AUTHOR=Yang Huanfen , Qin Zhen , Li Shengjiao , Shu Qingtai , Wang Mingxing , Zhang Yiran , Li Zeyu 

TITLE=Estimation of above-ground carbon stock of Dendrocalamus giganteus using ANUSPLIN-interpolated GEDI and ICESat-2/ATLAS parameters

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1676195

DOI=10.3389/fpls.2025.1676195

ISSN=1664-462X

ABSTRACT=GEDI and ICESat-2/ATLAS have significant limitations in estimating forest structure parameters. This study aims to enhance the estimation accuracy of above-ground carbon storage (AGC) of Dendrocalamus giganteus by applying the ANUSPLIN interpolation technique for spatial expansion. The results indicate that: (1) When the spline degree of ANUSPLIN interpolation was set to 4 for GEDI parameters (cover, pai, sensitivity) and ICESat-2/ATLAS parameters (toc_roughness, h_median_canopy_abs, h_canopy_abs, h_max_canopy_abs), the model’s accuracy was highest. The spline degree 2 for the digital_elevation_model parameter (GEDI) and the asr parameter (ICESat-2/ATLAS) yielded optimal results. (2) The interpolation accuracy and performance of ANUSPLIN outperformed that of co-kriging (CK). (3) The Extreme Gradient Boosting (XGBoost) model (Coefficient of Determination, R2 = 0.93; Root Mean Square Error, RMSE = 5.89 Mg/ha; Overall Estimation Accuracy, P = 85.84%; relative RMSE, rRMSE = 14.16%) outperformed the Light Gradient Boosting Machine (LightGBM) (R2 = 0.52, RMSE = 14.61 Mg/ha, P = 64.84%, rRMSE = 35.16%) and Random Forest Regression (RFR) (R2 = 0.90, RMSE = 8.23 Mg/ha, P = 79.79%, rRMSE = 20.21%), achieving relative improvements of 78.85%, 59.67%, 32.36%, and 59.71% over LightGBM, and 3.33%, 28.41%, 7.58%, and 29.94% over RFR, respectively. This study demonstrates the feasibility of using ANUSPLIN interpolation for satellite LiDAR data from GEDI and ICESat-2/ATLAS. The approach offers a new perspective on spatial interpolation of satellite LiDAR data at a regional scale, providing a valuable reference for cost-effective, high-precision estimation of forest structural parameters.