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Individual Tree AGB Estimation of Malania oleifera Based on UAV-RGB Imagery and Mask R-CNN

文献类型： 外文期刊

作者： Gong, Maojia 1 ; Kou, Weili 2 ; Lu, Ning 2 ; Chen, Yue 3 ; Sun, Yongke 2 ; Lai, Hongyan 1 ; Chen, Bangqian 4 ; Wang, Juan 5 ; Li, Chao 6 ;

作者机构： 1.Southwest Forestry Univ, Coll Forestry, Kunming 650224, Peoples R China

2.Southwest Forestry Univ, Coll Big Data & Intelligent Engn, Kunming 650224, Peoples R China

3.Southwest Forestry Univ, Coll Mech & Transportat, Kunming 650224, Peoples R China

4.Chinese Acad Trop Agr Sci CATAS, Rubber Res Inst RRI, Hainan Danzhou Agroecosyst Natl Observat & Res Stn, Haikou 571101, Peoples R China

5.Southwest Forestry Univ, Ecodev Acad, Kunming 650224, Peoples R China

6.China Tobacco Yunnan Ind Co Ltd, Cigarette Prod Qual Test Ctr, Kunming 650023, Peoples R China

关键词： Malania oleifera; aboveground biomass; UAV; Mask R-CNN; allometric growth model

期刊名称：FORESTS （ 影响因子：2.9； 五年影响因子：3.0 ）

ISSN：

年卷期： 2023 年 14 卷 7 期

页码：

收录情况： SCI

摘要： Forest aboveground biomass (AGB) is an important research topic in the field of forestry, with implications for carbon cycles and carbon sinks. Malania oleifera Chun et S. K. Lee (M. oleifera) is a valuable plant species that is listed on the National Second-Class Protected Plant checklist and has received global attention for its conservation and resource utilization. To obtain accurate AGB of individual M. oleifera trees in a fast, low-finance-cost and low-labor-cost way, this study first attempted to estimate individual M. oleifera tree AGB by combining the centimeter-level resolution RGB imagery derived from unmanned aerial vehicles (UAVs) and the deep learning model of Mask R-CNN. Firstly, canopy area (CA) was obtained from the 3.5 cm high-resolution UAV-RGB imagery using the Mask R-CNN; secondly, to establish an allometric growth model between the diameter at breast height (DBH) and CA, the correlation analysis of both was conducted; thirdly, the AGB estimation method of individual M. oleifera trees was presented based on an empirical equation. The study showed that: (1) The deep learning model of Mask R-CNN achieved an average segmentation accuracy of 90% in the mixed forests to the extraction of the canopy of M. oleifera trees from UAV-RGB imagery. (2) The correlation between the extracted CA and field-measured DBH reached an R-2 of 0.755 (n = 96). (3) The t-test method was used to verify the predicted and observed values of the CA-DBH model presented in this study, and the difference in deviation was not significant (p > 0.05). (4) AGB of individual M. oleifera was estimated for the first time. This study provides a reference method for the estimation of individual tree AGB of M. oleifera based on centimeter-level resolution UAV-RGB images and the Mask R-CNN deep learning.

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