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The land cover in the coastal zone is characterized by frequent changes, fragmented landscape and strong spatial heterogeneity, which makes accurate assessment and analysis of coastal ecosystem carbon storage challenging. This study developed a coastal ecosystem carbon storage assessment framework by integrating Landsat time-series analysis with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. First, using the GEE cloud platform and Landsat long-term satellite remote sensing data, this study applied median compositing algorithms to mitigate the impact of periodic tidal inundation on land boundaries. Second, by integrating multiple feature parameters and utilizing the random forest method, accurate information on land use/cover change was obtained. Subsequently, carbon density parameters were determined, and coastal ecosystem carbon storage was assessed using the InVEST model. Finally, a spatiotemporal pattern analysis of coastal ecosystem carbon storage in Hangzhou Bay over nearly four decades was conducted. The findings yielded the subsequent outcomes: (1) The random forest algorithm integrated multiple feature parameters is stable, and can extract LUCC information accurately. (2) The overall coastal ecosystem carbon storage of Hangzhou Bay, China, witnessed a decline over the preceding four decades, dropping from 108.15 Mt in 1985 to 82.47 Mt in 2023. (3) The decrease of vegetation area and the expansion of build-up area are the main reasons for the change of carbon storage. This study furnishes valuable data support to underpin the strategic governance of land resources in the Hangzhou Bay region, while the resultant carbon storage dataset holds critical ramifications for regional sustainable development.
Chen et al. (Tue,) studied this question.