A Biomass Index-Based Approach on Aboveground Biomass Estimation With ICESat-2 Photon Data

Abstract

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) photon-counting lidar (PCL) provides new opportunities for large-scale forest aboveground biomass (AGB) estimation. Currently, most AGB estimation models rely on relative height (RH) metrics that lack canopy stem and crown information, limiting the AGB estimation accuracy. This study incorporates a biomass index (BI) derived from the gap probability and an allometric formulation to represent the vertical canopy structure. A composite AGB regression model is proposed by integrating the linear RH metrics and power-law BI metric. Using ICESat-2 ATL08 data and the reference AGB maps in the northern Rocky Mountains, the relationships of AGB with the BI and RH metrics are investigated. Results show that BI exhibits a stronger correlation with AGB than RH100, RH75, and RH50. The performance of the proposed nonlinear AGB model is evaluated by comparing it with the RH-based linear regression and random forest (RF) models based on the coefficient of determination ( $\mathrm{R}^{2}$ ) and the root-mean-square error (RMSE). The proposed AGB model can achieve the best performance with an R2 of 0.78 and an RMSE of 34.14 Mg/ha. The results demonstrate that the BI can provide a quantitative metric for representing the vertical canopy structure and may be a crucial factor in the estimation of the large-scale AGB using ICESat-2 photon data.