Satellite-based mapping and modeling of mangrove loss: A systematic review of significant parameters and methods with an integrated meta-analysis of local ecological knowledge

Abstract

To accurately map and estimate mangrove loss, it is crucial to develop insights from scientific literature, data analytics, and Local Ecological Knowledge (LEK) to characterize the dynamic interplay of anthropogenic impacts, climate change, and conservation efforts. This study presents a meta-analysis of 159 included articles, selected from 1316 publications retrieved through Scopus, Web of Science, and other open-access and journal databases. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the study identifies significant parameters and methodologies applied to satellite data for quantifying mangrove loss and degradation at local, regional, and global scales.

This parameter-centric meta-analysis identified 76 significant mapping and modeling parameters across categories including optical and Synthetic Aperture Radar (SAR) bands, vegetation and moisture indices, soil and built-up indices, elevation, environmental and temporal dynamics, climatic and geographical factors, proximity metrics, and socio-economic factors. Vegetation indices emerged as the most frequently utilized parameter with 110 articles, highlighting the substantial focus on vegetation metrics in existing research. The highest loss proportions were associated with the use of built-up indices, vegetation indices, SAR bands, and temporal dynamics, ranging between 31 % and 37 %. However, the high standard deviation in vegetation and temporal dynamics suggests the importance of selecting specific indices to accurately capture mangrove degradation. The meta-analysis highlighted a significant research gap in understanding mangrove loss, with only 7.5 % of studies focusing on predictive modeling. Additionally, the supplementary analysis of LEK provided community-perceived factors affecting mangrove loss, offering context-specific insights for effective management. This review highlights the need for more studies on forecasting mangrove loss under various future scenarios, the use of open-source software and adaptive modeling tools, and the integration of LEK and community engagement to enhance the relevance, accuracy, and impact of mangrove conservation strategies.