Mapping global distribution of mangrove forests at 10-m resolution

Analysis of the causes of N/P imbalance in mangrove water caused by high elevation shrimp ponds

Due to its unique estuarine location at the junction of land and sea, mangrove wetlands are surrounded by numerous high-elevation shrimp ponds. The high-elevation shrimp ponds around the mangrove forest undergo 2.3 clearances by quicklime (CaO) disinfectant per year in China, but the impact of the quicklime disinfectant used and emitted on the mangrove wetland ecosystem is seriously underestimated. Due to the relatively limited data provided by high-elevation shrimp pond aquaculture in estuarine areas for the mangrove ecosystem, this study established an algorithm for calculating the reaction rate of quicklime disinfectants used in high-elevation shrimp pond aquaculture, which is the fundamental reason for the imbalance of N/P ratio in mangrove wetlands. Results showed that the amount of Ca(OH)2 produced by quicklime during the initial cleaning of the shrimp pond was 1303.4 t/a. The annual consumption of Ca(OH)2 by organic acids, strong chlorine disinfectants, and TP in the marine system was 154.6-171.5 t, 1.7 t, and < 284.5 t, respectively. The lack of phosphorus and the imbalance of N/P ratio caused by quicklime disinfectants may be a factor in the changes of mangrove wetlands and surrounding nearshore waters, the growth and decline of marine species, and even global changes.

An Approach for Detecting Mangrove Areas and Mapping Species Using Multispectral Drone Imagery and Deep Learning

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.

Mangrove Against Invasive Snails: Aegiceras corniculatum Shows a Molluscicidal Effect on Exotic Apple Snails (Pomacea canaliculata) in Mangroves

Apple snails (Pomacea canaliculata), one of the 100 most serious invasive species in the world, have invaded mangrove wetlands due to their salinity tolerance. We firstly prepared a plant molluscicide against apple snails based on the mangrove Aegiceras corniculatum in coastal wetland. The effects of four mangrove extracts from A. corniculatum, including ethanol extract (EE), petroleum ether extract (PEE), ethyl acetate extract (EAE), and n-butanol extract (BE), were studied for molluscicidal activity against apple snails in a saline environment. The LC50 values at 48 h of EE, PEE, EAE, and BE were 25 mg/L, 123 mg/L, 170 mg/L, and 14 mg/L, respectively. BE had the highest molluscicidal value (96.7%) against apple snails at 48 h. At 48 h, BE of A. corniculatum leaves significantly decreased the soluble sugar content, soluble protein content, acetylcholinesterase, and glutathione of apple snails to 4.25 mg/g, 29.50 mg/g, 947.1 U/gprot, and 6.22 U/gprot, respectively, compared to those in the control. The increased BE concentration significantly enhanced the malondialdehyde and aspartate aminotransferase contents to 4.18 mmol/gprot and 18.9 U/gprot at 48 h. Furthermore, the damage in the hepatopancreas tissue of apple snails increased, and the cellular structure became necrotic as the concentration of BE from A. corniculatum increased. The content of palmitic acid in BE of A. corniculatum leaves was the highest (10.9%), possibly be a toxic ingredient against apple snails. The n-butanol extract of A. corniculatum leaves showed a potential to control apple snails in the brackish water, and its plantation was beneficial to control the further spread of apple snails in mangrove wetlands.

The trajectory of wetland change in China between 1980 and 2020: hidden losses and restoration effects

Understanding wetland change is critical to establishing and implementing international conservation and management conventions. With such knowledge, supporting sustainable development, making management decisions, improving policies, and conducting scientific research become possible. However, consistent information on changes in Chinese wetlands has been unavailable. We applied the hybrid object-based and hierarchical classification approach to ∼53,000 scenes of Landsat images acquired between 1980 and 2020 and created a national wetland mapping product (China_Wetlands) for six periods (e.g., 1980, 1990, 2000, 2010, 2015, and 2020). China_Wetlands revealed diverse changes in Chinese wetlands and their trajectories in response to climate change and human impacts over the past four decades. Specifically, there was a substantial shrinkage in wetland area before 2015, with a small rebound between 2015 and 2020. The net loss was ∼60.9 × 103 km2, which represents 12% of the area in 1980. However, the loss of natural wetlands was hidden by human-made wetland gain with an offset of 15.6 × 103 km2. Additionally, the expansion of surface water extent approximately 14.0 × 103 km2 obscured the loss of vegetated wetlands. Wetland loss in hotspot areas (e.g., Sanjiang Plain and Yangtze River Delta) should not be neglected. The sustainable management and effective conservation of wetlands in China should target wetland areas, landscape structures, and small wetlands delivering important ecosystem services. Moreover, the conversion of wetland types and the invasion of alien species need to be monitored and regulated. China_Wetlands will be a critical wetland dataset for ecological research and the assessment of national and global environmental objectives (e.g., the United Nation's sustainable development goals).

Microbial diversity and biotechnological potential of mangrove leaf litter in Kebun Raya Mangrove, Surabaya, Indonesia

Mangrove ecosystems play a crucial role in maintaining ecological balance with leaf litter serving as an important substrate for diverse microbial communities. This study investigates the microbial communities inhabiting leaf litter from four different mangrove species: Rhizophora apiculata, Rhizophora stylosa, Sonneratia caseolaris, and Avicennia marina collected from Kebun Raya Mangrove, Surabaya, Indonesia. Using metagenomic sequencing, we revealed that Proteobacteria were predominant, followed by Chlorobi and Actinobacteria in the samples. Interestingly, we detected notable populations of anaerobic bacteria, including genus of Chlorobaculum and Allochromatium. Metagenomic analyses exhibited high levels of adaptation to stressors, evidenced by the prevalence of genes conferring resistance to antibiotics (e.g., beta-lactams, tetracyclines), heavy metals (e.g., chromium, arsenic), and hydrocarbons. Furthermore, the metagenomic analysis revealed the presence of genes involved in the biosynthesis of polyunsaturated fatty acids (PUFAs), antimicrobial compounds, and plant growth-promoting activities. These findings highlight the potential of mangrove leaf litter as a reservoir of beneficial microbes with diverse biotechnological applications, including bioremediation, nutraceuticals, pharmaceuticals, and agriculture.

GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020

The coastline reflects coastal environmental processes and dynamic changes, serving as a fundamental parameter for coast. Although several global coastline datasets have been developed, they mainly focus on coastal morphology, the typology of coastlines are still lacking. We produced a Global CoastLine Dataset (GCL_FCS30) with a detailed classification system. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index and an adaptive threshold segmentation method. The coastline classification was performed a hybrid transect classifier that integrates a random forest algorithm with stable training samples derived from multi-source geophysical data. The GCL_FCS30 offers significant advantages in capturing artificial coastlines, reflecting strong alignment with location validation data. The GCL_FCS30 classification was found to achieve an overall accuracy and Kappa coefficient over 85% and 0.75. Each coastline category accurately covered the majority of the area represented in third-party data and exhibited a high degree of spatial relevance. Therefore, the GCL_FCS30 is the first global coastline category dataset covering the high latitudes in a continuous and smooth line vector format.

Drivers of mangrove area change and suppression in Brazil from 2000 to 2020

Mangrove area loss is increasing globally, and drivers of loss differ depending not only on natural conditions but also on national and regional policies. Some countries with the most mangrove area, for instance, Brazil, lack broad systematic quantification of specific drivers of mangrove land-use and land-cover (LULC) change dynamics. We investigated the direct conversion (i.e., replacement) of mangrove forests due to changes in 21 types of LULC across Brazil from 2000 to 2020 based on annual LULC maps developed by the MapBiomas project. We quantified the area changes at national, regional, and state scales. We also determined and quantified mangrove forest conversion for each of the 21 LULC types with a pixel comparison analysis and identified temporal trends with a time-series analysis. The total conversion of mangrove area (3429 km2) was offset by a gain that was twice as large (6776 km2). Forest formations and water bodies, which may be interpreted as natural or indirect anthropogenic changes, were associated with most of the areas where mangrove cover was lost. Land-use modifications, mainly creation of pastures, accounted for 4% of direct mangrove conversions. We found that changes in LULC categories and patterns of gain and loss of mangrove areas differed among Brazilian states and regions. Based on other research, they also differ between Brazil and other countries. Thus, integrated mangrove forest conservation and management efforts that transcend political boundaries are essential to effectively address negative impacts on mangrove forests. We provide an interactive map to allow qualitative assessments of mangrove conversion drivers by different stakeholders, such as managers, policymakers, and nongovernmental organizations.

Mangrove Extraction Algorithm Based on Orthogonal Matching Filter-Weighted Least Squares

High-precision extraction of mangrove areas is a crucial prerequisite for estimating mangrove area as well as for regional planning and ecological protection. However, mangroves typically grow in coastal and near-shore areas with complex water colors, where traditional mangrove extraction algorithms face challenges such as unclear region segmentation and insufficient accuracy. To address this issue, in this paper we propose a new algorithm for mangrove identification and extraction based on Orthogonal Matching Filter-Weighted Least Squares (OMF-WLS) target spectral information. This method first selects GF-6 remote sensing images with less cloud cover, then enhances mangrove feature information through preprocessing and band extension, combining whitened orthogonal subspace projection with the whitened matching filter algorithm. Notably, this paper innovatively introduces Weighted Least Squares (WLS) filtering technology. WLS filtering precisely processes high-frequency noise and edge details in images using an adaptive weighting matrix, significantly improving the edge clarity and overall quality of mangrove images. This innovative approach overcomes the bottleneck of traditional methods in effectively extracting edge information against complex water color backgrounds. Finally, Otsu's method is used for adaptive threshold segmentation of GF-6 remote sensing images to achieve target extraction of mangrove areas. Our experimental results show that OMF-WLS improves extraction accuracy compared to traditional methods, with overall precision increasing from 0.95702 to 0.99366 and the Kappa coefficient rising from 0.88436 to 0.98233. In addition, our proposed method provides significant improvements in other metrics, demonstrating better overall performance. These findings can provide more reliable technical support for the monitoring and protection of mangrove resources.

Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020

In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

How do soil processes control the provision of ecosystem services in coastal wetlands?

Coastal wetlands are known for their diverse ecosystems, yet their soil characteristics are often misunderstood and thought to be monotonous. These soils are frequently subjected to saline water saturation, leading to unique soil processes. However, the combination and intensity of these processes can vary considerably across different ecosystems. In this study, we hypothesize that these diverse soil processes not only govern the geochemical conditions in coastal ecosystems but also influence their ability to deliver ecosystem services. To test this hypothesis, we conducted soil analyses in mangroves, seagrass meadows, and hypersaline tidal flats along the Brazilian coast. We used key soil properties as indicators of soil processes and developed a conceptual model linking soil processes and soil-related ecosystem services in these environments. Under more anoxic conditions, the intense soil organic matter accumulation and sulfidization processes in mangroves evidence their significance in terms of climate regulation through organic carbon sequestration and contaminants immobilization. Similarly, pronounced sulfidization in seagrasses underscores their ability to immobilize contaminants. In contrast, hypersaline tidal flats soils exhibit increased intensities of salinization and calcification processes, leading to a high capacity for accumulating inorganic carbon as secondary carbonates (CaCO3), underscoring their role in climate regulation through inorganic carbon sequestration. Our findings show that contrary to previously thought coastal wetlands are far from monotonous, exhibiting significant variations in the types and intensities of soil processes, which in turn influence their capacity to deliver ecosystem services. This understanding is pivotal for guiding effective management strategies to enhance ecosystem services in coastal wetlands.

Diversity of hydrocarbon-degrading bacteria in mangroves rhizosphere as an indicator of oil-pollution bioremediation in mangrove forests

Mangrove ecosystems, characterized by high levels of productivity, are susceptible to anthropogenic activities, notably oil pollution arising from diverse origins including spills, transportation, and industrial effluents. Owing to their role in climate regulation and economic significance, there is a growing interest in developing mangrove conservation strategies. In the Arabian Gulf, mangroves stand as the sole naturally occurring green vegetation due to the region's hot and arid climate. However, they have faced persistent oil pollution for decades. This review focuses on global mangrove distribution, with a specific emphasis on Qatar's mangroves. It highlights the ongoing challenges faced by mangroves, particularly in relation to the oil industry, and the impact of oil pollution on these vital ecosystems. It outlines major oil spill incidents worldwide and the diverse hydrocarbon-degrading bacterial communities within polluted areas, elucidating their potential for bioremediation. The use of symbiotic interactions between mangrove plants and bacteria offers a more sustainable, cost-effective and environmentally friendly alternative. However, the success of these bioremediation strategies depends on a deep understanding of the dynamics of bacterial communities, environmental factors and specific nature of the pollutants.

Chinese cities show different trend toward carbon peak

Understanding the disparities in carbon emission trend among cities is critical for achieving carbon peak goal. However, the status and trends of carbon peaking and reduction in various city types are still unclear. Therefore, this study classified 315 Chinese cities according to their economic and industrial structure by SOM-K-means, aiming to evaluate the trends and dynamic drivers of carbon peaking progress in different city types. The findings reveal a decline in carbon emissions in 110 cities (34.9 %) since 2020. Notably, all city types show potential for carbon reduction and achieving carbon peaking. Specifically, resource-based cities and high-end service cities have the most effect on reducing emissions, with 48.4 % and 42.1 % of the cities declining in carbon emissions. Energy-based and heavy industrial cities face heightened pressure to reduce carbon emissions. Additionally, in high-end service cities, energy efficiency and investment intensity contribute to emission reduction, while industrial structure adjustment decrease carbon emissions in resource-based cities. Furthermore, enhancing energy efficiency effects and R&D intensity are effective ways to significantly reduce carbon emissions in heavy industrial cities. We conclude that differentiating carbon reduction pathways for different cities should constitute be a breakthrough in achieving the goal of carbon peaking. These insights provide recommendations for cities that have yet to reach their carbon peak for both China and other developing countries.

Utilities of environmental radioactivity tracers in assessing sequestration potential of carbon in the coastal wetland ecosystems

Demand for accurate estimation of coastal blue carbon sequestration rates in a regular interval has recently surged due to the increasing awareness of nature-based climate solutions to alleviate adverse impacts stemming from the recent global warming. The robust estimation method is, however, far from well-established. The international community requires, moreover, to quantify its effect of "management." This article tries to provide the environmental isotope community with basic biophysical features of coastal blue carbon ecosystems to identify a suitable set of environmental isotopes for promoting coastal ocean-based climate solutions. This article reviews (i) the primary biophysical characteristics of coastal blue carbon ecosystems and hydrology, (ii) their consequential impact on the accumulation and preservation of organic carbon (OC) in the sediment column, (iii) suitable environmental isotopes to quantifying the sedimentary organic carbon accumulation, outwelling of the carbon-containing byproducts of decomposition of biogenic organic matter and acid neutralizing alkalinity produced in situ sediment to the offshore. Above-ground biomass is not cumulative over the years except for mangrove forests within coastal blue carbon systems. Non-gaseous carbon sequestration and loss occur mainly as a form of sediment organic carbon (SOC) and dissolved carbon in an intertidal and subtidal bottom sediment body in a slow, patchy, and dispersive way, on which this article focuses. Investigating environmental radionuclides is probably the most cost-effective effort to contribute to defining the offshore spatial extent of coastal blue carbon systems except for seagrass beds (e.g., Ra isotopes), to quantify millimeter per year scale carbon accretion and loss within the systems (e.g., 7Be, 210Pb) and a liter per meter of coastline per a day scale water movement from the systems (Ra isotopes). A millimeter-scale spatial and an annual (or less) time-scale resolution offered by the use of environmental isotopes would equip us with a novel tool to enhance the carbon storage capacity of the coastal blue carbon system.

2,2',4,4'-Tetrabromodiphenyl Ether (BDE-47) at Environmental Levels Influenced Photosynthesis in the Mangrove Species Kandelia obovata

2,2',4,4'-tetra-bromodiphenytol ether (BDE-47) is one of the ubiquitous organic pollutants in mangrove sediments. To reveal the toxic effects of BDE-47 on mangrove plants, the mangrove species Kandelia obovate was used to investigate the photosynthetic capacity effects and the molecular mechanisms involved after BDE-47 exposure at environment-related levels (50, 500, and 5000 ng g-1 dw). After a 60-day exposure, the photosynthetic capacity was inhibited in K. obovata seedlings, and a decrease in the stomatal density and damage in the chloroplast ultrastructure in the leaves were found. Transcriptome sequencing showed that, following exposure to BDE-47, gene expression in photosynthesis-related pathways was predominantly suppressed in the leaves. The bioinformatics analysis indicated that BDE-47 exerts toxicity by inhibiting photosystem I activity and chlorophyll a/b-binding protein-related genes in the leaves of K. obovata. Thus, this study provides preliminary theoretical evidence for the toxic mechanism effect of BDE-47 on photosynthesis in mangrove species.

Characterising the distribution of mangroves along the southern coast of Vietnam using multi-spectral indices and a deep learning model

Mangroves are an ecologically and economically valuable ecosystem that provides a range of ecological services, including habitat for a diverse range of plant and animal species, protection of coastlines from erosion and storms, carbon sequestration, and improvement of water quality. Despite their significant ecological role, in many areas, including in Vietnam, large scale losses have occurred, although restoration efforts have been underway. Understanding the scale of the loss and the efficacy of restoration requires high resolution temporal monitoring of mangrove cover on large scales. We have produced a time series of 10-m-resolution mangrove cover maps using the Multispectral Instrument on the Sentinel 2 satellites and with this tool measured the changes in mangrove distribution on the Vietnamese Southern Coast (VSC). We extracted the annual mangrove cover ranging from 2016 to 2023 using a deep learning model with a U-Net architecture based on 17 spectral indices. Additionally, a comparison of misclassification by the model with global products was conducted, indicating that the U-Net architecture demonstrated superior performance when compared to experiments including multispectral bands of Sentinel-2 and time-series of Sentinel-1 data, as shown by the highest performing spectral indices. The generated performance metrics, including overall accuracy, precision, recall, and F1-score were above 90 % for entire years. Water indices were investigated as the most important variables for mangrove extraction. Our study revealed some misclassifications by global products such as World Cover and Global Mangrove Watch and highlighted the significance of our study for local analysis. While we did observe a loss of 34,778 ha (42.2 %) of mangrove area in the region, 47,688 ha (57.8 %) of new mangrove area appeared, resulting in a net gain of 12,910 ha (15.65 %) over the eight-year period of the study. The majority of new mangrove areas were concentrated in Ca Mau peninsulas and within estuaries undergoing recovery programs and natural recovery processes. Mangrove loss occurred in regions where industrial development, wind farm projects, reclaimed land, and shrimp pond expansion is occurring. Our study provides a theoretical framework as well as up-to-date data for mapping and monitoring mangrove cover change that can be readily applied at other sites.

Quantifying the scale of erosion along major coastal aquifers of Pakistan using geospatial and machine learning approaches

Insufficient freshwater recharge and climate change resulted in seawater intrusion in most of the coastal aquifers in Pakistan. Coastal aquifers represent diverse landcover types with varying spectral properties, making it challenging to extract information about their state hence, such investigation requires a combination of geospatial tools. This study aims to monitor erosion along the major coastal aquifers of Pakistan and propose an approach that combines data fusion into the machine and deep learning image segmentation architectures for the erosion and accretion assessment in seascapes. The analysis demonstrated the image segmentation U-Net with EfficientNet backbone achieved the highest F1 score of 0.93, while ResNet101 achieved the lowest F1 score of 0.77. Resultant erosion maps indicated that Sandspit experiencing erosion at 3.14 km2 area. Indus delta is showing erosion, approximately 143 km2 of land over the past 30 years. Sonmiani has undergone substantial erosion with 52.2 km2 land. Miani Hor has experienced erosion up to 298 km2, Bhuri creek has eroded over 4.11 km2, east Phitii creek over 3.30 km2, and Waddi creek over 3.082 km2 land. Tummi creek demonstrates erosion, at 7.12 km2 of land, and East Khalri creek near Keti Bandar has undergone a measured loss of 5.2 km2 land linked with quantified reduction in the vertical sediment flow from 50 (billion cubic meters) to 10 BCM. Our analysis suggests that intense erosions are primarily a result of reduced sediment flow and climate change. Addressing this issue needs to be prioritized coastal management and climate change mitigation framework in Pakistan to safeguard communities. Leveraging emerging solutions, such as loss and damage financing and the integration of nature-based solutions (NbS), should be prioritized for the revival of the coastal aquifers.

Mangrove ecosystems in Southeast Asia region: Mangrove extent, blue carbon potential and CO2 emissions in 1996-2020

This study aimed to analyze mangrove extent (ME), carbon stock, blue carbon potential, and CO2 emission from 1996 to 2020 in Southeast Asia region. The data was obtained through the Global Mangrove Alliance (GMA) on the platform www.globalmangrovewatch.org v.3. Furthermore, ME was analyzed descriptively and the triggers for mangrove land changes in each country were investigated through a relevant literature review. The spatial analysis was conducted for blue carbon potential, while CO2 emission was derived by multiplying net change by emission factor (EF) of mangrove ecosystem. The results showed that the total ME in Southeast Asia was 5.07 million hectares (Mha) in 1996, decreasing to 4.82 Mha by 2020 due to various land uses, primarily shrimp farming. The total carbon stock potential was 2367.68 MtC, while a blue carbon potential was 8682.32 MtCO2-e, consisting of 1304.33 MtCO2-e and 7377.99 MtCO2-e from above-ground and soil carbon. Indonesia contributed 5939.57 MtCO2-e to blue carbon potential, while Singapore and Timor-Leste had the lowest contributions of 1.05 MtCO2-e and 1.37 MtCO2-e, respectively. Carbon stock potential (AGC and SOC) in Southeast Asia was influenced by ME conditions. The relationship between ME and AGC was found to be exponential (AGC = 0.0307e0.8938x; R2 = 0.9331; rME-AGC = 0.9964, P < 0.01). Similarly, ME and SOC, or AGC and SOC showed a relationship where SOC = 0.2e0.8829x (R2 = 0.937, rME-SOC = 0.9965 and rAGC-SOC = 0.9989, P < 0.01). The average CO2-e emission in Southeast Asia reached 17.0760 MtCO2-e yr-1 and the largest were attributed to Indonesia at 16.3817 MtCO2-e yr-1. Meanwhile, Brunei and Timor Leste did not show CO2-e emission as mangrove in these countries absorbed more CO2 from the atmosphere at -0.034 MtCO2-e yr-1 and -0.0002 MtCO2-e yr-1, respectively.

Spatiotemporal disturbances and attribution analysis of mangrove in southern China from 1986 to 2020 based on time-series Landsat imagery

As one of the most productive ecosystems in the world, mangrove has a critical role to play in both the natural ecosystem and the human economic and social society. However, two thirds of the world's mangrove have been irreversibly damaged over the past 100 years, as a result of ongoing human activities and climate change. In this paper, adopting Landsat for the past 36 years as the data source, the detection of spatiotemporal changes of mangrove in southern China was carried out based on the Google Earth Engine (GEE) cloud platform using the LandTrendr algorithm. In addition, the attribution of mangrove disturbances was analyzed by a random forest algorithm. The results indicated the area of mangrove recovery (5174.64 hm2) was much larger than the area of mangrove disturbances (1625.40 hm2) over the 35-year period in the study area. The disturbances of mangrove in southern China were dominated by low and low-to-medium-level disturbances, with an area of 1009.89 hm2, accounting for 57.50 % of the total disturbances. The mangrove recovery was also dominated by low and low-to-medium-level recovery, with an area of 3239.19 hm2, accounting for 62.61 % of the total recovery area. Both human and natural factors interacted and influenced each other, together causing spatiotemporal disturbances of mangrove in southern China during 1986-2020. The mangrove disturbances in the Phase I (1986-2000) and Phase III (2011-2020) were characterized by human-induced (50.74 % and 58.86 %), such as construction of roads and aquaculture ponds. The mangrove disturbances in the Phase II (2001-2010) were dominated by natural factors (55.73 %), such as tides, flooding, and species invasions. It was also observed that the area of mangrove recovery in southern China increased dramatically from 1986 to 2020 due to the promulgation and implementation of the Chinese government's policy on mangrove protection, as well as increased human awareness of mangrove wetland protection.

Exploring the spatio-temporal patterns of global mangrove gross primary production and quantifying the factors affecting its estimation, 1996-2020

Mangrove ecosystems, as an important component of "Blue Carbon", play a curial role on global carbon cycling; however, the lack of the global estimates of mangrove ecosystem gross primary production (GPP) and the underlying environmental controls on its estimation remain a gap in knowledge. In this study, we utilized global mangrove eddy covariance data and applied Gaussian Process Regression (GPR) to estimate GPP for global mangrove ecosystems, aiming to elucidate the factors influencing these estimates. The optimal GPR achieved favorable estimation performance through cross-validation (R2 = 0.90, RMSE = 0.92 gC/m2/day, WI = 0.86). Over the study period, the globally annual averaged GPP was 2054.53 ± 38.51 gC/m2/yr (comparable to that of evergreen broadleaf forests and exceeds the GPP of most other plant function types), amounting to a total of 304.82 ± 7.71TgC/yr, hotspots exceeding 3000 gC/m2/yr observed near the equator. The analysis revealed a decline in global mangrove GPP during 1996-2020 of -0.89 TgC/yr. Human activities (changes in mangrove cover area) played a relatively consistent role in contributing to this decrease. Conversely, variations in external environmental conditions showed distinct inter-annual differences in their impact. The spatio-temporal distribution patterns of mangrove ecosystems GPP (e.g., the bimodal annual pattern, latitudinal gradients, etc.) demonstrated the regulatory influence of external environmental conditions on GPP estimates. The model ensemble attribution analysis indicated that the fraction of absorbed photosynthetically active radiation exerted the dominant control on GPP estimations, while temperature, salinity, and humidity acted as secondary constraints. The findings of this study provide valuable insights for monitoring, modeling, and managing mangrove ecosystems GPP; and underscore the critical role of mangroves in global carbon sequestration. By quantifying the influences of environmental factors, we enhance our understanding of mangrove carbon cycling estimates, thereby helping sustain of these disproportionately productive ecosystems.

Spatiotemporal response of water quality in fragmented mangroves to anthropogenic activities and recommendations for restoration

Mangroves have received substantial attention for their pivotal role as ecological barriers between land and sea, owing to their capacity to effectively capture considerable quantities of terrestrial pollutants. Mangrove fragmentation has been a widespread global trend. There is limited information on the water quality status of these small scattered mangrove patches in coastal sub-developed areas, coupled with a paucity of efficient and intuitive assessment methodologies. To address this gap, the Water Quality Index (WQI) was introduced to evaluate the spatiotemporal characteristics of mangrove water quality. The major sources of pollution and anthropogenic activities that affect mangrove water quality were identified. The results revealed an average WQI value of 44.1 ± 13.3 for mangrove patches, consistently indicating a "low" water quality classification throughout all seasons. Both the size and natural conditions impact the water quality of mangroves. The large artificial patch (WQI: 56.4 ± 7.61) and the natural patch (WQI: 46.6 ± 13.6) exhibited relatively superior water quality, while the WQI value of a size-equivalent artificial patch compared with the natural patch is 38.6 ± 11.8. Aquaculture was the primary human activity that adversely affected the water quality of mangroves, and the potential sources of pollution were rainfall runoff and river discharge. These findings elucidate the unfavorable water quality characteristics and dominant pollution of fragmented mangroves, and validate the applicability of the WQI method for long-term evaluation of the water quality in mangrove patches. This study provides a basis for decision-making in water quality assessment and management of coastal wetlands and marine ecosystems. Scientific guidance to the management for mangrove protection and restoration was offered, such as regulating aquaculture activities, controlling non-point source pollution, implementing mangrove reforestation by using native species in historical mangrove sites.

Mangrove expansion on the low wooded islands of the Great Barrier Reef

Mangrove forests are the dominant vegetation growing on low wooded islands, which occur in the Caribbean, Indian and Pacific Oceans. In the northern Great Barrier Reef, we map remarkable, undocumented mangrove forest extension on 10 low wooded islands in the Howick Group that collectively equates to an area of 667 000 m2 (66.7 ha). We combine extensive field survey with canopy height models derived from RPA imagery and allometric scaling to quantify above ground biomass in both old (pre-1973) and new (post-1973) forest areas. Forest expansion added approximately 10 233 tonnes of new biomass since the early 1970s. We suggest that such substantial expansion of mangrove forest has occurred within a short time span in response to changing environmental controls. These may include sea-level rise, sediment transport and deposition, cyclone impact and the development of associated reef flat sedimentary landforms including unconsolidated and lithified shingle ridges, which influence reef flat hydrodynamics. Our observations highlight the globally dynamic response of mangrove distribution and forest structure to environmental change and provide timely new estimates from understudied reef island settings.