A Scalable Potential of Alpha Earth and Multisensor Datasets for Assessing Mangrove Intactness and Degradation Using Deep and Machine Learning Algorithms

Abstract

Mangrove ecosystems act as highly efficient carbon sinks and provide critical ecological services for climate regulation; however, they are increasingly threatened by anthropogenic pressures and climate change. Recent advances in annual satellite-based Earth Observation embedding (EM) datasets have enabled the integration of multisource data fusion, improving land use and land cover analyses. Nevertheless, EM datasets have rarely been applied for the monitoring of mangrove ecosystems. Within this context, the present study compares the performance of EM with combined Sentinel-1, Sentinel-2, and Landsat 8/9 datasets (S2S1L8/9) through the integration of a pretrained ResNet152–U-Net architecture and random forest (RF) modeling to assess spatiotemporal patterns of mangrove intactness and degradation from 2017 to 2024. A ResNet152 encoder pretrained on ImageNet was employed to train a U-Net model using the Global Mangrove Dataset (2020) for the mapping of potential mangrove extent. The EM dataset outperformed the S2S1L8/9 combination, achieving validation Intersection over Union scores of 0.85 and 0.84, and Dice coefficients of 0.89 and 0.88, respectively. Spatial comparison further indicated that EM yielded a lower root mean square error (6.51 ha) compared to S2S1L8/9 (7.27 ha). The RF model, trained on intact and nonintact mangrove samples across multiple years, confirmed the superior performance of EM in delineating intact mangrove areas along coastlines, with an overall accuracy of 0.97, an F1-score of 0.97, and a Matthews Correlation Coefficient of 0.93. Degraded mangrove areas were identified by masking intact regions, and gain–loss analysis revealed a decline of approximately 3% in 2021 and 2022 relative to the 2017 baseline. These findings demonstrate that EM provides a more accurate and spatially consistent approach than conventional multisource datasets for mapping mangrove intactness and degradation. By minimizing classification errors and improving coastline delineation, EM establishes a robust framework for large-scale monitoring of mangrove dynamics, supporting conservation planning, carbon accounting, and climate resilience strategies across diverse coastal and terrestrial systems at global scales.