Title: Estimating aboveground biomass in subtropical forests of China by integrating multisource remote sensing and ground data
Authors: ZHANG RONGZHOU XUHUIOUYANG ZUTAOAVITABILE VALERIOQI JIAGUOCHEN JIQUANGIANNICO VINCENZO
Citation: REMOTE SENSING OF ENVIRONMENT vol. 232 p. 111341
Publisher: ELSEVIER SCIENCE INC
Publication Year: 2019
JRC N°: JRC112199
ISSN: 0034-4257 (online)
URI: https://www.sciencedirect.com/science/article/pii/S0034425719303608?via%3Dihub
http://publications.jrc.ec.europa.eu/repository/handle/JRC112199
DOI: 10.1016/j.rse.2019.111341
Type: Articles in periodicals and books
Abstract: The biomass of the subtropical forests of China is an important component of the global carbon cycle. Recently, several above ground biomass (AGB) maps have been produced using a variety of approaches to assess the carbon stock of the subtropical forest in China. However, due to the lack of reliable ground observations and the limitations of AGB mapping methods at regional scales, estimates of the spatial distribution of AGB vary greatly, leading to large uncertainties in the carbon stock estimations. In this study, we produced a new 1-km spatial resolution AGB map by synthesizing an unprecedented number of ground AGB observations from published studies, developed an AGB mapping method using a combination of ground observations, MODIS data, forest cover/gain/loss maps based on Landsat, GLAS forest canopy height, and climatic and terrain data. In addition, we validated our estimates using independent testing data and compared our estimates with three previous AGB maps. The results indicate that the total AGB stock in the subtropical forest of China is (266 ± 9.1) × 106 Mg, with an average AGB of 123.2 Mg/ha. Based on sixteen explanatory variables, our ensemble mean model explains 75% of the variance in forest AGB, with an RMSE of 45.5 Mg/ha. Comparison using all observation data shows that our map has a significantly lower RMSE and bias than previous maps, where the RMSE and bias tended to vary with forest type. This study not only improved the accuracy of AGB estimation for the subtropical forests but also highlighted the importance of forest type for regional AGB estimation.
JRC Directorate:Sustainable Resources

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