ArticleFrontiers in plant science2024
Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- DFS: A Feature-Sample Collaborative Optimization Framework for Machine Learning-Based Forest Aboveground Biomass Estimation Using Multi-Source Remote Sensing.Plants (Basel, Switzerland) · 2026Article
- Regional forest stock volume mapping using GEDI-based interpolation, multi-source remote sensing, and a multi-level stacking ensemble model in complex terrain.Frontiers in plant science · 2026Article
- Trend and drivers of forest biomass change in the Greater Bay Area of China from 2000 to 2022.Scientific reports · 2025Article
- Unlocking vegetation health: optimizing GEDI data for accurate chlorophyll content estimation.Frontiers in plant science · 2024Article
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Authors and funding
6 authors.
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Abstract
Estimation of forest biomass at regional scale based on GEDI spaceborne LiDAR data is of great significance for forest quality assessment and carbon cycle. To solve the problem of discontinuous data of GEDI footprints, this study mapped different echo indexes in the footprints to the surface by inverse distance weighted interpolation method, and verified the influence of different number of footprints on the interpolation results. Random forest algorithm was chosen to estimate the spruce-fir biomass combined with the parameters provided by GEDI and 138 spruce-fir sample plots in Shangri-La. The results show that: (1) By extracting different numbers of GEDI footprints and visualize it, the study revealed that a higher number of footprints correlates with a denser distribution and a more pronounced stripe phenomenon. (2) The prediction accuracy improves as the number of GEDI footprints decreases. The group with the highest R
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