Individual Tree Aboveground Biomass Estimation Based on UAV Stereo Images in a Eucalyptus Plantation

Abstract

As one of the three fastest-growing tree species in the world, eucalyptus grows rapidly, with a monthly growth rate of up to 1 m and a maximum annual growth rate of up to 10 m. Therefore, ways to accurately and quickly obtain the aboveground biomass (AGB) of eucalyptus in different growth stages at a low cost are the foundation of achieving eucalyptus growth-change monitoring and precise management. Although Light Detection and Ranging (LiDAR) can achieve high-accuracy estimations of individual eucalyptus tree biomasses, the cost of data acquisition is relatively high. While the AGB estimation accuracy of high-resolution images may be affected by a lack of forest vertical structural information, stereo images obtained using unmanned aerial vehicles (UAVs) can not only provide horizontal structural information but also vertical structural information through derived point data, demonstrating strong application potential in estimating the biomass of eucalyptus plantations. To explore the potential of UAV stereo images for estimating the AGB of individual eucalyptus trees and further investigate the impact of stereo-image-derived features on the construction of biomass models, in this study, UAVs equipped with consumer-grade cameras were used to obtain multitemporal stereo images. Different features, such as spectral features, texture, tree height, and crown area, were extracted to estimate the AGB of individual eucalyptus trees of five different ages with three algorithms. The different features extracted based on the UAV images had different effects on estimating AGB in individual eucalyptus trees. By estimating eucalyptus AGB using only spectrum features, we found that tree height had the greatest impact, with its R2 value increasing by 0.28, followed by forest age. Other features, such as spectrum, texture, and crown area, had relatively small effects. For the three algorithms, the estimation accuracy of the CatBoost algorithm was the highest, with an R2 ranging from 0.65 to 0.90, and the normalized root-mean-square error (NRMSE) ranged from 0.08 to 0.15. This was followed by the random forest algorithm. The ridge regression algorithm had the lowest accuracy, with an R2 ranging from 0.34 to 0.82 and an NRMSE value ranging from 0.11 to 0.21. The AGB model that we established with forest age, TH, crown area, and HOM-B feature variables using the CatBoost algorithm had the best estimation accuracy, with an R2 of 0.90 and an NRMSE of 0.08. The results indicated that accurately estimating the AGB of individual eucalyptus trees can be achieved based on stereo images obtained using UAVs equipped with affordable, consumer-grade cameras. This paper can provide methodological references and technical support for estimating forest biomass, carbon storage, and other structural parameters based on UAV images.