Integrating Satellite Remote Sensing and Local Knowledge to Decipher Two Decades of Coastal Change in the Ensenada de La Paz, Mexico

Abstract. This study presents a comprehensive, two-decade (2005–2025) assessment of coastal morphodynamics in the Ensenada de La Paz, Mexico, by synergistically integrating satellite remote sensing with local knowledge. We employed a multi-sensor approach (Landsat 7 ETM+, Sentinel-2 MSI, and very-high-resolution imagery) to quantify spatiotemporal changes in bathymetry, shoreline position, and turbidity patterns. The analysis reveals a persistent trend of bay-wide shallowing (with an average depth reduction of 0.10 m per five-year period), significant net coastal erosion (21.34 ha), and increased nearshore turbidity, particularly adjacent to urban areas. Hurricanes Newton (2016) and Lorena (2019) triggered distinct, spatially heterogeneous geomorphic responses, driven by differences in rainfall distribution and fluvial sediment inputs. Crucially, structured engagement with local fishers, aquaculturists, and coastal residents provided essential ground-truthing and causal explanations for the remotely sensed patterns, identifying anthropogenic pressures such as illegal fishing, vessel traffic, waste discharge, and proposed infrastructure as key drivers. This integrated framework not only validates local observations with quantitative evidence but also bridges the gap between large-scale change detection and process-based understanding. The findings underscore the system’s vulnerability and provide a robust, evidence-based foundation for participatory coastal management and climate adaptation strategies in data-scarce regions.