Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data

Accurate estimation of aboveground forest biomass (AGB) at a large scale is important in global carbon cycle, forest productivity, and climate change. Coarse resolution remote sensing data of long time series are often used to estimate large scale AGB, but the result is inaccurate due to the scaling effect caused by nonlinearity in data representation and the existence of mixed pixels containing different forest types and land uses. Improvement in the accuracy of AGB estimated from coarse resolution remote sensing data is urgently needed. Research on spatial scaling of AGB is still lacking, therefore, this article proposed an approach based on structural analysis of mixed pixels and the Random Forest model (SMPRF) to increase the accuracy of AGB estimated from coarse resolution data. MODIS and SPOT 5 data were used to create forest biomass distribution maps of the study area at two scales. The scaling effect on estimating forest biomass based on remote sensing was analyzed by comparing data from these two datasets. SMPRF, which included a correction factor for the scaling effect on AGB estimated from coarse resolution MODIS data, was used to create a model that scaled from the fine resolution data (SPOT 5) to the coarse resolution data (MODIS). The results showed that the accuracy of AGB estimated from MODIS data was increased using this method. The Pearson correlation coefficient (
$r$
) for data verification increased from 0.63 to 0.89 and the root mean squared error decreased from 51.6 Mg
$\cdot $
ha
⁻¹
to 26.8 Mg
$\cdot $
ha
⁻¹
. The difference tests showed that the changes were extremely significant (
$p = 0$
). Thus, SMPRF can significantly improve the accuracy of large scale AGB estimation based on coarse resolution remote sensing data and the feasibility of applying the method proposed in this study to related fields is verified.