Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method.

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.

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics.

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC.

Estimation of forest canopy closure in northwest Yunnan based on multi-source remote sensing data colla-boration.

Forest canopy closure (FCC) is an important parameter to evaluate forest resources and biodiversity. Using multi-source remote sensing collaborative means to achieve regional forest canopy closure inversion with low cost and high-precision is a research hotspot. Taking ICESat-2/ATLAS data as the main information source and combined with data of 54 measured plots, we estimated FCC value by the Bayesian optimization (BO) algorithm improved random forest (RF), K-nearest neighbor (KNN), and gradient boosting regression tree (GBRT) model at footprint-scale.