Search Results (329)

Climbing vines have recently induced increasing threats to forest growth under favourable environmental changes. In mangrove forests, the native vine Derris trifoliata became invasive and is now one of the main threats. Yet current management relies on manual removal with low efficiency. Exploring an alternative, cost-effective method is required. To assess the potential of a proposed biological control method, this study performed a pot-plant experiment using Cuscuta japonica to infect D. trifoliata and three common mangrove species in Beihai, China. Results showed that D. trifoliata had a higher infection rate and high host mortality (90%) than mangrove (0%). It also had significantly decreased moisture by 4%, nitrogen by 14%, phosphorus by 27%, potassium by 49% and increased soluble sugar by 49% and protein by 20%, whereas only moisture (2% reduction) and one or two minerals of Excoecaria agallocha and Aegiceras corniculatum were influenced. Only Kandelia obovata had neither effective haustoria nor any nutrients impact from the infection. This study indicated that C. japonica can cause more damage to D. trifoliata than to mangrove species and has the potential to be used as a biological control agent for the threatened mangrove forests of A. corniculatum and K. obovata with monitoring and control. Further field tests are required to bring this method into practice. Full article

Coastal ecosystems are increasingly threatened by climate change and anthropogenic pressures, which can disrupt microbial communities and favor the emergence of pathogenic organisms. In this study, we applied metagenomic analysis to characterize fungal communities in sediment samples from an urban mangrove subjected to environmental stress. The results revealed a fungal community with reduced richness—28% lower than expected for similar ecosystems—likely linked to physicochemical changes such as heavy metal accumulation, acidic pH, and eutrophication, all typical of urbanized coastal areas. Notably, we detected an increase in potentially pathogenic genera, including Candida, Aspergillus, and Pseudoascochyta, alongside a decrease in key saprotrophic genera such as Fusarium and Thelebolus, indicating a shift in ecological function. The fungal assemblage was dominated by the phyla Ascomycota and Basidiomycota, and despite adverse conditions, symbiotic mycorrhizal fungi remained present, suggesting partial resilience. A considerable fraction of unclassified fungal taxa also points to underexplored microbial diversity with potential ecological or health significance. Importantly, this study does not aim to compare pristine and contaminated environments, but rather to provide a sanitary alert by identifying the presence and potential proliferation of pathogenic fungi in a degraded mangrove system. These findings highlight the sensitivity of mangrove fungal communities to environmental disturbance and reinforce the value of metagenomic approaches for monitoring ecosystem health. Incorporating fungal metagenomic surveillance into environmental management strategies is essential to better understand biodiversity loss, ecological resilience, and potential public health risks in degraded coastal environments. 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

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. 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

Mangrove forests serve as critical ecological barriers in coastal zones and play a vital role in global blue carbon sequestration strategies. In recent decades, China’s mangrove ecosystems have experienced complex interactions between degradation and restoration under intense coastal urbanization and systematic conservation efforts. However, the long-term spatiotemporal patterns and driving mechanisms of mangrove ecosystem health changes remain insufficiently quantified. This study developed a multi-temporal analytical framework using Landsat imagery (1986–2021) to derive kernel normalized difference vegetation index (kNDVI) time series—an advanced phenological indicator with enhanced sensitivity to vegetation dynamics. We systematically characterized mangrove growth patterns along China’s southeastern coast through integrated Theil–Sen slope estimation, Mann–Kendall trend analysis, and Hurst exponent forecasting. A Deep Forest regression model was subsequently applied to quantify the relative contributions of environmental drivers (mean annual sea surface temperature, precipitation, air temperature, tropical cyclone frequency, and relative sea-level rise rate) and anthropogenic pressures (nighttime light index). The results showed the following: (1) a nationally significant improvement in mangrove vitality (p < 0.05), with mean annual kNDVI increasing by 0.0072/yr during 1986–2021; (2) spatially divergent trajectories, with 58.68% of mangroves exhibiting significant improvement (p < 0.05), which was 2.89 times higher than the proportion of degraded areas (15.10%); (3) Hurst persistence analysis (H = 0.896) indicating that 74.97% of the mangrove regions were likely to maintain their growth trends, while 15.07% of the coastal zones faced potential degradation risks; and (4) Deep Forest regression id the relative rate of sea-level rise (importance = 0.91) and anthropogenic (nighttime light index, importance = 0.81) as dominant drivers, surpassing climatic factors. This study provides the first national-scale, 30 m resolution assessment of mangrove growth dynamics using kNDVI, offering a scientific basis for adaptive management and blue carbon strategies in subtropical coastal ecosystems. 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

The focus of this study was on young populations of Kandelia obovata within a constructed coastal wetland in Haicang Bay, Xiamen, Southeast China. The objective was to systematically examine their allometric growth characteristics and carbon sequestration potential over an 8-year monitoring period (2016–2024). Allometric equations were developed to estimate biomass, and the spatiotemporal variation in both plant and soil carbon stocks was estimated. There was a significant increase in total biomass per tree, from 120 ± 17 g at initial planting to 4.37 ± 0.59 kg after 8 years (p < 0.001), with aboveground biomass accounting for the largest part (72.2% ± 7.3%). The power law equation with D²H as an independent variable yielded the highest predictive accuracy for total biomass (R² = 0.957). Vegetation carbon storage exhibited an annual growth rate of 4.2 ± 0.8 Mg C·ha⁻¹·yr⁻¹. In contrast, sediment carbon stocks did not show a significant increase throughout the experimental period, although long-term accumulation was observed. The restoration of mangroves in urban coastal constructed wetlands is an effective measure to sequester carbon, achieving a carbon accumulation rate of 21.8 Mg CO₂eq·ha⁻¹·yr⁻¹. This rate surpasses that of traditional restoration methods, underscoring the pivotal role of interventions in augmenting blue carbon sinks. This study provides essential parameters for allometric modeling and carbon accounting in urban mangrove afforestation strategies, facilitating optimized restoration management and low-carbon strategies. Full article

This paper systematically reviews the progress of remote-sensing technology in coastal-ecosystem health assessment. Coastal ecosystems, as transitional zones between land and ocean, play vital roles in maintaining biodiversity, carbon sequestration, and coastal protection, but currently face severe challenges from climate change and human activities. Remote-sensing technology, with its capability for large-scale, long time-series observations, has become a key tool for coastal-ecosystem health assessment. This paper analyzes the technical characteristics and advantages of optical remote sensing, radar remote sensing, and multi-source data fusion in coastal monitoring; constructs a health-assessment framework that includes water-quality indicators, vegetation and ecosystem function indicators, and human disturbance and landscape change indicators; discusses the application of advanced technologies from traditional methods to machine learning and deep learning in data processing; and demonstrates the role of multi-temporal analysis in revealing coastal-ecosystem change trends through typical case studies of mangroves, salt marshes, and coral reefs. Research indicates that, despite the enormous potential of remote-sensing technology in coastal monitoring, it still faces challenges such as sensor limitations, environmental interference, and data processing and validation. Future development should focus on advanced sensor technology, platform innovation, data-processing method innovation, and multi-source data fusion, while strengthening the effective integration of remote-sensing technology with management practices to provide scientific basis for the protection and sustainable management of coastal ecosystems. Full article

The world’s largest mangrove, Sundarbans, Bangladesh, is the habitat of the euryhaline catfish Nona Tengra (Mystus gulio). This study aimed to assess the stock status of M. gulio and provide reference points for sustainable fisheries’ management. One-year length–frequency (LF) data were collected from the Sundarbans region of Bangladesh and analyzed using the Length-Based Bayesian Biomass (LBB) method and the Length-Based Spawning Potential Ratio (LBSPR) model. The findings showed healthy biomass (B/B_MSY = 1.2), with 57% of the wild stock of this species being harvested (B/B₀ = 0.43). The calculated fishing mortality ratio indicated the underfishing conditions (F/M = 0.9). Safe exploitation (E = 0.46) was depicted, as E was smaller than the permitted level of 0.5. The value of capture length (L_c = 12.8 cm) was larger than the optimum capture length (L_{c_opt} = 10.0 cm) and the optimum length for maximum yield per recruit (L_opt = 12.0 cm) and larger than the maturity length (L_m = 9.16 cm), indicating the capture of mature individuals. The calculated Spawning Potential Ratio (SPR = 48%) was higher than the target reference points (SPR = 40%). This research evaluated the sustainable stock status. Although the margin between L_{c_opt} and L_m is very narrow, setting the minimum capture size at L_opt would be a conservative buffer to ensure long-term sustainability. The recommended minimum harvest size is 12 cm for M. gulio. Current fishing gear selectivity can ensure the sustainability of M. gulio in Sundarbans, Bangladesh; however, maintaining current fishing practice through careful management is suggested. Further assessments with length-based and other low-data methods should be conducted to refine exploitation estimates and trends. Full article

This review synthesizes four decades (1980–2024) of marine oil spill research in the Philippines, analyzing 80 peer-reviewed publications sourced from Scopus, Web of Science, Clarivate, and Google Scholar. Findings show that oil spill research activity spikes after major spills, particularly the 2006 Guimaras incident, which accounts for over half of the reviewed studies and were mostly concentrated in the field of biology, followed by social sciences. Mangroves are the most studied as they are the widely affected ecosystem in the Philippines. Despite the number of published articles on oil spills in the Philippines, only the major events were emphasized, and small-scale spills remain under documented. Research on small-scale oil spills and the country’s two recent big oil spills (Mindoro Oil Spill and Manila Bay Oil Spill), particularly in a country’s environmentally sensitive areas, must be conducted in collaboration with academic institutions and relevant stakeholders to gain a deeper understanding and formulate appropriate countermeasures in the event of future spills. The review also highlights limited application of advanced techniques such as hydrocarbon fingerprinting, geospatial analysis, and next-generation DNA sequencing, limiting comprehensive assessments of oil fate and ecological effects. Addressing these gaps through interdisciplinary collaboration is critical to improving oil spill response, environmental management, and policy formulation in the Philippines’ complex archipelagic setting. Full article

The integration of artificial intelligence (AI) and forestry is driving transformative advances in precision monitoring, disaster management, carbon sequestration, and biodiversity conservation. However, significant knowledge gaps persist in cross-ecological model generalisation, multi-source data fusion, and ethical implementation. This review provides a comprehensive overview of AI’s transformative role in forestry, focusing on three key areas: resource monitoring, disaster management, and sustainability. Data were collected via a comprehensive literature search of academic databases from 2019 to 2025. The review identified several key applications of AI in forestry, including high-precision resource monitoring with sub-metre accuracy in delineating tree canopies, enhanced disaster management with high recall rates for wildfire detection, and optimised carbon sequestration in mangrove forests. Despite these advancements, challenges remain in cross-ecological model generalisation, multi-source data fusion, and ethical implementation. Future research should focus on developing robust, scalable AI models that can be integrated into existing forestry management systems. Policymakers and practitioners should collaborate to ensure that AI-driven solutions are implemented in a way that balances technological innovation with ecosystem resilience and ethical considerations. Full article

Mangrove forests are vital ecosystems with the highest global carbon absorption capacity, playing a crucial role in climate change mitigation. Therefore, their conservation and management are essential. However, as mangroves are primarily found in tropical regions, frequent cloud cover and limited accessibility pose significant challenges to effective monitoring using optical satellite imagery. In addition, many developing countries with extensive mangrove coverage face challenges in conducting precise monitoring due to limited technological infrastructure. To overcome these limitations, this study integrated open-access synthetic aperture radar (SAR) data with optical imagery to enhance the classification accuracy of mangrove forests in the Bali Denpasar–Badung region. The Sentinel-1 and Sentinel-2 datasets were used, and the U-Net deep learning model was employed for training and classification. A digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) was applied to exclude areas higher than 10 m above sea level, thereby improving the classification accuracy. Additionally, a time-series analysis was performed to assess changes in the mangrove distribution over the past decade, revealing a consistent increase in mangrove extent in the study area. The classification performance was evaluated using a confusion matrix, demonstrating that the combined SAR-optical model outperformed single-source models across all key metrics including precision, accuracy, recall, and F1-score. The findings highlight the effectiveness of integrating SAR and optical data for capturing the complex ecological and geographical characteristics of mangrove forests. Notably, SAR imagery, which is resistant to cloud cover, shows considerable potential for independent application in tropical mangrove monitoring, warranting further research to explore its capabilities in greater depth. Full article

Coastal blue carbon ecosystems serve as vital carbon sinks in global climate regulation, yet their long-term carbon storage dynamics remain poorly quantified at regional scales. This study quantified the spatiotemporal evolution of mangrove and salt marsh carbon storage in Guangdong Province, China, over three decades (1986–2020), by integrating a new mangrove and salt marsh detection framework based on Landsat image time series and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The proposed detection framework provided two coastal vegetation detection methods, exploring the potential of utilizing phenological features to improve the mangrove and salt marsh discrimination accuracy with Landsat data. The overall accuracies of both mangrove and salt marsh detection results exceeded 90%, suggesting good consistency with the validation data. The mangrove extent showed a trend of decreasing from 1986 to 1995, then fluctuated from 1995 to 2005, and presented an upward trend from 2005 to 2020. The overall trend of the salt marsh area was upward, with small fluctuations. The mangrove carbon storage in Guangdong increased from 414.66 × 10⁴ Mg C to 490.49 × 10⁴ Mg C during 1986–2020, with Zhanjiang having the largest mangrove carbon storage increase. The salt marsh carbon storage in Guangdong grew from 8.73 × 10⁴ Mg C in 1986 to 14.39 × 10⁴ Mg C in 2020, with Zhuhai as the salt marsh carbon sequestration hotspot. The temporal dynamics of carbon storage in mangroves and salt marshes could be divided into three stages, namely a decreasing period, a fluctuating period, and a rapid increase period, during which ecological and economic policies played a crucial role. The multi-decadal blue carbon datasets and their temporal-spatial change analysis results here can provide a scientific basis for nature-based climate solutions and decision-support tools for carbon offset potential realization and sustainable coastal zone management. Full article