Salt-tolerant mangrove ferns, palms and woody plants, thrive in tropical and subtropical intertidal zones and form vital interfaces between land and sea along sheltered coastlines, estuaries, and deltas (Barbosa et al., 2001; Saenger and Saenger, 2002; FAO, 2007). Their unique adaptations such as prop roots and pneumatophores for structural support and gas exchange, porous tissues for efficient oxygen diffusion, salt-exclusion mechanisms, and buoyant propagules for water dispersal, are key to their survival in saline, waterlogged, and tidal environments (Lugo and Snedaker, 1974; Rabinowitz, 1978; Wang et al., 2011; Duke and Schmitt, 2015). These traits also shape clear zonation patterns across intertidal gradients, which in turn support ecosystem resilience and functional diversity (Lugo et al., 1975; Duke et al., 1998).

As one of the planet’s most productive ecosystems, mangroves support exceptional biodiversity, by serving as critical nursery habitats for countless marine species, and providing critical habitat for migratory birds (Duke et al., 2007; FAO, 2007; Spalding and Leal, 2024). Moreover, mangroves trap sediments, organic matter, and pollutants through dense canopies and root networks (Tam and Wong, 1993), contributing significantly to carbon sequestration, averaging 394 tons per hectare, with peaks of 650 tons in high-density areas (Spalding and Leal, 2024). Socioeconomically, mangroves provide vital resources for coastal communities, valued between USD 200,000 and 900,000 per km² annually (UNEP-WCMC, 2006; Fatoyinbo et al., 2008). They also offer seafood, fuelwood, fibers, construction materials, honey, edible leaves, medicinal plants, and even ground for ecotourism, thereby supporting food security, livelihoods, and local economies (Rönnbäck, 1999; Barbier, 2000; UNEP-WCMC, 2006; Fatoyinbo et al., 2008; Lee et al., 2014; Friess, 2017; Goldberg et al., 2020).

Mangroves are fundamental to coastal protection, particularly as climate change intensifies storm activity (McIvor et al., 2015). Their structural complexity slows water flow, reduces wave energy, and mitigates storm surge impacts, lessening flood and erosion risks across tropical shorelines (Massel et al., 1999; Mazda et al., 2006; Barbier et al., 2011; Blankespoor et al., 2017). Broad mangrove belts spanning several kilometers buffer wave energy and floodwater, while even smaller stands provide measurable protection to nearby areas (Spalding and Leal, 2024).

Mozambique hosts Africa’s second-largest mangrove coverage, approximately 54.6% of its 11,768 km coastline, offering vital protective functions and supporting high species diversity, with 9–10 species recorded (Fatoyinbo et al., 2008; Charrua et al., 2020; Global Mangrove Watch, 2024; Barbosa et al., 2001). However, besides the significant degradation driven by aquaculture, urban expansion, land reclamation, and unsustainable logging, mangrove loss is further aggravated by sea level rise, cyclones, and extreme rainfall (Farnsworth and Ellison, 1997; Giri et al., 2008; Fatoyinbo et al., 2008; Baloloy et al., 2020). Globally, mangrove cover has declined by roughly one-third over the past five decades, with Mozambique experiencing sharp losses—particularly after Cyclone Idai (2019), a Category 4 storm that caused widespread ecological and human devastation (Spalding et al., 1997; Lovelock et al., 2015; Devi, 2019; Charrua et al., 2020). With tropical cyclones accounting for 45% of global mangrove mortality in the last 60 years, and projections indicating increased frequency and intensity, growing risks to both ecosystems and human settlements are anticipated (Hoque et al., 2018; Charrua et al., 2020; Phiri et al., 2021). The intrusion of salt water inland is a direct consequence of climate change, mainly linked to rising sea levels. In this context, it has been documented that mangroves have the ability to migrate inland when conditions are favorable in terms of ecological timing and availability of suitable soil (Visschers et al., 2022). In addition, while mangroves are threatened by seawater intrusion (flooding and sea level rise), they are also often threatened by altered freshwater inflows from inland areas (e.g., dam construction that alters water flow and pollutants).

The conservation of these ecosystems is imperative; deforestation or natural erosion not only put these carbon stocks at risk, but also risk releasing significant amounts of greenhouse gases into the atmosphere, thus aggravating climate change. Protecting mangroves is essential to sustain their role in global carbon sequestration and to prevent further acceleration of climate impacts (Charrua et al., 2020; Bourgeois et al., 2024).

Accelerating mangrove loss and intensifying climate threats highlight the urgent need for research to guide conservation and restoration efforts. Nature-based Solutions that restore ecosystem function and enhance coastal resilience are essential, particularly as coastal regions face growing erosion and flooding risks from storms and sea-level rise. Understanding and preserving the protective role of natural habitats is critical for sustainable coastal management, aligning with global goals like the SDGs and regional initiatives under the UNEP-Nairobi Convention (Bosire et al., 2015; Tallis et al., 2015; Charrua et al., 2020).

In the present study we examine the spatial and temporal dynamics of degradation, evaluate mangroves’ protective capacity against floods and erosion, and explore strategies to maintain their ecological and socioeconomic value. Through this work, we aim to support Mozambique’s efforts to protect vulnerable coastlines and strengthen coastal communities facing increasing climate-related challenges.

Several studies have assessed the extent and condition of Mozambique’s mangroves over the past three decades, using diverse methods ranging from aerial surveys (Saket and Matusse, 1994) and field-based inventories (Barbosa et al., 2001) to satellite remote sensing (Fatoyinbo et al., 2008) combined Landsat ETM + and Shuttle Radar Topography Mission data to estimate mangrove extent, height, and biomass, while Charrua et al. (2020) used field data and environmental parameters to model the distribution of mangrove forests and an EI to climate hazards and erosion. Charrua et al. (2021) used Landsat time-series analysis to analyze temporal changes in Land Use and Land Cover (LULC) after the passage of Cyclone Idai across Sofala Province. More recently, the Global Mangrove Watch (2024) has produced global baseline maps at 30 m resolution. Although these studies provide valuable insights into mangrove dynamics, they differ in scale, resolution, and methodological consistency, limiting their utility for long-term, standardized national monitoring. Moreover GIS and remote sensing have been effectively used for produce datasets used multi-criteria decision analyses (MCDA) in the context of land use planning, such as forest fire (Akay and Erdoğan, 2017), protected areas management (Nelson and Burnside, 2019), evaluation of environmental hazards (Arabameri et al., 2019; Darwish, 2023). However this approach is never been applied in mangroves ecosystems in Mozambique, as highlighted by Frosi et al. (2025). By integrating the Mangrove MVI with the InVEST (see Appendix) Coastal Vulnerability Model, our study addresses these gaps, offering a novel framework to assess both mangrove cover change and coastal vulnerability in Mozambique at high spatial resolution.

The MVI analysis revealed that Mozambique’s mangrove cover decreased from 2,114 km² in 2013 to 1,739 km² in 2023 (Figure 3; Table 4), reflecting a net loss of 376 km² (−18%) over the decade. The InVEST Coastal Vulnerability model classified the entire Mozambican coastline using the EI (Figure 4), ranging from 1 (low vulnerability) to 5 (high vulnerability; Table 3). Quantile-based grouping (0.8 increments) indicated that 42% of the coastline (1,150 km out of 11,768 km) exhibits intermediate to high vulnerability to flooding and erosion (Figure 5). This underscores the need for disaster prevention measures, particularly in areas affected by cyclone landfall. All coastal provinces are impacted, with Maputo (including Maputo City), Sofala, and Zambezia identified as the most vulnerable. Detailed results for the seven coastal provinces, ordered by mangrove loss (from highest to lowest), are presented below. For clarity, Maputo Province and Maputo City are combined. Province-specific maps are provided in the Supplementary materials.

The analyses showed that the most vulnerable provinces are also those that have experienced the greatest mangrove loss—namely, Zambezia, Sofala, Maputo.

MVI analysis indicated that Zambezia suffered the larger mangrove area decrease in Mozambique, from 813 km² in 2013 to 554 km² in 2023—a loss of 258 km² (−32%). The Coastal Vulnerability Model showed that 46% of Zambezia’s coastline (classified from “Intermediate” to “Very High”) is highly exposed to flooding events and would therefore need intervention. Namacurra District exhibited the highest vulnerability, with coastal segments classified as EI “Very High” (see Supplementary Figure 15).

The province with the next highest mangrove surface loss is Sofala, where mangrove area shrank from 451 km² in 2013 to 366 km² in 2023—a loss of 84 km² (−19%). EI values range from 1.1 to 5, with a mean value of 2.5. The Coastal Vulnerability Model showed that 40% of Sofala’s coastline (classified from “Intermediate” to “Very High”) is highly exposed to flooding event. Cidade da Beira District displayed the highest vulnerability, with coastal segments ranked as “High” and “Very High” EI. Notably, Cyclone Idai severely impacted Beira, and the MVI analysis of the Buzi River area revealed a mangrove cover reduction from 76 km² pre-cyclone (2018) to 23 km² post-cyclone (2019)—a loss of 53 km² (−69%; Figure 6). This underscores the cyclone’s significant impact on mangrove ecosystems, as well as on human life and infrastructe.

In Nampula Province, MVI analysis indicated a mangrove area decrease from 370 km² in 2013 to 338 km² in 2023—a loss of 31 km² (−8%). EI values range from 1.1 to 4.8, with a mean value of 2.4. The Coastal Vulnerability Model showed that 39% of Napula’s coastline (classified from “Intermediate” to “Very High”) is highly exposed to flooding. Moma and Angoche Districts showed the highest vulnerability, with coastal segments classified as EI “Very High” (see Supplementary Figure 18).

In Maputo Province, MVI analysis revealed that mangrove area decreased from 41 km² in 2013 to 36 km² in 2023—a loss of 5 km² (−12%), equating to approximately 0.5 km² per year. EI values range from 1 to 4.5 (mean: 2.6). The Coastal Vulnerability Model showed that 46% of Maputo’s coastline (classified from “Intermediate” to “Very High”) is highly exposed to flooding events. Marracuene and Manhiça Districts exhibited the most “Intermediate-High” EI values, while Cidade de Maputo had “Very High” EI segments.

In Inhambane Province, the MVI analysis showed a slight decrease in mangrove area—from 156 km² in 2013 to 153 km² in 2023—a loss of 3 km² (−2%). EI values range from 1 to 4.2 (mean: 2.4). The Coastal Vulnerability Model showed that 38% of the coastline of Inhambane Province (values ranging from “Intermediate” to “High” - no “Very High” values were recorded) is found to be highly exposed to flooding. Zavala District exhibited the most “Intermediate-High” EI values.

Conversely, Cabo Delgado Province experienced a mangrove area increase, from 281 km² in 2013 to 287 km² in 2023—a gain of 6 km² (+2%). EI values range from 1 to 4.6 (mean: 2.4). The Coastal Vulnerability Model showed that 39% of the coastline of Cabo Delgado Province (values ranging from “Intermediate” to “Very High”) is highly exposed to flooding. Palma District exhibited the highest vulnerability, with “Very High” EI coastal segments, followed by Mocímboa da Praia.

Similarly, MVI analysis revealed a mangrove area increase in Gaza Province from 2.8 km² in 2013 to 3.2 km² in 2023—a gain of 0.5 km² (+15%). These mangroves, located primarily in the protected Limpopo River delta, showed no significant disturbance. EI values range from 1.4 to 4 (mean: 2.6). The Coastal Vulnerability Model showed that 50.7% of the coastline of Gaza Province (values ranging from “Intermediate” to “High”; no “Very High” values recorded) is highly exposed to flooding. Mandlakaze District exhibited the most “Intermediate-High” EI values, indicating higher vulnerability.

For more details on the results for each coastal province, see Supplementary materials section B. In addition, maps showing mangrove loss and gain for certain areas of Mozambique are available in Supplementary materials section C.

This study presents a robust framework for evaluating the dynamics of mangrove ecosystems and the vulnerability of the coastline in Mozambique, an area that is particularly vulnerable to the effects of climate change, such as intensifying cyclones and rising sea levels (Chichava et al., 2024). Our approach enhances the precision of mangrove monitoring and coastal exposure analysis by integrating the MVI with the InVEST Coastal Vulnerability Model at a 100 m resolution. The standardized, cloud-based workflow utilizing Google Earth Engine and QGIS offers a scalable alternative to traditional field surveys, enabling the efficient identification of priority areas for conservation and restoration. This methodology strengthens Mozambique’s disaster risk management framework and makes a significant contribution to global research on coastal resilience in regions vulnerable to climate change.

This study rigorously quantifies mangrove loss in Mozambique from 2013 to 2023, documenting a 18% reduction (377 km²), which aligns with global mangrove decline rates of 1–2% annually (Zhang et al., 2017). By applying the MVI within Google Earth Engine, we effectively mapped mangroves at 10 m and 30 m resolutions, leveraging its ability to distinguish mangroves from other vegetation types (Baloloy et al., 2020). The MVI’s simplicity and automation potential position it as a scalable tool for rapid, cloud-based monitoring, surpassing labor-intensive field surveys (Liu et al., 2008; Baloloy et al., 2020). However, the MVI’s upper limit in Mozambique exceeds reported thresholds, suggesting denser mangrove stands and highlighting the need for region-specific calibration to optimize classification accuracy (Baloloy et al., 2020).

The provinces of Zambezia and Sofala, in addition to being the provinces with the highest mangrove coverage in the country, are also those that have suffered the greatest losses. In the case of Zambezia province, we are talking about a 32% (−259 km²) reduction in 10 years, concentrated mainly in the districts of Maquival and Namacurra. It is believed that among the main causes of this loss are the construction of the Kariba Dam in 1960 and the Cahora Bassa Dam in 1975 on the Zambezi River, which over the years have altered both the total flow of fresh water into the delta and the cyclical flooding of the floodplain (Fatoyinbo et al., 2008). Those districts also result in high coastal vulnerability to flooding and erosion, as assessed by the InVEST Coastal Vulnerability Model (Tallis et al., 2015). About 14% of Zambezia’s coastline is classified as highly or very highly vulnerable, due to its geomorphology like low-lying land and large estuaries (Cabral et al., 2017; Charrua et al., 2020).

As for the province of Sofala, there has been an 18% (−85 km²) loss in 10 years, with the greatest losses concentrated along the Buzi and Púnguè rivers (hit in 2019 by Cyclone Idai). The cyclone reduced Buzi and Púnguè River mangrove cover by 69.33% (54 km²), highlighting the destructive impact of extreme weather events (Charrua et al., 2021; Phiri et al., 2021). This is an area highly prone to cyclones, which cause severe flooding (Sete et al., 2002). While mangroves have the ability to protect coastlines from storm surges—reducing wave speed thanks to their dense system of aerial roots (Quartel et al., 2007; Zhang et al., 2012; Pinsky et al., 2013; Spalding and Leal, 2024)—these events can also pose a serious threat, leading to extensive destruction of trunks and roots (Charrua et al., 2021; Phiri et al., 2021). This study quantitatively assessed the degradation of mangroves after Cyclone Idai struck the Beira area (in Sofala province), but further research on estimating the recovery time of these plants is needed. It should be noted that the greater the frequency of these extreme weather events, the less time these plants have to grow and recolonize areas, and the less ecosystem services they can provide to coastal populations. Timely intervention to promote restoration in degraded areas that are highly susceptible to cyclones could be an effective strategy for maintaining active coastal protection ecosystem services to limit damage to people and infrastructure in preparation for the next flood. In addition to being an area highly prone to this type of event, mangroves are also threatened by deforestation (Barbosa et al., 2001).

In contrast, Gaza and Cabo Delgado provinces showed modest mangrove gains (14 and 2%, respectively), possibly due to favorable riverine conditions and lower anthropogenic pressures (Fatoyinbo et al., 2008). In particular, with regard to the province of Cabo Delgado, this area is known to be sparsely populated and to have security problems. We believe that this may have contributed to both a better EI (despite being an area highly prone to cyclones) and less mangrove loss (due to less human influence).

Maputo Province, despite pollution from oil spills and urban waste (Sete et al., 2002; Simbine, 2023), experienced a moderate loss of 18%, which may suggest localized resilience or restoration efforts. Notably, initiatives such as the Italian Agency for Development Cooperation’s project in the Maputo Baia (AICS, 2022) and recent ecological assessments on the mangroves of Matola District (Simbine, 2023; Litulo et al., 2023) reflect a growing institutional focus on conservation and rehabilitation. These provincial variations underscore the interplay of local ecological conditions and human activities in shaping mangrove dynamics. The main threats to mangroves reported in the literature for Maputo Bay are firstly that Maputo City hosts one of the major ports in Southeast Africa, increasing the risk of oil spills (Sete et al., 2002). A total of 14 minor and two major crude oil spills involving mangroves have occurred in Maputo Harbor, including one in 1992 involving the tanker Katina P (Barbosa et al., 2001). In addition, five rivers, some of which are international, flow into the bay and are used for agricultural purposes in the upper part of the riviera, and thus there is an influx of pollutants and reduced river flow in the lower part of the riviera (Sete et al., 2002). Another problem is that the existence of mangroves and the high level of nutrients from the rivers encourages the development of commercially valuable fisheries, such as shallow-water shrimp and small pelagic species (Sete et al., 2002). Moreover, Maputo City, which is connected to the bay, is overpopulated, with about 2 million people, without adequate treatment of domestic waste. The country’s major industries are located in Maputo and Matola and dump their waste into the bay (Sete et al., 2002). All these factors contribute to the possible pollution of the bay through domestic, industrial, and agricultural wastes, which, in turn, can affect water quality and thus ecosystem productivity. Finally, regarding the main causes of mangrove destruction in Maputo Bay, Charrua et al. (2020) include deforestation and urban expansion.

Mangroves play a critical role in mitigating coastal hazards by reducing wave energy and storm surge impacts, as evidenced by modeling studies (Massel et al., 1999; Mazda et al., 2006; Barbier, 2008; Blankespoor et al., 2017). Their protective capacity depends on forest width, density, and coastal topography (Spalding and Leal, 2024). In Mozambique, 9.5% of the 11,768.11 km coastline is highly or very highly vulnerable, with Zambezia, Sofala, and Maputo facing elevated risks (Charrua et al., 2020). The severe mangrove loss in these provinces likely amplifies coastal exposure, as mangroves provide critical stabilization and flood protection (Alongi, 2008; UNEP-WCMC, 2006).

The combined use of the MVI and InVEST models offers a robust framework for identifying priority areas for mangrove management. High-vulnerability districts, such as Buzi and Cidade da Beira (Sofala) and Maquival and Namacurra (Zambezia), which also experience significant mangrove loss and population pressures, warrant urgent intervention (Charrua et al., 2020). Restoration projects, prioritizing flood-tolerant species like Avicennia marina (Alongi, 2008), could enhance coastal resilience in these areas. Moreover, stakeholder engagement and enforced regulations are essential to ensure community development, biodiversity conservation, and social equity in mangrove management (Charrua et al., 2020).

The mangrove loss rate observed in Mozambique from 2013 to 2023 (−18%) is consistent with values found in other tropical and subtropical regions but slightly exceeds the global annual average of 0.16–2% (Hamilton and Casey, 2016; Zhang et al., 2017). The Global Mangrove Watch (2024) reports that, among the countries with the highest mangrove coverage, Mozambique ranks eighth among the top 10 mangrove-holding countries in terms of loss between 1996 and 2020. Globally, the most severe recent declines have occurred in Indonesia, Australia, Mexico, Myanmar, and Cuba (Hamilton and Casey, 2016; Global Mangrove Watch, 2024). Despite these trends, our findings suggest that the rate of mangrove loss in Mozambique is higher than the global average, indicating a particularly critical situation.

Despite its strengths, this study does not include accuracy estimates for the MVI, limiting confidence in its precision. The discrepancy between our 2023 mangrove estimate (1,739.44 km²) and GMW’s 2020 estimate (3,380.27 km²) likely reflects methodological or resolution differences, which should be further investigated (Global Mangrove Watch, 2024). Future research should focus on validating MVI performance across ecosystems, incorporating time-series analyses (e.g., NDVI) to detect flooding events, and assess restoration outcomes. Additionally, investigating cyclone impacts under climate change scenarios is critical for designing Nature-based Solutions, particularly in under-studied regions such as Sofala (Charrua et al., 2021).

The choice to resample Sentinel-2 imagery from 10 m to 30 m resolution was made to ensure direct comparability with Landsat-8 data, allowing consistent application of the MVI across both time periods. Although alternative approaches such as pan-sharpening Landsat imagery to 15 m using the panchromatic band have been applied in other land cover studies, this option was not suitable for our analysis. The Landsat panchromatic band does not include the shortwave infrared (SWIR) wavelengths that are essential for MVI calculation, and pan-sharpening can introduce spectral distortions that may reduce the reliability of vegetation indices in complex coastal environments. For these reasons, we prioritized spectral integrity and methodological consistency. Nevertheless, future studies could explore pan-sharpened datasets as complementary inputs, particularly for object-based classifications or fine-scale land cover mapping.

The InVEST Coastal Vulnerability Model simplifies the EI calculation by using a geometric mean and does not incorporate habitat quality and conservation status (Cabral et al., 2017). Data unavailability for sea level rise and geomorphology limited the analysis. Furthermore, the model does not account for cyclone damage occurring inland—a significant omission in Mozambique, where lightweight housing often results in severe impacts hundreds of kilometers from the coast (Cabral et al., 2017).

Mozambique’s multi-tiered disaster management system—led by the National Institute for Disaster Management (INGC) and supported by regional centers and community committees—is reinforced by anticipatory action protocols and early warning systems (IFRC, 2013). This article strengthens that framework by providing a spatially explicit assessment of mangrove loss and cyclone vulnerability using MVI and the InVEST model. The findings support targeted intervention, mangrove restoration, and integration of Nature-based Solutions into national disaster preparedness and coastal adaptation strategies.

Mozambique has developed a comprehensive, multi-level approach to cyclone preparedness and response, combining strong national coordination through the National Institute for Disaster Management (INGC), regional specialization via emergency operation centers (CENOE) in Vilankulos, Caia, and Nacala, and grassroots engagement through local Disaster Risk Management Committees. This system is complemented by community-based early warning mechanisms and anticipatory action frameworks, including the Early Action Protocol (EAP) for cyclones and forecast-based financing supported by the Mozambique Red Cross Society and the World Food Program (IFRC, 2013). Within this framework, the present article provides a timely and valuable contribution by delivering a spatially explicit assessment of mangrove loss and cyclone vulnerability. It strengthens Mozambique’s national DRR planning, aligning closely with the objectives of the Plano Director para a Redução do Risco de Desastres 2017–2030 (PDRRD), which emphasizes integrated risk management, territorial planning, and ecosystem-based adaptation (PDRRD, 2017).

The combined use of the MVI and the InVEST Coastal Vulnerability Model offers operational decision support for implementing Nature-based Solutions (NbS), identifying restoration priorities, and mainstreaming resilience into development strategies. By identifying priority zones for mangrove restoration and highlighting high-risk coastal segments, this study supports both defense and environmental institutions in proactively reducing cyclone-related impacts. It also serves broader cross-sectoral goals—from blue economy development to land-use planning—ultimately strengthening Mozambique’s resilience in the face of escalating climate risks.

Despite its strengths, this study does not include accuracy estimates for the MVI, limiting confidence in its precision, a common limitation in large-scale mangrove remote sensing studies, where logistical and financial constraints often hinder systematic ground data collection (Giri et al., 2011; Zhang et al., 2017; Baloloy et al., 2020). Future research should focus on validating MVI performance across ecosystems, incorporating time-series analyses, and targeted field validation.

In conclusion, this study advances mangrove monitoring by combining MVI mapping with coastal vulnerability modeling, emphasizing the urgent need for conservation in Mozambique’s most at-risk provinces. By providing a scalable, data-driven approach, our workflow supports global mangrove management efforts and contributes to safeguarding ecosystem services that are vital to coastal communities (Liu et al., 2008; Zhang et al., 2017; Baloloy et al., 2020).

This study provides the first national-scale analysis combining mangrove cover change and coastal vulnerability in Mozambique using the MVI and the InVEST Coastal Vulnerability Model. Results reveal a significant decline in mangrove extent between 2013 and 2023, with a 18% national loss and more severe reductions in provinces facing the highest exposure to coastal hazards. This spatial correlation underscores the urgent need to integrate mangrove conservation into coastal risk management strategies.

The MVI proved effective for rapid, large-scale mangrove monitoring, while the InVEST model highlighted areas where natural habitats play a critical role in mitigating erosion and flooding. However, both tools have limitations. The MVI requires further validation, and the InVEST model excludes important variables such as inland cyclone impacts and habitat condition. These gaps call for targeted field validation and the integration of additional data layers, including sea-level rise and geomorphology, to enhance future assessments.

Given Mozambique’s high vulnerability to climate extremes and rapid coastal development, the findings support the prioritization of Nature-based Solutions, particularly mangrove restoration, in spatial planning. Protecting and rehabilitating mangroves in high-risk areas such as Zambezia, Sofala, and Maputo provinces will be essential to safeguard ecosystems, livelihoods, and infrastructure in the face of escalating climate threats.