Abstract
Forest aboveground biomass (AGB) is of great significance since it represents large carbon storage and may reduce global climate change. However, there are still considerable uncertainties in forest AGB estimates, especially in rugged regions, due to the lack of effective algorithms to remove the effects of topography and the lack of comprehensive comparisons of methods used for estimation. Here, we systematically compare the performance of three sources of remote sensing data used in forest AGB estimation, along with three machine-learning algorithms using extensive field measurements (N = 1058) made in the Khingan Mountains of north-eastern China in 2008. The datasets used were obtained from the LiDAR-based Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation satellite (ICESat/GLAS), the optical-based Moderate Resolution Imaging Spectroradiometer (MODIS), and the SAR-based Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR). We show that terrain correction is effective for this mountainous study region and that the combination of terrain-corrected GLAS and PALSAR features with Random Forest regression produces the best results at the plot scale. Including further MODIS-based features added little power for prediction. Based upon the parsimonious data source combination, we created a map of AGB circa 2008 and its uncertainty, which yields a coefficient of determination (R2) of 0.82 and a root mean squared error of 16.84 Mg ha−1 when validated with field data. Forest AGB values in our study area were within the range 79.81 ± 16.00 Mg ha−1, ~25% larger than a previous, SAR-based, analysis. Our result provides a historic benchmark for regional carbon budget estimation.
Highlights
Forest ecosystems play a vital role in the terrestrial carbon cycle
Geoscience Laser Altimeter System (GLAS)-based estimation outperformed estimation based on both Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array type L-band Synthetic Aperture Radar (PALSAR) data, as might be expected when single source data are considered
The analysis shows that when we set the initial model as a combination of GLAS and PALSAR, the α for NDVI, Enhanced Vegetation Index (EVI), Vegetation Continuous Fields (VCF), and Leaf Area Index (LAI) equals 0.02, 0.11, −0.02 and −0.01, respectively, with the α for seven NBAR bands significantly lower than the abovementioned vegetation indices, which all have values lower than −0.01
Summary
Forest ecosystems play a vital role in the terrestrial carbon cycle. They contain 85–90%of the carbon in the terrestrial vegetation biomass and may help to alleviate the effects of increasing global climatic change and anthropogenic emissions by capturing large amounts of carbon [1,2,3]. Forest ecosystems play a vital role in the terrestrial carbon cycle. Of the carbon in the terrestrial vegetation biomass and may help to alleviate the effects of increasing global climatic change and anthropogenic emissions by capturing large amounts of carbon [1,2,3]. Fundamental understanding of carbon dynamics, especially under global climatic change [4], would be increased if an accurate spatially explicit estimation of forest aboveground biomass (AGB) was available. 2022, 14, 1039 estimates of AGB for local or regional scales [5] This method is both time consuming and labor intensive, and unsuitable for monitoring fast carbon dynamics at large scales [6].
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