Accurate estimation of forest canopy height and understory terrain in mountainous regions is crucial for carbon stock assessment under the Paris Agreement but remains challenging. This study aimed to evaluate ICESat-2’s performance in these complex environments. To achieve this, ICESat-2 ATL03 Version 6 photon data were processed using a novel adaptive DBSCAN algorithm (BDT-ADBSCAN) in Pu’er City, China, a biodiversity hotspot, and results were validated against airborne LiDAR. ICESat-2 achieved high terrain retrieval accuracy (R² = 1.00, RMSE = 0.91 m), primarily affected by slope, while canopy height retrieval was less accurate (R² = 0.53, RMSE = 6.45 m) with systematic underestimation, mainly influenced by canopy height itself. Nighttime strong-beam acquisitions substantially improved accuracies for both products. This research demonstrates ICESat-2’s viability for high-resolution digital terrain modeling and provides quality control thresholds for forest structure estimation in challenging regions, addressing validation gaps in Asian biodiversity hotspots and supporting carbon monitoring for UN Sustainable Development Goals.

The Leaf Area Index (LAI) plays a crucial role in assessing the health of forest ecosystems. This study utilized ICESat-2/ATLAS as the primary information source, integrating 51 measured sample datasets, and employed the Sequential Gaussian Conditional Simulation (SGCS) method to derive surface grid information for the study area. The backscattering coefficient and texture feature factor from Sentinel-1, as well as the spectral band and vegetation index factors from Sentinel-2, were integrated. The random forest (RF), gradient-boosted regression tree (GBRT) model, and K-nearest neighbor (KNN) method were employed to construct the LAI estimation model. The optimal model, RF, was selected to conduct accuracy analysis of various remote sensing data combinations. The spatial distribution map of Dendrocalamus giganteus in Xinping County was then generated using the optimal combination model. The findings reveal the following: (1) Four key parameters—optimal fitted segmented terrain height, interpolated terrain surface height, absolute mean canopy height, and solar elevation angle—are significantly correlated. (2) The RF model constructed using a combination of ICESat-2/ATLAS, Sentinel-1, and Sentinel-2 data achieved optimal accuracy, with a coefficient of determination (R²) of 0.904, root mean square error (RMSE) of 0.384, mean absolute error (MAE) of 0.319, overall estimation accuracy (P₁) of 88.96%, and relative root mean square error (RRMSE) of 11.04%. (3) The accuracy of LAI estimation using a combination of ICESat-2/ATLAS, Sentinel-1, and Sentinel-2 remote sensing data showed slight improvement compared to using either ICESat-2/ATLAS data combined with Sentinel-1 or Sentinel-2 data alone, with a significant enhancement in LAI estimation accuracy compared to using ICESat-2/ATLAS data alone. (4) LAI values in the study area ranged mainly from 2.29 to 2.51, averaging 2.4. Research indicates that employing ICESat-2/ATLAS spaceborne LiDAR data for regional-scale LAI estimation presents clear advantages. Incorporating SAR data and optical imagery and utilizing diverse data types for complementary information significantly enhances the accuracy of LAI estimation, demonstrating the feasibility of LAI inversion with multi-source remote sensing data. This approach offers an innovative framework for utilizing multi-source remote sensing data for regional-scale LAI inversion, demonstrates a methodology for integrating various remote sensing data, and serves as a reference for low-cost high-precision regional-scale LAI estimation.

The spectrophotometer method is costly, time-consuming, laborious, and destructive to the plant. Samples will be lost during the transportation process, and the method can only obtain sample point data. This poses a challenge to the estimation of chlorophyll content at the regional level. In this study, in order to improve the estimation accuracy, a new method of collaborative inversion of chlorophyll using Landsat 8 and Global Ecosystem Dynamics Investigation (GEDI) is proposed. Specifically, the chlorophyll content data set is combined with the preprocessed two remote-sensing (RS) factors to construct three regression models using a support vector machine (SVM), BP neural network (BP) and random forest (RF), and the better model is selected for inversion. In addition, the ordinary Kriging (OK) method is used to interpolate the GEDI point attribute data into the surface attribute data for modeling. The results showed the following: (1) The chlorophyll model of a single plant was y = 0.1373x^1.7654. (2) The optimal semi-variance function models of pai, pgap_theta and pgap_theta_a3 are exponential models. (3) The top three correlations between the two RS data and the chlorophyll content were B2_3_SM, B2_3_HO, B2_5_EN and pai, pgap_theta, pgap_theta_a3. (4) The combination of the Landsat 8 imagery and GEDI resulted in the highest modeling accuracy, and RF had the best performance, with R², RMSE and P values of 0.94, 0.18 g/m² and 83.32%, respectively. This study shows that it is reliable to use Landsat 8 images and GEDI to retrieve the chlorophyll content of Dendrocalamus giganteus (D. giganteus), revealing the potential of multi-source RS data in the inversion of forest ecological parameters.

Bamboo forests, as some of the integral components of forest ecosystems, have emerged as focal points in forestry research due to their rapid growth and substantial carbon sequestration capacities. In this paper, satellite-borne lidar data from GEDI and ICESat-2/ATLAS are utilized as the main information sources, with Landsat 9 and DEM data as covariates, combined with 51 pieces of ground-measured data. Using random forest regression (RFR), boosted regression tree (BRT), k-nearest neighbor (KNN), Cubist, extreme gradient boosting (XGBoost), and Stacking-ridge regression (RR) machine learning methods, an aboveground carbon (AGC) storage model was constructed at a regional scale. The model evaluation indices were the coefficient of determination (R²), root mean square error (RMSE), and overall estimation accuracy (P). The results showed that (1) The best-fit semivariogram models for c_dem, f_dem, f_ndvi, p_dem, and a_ndvi were Gaussian models, while those for h1_b7, h2_b7, h3_b7, and h4_b7 were spherical models; (2) According to Pearson correlation analysis, the AGC of Dendrocalamus giganteus showed an extremely significant correlation (p < 0.01) with c_dem and p_dem from GEDI, and also showed an extremely significant correlation with a_ndvi, h1_b7, h2_b7, h3_b7, and h4_b7 from ICESat-2/ATLAS; moreover, AGC showed a significant correlation (0.01 < p < 0.05) with f_dem and f_ndvi from GEDI; (3) The estimation accuracy of the GEDI model was superior to that of the ICESat-2/ATLAS model; additionally, the estimation accuracy of the Stacking-RR model, which integrates GEDI and ICESat-2/ATLAS (R² = 0.92, RMSE = 5.73 Mg/ha, p = 86.19%), was better than that of any single model (XGBoost, RFR, BRT, KNN, Cubist); (4) Based on the Stacking-RR model, the estimated AGC of Dendrocalamus giganteus within the study area was 1.02 × 10⁷ Mg. The average AGC was 43.61 Mg/ha, with a maximum value of 76.43 Mg/ha and a minimum value of 15.52 Mg/ha. This achievement can serve as a reference for estimating other bamboo species using GEDI and ICESat-2/ATLAS remote sensing technologies and provide decision support for the scientific operation and management of Dendrocalamus giganteus.

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted observed regression slope from a base model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying the canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments.

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted-observed regression slope from a base-model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments.