Mozambique holds approximately 300,000 hectares of mangroves, the third largest extent in Africa and the thirteenth largest in the world. These salt-tolerant forests, dominated in the south by species such as Avicennia marina, Rhizophora mucronata, Bruguiera gymnirrhiza, Ceriops tagal and Xylocarpus granatum, buffer shorelines against storms, sustain fisheries, cycle nutrients, and store substantial quantities of carbon. Yet the southern provinces have not been spared. Between 2008 and 2015, an estimated 5,898 hectares of mangrove were lost in the region, with the heaviest impacts recorded in Maputo, Inhambane and Gaza provinces. Timber extraction, coastal population growth, agricultural expansion, aquaculture, and intensifying climate impacts have all contributed to the pressure.

In response, Mozambique has woven mangrove conservation into its national policy framework. The country’s commitments under Sustainable Development Goal 14 were formalized through Decree 103/2019, which established eight marine protected areas, all of which contain mangroves. A National Mangrove Restoration Strategy and Action Plan covering 2015 to 2024 remains in effect, and it identifies systematic assessment and monitoring as central pillars of successful restoration. The new study speaks directly to that need, providing exactly the kind of long-term, spatially explicit evidence the strategy calls for.

Technically, the research is a tour de force of open satellite data. The team combined imagery from Landsat 7 ETM+, Landsat 8 OLI/TIRS, and Sentinel-2 MSI, harmonized to a common 30-meter resolution and processed on the Google Earth Engine platform. Cloud screening with a threshold of 20 percent or less ensured radiometric reliability, and Sentinel-2 data were resampled to match the Landsat record. The researchers then deployed a Random Forest classifier, configured with 100 trees, to sort every pixel into seven land-cover classes: water, mangrove, forest, habitation, cultivation, prairie, and exposed soil. Between roughly 209 and 896 training points were collected per class in each region, totaling close to 3,000 samples per site, with validation points collected independently to avoid inflated accuracy estimates.

The classification performed robustly. Overall accuracy exceeded 0.80 in all four regions and reached a maximum of 0.96, while Kappa coefficients ranged from 0.76 to 0.95, indicating strong to excellent agreement with reference data. Water and mangrove classes were the most reliably mapped, with producer’s and user’s accuracy consistently above 0.94 in most regions. Forest also performed well. The greatest confusion arose in the heterogeneous landscapes of Tembe and Limpopo, where settlements, cultivation, and savanna-like vegetation overlap spectrally, a well-known challenge in coastal land-cover mapping.

Beyond the classified maps, the team computed a battery of spectral indices to probe vegetation health and hydrology. These included the Normalized Difference Vegetation Index and Enhanced Vegetation Index as general vigor measures, the Mangrove Vegetation Index as a mangrove-specific probe, and water- and soil-related indices such as NDWI, MNDWI, and NDSI. Temporal trends were tested with the non-parametric Mann-Kendall test, quantified with Kendall’s tau and 95 percent confidence intervals. The results showed statistically significant upward trends in vegetation indices across all regions, while water-related indices consistently indicated hydrological reduction or retraction. The mangrove-specific indices confirmed the same modest improvement, though the confidence intervals included zero, signaling uncertainty in effect size even where the direction of change was clear.

The spatial patterns are where the story becomes genuinely compelling. Tembe recorded a net mangrove gain of 41 hectares relative to its base year, with the water class stable throughout. Limpopo and Belavista also registered slight increases, which the authors link to restoration measures including hydrological rehabilitation and the restoration of tidal dynamics, alongside mild temperatures favorable to mangrove growth. In Belavista, declining cultivation and prairie classes appear to have been colonized by forest and mangrove vegetation, suggesting that abandoned or degraded land is being reclaimed naturally where pressures have eased. Limpopo’s estuary, with its sediment accretion and community-based management, has previously been identified as a zone of natural recovery, and the new analysis is consistent with that picture.

Inhambane stands out as the exception. Mangrove cover there declined by more than 2,000 hectares despite years of relative stability, a trajectory the researchers attribute to the late start of local restoration efforts, which only began in 2020, combined with anthropogenic pressure, forest encroachment into mangrove zones, and reduced rainfall. The contrast with the other three sites underscores a central lesson of restoration ecology: timing matters, and interventions launched after degradation has gained momentum face a steeper uphill battle. It also echoes national assessments that have linked urban expansion and economic activity to mangrove decline in some coastal systems even as others expand under favorable geomorphological and hydrological conditions.

Climate emerges as a silent but powerful actor throughout the study period. Although temperatures remained within the normal range for mangrove growth, roughly between 22 and 35 degrees Celsius, rainfall was persistently scarce, and the period coincided with severe and moderate El Niño events in southern Mozambique. Previous research has shown that precipitation and mangrove cover follow a sigmoid relationship, with a reflection point near 1,368 millimeters, and that rainfall below 1,000 millimeters can induce physiological stress. Comparable El Niño episodes have driven catastrophic mangrove dieback in northern Australia and limited restoration success elsewhere in Mozambique, including massive dieback documented at the Maputo River estuary. The modest gains detected in this study are best understood against that climatic backdrop: restoration can work, but drought and hydrological stress can throttle seedling survival and canopy recovery even where human pressures are controlled.

The study is candid about its limits. It rests entirely on remote sensing, without field or socioeconomic data, and the 30-meter resolution of the imagery means mixed pixels can blur the boundary between mangroves and adjacent vegetation, a recognized source of uncertainty in mangrove classification. Tidal variation can also distort spectral indices when images are not acquired under comparable conditions, though multi-temporal image extraction was used to mitigate this. The authors recommend that future assessments incorporate full hydrological and socioeconomic datasets to strengthen causal interpretation.

Even so, the implications for policy are clear. Restoration alone, the findings suggest, is not a guarantee of recovery; it must be paired with systematic monitoring, coordination between government and scientific institutions, and genuine participation by local communities. The mangroves of southern Mozambique occupy narrow intertidal zones where every hectare counts, and the satellite record now shows precisely where recovery is taking root and where it is slipping away. As climate variability intensifies, that kind of evidence will be indispensable for deciding where to plant, where to protect, and where to let the tide do the work.

Subject of Research: Spatiotemporal monitoring of mangrove cover change in restoration areas of southern Mozambique using satellite remote sensing from 2009 to 2025

Article Title: Mangrove spatiotemporal changes in four areas with restoration projects in Southern Mozambique from 2009 to 2025

Article References: Mangrove spatiotemporal changes in four areas with restoration projects in Southern Mozambique from 2009 to 2025. (n.d.). https://doi.org/10.1016/j.envc.2026.101669

Image Credits: AI Generated

DOI: 10.1016/j.envc.2026.101669

Keywords: mangroves, Mozambique, remote sensing, restoration, Random Forest classification, Google Earth Engine, spectral indices, El Niño, coastal ecosystems, land cover change, blue carbon, Mann-Kendall trend test