Assessment and Optimization of Forest Aboveground Biomass in Liaoning Province

Forests are the largest terrestrial carbon reservoirs and the most cost-effective carbon sinks. Accurate estimation of forest aboveground biomass (AGB) can significantly reduce uncertainty in carbon stock assessments. However, due to the limitations of timely and reliable forestry surveys, as well as high-resolution remote sensing data, mapping high-resolution and spatially continuous forest AGB remains challenging. The Global Ecosystem Dynamics Investigation (GEDI) is a remote sensing mission led by NASA, aimed at obtaining global forest three-dimensional structural information through LiDAR data, and has become an important tool for estimating forest structural parameters at regional scales. In 2019, the GEDI L4A product was introduced to improve AGB estimation accuracy. Currently, forest AGB maps in China have not been consistently evaluated, and research on biomass at the provincial level is still limited. Moreover, scaling GEDI’s footprint-based data to regional-scale gridded data remains a pressing issue. In this study, to verify the accuracy of GEDI L4A data and the reliability of the filtering parameters, the filtered GEDI L4A data were extracted and validated against airborne data, resulting in a Pearson correlation coefficient (ρ) of 0.69 (p < 0.001, statistically significant). This confirms the reliability of both the GEDI L4A data and the proposed filtering parameters. Taking Liaoning Province as an example, this study evaluated three forest AGB maps (Yang’s, Su’s, and Zhang’s maps), which were obtained as nationwide AGB product maps, using GEDI L4A data. The comparison with Su’s map yields the highest ρ value of 0.61. To enhance comparison accuracy, Kriging spatial interpolation was applied to the extracted GEDI footprint data, yielding continuous data. This ρ value increased to 0.75 when compared with Su’s map, with significant increases also observed against Yang’s and Zhang’s maps. The study further proposes a method to subtract the extracted GEDI data from the AGB values of the three maps, followed by Kriging interpolation, resulting in ρ values of 0.70, 0.80, and 0.69 for comparisons with Yang’s, Su’s, and Zhang’s maps, respectively. Additionally, comparisons with field measurements from the Mudanjiang Ecological Research Station yielded ρ values of 0.66, 0.65, and 0.50, indicating substantial improvements over direct comparisons. All the ρ values were statistically significant (p < 0.001). This study also conducted comparisons across different cities and forest cover types. The results indicate that cities in eastern Liaoning Province, such as Dalian and Anshan, which have larger forest cover areas, produced better results. Among the different forest types, evergreen needle-leaved forests and deciduous needle-leaved forests yielded better results.