Abstract

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

Highlights

  • Into the univariable base model, there was a considerable enhancement in model fitting; the root mean square error (RMSE) decreased by about 30% and the R2a increased by about

  • There was a further improvement in the model fitting after introducing site random effect parameters of u0, u1, and u5 ; the nonlinear mixed-effects (NLME) model achieved a higher R2a and likelihood values (LL) and a lower RMSE and Akaike information criterion (AIC) than the generalized model

  • This study introduces a framework for model-based individual tree diameter at breast height (DBH) estimation in Unmanned aerial vehicle laser scanning (UAVLS) forest inventories

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Summary

Introduction

Forest inventories depend on the sampling of the ground truth (in situ measurements), where each selected individual’s attributes are obtained through tree-by-tree measurements [3,4]. Such inventories are not cost-effective since the field measurements are often labor-intensive and time-consuming, limiting the sampling intensity and number of tree attributes measured [4]. Developments in remote sensing technologies have brought about massive breakthroughs in terms of improving the performance of forest inventory, with respect to the measurement scale and efficiency [5].

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