4.7 Article

Estimating Individual Tree Above-Ground Biomass of Chinese Fir Plantation: Exploring the Combination of Multi-Dimensional Features from UAV Oblique Photos

期刊

REMOTE SENSING
卷 14, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/rs14030504

关键词

Chinese fir; individual tree above-ground biomass; UAV oblique photogrammetry; multi-dimensional feature; intensity

资金

  1. National Key R&D Program of China project Research of Key Technologies for Monitoring Forest Plantation Resources [2017YFD0600900]

向作者/读者索取更多资源

This study proposes a novel approach to estimate individual tree above-ground biomass (IT-AGB) in Chinese fir using Unmanned Aerial Vehicle (UAV) oblique photogrammetry technology. By incorporating color space intensity information and point cloud data, the model achieved high estimation accuracy, especially at a spatial resolution of 0.2 m. Moreover, the estimation capacity varied significantly in different directions, with the West-model performing the best.
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 (R-2 = 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.

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