Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

•Grassland biomass estimation and uncertainty were analyzed in Northern China.•Uncertainty was traced to data sources, model forms and model parameters.•Biomass allocation was most influential to grassland biomass estimation. Accurate estimation of grassland biomass and its dynamics are crucial not...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Ecological indicators 2016-01, Vol.60, p.1031-1040
Hauptverfasser: Jia, Wenxiao, Liu, Min, Yang, Yuanhe, He, Honglin, Zhu, Xudong, Yang, Fang, Yin, Cai, Xiang, Weining
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•Grassland biomass estimation and uncertainty were analyzed in Northern China.•Uncertainty was traced to data sources, model forms and model parameters.•Biomass allocation was most influential to grassland biomass estimation. Accurate estimation of grassland biomass and its dynamics are crucial not only for the biogeochemical dynamics of terrestrial ecosystems, but also for the sustainable use of grassland resources. However, estimations of grassland biomass on large spatial scale usually suffer from large variability and mostly lack quantitative uncertainty analyses. In this study, the spatial grassland biomass estimation and its uncertainty were assessed based on 265 field measurements and remote sensing data across Northern China during 2001–2005. Potential sources of uncertainty, including remote sensing data sources (DATsrc), model forms (MODfrm) and model parameters (biomass allocation, BMallo, e.g. root:shoot ratio), were determined and their relative contribution was quantified. The results showed that the annual grassland biomass in Northern China was 1268.37±180.84Tg (i.e., 532.02±99.71g/m2) during 2001–2005, increasing from western to eastern area, with a mean relative uncertainty of 19.8%. There were distinguishable differences among the uncertainty contributions of three sources (BMallo>DATsrc>MODfrm), which contributed 52%, 27% and 13%, respectively. This study highlighted the need to concern the uncertainty in grassland biomass estimation, especially for the uncertainty related to BMallo.
ISSN:1470-160X
1872-7034
DOI:10.1016/j.ecolind.2015.09.001