Satellite Topographic Mapping in Complex Intertidal Wetlands: A Canopy-Height-Constrained Fusion of ICESat-2 Photon-Derived Structural Samples and Single-Phase Submeter Optical Imagery
2026· IEEE Transactions on Geoscience and Remote Sensing· Vol 64, pp. 4414912-4414912· 0 citations· 39 references
Abstract
High-resolution topographic mapping of intertidal wetlands is essential for geomorphic analysis, yet existing remote sensing methods often struggle with vegetation interference, dependence on dense time-series data, and limited representation of fine geomorphic features. We propose a canopy-height-constrained stratified cooperative inversion framework for the entire intertidal wetland, integrating single-phase submeter optical imagery (Jilin-1), spaceborne photon-counting light detection and ranging (LiDAR) Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), and machine learning. To accurately construct digital elevation model (DEM) and canopy height model (CHM) training samples in salt-marsh environments, we developed an ATL03 photon-classification workflow combining histogram-based control-point extraction and morphological refinement to generate these samples directly from ICESat-2 ATL03 photons. The retrieved CHM was then introduced as a structural constraint in the DEM retrieval model to support canopy-terrain signal decoupling in vegetated salt-marsh areas. A case study on Chongming Island, Shanghai, China, demonstrated that the DEM retrieval achieved high accuracy on the test set (R ${}^{2} =0.94$ , root-mean-squared error (RMSE) = 0.28 m) and maintained consistent performance against independent UAV-LiDAR validation data (R ${}^{2} = 0.53-0.77$ and RMSE = 0.34–0.53 m). The retrieved 0.5-m DEM reproduced regional elevation gradients, tidal-creek networks, and microtopographic variations across bare flats and vegetated marshes. Shapley additive explanation (SHAP) analysis showed that elevation retrieval over bare mudflats relied mainly on spectral predictors, whereas vegetated areas exhibited a complementary spectral-texture-CHM structure, with CHM consistently ranking as a mid-to-high predictor (fourth–seventh). This further supports the role of CHM as an effective structural constraint. By using only single-phase imagery and ATL03-derived DEM/CHM samples, the framework enables intertidal topographic retrieval that includes vegetated areas. It therefore provides an efficient and low-cost pathway for high-accuracy intertidal topographic monitoring under complex environmental conditions and limited image availability.
Reliable monitoring of lake turbidity is often constrained by the trade-off between the spatial coverage of passive optical imagery and the vertical profiling capability of active LiDAR observations. This study proposes a multi-source retrieval framework integrating ICESat-2 ATL03 photon data with Sentinel-2 multispectral imagery for large-scale turbidity mapping in Lake Erie. An adaptive quadtree pruning strategy combined with Otsu thresholding was applied to isolate high-confidence surface water photons. Vertical distribution descriptors, including penetration depth and attenuation-related photon metrics, were quantified along 500-m segments. A Random Forest inversion model was established using these LiDAR-derived features, achieving an RMSE of 2.67 NTU. To overcome the spatial discontinuity of ICESat-2 tracks, the LiDAR-derived turbidity estimates were incorporated as virtual buoy constraints to calibrate temporally matched Sentinel-2 reflectance products. A Bayesian-optimized fusion framework was subsequently developed to generate spatially continuous turbidity fields. Validation results indicate that the synergistic model achieved an RMSE of 2.95 NTU, representing a 39% improvement over conventional optical-only retrieval methods. The proposed framework demonstrates the potential of cross-modal remote sensing synergy for large-scale inland water quality monitoring.
Rong He, Heng Chen, Guang-Hui Zhu· GEOINFORMATICS· 0 citations
In understory terrain research, high-precision photon point cloud classification is essential for comprehensive analysis of forest ecosystems. However, ICESat-2/Advanced Topographic Laser Altimeter System (ATLAS) photon data contain substantial noise, and the raw photon data lack explicit terrain attributes, which can lead to the misclassification of noise photons as ground signals and reduce terrain-extraction accuracy. To overcome this challenge, we develop a deep learning framework that fuses a dynamic graph convolutional neural network (DGCNN) with a convolutional block attention module (CBAM), enabling robust classification of ICESat-2 photon point clouds into ground, canopy, canopy top, and noise categories. Across three contrasting forest regions—California, South Carolina, and Puerto Rico—the proposed framework consistently outperformed NASA’s official algorithm and density-based spatial clustering of applications with noise (DBSCAN). Using digital terrain model (DTM)/digital surface model (DSM) as reference, the average <inline-formula> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula>-scores were 0.85 (macro) and 0.87 (weighted); using manual reference, they were 0.77 (macro) and 0.81 (weighted). For understory terrain estimation, the extracted ground photons achieved an average R2 of 0.99 and an average root-mean-square error (RMSE) of 1.70 m, outperforming FABDEM (R<inline-formula> <tex-math notation="LaTeX">${}^{2} = 0.99$ </tex-math></inline-formula>, RMSE = 4.99 m) and Shuttle Radar Topography Mission (SRTM) (R<inline-formula> <tex-math notation="LaTeX">${}^{2} = 0.99$ </tex-math></inline-formula>, RMSE = 9.05 m). These results demonstrate the strong capability of the proposed model for photon point cloud classification and precise understory terrain recovery.
Jiapeng Huang, Yunqiu Wang, Hua Wu et al.· IEEE Transactions on Geoscie...· 0 citations
Accurate quantification of forest biomass in the karst mountainous region of Southwest China is critical for regional carbon sink accounting. However, remote sensing-based inversion of forest biomass in this region is subject to the dual constraints of scale effects and spatial heterogeneity. Taking two typical karst landforms—plateau karst and peak-cluster depression karst—as case studies, this study developed an inversion framework that integrates multi-scale synergy (plot—small watershed—region) with geomorphological zoning modeling. Specifically, the high-precision forest aboveground biomass (AGB), retrieved by integrating high-resolution satellite imagery (2 m) of small watersheds with field plot data, was used as the scale-conversion bridge. The dominant class variability-weighted method was applied to upscale the spatial resolution from 2 m to 30 m. Subsequently, Landsat-8 OLI imagery, land use/land cover data, and topographic factors were integrated to construct landform-specific neural network models, namely BPANN-GY for plateau karst and BPANN-FC for peak-cluster depression karst, with validation RMSEs of 11.02% and 12.39%, respectively. The results showed that the mean forest AGB in the plateau karst region was 110.66 t·ha−1 in 2013 and 111.46 t·ha−1 in 2024; in the peak-cluster depression karst region, the mean forest AGB was 123.98 t·ha−1 in 2014 and 125.57 t·ha−1 in 2024. Compared with the baseline years (2013/2014), total forest AGB in both regions increased significantly by 2024, up to 11.43% in the plateau karst region and 6.43% in the peak-cluster depression karst region. Spatially, the most marked increase in AGB occurred in the mid-to-high slope zones, while the differences in forest AGB among slope grades gradually narrowed, indicating progressively enhanced forest structural integrity and functional stability under effective land management. This study methodologically validates the scientific soundness and feasibility of using high-precision AGB retrieved from high-resolution satellite imagery as a scale-conversion bridge. The established AGB inversion system provides a methodological framework and data foundation for carbon sink accounting, ecological restoration, and land management in the karst region of Southwest China, and offers a transferable approach for forest AGB inversion in other highly heterogeneous landscapes.
Supraglacial lake depth is a key variable for quantifying surface meltwater storage and assessing ice-shelf stability, yet spatially continuous and reliable bathymetric information remains difficult to obtain in polar regions because in situ measurements are scarce and optical imagery cannot directly provide water depth. This study develops an integrated framework for supraglacial lake identification and bathymetry retrieval by combining ICESat-2 ATL03 photon-counting lidar data with Sentinel-2 multispectral imagery. ICESat-2 lake photons were used to constrain lake-region extraction from Sentinel-2 imagery, and the photon-derived along-track depths were corrected for scattering and refraction before being converted into Sentinel-2 pixel-level depth labels. Based on these labels, four retrieval models were constructed and evaluated, including an empirical model, CatBoost, a convolutional neural network (CNN), and a residual dense network (RDN). CatBoost generated initial depth estimates, while CNN and RDN further incorporated the CatBoost-derived depth prior and Sentinel-2 multispectral features for pixel-level depth prediction. Experiments over four investigated supraglacial lakes showed that RDN achieved the best average performance across the investigated lakes, with mean R2, RMSE, and MAE values of 0.927, 0.187 m, and 0.144 m, respectively. For the investigated lakes, the integration of ICESat-2 and Sentinel-2 extended discrete along-track reference-depth observations to spatially continuous bathymetry maps. Because the training and validation samples were obtained from different spatial blocks within the same four lake scenes, the reported performance primarily reflects within-lake spatial generalization under the investigated conditions, and transferability to unseen lakes remains to be evaluated. These maps may provide inputs for future lake-volume estimation and ice-shelf hydrological analyses, while their applicability to lakes with different morphological and optical conditions requires further evaluation.
Yuzhou Wu, Yinqiang Zheng, Yi Shen et al.· Remote Sensing· 0 citations
Peat swamp forests play a critical role in maintaining the ecological integrity of tropical peatlands. The conservation and restoration efforts on these ecosystems have gained considerable attention considering their vulnerability. Accurate spatial mapping of aboveground biomass (AGB) is important to support such measures, and it can be accurately implemented using airborne LiDAR. However, the high operational cost of LiDAR surveys typically restricts their spatial coverage. This study estimated AGB of peat swamp forests by combining two remote sensing datasets, i.e., Planet NICFI imagery (2023–2024) and partially covered airborne LiDAR (10.56% of the total area), in the Katingan–Mentaya peat swamp forest, Central Kalimantan, Indonesia. Two workflows were proposed and compared. The first, DL-PowerReg, estimated canopy height model (CHM) using a U-Net deep learning model trained on Planet imagery, followed by AGB mapping through power regression. The second, StepwiseReg-DL, derived LiDAR-based AGB through stepwise regression of LiDAR metrics, then upscaled it to the full study area using U-Net with Planet imagery as input. DL-PowerReg (MAE = 52.24 t/ha) outperformed StepwiseReg-DL (MAE = 62.08 t/ha) and additionally produced an intermediate CHM map, providing complementary information on forest structure. The two approaches estimated total AGB storage in the study area at 47.75 Mt (mean = 239.91 t/ha) and 40.07 Mt (mean = 201.30 t/ha), respectively. This study demonstrates a methodological framework for leveraging spatially incomplete LiDAR data in high-resolution wall-to-wall forest biomass mapping.
D. Umarhadi, T. Darusman, D. Lestari et al.· Remote Sensing· 0 citations
Urban forests play a crucial role in sustaining habitable urban environments, and tree canopy height (TCH) is a key parameter for characterizing urban forest structure and ecological functions. However, accurate TCH retrieval using spaceborne lidar remains challenging in urban built-up areas because of the high spatial heterogeneity of urban land cover. This study proposes an urban-oriented method for ICESat-2 data processing and TCH extraction and evaluates it in Peoria, Illinois, USA. By integrating spectral constraints with photon spatial distribution patterns, the method further separates non-ground photons into vegetation photons and building photons. Individual tree crown polygons are then introduced as spatial constraints for TCH extraction, helping to overcome the limitations of regular grids in representing both the laser footprint scale and fine urban landscape detail. In addition, a non-exclusive photon-to-crown assignment strategy based on energy weighting allowed each photon to contribute to multiple crowns in proportion to the footprint energy intercepted by each crown. Among the 1391 crowns associated with vegetation photons, 286 met the thresholds for both total and high-weight photon counts and were retained for accuracy assessment. Validation against canopy heights derived from airborne laser scanning (ALS) reference data shows that the proposed method achieves an R2 of 0.55 and an RMSE of 2.71 m. Further analysis indicates that retrieval accuracy is primarily controlled by beam strength: strong beams yield higher accuracy (R2 ≈ 0.60), whereas daytime and nighttime observations differ only slightly. Overall, after targeted processing, ICESat-2 can provide a set of individual tree canopy height samples in urban built-up areas, which may support local calibration and serve as potential labels for subsequent regional canopy height mapping.
