Mangrove forests in southern China’s Gaoqiao Mangrove National Nature Reserve (Guangdong–Guangxi border) have undergone significant decline followed by partial recovery, driven by human activities and conservation efforts. Traditional monitoring methods struggle to capture their complex spatiotemporal dynamics. This study develops a practical two-stage deep learning framework: an enhanced U-Net with Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) first extracts high-quality annual mangrove masks from multi-temporal Landsat imagery (1993–2023), achieving IoU = 0.815 and F1-score = 0.928. These masks are then used for spatiotemporal forecasting, with U-Net–ConvLSTM recommended as the primary architecture due to its excellent balance of accuracy, simplicity, and computational efficiency. An optional asymmetric Ecological Constraint Loss (ECOLOSS) can be added to form the ConvLSTM+ECOLOSS variant, providing marginal additional accuracy (IoU = 0.793 vs. 0.787, MAE = 6.70% vs. 6.83%) on the test period (2019–2023) by acting mainly as an ecological safeguard against unrealistic long-term runaway trends. Forecasts for 2024–2026 indicate continued slow recovery under current management. The U-Net–ConvLSTM pipeline offers a transparent and efficient tool for operational mangrove monitoring and conservation planning in subtropical China.

Tidal inundation plays a critical role in maintaining the ecosystem services of the Sundarbans mangrove forest. In this study, we configured and calibrated a coupled one-dimensional (1D) river network and two-dimensional (2D) floodplain hydrodynamic model for the Sundarbans in Bangladesh. Model calibration was performed using gauged water levels, inundation maps, and Google Earth (Version 7.3.6) imagery. Using the calibrated model, we assessed potential changes in inundation extent, depth, and duration across the Sundarbans for varying freshwater inflow and tidal height scenarios. Results show variation in inundation extent, depth, and duration spatially and temporarily across the Sundarbans. Inundation is relatively less during February-March (end of the dry season) and high in July-August (mid-wet season). Approximately 3158 km2 (85.1%) of the Sundarbans experiences at least one inundation in March, increasing to about 3658 km2 (98.6%) in July. Although a large proportion of the Sundarbans inundate during daily tidal cycles, the mean inundation depth remains shallow (0.24 to 0.33 m) due to flat topography. The influence of freshwater inflow on inundations is small (<2%). In contrast, the impacts of tidal magnitude are substantial on both inundation extent and depth. These findings provide valuable insights on inundation dynamics for understanding the hydrological and ecological functioning of the Sundarbans.

ABSTRACT Temporal unmixing, an extension of traditional spectral unmixing in a multi-temporal context, leverages endmembers defined by their temporal signatures to decompose mixed pixel responses into fractional cover. Although numerous methods that account for endmember variability have been successfully applied, there is a notable scarcity of theoretical frameworks to quantitatively elucidate how endmember variability induced by various spectral-temporal feature combinations impacts unmixing accuracy. The primary objective of this study is to formulate and analyze the quantitative relationship between endmember variability and temporal unmixing performance. To this end, we developed corresponding endmember variability measures (EMvar) and unmixing error measures at both the bi-class confusion level and the one-class aggregation level. We comprehensively investigated their relationship by constructing univariate linear and exponential models alongside a bivariate nonlinear model that integrates dataset dimensionality as an explanatory factor. Evaluations were conducted using extensive real and synthetic multi-temporal Sentinel-2 datasets covering Spartina alterniflora-invaded mangrove forest. Results demonstrate a robust correlation between EMvar and unmixing error measures at both levels (0.4 ≤ |r| ≤ 0.8), and this correlation is notably more pronounced at the one-class aggregation level. Beyond EMvar, dataset dimensionality played a pivotal role in explaining unmixing accuracy. While linear models sufficed for datasets of fixed dimensionality (0.3 ≤ R 2 ≤ 0.6), the exponential model provided a superior fit across varying dimensionalities (0.3 ≤ R 2 ≤ 0.7). Significantly, the bivariate model outperformed the univariate counterparts and achieved high predictive accuracy (R 2 ≈ 0.8). These results provide the first quantitative evidence that EMvar is a direct and predictable driver of unmixing accuracy. Consequently, our EMvar-unmixing error models establish a practical, predictive framework that shifts temporal unmixing from an empirical endeavor to a model-driven optimization process. This framework enables the a priori identification of optimal spectral-temporal features, paving the way for more efficient and accurate sub-pixel land cover monitoring.

Co-occurrence of metals and per- and polyfluoroalkyl substances (PFAS) in Ecuadorian mangrove sediments: influence of forest age and position along a salinity gradient.

Mangrove ecosystems play a critical role in sequestering heavy metals pollutants, yet the dynamics of heavy metals accumulation during mixed litter decomposition remain poorly understood. This study investigated the seasonal and species-specific variations in heavy metals accumulation during the decomposition of Kandelia obovata (KO) and Avicennia marina (AM) leaf litter mixtures in a subtropical mangrove forest in the Jiulong River Estuary, Fujian, China. Using the litterbag technique, we monitored eight heavy metals (V, Cr, Ni, Cu, Zn, As, Se, Cd) across three mixing ratios (KO:AM = 1:2, 1:1, 2:1) in summer and winter. Results revealed that V concentrations were influenced by both season and litter ratio, with higher KO proportions enhancing V accumulation in summer but reducing it in winter. In contrast, Cr, Ni, Cu, As, Se, and Cd were primarily regulated by litter ratios: KO-dominated mixtures promoted Cr and Ni accumulation, while AM-dominated mixtures favored Cu, As, Se, and Cd. Zn exhibited the highest variability and was unaffected by season or ratio. Total organic carbon (TOC) and carbon/metal (C/M) ratios significantly correlated with reduced bioavailability of most heavy metals, whereas total nitrogen (TN) and C/N ratios showed no consistent relationship. The heavy metals accumulation index (MAI) indicated higher accumulation in summer than in winter, with the highest MAI observed in the KO:AM = 2:1 treatment group during summer (MAI = 1.36), whereas winter decomposition slowed accumulation rates. These findings highlight the dual regulatory roles of species composition and environmental factors in mangrove heavy metals cycling, offering critical insights for ecological risk assessment and contaminated soil remediation strategies in coastal ecosystems.

Sediment physicochemistry outweighs anthropogenic factors in driving sediment heavy metals accumulation within mangrove forests across Hainan Island, China.

Abrasion can cause environmental damage, damage coastal infrastructure, and threaten the lives of communities living around the coast. One effort that can be made to prevent abrasion is planting mangroves along the coastal area. Mangrove forests are important habitats for marine organisms and act as coastal guards against abrasion. One area that has mangrove forests and is quite vulnerable to abrasion is Pulau Ketam Kuala Perlis. This activity aims to increase community awareness about the importance of mangroves in preventing abrasion, specifically in Pulau Ketam Kuala Perlis. The methods used include observation, interviews, and planting. The results of the Community Service Activity show that the community of understands and greatly benefits from mangrove planting efforts and abrasion prevention.

Seaward dynamic changes in mangrove forest over a tide-dominated estuary between 1986 and 2023

Ecological risk and source attribution of macro litter in the biodiverse blue carbon mangrove ecosystem along the Gulf of Mannar.

Tracking pesticides from upstream plantations to native bivalves in a tropical wetland habitat in Costa Rica.

The sources and export mechanisms of dissolved organic carbon in pore water of a mangrove swamp revealed by dual carbon isotopes

What drives mangrove forest gains? Evidence from six Southeast Asian countries using fixed effects panel data models

This study aims to estimate the community’s willingness to pay (WTP) for mangrove forest conservation efforts in Pantai Lango Village. Well as to explore public perceptions regarding the existence and benefits of mangroves in the area. The sampling technique employed was purposive sampling. Data were analyzed using non-parametric methods, including the Turnbull, Kaplan–Meier–Turnbull (K-M-T), and Spearman–Karber (S-K) approaches, along with descriptive analysis using a Likert scale. The results indicate that the average WTP estimated using the Turnbull method was Rp 27,000 per person. The K-M-T method produced an average value of Rp 34,500 per person, while the S-K method yielded Rp 25,750 per person. Community perceptions of mangrove conditions and the benefits provided by mangrove forests were generally categorized as fairly good and valuable. In contrast, perceptions related to mangrove management, existing threats, and the impact of the development of Indonesia’s new capital city (IKN) were classified as poor, moderately threatened, and highly influential.

Biomass characterisation is important for knowing the contained components in biomass. Some types of biomass studied were wood of acacia (Acacia mangium), gamal (Gliricidia sepium), coconut shell (Cocos nucifera) charcoal, and wood charcoal, for the gasification process through proximate analysis, ultimate analysis, and determination of high heating value (HHV). Ultimate analysis data utilised thermogravimetric analysis (TGA) for efficiency. The characterisation results show that coconut shell charcoal and wood charcoal have the highest fixed carbon content (72.66% and 68.98%) and higher C element (57.87% and 54.94%) as well as lower ash content compared to acacia wood and gamal wood, resulting in the highest calorific value (30.01 and 22.05 mJ/kg) and potentially better gasification performance. Conversely, acacia wood and gamal wood show relatively higher volatile matter content (64.03% and 76.38%) and ash (18.88%), resulting in lower calorific values. Overall, the research results indicate that coconut shell charcoal and wood charcoal are more recommended as the primary fuel in the gasification system.

Introduction: Mangroves provide many ecosystem services, yet they continue to be degraded and decline in numbers. Effective management strategies need baseline information to assess habitat condition and potential decline. In Costa Rica, mangroves on the North Pacific are the least studied. Objective: To provide the first characterization of forest structure and carbon content of the Palmares mangrove. Methods: A total of 58 square plots (5 × 5 m) were sampled between March 2022 and April 2023. In each plot, mangrove species, stem height, and circumference were quantified. Samples were collected to estimate interstitial water salinity, fine root content, and sediment grain size, bulk density, and carbon content. Mangrove biomass and organic carbon (OC) were calculated using allometric equations. Water quality was assessed at six sites in the main channel during the dry and rainy seasons of 2023. Results: Six mangrove species were identified at Palmares. Rhizophora spp. (55 % of plots) and Avicennia spp. (29 %) were the most abundant, while Laguncularia and Conocarpus were rare. Average tree height was 8 ± 7 m, width 11 ± 12 cm, and density 1 833 ± 1 757 stems ha-1. Rhizophora spp. were taller and wider than Avicennia spp. Interstitial water salinity at Palmares was 34 ± 14, which was higher during the dry season and in Avicennia spp. plots. Sediment was mainly composed of silt-clay (52 %), which was higher in Rhizophora spp. plots. Sediment bulk density was 0.8 ± 0.3 g cm-3 and fine roots < 1 %. Above-ground biomass OC averaged 224 ± 335 Mg ha-1 and was higher in Rhizophora spp. plots. Sediment OC was 8 ± 3 % and inorganic carbon 3 ± 1 %. OC was higher in sediments of Rhizophora spp., with higher silt-clay, larger mangroves, and lower densities. There was no clear pattern of variation in water parameters along the main channel. Conclusions: This first characterization of Palmares mangrove can serve as a baseline for further studies and effective management and conservation strategies.

Mangrove forests are one of the most effective carbon assimilation and storage ecosystems, providing crucial ecosystem services in coastal regions. However, they face potential threats from climate change, human activities, as well as their interactive effects, which have not yet been well investigated. In this study, we investigated net ecosystem production (NEP) of a temperate mangrove forest next to salt-production ponds in South Australia. The hypersaline salt ponds next to the land side of the mangrove forest create a salinity gradient in the forest. The study was based on eddy covariance measurement of carbon flux from November 2017 to December 2019, along with a salinity stress index (SSI), a new sea/land breeze index ( W b ), and flooded fraction ( F ). The result shows significant difference in mangrove NEP between seaside and landside sources, which can be attributed to salinity difference and tidal inundation. The negative impact of salinity stress on mangrove NEP can be mitigated by tidal flooding but the tidal flushing effect is limited toward the landside end. Additionally, an optimal temperature range (16–24 °C) is identified, above and below which the NEP rate reduces, and this range is lower than that reported for mangroves in tropical and subtropical areas. The negative impact of high temperature on NEP can be slightly relieved by sea breezes. At this particular mangrove site, sea level rise may enhance salt flushing and thus the carbon sink. The carbon flux data measured in this study provide a baseline before a dieback event in 2020, aiding in assessing losses and informing temperate mangrove management in salinity-affected areas.

Understanding the services provided by coastal ecosystems is vital for their study, preservation, and restoration. Mangrove forests, in particular, provide key ecosystem services: they sequester carbon, support fisheries and biodiversity, and facilitate sustainable tourism. In the Caribbean and the Gulf of Mexico, mangrove-related services have been studied extensively, but often via fragmented approaches. This meta-analysis combines a literature review, bibliometric tools, and thematic mapping to identify emerging trends and long-standing gaps. We analyzed 61 peer-reviewed studies across 21 sovereign states and U.S. states, which highlighted shifting research priorities and a lack of convergence—defined herein as the failure of individual studies to examine multiple ecosystem service categories (regulating, cultural, supporting, and provisioning) simultaneously to assess potential trade-offs. While early research emphasized supporting services such as fishery nurseries, recent studies focus on regulating services, especially carbon sequestration. Stakeholder engagement remains limited, with only 18% of studies incorporating local perspectives. We argue for greater integration of stakeholder input and convergence across service categories to enhance the scientific basis for mangrove management and policy design.

The mangrove ecosystem has good biodiversity in the form of flora and fauna. Mangroves function in coastal areas as a habitat in sea waters. Mangroves have a major impact on nature and living things. One of the ecosystems in the coastal area is the mangrove ecosystem, likewise the Oesapa coast has a mangrove ecosystem, but the condition of the mangrove forest on the Oesapa coast has long been buried by garbage. The garbage found consists of three categories, namely garbage from coastal communities, tourists and nature such as tree branches. Plastic waste is a problem that needs attention because it can have a negative impact on the marine ecosystem and the environment around the coast. Reprocessing plastic waste is an effort to avoid environmental pollution by plastic waste and can minimize the risk of spreading diseases due to microorganisms contained in organic waste. The community can also sell the waste to get extra money. This activity gave an exciting and extraordinary impression to the participants of the Inbound PMM 2 Nusa Cendana University, and this mangrove coastal cleanup movement succeeded in increasing public awareness and concern in maintaining the cleanliness of the Oesapa mangroves.

Accurate estimation of gross primary production (GPP) and above-ground biomass (AGB) is fundamental to assessing the carbon sequestration potential of artificial mangrove wetlands. However, pronounced spatiotemporal heterogeneity in stand structure, particularly in restored mangrove forests with diverse age compositions, introduces substantial uncertainty in GPP and AGB quantification. This study presents an innovative framework that explicitly incorporates stand age into the light use efficiency (LUE) model as a physiological constraint, thereby enhancing the accuracy of GPP and AGB estimations. Stand age was mapped using Landsat-7 and sentinel-2 time-series imagery and a random forest classification approach on the Google Earth Engine platform, providing high spatial resolution age distributions. Age-dependent productivity constraints, derived from net primary production–age relationships observed in evergreen broadleaf ecosystems, were incorporated into the LUE model to refine photosynthetic efficiency estimations. Application of this framework to mangrove plantations in the Luoyangjiang Estuary (2000–2022) yielded high accuracy in GPP (RMSE = 9.66 g d −1 , R 2 = 0.95) and AGB (RMSE = 1,051 g·m −2 , R 2 = 0.63) estimations. The results captured exponential AGB growth with stand development, and spatial analysis demonstrated a strong correspondence between biomass distribution and stand age, with mature stands (≥20 years) contributing disproportionately to carbon accumulation. This stand age–integrated approach delivers fine spatial and temporal resolution, offering a practical and transferable tool for monitoring carbon dynamics and informing adaptive management strategies in restored coastal wetlands, thereby supporting the long-term assessment of blue carbon projects.

Abstract Polymesoda erosa inhabits mangrove forests and serves as a food source for local people. However, the accumulation of heavy metals in P. erosa may pose potential risks to consumers. This study aimed to investigate the bioaccumulation of heavy metals in P. erosa across small, medium, and big sizes and to assess the preference of environmental accumulation sources. Heavy metals were analyzed in the tissue of P. erosa : As, Se, Cr, Co, Ni, Cu, Zn, Mo, Ag, Cd, Sn, Ba, and Pb. Bioaccumulation factors (BAF) were used to examine the bioaccumulation behavior. The results showed that Zn had the highest concentration in P. erosa for all sizes: small (11.13 mg·kg⁻¹), medium (13.54 mg·kg⁻¹), and big (13.69 mg·kg⁻¹). The total amount of accumulated heavy metals increased with P. erosa sizes, with values of 0.0655 mg, 0.2554 mg, and 0.5533 mg for small, medium, and big sizes, respectively. Although accumulated heavy metals were different in sizes, Bioaccumulation ratio of P. erosa was approximately 0.02 mg of heavy metals per gram of tissue for all 3 sizes. P. erosa was found to absorb more heavy metals from the water than from the sediment. In addition, target hazard quotient values (THQ > 1) indicated a potential non-carcinogenic health risk to consumers.