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Forest structure, notably the canopy height above the ground, can be obtained from the full-waveform data acquired by satellite laser altimetry. On sloped terrain, the laser pulse can simultaneously reflect from both the vegetation and the ground within their intersecting vertical extents, causing their signals to overlap. This overlap complicates the separation of canopy and ground in the analysis. Consequently, accurately obtaining canopy height and its variation, while considering the influence of terrain factors, poses a significant challenge.In this paper, to counter this, we introduce an innovative approach to remove terrain effects for more reliable identification of canopy and ground. Overlapping footprints from the Ice, Cloud, and land Elevation Satellite (ICESat)/Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) campaigns are extracted as coincidental observations. Utilizing the different footprint sizes and by minimizing the center and width of each decomposed Gaussian component pair, optimized ground returns can be obtained. Validation using airborne LiDAR data from the Bartlett Experimental Forest, New Hampshire, USA, indicate that the proposed approach can reduce the ground elevation root-mean-square error (RMSE) by approximately 3 m, compared to the prevalent methods and standard products. While the GEDI product algorithms exhibit a certain accuracy in canopy height retrieval, the proposed approach presents a more constrained error band, signifying less impact from ground elevation discrepancies.While the proposed method enhances canopy height precision, its reliance on overlapping footprints means sacrificing some observations. However, the proposed method is effective in scenarios such as change detection that require repeat observations. As a case study, we applied the proposed method in Porto Velho, Rondônia, Brazil, which is a deforestation hotspot. Using the GlobeLand30 dataset, we investigated the land-cover dynamics from co-located laser footprints, identifying significant forest loss areas and validating our findings with the Hansen Global Forest Change dataset, achieving an accuracy of 83.81 %.In conclusion, this research emphasizes the significance of terrain topography effects in vegetation structure analysis, and positions satellite laser altimetry as instrumental for forest monitoring. |
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