Monitoring global mangrove spatial evolution and functional degradation using BTSAD and CBPA methods over a 36-year dense Landsat and HLSv2.0 time series

Mangroves constitute one of the world's most valuable natural ecosystems in terms of ecological service provision. Over recent decades, however, these ecosystems have confronted the dual pressures of spatial shrinkage and persistent functional deterioration. Conventional remote sensing monitoring methods focus on binary area change detection, often overlooking the complex, multi-phase successional trajectories and the latent functional degradation risks that can persist beneath spatial stability, which limits our capacity to assess ecological resilience and impedes progress toward the United Nations Sustainable Development Goals (SDGs). To address this critical gap, this study proposes a comprehensive remote sensing framework that captures mangrove spatiotemporal evolution across six representative global mangrove sites using a 36-year multispectral time series (Landsat and HLSv2.0, 1988–2024) on the Google Earth Engine (GEE) platform. We develop a Bidirectional Temporal State-Aware Detection (BTSAD) algorithm to capture nonlinear evolutionary trajectories, including explicit identification of secondary expansion and loss events. We further integrate Space-Time Pattern Mining (STPM) with DTW-Kmeans clustering to construct an adaptive ecological signal analysis framework (CBPA) for quantifying functional degradation risk. Finally, we propose a Time-Lagged Response Structural Equation Model (TLR-SEM) to disentangle the lagged effects of environmental and anthropogenic drivers. Our results demonstrate that: (1) BTSAD effectively identifies eight distinct evolutionary trajectory types with an average overall accuracy of 81.92%; secondary events account for 40.65% of total expansions and 17.43% of losses. (2) Persistent hot/cold spot patterns dominated functional trait evolution, representing 27.61%–93.79% of observed modes. (3) Human-dominated mangroves (e.g., China-BER) exhibit the highest intrinsic instability (RSI = 23.648), while extreme-environment mangroves (e.g., Brazil-RM) present the greatest functional degradation risk (RDR = 4.535). Functional growth risk ranked in descending order as Brazil-RM > China-BER > India-SW ≈ Gabon-PNP > USA-ENP > Indonesia-SNP, with India-SW and Gabon-PNP exhibiting comparable levels. (4) Salinity and wind exerted short-term negative effects on mangroves (1–2 months lag, −0.132 to −0.378), while precipitation and temperature exhibited cumulative regulation effects (3–6 months lag, −0.852 to 0.405). This spatiotemporal framework provides robust methodological advancements for large-scale mangrove degradation early warning and restoration evaluation.