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Capturing woody aboveground biomass historical change and potential under climate change using Landsat time-series for afforestation in dryland of China

文献类型：期刊论文

作者Wang, Zhihui4; Shi, Yonglei3,4; Tang, Qiuhong2; Cheng, Miaomiao1; Zhang, Yi1
刊名SCIENCE OF THE TOTAL ENVIRONMENT
出版日期2024-09-15
卷号943页码:13
关键词Woody AGB change Landsat time-series Woody AGB potential Afforestation Dryland ecosystem
ISSN号0048-9697
DOI10.1016/j.scitotenv.2024.173886
英文摘要Capturing long-term dynamics and the potential under climate change of woody aboveground biomass (AGB) is imperative for calculating and raising carbon sequestration of afforestation in dryland. It is always been a great challenge to accurately capture AGB dynamics of sparse woody vegetation mixed with grassland using only Landsat time -series, resulting in changing trajectory of woody AGB estimates cannot accurately reflect woody vegetation growth regularity in dryland. In this study, surface reflectance (SR) sensitive to woody AGB was firstly selected and interannual time -series of composited SR was smoothed using S -G filter for each pixel, and then optimal machine learning algorithm was selected to estimate woody AGB time -series. Pixels that have reached AGB potential were detected based on the AGB changing trajectory, and the potential was spatial -temporal extended using random forest model combining environmental variables under current climate condition and CMIP6 climate models. Results show that: 1) minimum value composite based on NIRv during Jul. -Sep. is more capable of explaining woody AGB variation in dryland ( R = 0.87, p < 0.01), and Random Forest (RF) model has the best performance in estimating woody AGB (R(2)2 = 0.75, RMSE = 4.74 t center dot ha(-1)) among sis commonly used machine learning models. 2) Annual woody AGB estimates can be perfectly fitted with a logistic growth curve (R-2 = 0.97, p < 0.001) indicating explicit growth regularity of woody vegetation, which provides physiological foundation for determining woody AGB potential. 3) Woody AGB potential can be accurately simulated by RF combining environmental variables (R-2 = 0.95, RMSE = 2.89 t center dot ha(-1)), and current woody AGB still has a potential of small increase, whereas the overall losses of woody AGB potential were observed in 2030, 2040 and 2050 under CMIP6 SSP-RCP scenarios.
WOS关键词FOREST BIOMASS ; VEGETATION ; WATER ; REGRESSION ; DYNAMICS ; PROGRESS ; INDEX
资助项目National Key Research and Development Program of China[2022YFC3205200] ; National Natural Science Foundation of China[U2243212] ; Science Foundation for Young Elite Talents of YRCC[HQK-202307]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001253729700001
出版者ELSEVIER
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Science Foundation for Young Elite Talents of YRCC
源URL[http://ir.igsnrr.ac.cn/handle/311030/206265]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Shi, Yonglei
作者单位1.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454003, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
3.Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China
4.Yellow River Conservancy Commiss, Yellow River Inst Hydraul Res, Key Lab Soil & Water Conservat Loess Plateau, Minist Water Resources, Zhengzhou 450003, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhihui,Shi, Yonglei,Tang, Qiuhong,et al. Capturing woody aboveground biomass historical change and potential under climate change using Landsat time-series for afforestation in dryland of China[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2024,943:13.
APA Wang, Zhihui,Shi, Yonglei,Tang, Qiuhong,Cheng, Miaomiao,&Zhang, Yi.(2024).Capturing woody aboveground biomass historical change and potential under climate change using Landsat time-series for afforestation in dryland of China.SCIENCE OF THE TOTAL ENVIRONMENT,943,13.
MLA Wang, Zhihui,et al."Capturing woody aboveground biomass historical change and potential under climate change using Landsat time-series for afforestation in dryland of China".SCIENCE OF THE TOTAL ENVIRONMENT 943(2024):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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