Search Results (235)

Mangroves in Sri Lanka provide critical ecosystem services, yet they have undergone significant changes due to anthropogenic and natural drivers. This study presents the first national-scale assessment of mangrove dynamics in Sri Lanka using remote sensing techniques. A total of 4670 Landsat images from Landsat 5, 7, 8, and 9 were selected to detect mangrove distribution, changes in extent, and structure and stability patterns from 1987 to 2022. A Random Forest classification model was applied to elucidate the spatial changes in mangrove distribution in Sri Lanka. Using national-scale data enhanced mapping accuracy by incorporating region-specific spectral and ecological characteristics. The average overall accuracy of the maps was over 96.29%. The total extent of mangroves in 2022 was 16,615 ha, representing 0.25% of the total land of Sri Lanka. The results further indicate that, at the national scale, mangrove extent increased from 1989 to 2022, with a net gain of 1988 ha (13.6%), suggesting a sustained and continuous recovery of mangroves. Provincial-wise assessments reveal that the Eastern and Northern Provinces showed the largest mangrove extents in Sri Lanka. In contrast, the Colombo, Gampaha, and Kalutara districts in the Western Province showed persistent declines. The top mangrove spatial structure and stability districts were Jaffna, Trincomalee, and Gampaha, while the most degraded mangrove districts were Batticaloa, Colombo, and Kalutara. This study offers critical insights into sustainable mangrove management, policy implementation, and climate resilience strategies in Sri Lanka. Full article

Mangrove forests are among the world’s most vital coastal ecosystems. Mapping mangrove cover from local to global scales using spectral data and machine learning models is a well-established method. While non-spectral contextual datasets (spatial features) have also been incorporated into such models, the contribution of these additional features to improving mangrove mapping remains underexplored. Using the Mesoamerican Reef Region as a case study, we evaluate the effectiveness of incorporating spatial features in binary mangrove classification to enhance mapping accuracy. We compared an aspatial model that includes only spectral data with three spatial models: two included features such as geographic coordinates, elevation, and proximity to coastlines and streams, while the third integrated a geostatistical approach using Inverse Distance Weighted (IDW) interpolation. Spectral inputs included bands and indices derived from Sentinel-1 and Sentinel-2, and all models were implemented using the Random Forest algorithm in Google Earth Engine. Results show that spatial features reduced omission errors without increasing commission errors, enhancing the model’s ability to capture spatial variability. Models using geographic coordinates and elevation performed comparably to those with additional environmental variables, with storm frequency and distance to streams emerging as important predictors in the Mesoamerican Reef region. In contrast, the IDW-based model underperformed, likely due to overfitting and limited representation of local spectral variation. Spatial analyses show that models incorporating spatial features produced more continuous mangrove patches and removed some false positives in non-mangrove areas. These findings highlight the value of spatial features in improving classification accuracy, especially in regions with ecologically diverse mangroves across varied environments. By integrating spatial context, these models support more accurate, locally relevant mangrove maps that are essential for effective conservation and management. Full article

Mangroves play a crucial part in the worldwide blue carbon cycle because they store a lot of carbon in their biomass and soil. Accurate estimation of aboveground biomass (AGB) is essential for quantifying carbon stocks and understanding ecological responses to climate and human disturbances. However, regional-scale AGB mapping remains difficult due to fragmented mangrove distributions, limited field data, and cross-site heterogeneity. To address these challenges, we propose a Spatial Attention Coupled Bayesian Aggregator (SAC-BA), which integrates field measurements with multi-source remote sensing (Landsat 8, Sentinel-1), terrain data, and climate variables using advanced ensemble learning. Four machine learning models (Random Forest (RF), Cubist, Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost)) were first trained, and their outputs were fused using Bayesian model averaging with spatial attention weights and constraints based on Local Indicators of Spatial Association (LISAs), which identify spatial clusters (e.g., high–high, low–low) to improve accuracy and spatial coherence. SAC-BA achieved the highest performance (coefficient of determination (${\mathrm{R}}^2$) = 0.82, root mean square error = 29.90 Mg/ha), outperforming all individual models and traditional BMA. The resulting 30-m AGB map of Indonesian mangroves in 2017 estimated a total of 217.17 × ${\mathrm{10}}^6$ Mg, with a mean of 103.20 Mg/ha. The predicted AGB map effectively captured spatial variability, reduced noise at ecological boundaries, and maintained high confidence predictions in core mangrove zones. These results highlight the advantages of incorporating spatial structure and uncertainty into ensemble modeling. SAC-BA provides a reliable and transferable framework for regional AGB estimation, supporting improved carbon assessment and mangrove conservation efforts. Full article

Mangrove forests are widely recognized for their effectiveness as carbon sinks and serve as critical ecosystems for mitigating the effects of climate change. Current research lacks comprehensive, large-scale carbon storage datasets for wetland ecosystems, particularly across Mexico and other understudied regions worldwide. Therefore, the objective of this study was to develop a high spatial resolution map of carbon stocks, encompassing both aboveground and belowground components, within the Marismas Nacionales system, which is the largest mangrove complex in northeastern Pacific Mexico. Our approach integrates primary field data collected during 2023–2024 and incorporates some historical plot measurements (2011–present) to enhance spatial coverage. These were combined with contemporary remote sensing data, including Sentinel-1, Sentinel-2, and LiDAR, analyzed using Random Forest algorithms. Our spatial models achieved strong predictive accuracy (R² = 0.94–0.95), effectively resolving fine-scale variations driven by canopy structure, hydrologic regime, and spectral heterogeneity. The application of Local Indicators of Spatial Association (LISA) revealed the presence of carbon “hotspots,” which encompass 33% of the total area but contribute to 46% of the overall carbon stocks, amounting to 21.5 Tg C. Notably, elevated concentrations of carbon stocks are observed in the central regions, including the Agua Brava Lagoon and at the southern portion of the study area, where pristine mangrove stands thrive. Also, our analysis reveals that 74.6% of these carbon hotspots fall within existing protected areas, demonstrating relatively effective—though incomplete—conservation coverage across the Marismas Nacionales wetlands. We further identified important cold spots and ecotones that represent priority areas for rehabilitation and adaptive management. These findings establish a transferable framework for enhancing national carbon accounting while advancing nature-based solutions that support both climate mitigation and adaptation goals. Full article

Mangrove forests are vital blue carbon (BC) ecosystems that significantly contribute to climate change mitigation through carbon sequestration. Accurate, scalable, and cost-effective methods for estimating carbon stocks in these environments are essential for conservation planning. In this study, we assessed the potential of drones, also known as unmanned aerial vehicles (UAVs), for estimating above-ground biomass (AGB) and BC in Avicennia marina stands by integrating drone-based canopy measurements with field-measured tree heights. Using structure-from-motion (SfM) photogrammetry and a consumer-grade drone, we generated a canopy height model and extracted structural parameters from individual trees in the Melgonze mangrove patch, southern Iran. Field-measured tree heights served to validate drone-derived estimates and calibrate an allometric model tailored for A. marina. While drone-based heights differed significantly from field measurements (p < 0.001), the resulting AGB and BC estimates showed no significant difference (p > 0.05), demonstrating that crown area (CA) and model formulation effectively compensate for height inaccuracies. This study confirms that drones can provide reliable estimates of BC through non-invasive means—eliminating the need to harvest, cut, or physically disturb individual trees—supporting their application in mangrove monitoring and ecosystem service assessments, even under challenging field conditions. Full article

Mangroves, as a key component of the blue-carbon ecosystem, have exceptional carbon sequestration capacity and are mainly distributed in tropical coastal regions. In the Solomon Islands, ongoing degradation of mangrove forests, primarily due to land conversion and timber exploitation, highlights an urgent need for high-resolution spatial data to inform effective conservation strategies. The present study introduces an efficient and accurate methodology for mapping mangrove habitats and prioritizing protection areas utilizing open-source satellite imagery and datasets available through the Google Earth Engine platform in conjunction with a U-Net deep learning algorithm. The model demonstrates high performance, achieving an F1-score of 0.834 and an overall accuracy of 0.96, in identifying mangrove distributions. The total mangrove area in the Solomon Islands is estimated to be approximately 71,348.27 hectares, accounting for about 2.47% of the national territory. Furthermore, based on the mapped mangrove habitats, an optimized hotspot analysis is performed to identify regions characterized by high-density mangrove distribution. By incorporating spatial variables such as distance from roads and urban centers, along with mangrove area, this study proposes priority mangrove protection areas. These results underscore the potential for using openly accessible satellite data to enhance the precision of mangrove conservation strategies in data-limited settings. This approach can effectively support coastal resource management and contribute to broader climate change mitigation strategies. Full article

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope. Full article

Mangrove wetlands, acting as significant traps for marine debris, have received insufficient attention in previous research. Here, we conduct the first comprehensive investigation into the magnitude, accumulation, source, and fate of marine debris across seven mangrove areas in the northern South China Sea (MNSCS) during 2019–2020. Systematic field surveys employed stratified random sampling, partitioning each site by vegetation density and tidal influence. Marine debris were collected and classified in sampling units by material (plastic, fabric, styrofoam), size (categorized into small, medium, and large), and origin (distinguishing between land-based and sea-based). Source identification and potential risk assessment were achieved through the integration of debris feature analysis. The results indicate relatively low debris levels in MNSCS mangroves, with plastics dominant. More than 70% of all debris weight with plastics (48.34%) and fabrics (14.59%) is land-based, and more than 70% comes from coastal/recreational activities. More than 90% of all debris items with plastics (52.50%) and Styrofoam (36.32%) are land-based, and more than 90% come from coastal/recreational activities. Medium/large-sized debris are trapped in mangrove wetlands under the influencing conditions of local tidal level, debris item materials, and sizes. Our study quantifies marine debris characteristics, sources, and ecological potential risks in MNSCS mangroves. From environmental, economic, and social sustainability perspectives, our findings are helpful for guiding marine debris management and mangrove conservation. By bridging research and policies, our work balances human activities with ecosystem health for long-term sustainability. Full article

This study presents the first comprehensive nationwide assessment of mangrove ecosystems in the Maldives. Surveys were conducted across 162 islands in 20 administrative atolls, integrating field data, the literature, and secondary sources to map mangrove distribution, confirm species presence, and classify habitat types. Twelve true mangrove species were identified, with Bruguiera cylindrica, Rhizophora mucronata, and Lumnitzera racemosa emerging as dominant. Species diversity was evaluated using Shannon (H′), Margalef (d′), Pielou’s evenness (J′), and Simpson’s dominance (λ′) indices. Atolls within the northern and southern regions, particularly Laamu, Noonu, and Shaviyani, exhibited the highest diversity and evenness, while central atolls such as Ari and Faafu supported mono-specific or degraded stands. Mangrove habitats were classified into four geomorphological types: marsh based, pond based, embayment, and fringing systems. Field sampling was conducted using standardized belt transects and quadrats, with species verified using photographic documentation and expert validation. Species distributions showed strong habitat associations, with B. cylindrica dominant in marshes, R. mucronata and B. gymnorrhiza in ponds, and Ceriops tagal and L. racemosa in embayments. Rare species like Bruguiera hainesii and Heritiera littoralis were confined to stable hydrological niches. This study establishes a critical, island-level baseline for mangrove conservation and ecosystem-based planning in the Maldives, providing a reference point for tracking future responses to climate change, sea-level rise, and hydrological disturbances, emphasizing the need for habitat-specific strategies to protect biodiversity. Full article

Mangrove ecosystems are crucial coastal habitats that support life and regulate the Earth’s atmosphere. However, these ecosystems face prominent threats due to anthropogenic activities and environmental constraints. For instance, the Saudi Arabian coast is particularly vulnerable to species extinction and biodiversity loss due to the fragility of the ecosystem; this highlights the need to understand the spatial and temporal dynamics of mangrove forests in desert environments. Hence, this is the first national study to quantify mangrove forests and analyze at-risk zone-based land use along Saudi Arabian coasts over 40 years. Thus, the primary contents of this research were (1) to produce a new long-term dataset covering the entire Saudi coastline, (2) to identify the patterns, analyze the trends, and quantify the change of mangrove areas, and (3) to determine vulnerability zoning of mangrove area-based land use and transportation networks. This study used Landsat satellite imagery via Google Earth Engine for national-scale mangrove mapping of Saudi Arabia between 1985 and 2024. Visible and infrared bands and seven spectral indices were employed as input features for the random forest classifier. The two classes used were mangrove and non-mangrove; the latter class included non-mangrove land-use and land-cover areas. Then, the study employed the output mangrove mapping to delineate vulnerable mangrove forest-based land use. The overall results showed a substantial increase in mangrove areas, ranging from 27.74 to 59.31 km² in the Red Sea and from 1.05 to 8.65 km² in the Arabian Gulf between 1985 and 2024, respectively. However, within this decadal trend, there were noticeable periods of decline. The spatial coverage of mangroves was larger on Saudi Arabia’s western coasts, especially the southwestern coasts, than on its eastern coasts. The overall accuracy, conducted annually, ranged between 91.00% and 98.50%. The results also show that expanding land uses and transportation networks within at-risk zones of mangrove forests may have a high potential effect. This study aimed to benefit the government, conservation agencies, coastal planners, and policymakers concerned with the preservation of mangrove habitats. Full article

: Mangrove forests are one of the ecosystems with the richest biodiversity and the highest functional value of ecosystem services in the world. For mangrove research, it is particularly important to facilitate mangrove mapping, plant species classification, biomass, and carbon sink estimation using remote sensing technologies. Recently, more and more studies have combined unmanned aerial vehicles and remote sensing technology to estimate plant traits and the biomass of mangrove forests. Various multispectral and hyperspectral data are used to establish various vegetation indices for plant classification, and data models for biomass estimation and carbon sink calculation. This study systematically reviews the use of remote sensing and unmanned aerial vehicles in mangrove studies during the past three decades based on 2424 peer-reviewed papers. By synthesizing these studies, we identify the pros and cons of different indices and models developed from remote sensing technologies by sorting out past cases. Specifically, we review the use of remote sensing technologies in mapping the past and present area, plant species composition, and biomass of mangrove forests and examine the threats to the degradation of mangrove forests. Our findings reveal that there is increasing integration of machine learning and remote sensing to facilitate mangrove mapping and species identification. Moreover, multiple sources of remote sensing data tend to be combined to improve species classification accuracy and enhance the precision of mangrove biomass estimates when integrated with field-based data. Full article

Mangroves serve as critical transitional ecosystems between land and sea. However, their large-scale possible impacts on coastal water quality have not been investigated. This study systematically examined the possible impacts of mangrove dynamics on coastal water quality in China over a 20-year period (1998–2018). Theil–Sen trend analysis and Mann-Kendall tests were employed to assess long-term trends of mangrove area and four water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), particulate attenuation coefficient at 660 nm (Cp660), and seawater transparency (Secchi disk depth, SDD). Partial correlation analysis and convergent cross-mapping (CCM) techniques were applied to evaluate the relationships between mangroves and water quality parameters, while a factor detector was used to quantify the specific contribution of mangroves to water quality improvement. The results revealed the following: (1) a significant nationwide expansion of mangroves, particularly after 2005, accompanied by accelerated recovery rates; (2) notable variations in water quality indicators, with SDD and CDOM experiencing degradation, while Chl-a and Cp660 showed varying degrees of improvement; (3) statistical evidence indicating that mangrove expansion was negatively partially correlated with Chl-a concentrations, and had moderate effects on CDOM, Cp660, and SDD. These findings highlight the measurable role of mangroves in improving coastal water quality at a national scale, provide a robust scientific basis for integrated coastal zone management, and underscore the need for further investigation into the underlying mechanisms, with comprehensive consideration of the dynamic impacts of climate change and anthropogenic activities. Full article

Mangroves are critical ecosystems that provide essential environmental services, such as climate regulation, carbon storage, biodiversity conservation, and shoreline protection, making their conservation vital. High-resolution remote sensing using unmanned aerial vehicles (UAVs) offers a powerful tool for detailed mapping of mangrove species and structure. This study applies geographic object-based image analysis (GEOBIA) combined with machine learning (ML) to classify mangrove species at two ecologically distinct sites in Brazil: Cardoso Island (São Paulo State) and Suape (Pernambuco State). UAV flights at 50 m and 120 m altitudes captured multispectral data, enabling species-level classification of Laguncularia racemosa, Rhizophora mangle, and Avicennia schaueriana. By integrating field measurements and advanced metrics such as texture and spectral indices, the workflow achieved precise delineation of tree crowns and spatial distribution mapping. The results demonstrate the superiority of this approach over traditional methods, offering scalable and adaptable tools for ecological monitoring and conservation. The findings highlight the potential of UAV-based multispectral imaging to improve mangrove conservation efforts by delivering actionable, fine-scale data for policymakers and stakeholders. Full article

Producing high-quality local land cover data can be cost-prohibitive, leaving gaps in reliable estimates of forest cover and loss for environmental policy and planning. Remote sensing data (RSD) offer accessible, globally consistent layers for forest mapping. However, being able to produce reliable RSD-based land cover products with high local fidelity requires ground truth data, which are scarce and cost-intensive to obtain in settings like Madagascar. Global land cover datasets that rely on models trained mostly in well-studied regions claim to alleviate the problem of label scarcity. However, studies have shown that these products often fail to fulfill this promise. Given downstream studies focused on Madagascar still rely on these global land cover products, in this study we compared seven global RSD products measuring forest extent and change in Madagascar to explore levels of similarity across different forest ecoregions over multiple years. We also conducted temporal correlation analysis by checking the correlation between forest area from the different products. We found that agreement levels among the different data products varied by forest type and region, with higher disagreement levels in drier forest ecosystems (dry and spiny forests) than in more humid ones (moist forests and mangroves). For instance, if high agreement is defined as a pixel being classified as a forest by all or all but one product in a year, the average percentage of high-agreement pixels between 2016 and 2020 is just about 8% in the spiny forest and 16% in the dry forest region. These findings underscore the limitations of global RSD products and the importance of localized data for accurate forest monitoring, building justification for efforts to develop a local forest cover product for Madagascar. Our temporal similarity analysis indicates that, although pixel-level maps may show low agreement, temporal aggregates tend to be highly correlated in most cases. We synthesized these results with existing applications of global RSDs in Madagascar to propose practical recommendations for end-users of these products in Madagascar. Full article

Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity and maintaining marine ecological balance. It is crucial to develop more efficient, intelligent, and accurate monitoring methods for mangroves to understand better and protect mangrove ecosystems. This study promotes a novel model, MangroveNet, for integrating multi-scale spectral and spatial information and detecting mangrove area. In addition, we also present an improved model, AttCloudNet+, to identify the distribution of mangrove species based on high-resolution multispectral drone images. These models incorporate spectral and spatial attention mechanisms and have been shown to effectively address the limitations of traditional methods, which have been prone to inaccuracy and low efficiency in mangrove species identification. In this study, we compare the results from MangroveNet with SegNet, UNet, and DeepUNet, etc. The findings demonstrate that the MangroveNet exhibits superior generalization learning capabilities and more accurate extraction outcomes than other deep learning models. The accuracy, F1_Score, mIoU, and precision of MangroveNet were 99.13%, 98.84%, 98.11%, and 99.14%, respectively. In terms of identifying mangrove species, the prediction results from AttCloudNet+ were compared with those obtained from traditional supervised and unsupervised classifications and various machine learning and deep learning methods. These include K-means clustering, ISODATA cluster analysis, Random Forest (RF), Support Vector Machines (SVM), and others. The comparison demonstrates that the mangrove species identification results obtained using AttCloudNet+ exhibit the most optimal performance in terms of the Kappa coefficient and the overall accuracy (OA) index, reaching 0.81 and 0.87, respectively. The two comparison results confirm the effectiveness of the two models developed in this study for identifying mangroves and their species. Overall, we provide an efficient solution based on deep learning with a dual attention mechanism in the acceptable real-time monitoring of mangroves and their species using high-resolution multispectral drone imagery. Full article