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Monitoring annual forest carbon stock loss using very high-resolution time series remote sensing images and earth-foundational data
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DOI：10.1016/j.jag.2026.105320.png)
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Abstract
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• A novel forest canopy height estimate method based on multi-modal data was proposed. • A novel annual forest change detection method was generated based on VHR images and forest canopy height. • A novel framework to estimate annual forest carbon stock loss combining forest canopy height and annual forest change results.
Keywords:
Deep learning
Carbon stocking loss
Forest three-dimensional change
High resolution remote sensing imagery
Forest canopy height
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