文献类型：期刊文献

英文题名：Estimation of Above-Ground Biomass for Pinus densata Using Multi-Source Time Series in Shangri-La Considering Seasonal Effects

作者：Chen, Chaoqing[1] He, Yunrun[1] Zhang, Jialong[1] Xu, Dongfan[2] Han, Dongyang[3,4] Liao, Yi[5] Luo, Libin[6] Teng, Chenkai[1] Yin, Tangyan[1]

第一作者：Chen, Chaoqing

通信作者：Zhang, JL[1]

机构：[1]Southwest Forestry Univ, Fac Forestry, Kunming 650224, Peoples R China;[2]Fudan Univ, Key Lab Biodivers Sci & Ecol Engn, Natl Observat & Res Stn Wetland Ecosyst Yangtze Es, Minist Educ, Shanghai 200433, Peoples R China;[3]Fudan Univ, Shanghai Inst EcoChongming SIEC, Shanghai 200433, Peoples R China;[4]Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China;[5]Northwest A&F Univ, Coll Mech & Elect Engn, Xianyang 712100, Peoples R China;[6]Forestry & Grassland Bur Diqing Prefecture, Shangri La 674499, Peoples R China

年份：2023

卷号：14

期号：9

外文期刊名：FORESTS

收录：;EI(收录号：20234014822291);Scopus(收录号：2-s2.0-85172726681);WOS:【SCI-EXPANDED(收录号:WOS:001073867600001)】；

基金：We would like to thank the editor and anonymous reviewers for their comments, which helped improve the manuscript. We thank the Copernicus Open Access Hub for free remote sensing imagery and the Google Earth Engine for providing the platform and algorithmic support for our data acquisition and processing. We also would like to acknowledge all the other individuals who contributed to this paper.

语种：英文

外文关键词：above-ground biomass (AGB); Pinus densata; Sentinel-1 and-2; seasonal effects; time series

摘要：Forest above-ground biomass (AGB) is the basis of terrestrial carbon storage estimation, and making full use of the seasonal characteristics of remote sensing imagery can improve the estimation accuracy. In this study, we used multi-source time series and sample plots with the Random Forest (RF) model to estimate the AGB. The sources included Sentinel-1 (S-1), Sentinel-2 (S-2), and the S-1 and S-2 combination (S-1S-2). Time series included single season, annual, and multi-season. This study aims to (1) explore the optimal image acquisition season to estimate AGB; (2) determine whether the ability to estimate the AGB of multi-seasonal imagery exceeded that of annual and single-season imagery; (3) discover the sensitivity of different data to AGB according to phenological conditions. The results showed that: (1) images acquired in autumn were more useful for AGB estimation than spring, summer, and winter; (2) the S-1 multi-seasonal AGB model had higher accuracy than the annual or single-season one; (3) in autumn and spring, S-1 had higher estimation accuracy than S-2, and in autumn and spring, estimation accuracy from S-1S-2 was higher than that from S-1 and S-2; (4) in 16 AGB estimation models, the best estimation accuracy was achieved by the autumn AGB model from S-1S-2 (R-2 = 0.90, RMSE = 16.26 t/ha, p = 0.82, and rRMSE = 18.97). This study could be useful to identify the optimal image acquisition season for AGB estimation, thus reducing the economic cost of image acquisition and improving the estimation accuracy.