气候变化领域集成服务门户(CCPortal): Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning

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DOI	10.3389/ffgc.2024.1338795
	Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning
	Lv, Shaofeng; Yuan, Ning; Sun, Xiaobo; Chen, Xin; Shi, Yongjun; Zhou, Guomo; Xu, Lin
发表日期	2024
EISSN	2624-893X
起始页码	7
卷号	7
英文摘要	Estimating the carbon sequestration potential of Moso bamboo (Phyllostachys pubescens) forests and optimizing management strategies play pivotal roles in enhancing quality and promoting sustainable development. However, there is a lack of methods to simulate changes in carbon sequestration capacity in Moso bamboo forests and to screen and optimize the best management measures based on long-term time series data from fixed-sample fine surveys. Therefore, this study utilized continuous survey data and climate data from fixed sample plots in Zhejiang Province spanning from 2004 to 2019. By comparing four different algorithms, namely random forest, support vector machine, XGBoost, and BP neural network, to construct aboveground carbon stock models for Moso bamboo forests. The ultimate goal was to identify the optimal algorithmic model. Additionally, the key driving parameters for future carbon stocks were considered and future aboveground carbon stocks were predicted in Moso bamboo forests. Then formulated an optimal management strategy based on these predictions. The results indicated that the carbon stock model constructed using the XGBoost algorithm, with an R-2 of 0.9895 and root mean square error of 0.1059, achieved the best performance and was considered the optimal algorithmic model. The most influential driving parameters for vegetation carbon stocks in Moso bamboo forests were found to be mean age, mean diameter at breast height, and mean culm density. Under optimal management measures, which involve no harvesting of 1-3 du bamboo, 30% harvesting of 4 du bamboo, and 80% harvesting of bamboo aged 5 du and above. Our predictions show that aboveground carbon stocks in Moso bamboo forests in Zhejiang Province will peak at 36.25 +/- 8.47 Tg C in 2046 and remain stable from 2046 to 2060. Conversely, degradation is detrimental to the long-term maintenance of carbon sequestration capacity in Moso bamboo forests, resulting in a peak aboveground carbon stock of 29.50 +/- 7.49 Tg C in 2033, followed by a continuous decline. This study underscores the significant influence of estimating carbon sequestration potential and optimizing management decisions on enhancing and sustaining the carbon sequestration capacity of Moso bamboo forests.
英文关键词	Phyllostachys pubescens; machine learning; climate change; management optimization; carbon sequestration capacity
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Forestry
WOS类目	Ecology ; Forestry
WOS记录号	WOS:001204713400001
来源期刊	FRONTIERS IN FORESTS AND GLOBAL CHANGE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305175
作者单位	Zhejiang A&F University; Zhejiang A&F University; Zhejiang A&F University
推荐引用方式 GB/T 7714	Lv, Shaofeng,Yuan, Ning,Sun, Xiaobo,et al. Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning[J],2024,7.
APA	Lv, Shaofeng.,Yuan, Ning.,Sun, Xiaobo.,Chen, Xin.,Shi, Yongjun.,...&Xu, Lin.(2024).Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning.FRONTIERS IN FORESTS AND GLOBAL CHANGE,7.
MLA	Lv, Shaofeng,et al."Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning".FRONTIERS IN FORESTS AND GLOBAL CHANGE 7(2024).