Abstract

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is one of the important tree species in plantation in southern China. Rapid and accurate acquisition of individual tree above-ground biomass (IT-AGB) information is of vital importance for precise monitoring and scientific management of Chinese fir forest resources. Unmanned Aerial Vehicle (UAV) oblique photogrammetry technology can simultaneously obtain high-density point cloud data and high spatial resolution spectral information, which has been a main remote sensing source for obtaining forest fine three-dimensional structure information and provided possibility for estimating IT-AGB. In this study, we proposed a novel approach to estimate IT-AGB by introducing the color space intensity information into a regression-based model that incorporates three-dimensional point cloud and two-dimensional spectrum feature variables, and the accuracy was evaluated using a leave-one-out cross-validation approach. The results demonstrated that the intensity variables derived from the color space were strongly correlated with the IT-AGB and obviously improved the estimation accuracy. The model constructed by the combination of point cloud variables, vegetation index and RGB spatial intensity variables had high accuracy (R2 = 0.79; RMSECV = 44.77 kg; and rRMSECV = 0.25). Comparing the performance of estimating IT-AGB models with different spatial resolution images (0.05, 0.1, 0.2, 0.5 and 1 m), the model was the best at the spatial resolution of 0.2 m, which was significantly better than that of the other four. Moreover, we also divided the individual tree canopy into four directions (East, West, South and North) to develop estimation models respectively. The result showed that the IT-AGB estimation capacity varied significantly in different directions, and the West-model had better performance, with the estimation accuracy of 67%. This study indicates the potential of using oblique photogrammetry technology to estimate AGB at an individual tree scale, which can support carbon stock estimation as well as precision forestry application.

Highlights

  • We evaluated the effects of different color spatial intensity information and different spatial resolution on the performance of the model, and deduced an optimal model for estimating individual tree Above-ground biomass (AGB) (IT-AGB)

  • (2) Based on point cloud data and Digital orthophoto model (DOM) data obtained from Unmanned Aerial Vehicle (UAV) oblique photos, point cloud variables, vegetation index and intensity variables were extracted respectively

  • The results showed that point cloud height variable had a high correlation with IT-AGB, while the two-dimensional vegetation index had a low correlation with IT-AGB

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Summary

Introduction

Strong wind resistance, fast growth speed and high economic value, it plays a role in maintaining the regional ecological environment, and makes an important contribution to the carbon balance. Above-ground biomass (AGB) is an important index to evaluate the carbon storage capacity [1,2] and potential carbon sink 4.0/). 2022, 14, 504 scale of forest ecosystems [3], so it is significant to timely and accurately obtain the individual tree AGB (IT-AGB) information. Destructive sampling is the most accurate and reliable method [4] to measure AGB information [5,6]. It is not practical to acquire large-scale forest

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