest inventory related expeditions [14][15][16][17][18][19][20][21].…”

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

“…Non-linear least-squares regression models (the power-law models) were used to model AGB across 30 where a 2014 and b 2014 are the estimates' parameters of the power-law models in 2014. Leave-one-out cross-validation (LOOCV) was developed (e.g., [14,15]), and the prediction precision of the LOOCV models was evaluated in terms of the coefficient of determination (R 2 ), absolute and relative Root Mean…”

“…Leave-oneout cross-validation (LOOCV) was developed (e.g., [14,15]), and the prediction precision of the LOOCV models was evaluated in terms of the coefficient of determination (R 2 ), absolute and relative Root Mean Square Error (RMSE), and absolute and relative bias from the linear relationship between observed and LOOCV predicted AGB values: …”

“…Lidar can provide high resolution, three-dimensional information on forest structure and the underlying topography [14,15]. Recently, d'Oliveira et al [7] and Andersen et al [8] have used airborne lidar for detecting selective logging activities and mapping AGB stocks and changes in the Brazilian Amazon forest.…”

Prediction of Diameter Distributions with Multimodal Models Using LiDAR Data in Subtropical Planted Forests

“…Attributes describing forest structure can be an imperative support tool in forest management planning [9]. Thus, timely, effectively, and accurately acquiring up-to-date and reliable information on the state and structure attributes of China's subtropical planted forests is crucial for enhancing forest productivity, understanding forest ecological function, supporting forest management decisions, improving silvicultural treatments (e.g., thinning, timber harvesting and assortment) and promoting sustainable management [1, [10][11][12].…”