Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau

Accurate estimates of forest aboveground biomass (AGB) are critical for supporting strategies of ecosystem conservation and climate change mitigation. The Jiuzhaigou National Nature Reserve, located in Eastern Tibet Plateau, has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles. However, an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global biomass model is available has not been determined. To provide this information, Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using linear regression, and two machine learning approaches-random forest and extreme gradient boosting, with 54 inventory plots. Regardless of forest type, Observed AGB of the Reserve varied from 61.7 to 475.1 Mg ha(-1) with an average of 180.6 Mg ha(-1). Results indicate that integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches. The results highlight a potential way to improve the prediction of forest AGB in mountainous regions. Modelled AGB indicated a strong spatial variability. However, the modelled biomass varied greatly with global biomass products, indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required, particularly for areas with complex terrain to improve model accuracy.