Predicting Individual Tree Diameter of Larch (Larix olgensis) from UAV-LiDAR Data Using Six Different Algorithms

Yusen Sun,Xingji Jin,Fengri Li,Timo Pukkala

doi:10.3390/rs14051125

Yusen Sun, Xingji Jin + Show 2 more

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https://doi.org/10.3390/rs14051125

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Abstract

Individual tree detection is an increasing trend in LiDAR-based forest inventories. The locations, heights, and crown areas of the detected trees can be estimated rather directly from the LiDAR data by using the LiDAR-based canopy height model and segmentation methods to delineate the tree crowns. However, the most important tree variable is the diameter of the tree stem at the breast height (DBH) which can seldom be interpreted directly from the LiDAR data. Therefore, the use of individually detected trees in forest planning calculations requires predictions for the DBH. This study tested six methods for predicting the DBH from laser scanning data collected by an unmanned aerial vehicle from Larix olgensis plantations located in northeast China. The tested methods were the linear regression model (LM), a linear model with ridge regularization (LMR), support vector regression (SVR), random forest (RF), artificial neural network (ANN), and the k-nearest neighbors (KNN) method. Both tree-level and stand-level metrics derived from the LiDAR point cloud data (for instance percentiles of the height distribution of the echoes) were used as potential predictors of DBH. Compared to the LM, all other methods improved the accuracy of the predictions. On the other hand, all methods tended to underestimate the DBH of the largest trees, which could be due to the inability of the methods to sufficiently describe nonlinear relationships unless different transformations of the LiDAR metrics are used as predictors. The support vector regression was evaluated to be the best method for predicting individual tree diameters from LiDAR data. The benefits of the methods tested in this study can be expected to be the highest in the case of little prior knowledge on the relationships between the predicted variable and predictors, a high number of potential predictors, and strong mutual correlations among the potential predictors.

Highlights

Introduction published maps and institutional affilForest ecosystems play an important role in maintaining ecological balance and carbon cycle, regulating local and regional climate, and preserving biosphere stability [1]
The average root mean square error (RMSE) stabilized after reaching the best accuracy, except linear regression model (LM) where a high number of predictors most likely resulted in overfitting, decreasing model performance in independent test data
Since artificial neural network (ANN) was sensitive to hyper-parameters, the overfitting pattern shown in Figure 7 may be explained by the fact that the hyper-parameters were not resulted in overfitting, decreasing model performance in independent test data

Summary

Introduction

Forest ecosystems play an important role in maintaining ecological balance and carbon cycle, regulating local and regional climate, and preserving biosphere stability [1]. Information on tree cover is required for the management of forest ecosystems and to support policies on ecological restoration and climate change mitigation [2]. Traditional forest inventories often employ intensive field samplings with accurate measurements in sample plots [3]. Field-based forest inventories are labor-intensive and time-consuming, and expensive for collecting data from large areas. Remote sensing is an effective tool for monitoring wide forest areas [4]. As an active remote sensing technology, airborne light detection and ranging (LiDAR) can directly capture detailed information on forest canopies in three dimensions from large areas [2,5]

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