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

Zhengnan Zhang,Yong Pang,Hao Liu,Zengyuan Li,Lin Cao,Christopher Mulverhill

doi:10.3390/f10020125

Abstract

Tree diameter distributions are essential for the calculation of stem volume and biomass, as well as simulation of growth and yield and to understand timber assortments. Accurate and reliable prediction of tree diameter distributions is critical for optimizing forest structure compositions, scheduling silvicultural operations and promoting sustainable management. In this study, we investigated the potential of airborne Light Detection and Ranging (LiDAR) data for predicting tree diameter distributions using a bimodal finite mixture model (FMM) and a multimodal k-nearest neighbor (KNN) model (compared to the unimodal Weibull model (UWM)) over a subtropical planted forest in southern China. To do so, we first evaluated the capability of various LiDAR predictions (i.e., the bimodality coefficient (BC) and Lorenz-based indicators) to stratify forest structural types into unimodal and multimodal stands. Once the best LiDAR prediction for the differentiation was determined, the parameters of UWM (in non-specific and species-specific models) and FMM (in structure-specific models) were estimated by LiDAR-derived metrics and the tree diameter distributions of stands were generated by the estimated LiDAR parameters. When KNN was applied for constructing diameter distributions, optimal KNN strategies, including number of neighbors k, response configurations and imputation methods (i.e., Most Similar Neighbor (MSN) and Random Forest (RF)) for different species were heuristically determined. Finally, the predictive performance of estimated LiDAR the parameters of UWM, FMM and KNN for predicting diameter distributions were assessed. The results showed that LiDAR-predicted Lorenz-based indicators performed best for differentiation. Parameters of UWM and FMM were predicted well and the species-specific models had higher accuracies than the non-specific models. Overall, RF imputation from KNN with an optimal response set (i.e., DBH) were was stable than MSN imputation when k = 5 neighbors. In addition, the inclusion of bimodal FMM for differentiated all plots generally produced a more accurate result (Mean eR = 40.85, Mean eP = 0.20) than multimodal KNN (Mean eR = 52.19, Mean eP = 0.26), whereas the UWM produced the lowest performance (Mean eR = 52.31, Mean eP = 0.26). This study demonstrated the benefits of multimodal models with LiDAR for estimating diameter distributions for supporting forest inventory and sustainable forest management in subtropical planted forests.

Highlights

As an indispensable component of the world’s forest resources, planted forests make up approximately 6.94% of global forest areas [1]
The aims of this study are: (1) To evaluate the capability of Light Detection and Ranging (LiDAR) for discriminating unimodal and multimodal stands by examining the modality of diameter distributions; (2) to implement multimodal finite mixture model (FMM) and k-nearest neighbor (KNN) models for predicting tree diameter distributions, and to investigate the sensitivity of various KNN strategies, including the number of neighbors (i.e., k), response configurations and imputations approaches (i.e., Most Similar Neighbor (MSN) and Random Forest (RF)) for KNN-imputed diameter distributions; and (3) to assess the performance of FMM and KNN models for predicting diameter distributions by comparing to those estimated by UWM
Vertical distribution dependent metrics, such as Lkurt, Oligo and coefficient of variation of leaf area density (CvLAD) contributed to the prediction of distribution-related bimodality coefficient (BC)

Summary

Introduction

As an indispensable component of the world’s forest resources, planted forests make up approximately 6.94% of global forest areas [1]. China currently has the largest planted forest area at 91.8 million ha, approximately 33.03% of total planted forests in the world [1,5]. Subtropical planted forests account for approximately 9% of planted forest areas in the world [5]. Especially in the southeastern region, contains the largest proportion of low latitude humid subtropical forests, which play an essential role in establishing an ecological balance and improving environmental quality [7,8]. 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]

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Methods

Results

Discussion

Conclusion