In the United Nations decade on ecosystem restoration 2021–2030, coastal ecosystems such as mangroves are listed as a priority for biodiversity restoration. Therefore, understanding large-scale mangrove species diversity and temporal changes are important for predicting ecosystem health, viability, and resilience against changing climate and human pressure. However, it is also crucial to understand the effects of conservation interventions when considering future conservation efforts and policies for mangroves. To address these concerns, we must improve our ability to gather reliable forest inventory measurements, spatial scale biodiversity predictions, and good practices for using Earth observation data. In this study, we investigated the knowledge gaps considering potential spatial diversity, intertidal zonation, and the historic state of mangrove forest species, and tested the role of environmental settings such as topography and anthropogenic (rehabilitation or plantation) settings on diversification. We have successfully integrated historic multi-satellite data, current ecological data, and micro-topographic measurements to establish a historic state and zonation for the mangrove forests in the Trat Province of Thailand. The method introduced in this study allows us to overcome the technical limitations of monitoring protocols and provides a powerful decision-support system to assess the forest recovery period, structural growth, and species composition of plantations and natural native stands over three decades. This study also identifies the main influencing factors that hinder the quality of Earth observation data and propose best practices specific to mangrove ecosystem monitoring. In addition, we developed the “automatic regrowth monitoring algorithm (ARMA)” tool and summarized the functional indicators (secondary succession) by type. ARMA can identify plantation years, recovery period, age, and structural development of rehabilitated mangroves compared wi