Estimation of Individual Tree Stem Biomass in an Uneven-Aged Structured Coniferous Forest Using Multispectral LiDAR Data

Nikos Georgopoulos,Lauri Korhonen,Dimitris Stavrakoudis,Alexandra Stefanidou,Ioannis Z Gitas

doi:10.3390/rs13234827

Nikos Georgopoulos, Lauri Korhonen + Show 3 more

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https://doi.org/10.3390/rs13234827

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Abstract

Stem biomass is a fundamental component of the global carbon cycle that is essential for forest productivity estimation. Over the last few decades, Light Detection and Ranging (LiDAR) has proven to be a useful tool for accurate carbon stock and biomass estimation in various biomes. The aim of this study was to investigate the potential of multispectral LiDAR data for the reliable estimation of single-tree total and barkless stem biomass (TSB and BSB) in an uneven-aged structured forest with complex topography. Destructive and non-destructive field measurements were collected for a total of 67 dominant and co-dominant Abies borisii-regis trees located in a mountainous area in Greece. Subsequently, two allometric equations were constructed to enrich the reference data with non-destructively sampled trees. Five different regression algorithms were tested for single-tree BSB and TSB estimation using height (height percentiles and bicentiles, max and average height) and intensity (skewness, standard deviation and average intensity) LiDAR-derived metrics: Generalized Linear Models (GLMs), Gaussian Process (GP), Random Forest (RF), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). The results showcased that the RF algorithm provided the best overall predictive performance in both BSB (i.e., RMSE = 175.76 kg and R2 = 0.78) and TSB (i.e., RMSE = 211.16 kg and R2 = 0.65) cases. Our work demonstrates that BSB can be estimated with moderate to high accuracy using all the tested algorithms, contrary to the TSB, where only three algorithms (RF, SVR and GP) can adequately provide accurate TSB predictions due to bark irregularities along the stems. Overall, the multispectral LiDAR data provide accurate stem biomass estimates, the general applicability of which should be further tested in different biomes and ecosystems.

Highlights

Stem volume and biomass are among the most significant productivity and carbon sequestration factors since they are vital for sustainable forest management and climate change mitigation [1]
The present study focuses on the potential of multispectral Light Detection and Ranging (LiDAR)-derived variables alongside with linear and machine learning (ML) regression algorithms for single-tree stem biomass estimation (i.e., total stem biomass (TSB) and barkless stem biomass (BSB)) in an uneven-aged structured forest
The results of our work demonstrated the ability of LiDAR data to estimate individual-tree stem biomass in a multilayered forest, which is critical for forest inventory management and could potentially replace the laborious field measurements required in traditional stem biomass estimation methods

Summary

Introduction

Stem volume and biomass are among the most significant productivity and carbon sequestration factors since they are vital for sustainable forest management and climate change mitigation [1]. According to [2], plant biomass (above and below ground) is the main conduit for CO2 removal from the atmosphere, storing more than 80% of the total aboveground carbon [3,4]. There has been an increasing demand for accurate and timely vegetation biomass estimation due to the rising consumption of biomass products, especially in managed forests. Above-ground biomass (AGB) is defined as the sum of the dry mass of every tree component standing above the soil level (e.g., stem, leaves, needles, branches, bark), typically expressed as mass at the individual tree level [5–7]. Among the different AGB components, the stem biomass is considered to be the most crucial, being the dominant material for timber products and paper production [8].

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