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AIM: To provide high‐resolution local, regional, national and global estimates of annual mangrove forest area from 2000 through to 2012 with the goal of driving mangrove research questions pertaining to biodiversity, carbon stocks, climate change, functionality, food security, livelihoods, fisheries support and conservation that have been impeded until now by a lack of suitable data. LOCATION: Global, covering 99% of all mangrove forests. METHODS: We synthesized the Global Forest Change database, the Terrestrial Ecosystems of the World database and the Mangrove Forests of the World database to extract mangrove forest cover at high spatial and temporal resolutions. We then used the new database to monitor mangrove cover at the global, national and protected area scales. RESULTS: Countries showing relatively high amounts of mangrove loss include Myanmar, Malaysia, Cambodia, Indonesia and Guatemala. Indonesia remains by far the largest mangrove‐holding nation, containing between 26% and 29% of the global mangrove inventory with a deforestation rate of between 0.26% and 0.66% per year. We have made our new database, CGMFC‐21, freely available. MAIN CONCLUSIONS: Global mangrove deforestation continues but at a much reduced rate of between 0.16% and 0.39% per year. Southeast Asia is a region of concern with mangrove deforestation rates between 3.58% and 8.08%, this in a region containing half of the entire global mangrove forest inventory. The global mangrove deforestation pattern from 2000 to 2012 is one of decreasing rates of deforestation, with many nations essentially stable, with the exception of the largest mangrove‐holding region of Southeast Asia. We provide a standardized spatial dataset that monitors mangrove deforestation globally at high spatio‐temporal resolutions. These data can be used to drive the mangrove research agenda, particularly as it pertains to monitoring of mangrove carbon stocks and the establishment of baseline local mangrove forest inventories required for payment for ecosystem service initiatives.

Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI + NDSI + DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI + NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (√k), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI + NDSI + DEM parameters with the number of trees = 100, mtry = all variables (k), and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.

The management of mangrove ecosystems faced to serious problem while these ecosystems are among the world’s richest ecosystems and provide a wealth of important services to human well-being. 35% of the areas of mangrove forests has globally been lost in the past two decades and supposed to be reach 60% by 2030. It is urgent to prospect the potential conservation research topics for the better management of mangrove areas. With a view to reducing mangrove degradation rate, this review aims to highlight restoration strategies and governance tenure of mangrove ecosystems in Africa. A total of 101 papers from Africa (31.68%), Asia (46.53%), America (11.85%), Europe (7.10%), and the rest of the world (2.84%) were considered. Indonesia, Thailand and India respectively recorded the top number of publications in Asia whereas Benin and Ghana accounted the higher number in Africa. 71.42% of publications have considered human activities including tree clearance and higher wood used (e.g. salt production activities) as the main mangrove degradation drivers in Africa. 4.80% of papers were found as the highest annual rate of mangrove degradation and less rate was 0.21% obtained in Africa. Many approaches were described for mangrove ecosystems restoration. 61.54% of mangrove governance publications showed that multi-level mangrove governance approach is widely applied. However, many studies suggested that this model of mangrove governance is the source of the resource’s degradation by the lack of a strong regulation. This review could help to set up a good program of mangrove restoration in West Africa.

The present systematic literature review (SLR) synthesized the literature on mangrove litterfall production and decomposition from studies published between 1985 and 2023 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Key questions about biophysical, chemical, and anthropogenic/societal factors influencing nutrient cycling via litterfall production and decomposition in mangrove forests were addressed. The SLR included 332 peer-reviewed original and review articles from the ScienceDirect, PubMed, and Google Scholar databases. The United States of America had the highest relative count (RC, 31.32%), followed by Japan (8.79%) and Indonesia (8.24%), and the lowest RCs were found in Bangladesh, Kenya, Philippines, and Thailand. We showed the increasing trend on these topics and discussed the milestones to enhance our understanding of litterfall production and decomposition processes and inform future research endeavors in the context of climate change. A positive trajectory for understanding litterfall production and decomposition for effective decision-making and management strategies towards mangrove conservation and sustainable use is also discussed. Ten-year research prospects were also identified, including studies on impacts of pollution, habitat degradation, climate change, and other destructive human activities. The trend in studies about mangrove litterfall production and decomposition suggests the growing recognition of mangroves’ ecological and societal importance. Future advancements can be made to better understand the biophysical, chemical, and anthropogenic factors influencing litterfall production and decomposition through the identified future research directions. Finally, the findings of the present review are relevant to supporting effective conservation and management strategies for mangroves in a changing climate.

The mangrove ecosystem in Bintan Regency provides many benefits, both directly and indirectly, for the people around it. Mangrove ecosystem services also vary and play a role in adaptation and mitigation processes in the face of climate change. Oil pollution that occurs every year in Bintan Regency has a negative impact both ecologically and economically. This research aims to estimate the value of mangrove ecosystem services in the East and North Coastal Areas of Bintan Island, which can be used as a basis for the management of the Area. The data collection method used was a survey using a questionnaire on fishermen, tourists, tourism managers, and the surrounding community. In addition, it also collects some secondary data to support data analysis. The results of the study state that the estimated economic value of mangrove ecosystem services is IDR. 135,663,899,478.30/year from a mangrove area of 4354.11 ha. The economic value consists of providing services by 61%, regulatory services by 21%, supporting services by 2%, and cultural services by 16%. The economic value of the mangrove ecosystem services in the coastal area of Bintan Island, Indonesia, has significant implications for policy-making related to mangrove conservation, social welfare, and environmental damage claims. The research provides evidence of the economic value of mangroves, which can be used to support policies and claims aimed at protecting these crucial ecosystems and the benefits they provide to local communities.

To date, discourse associated with the potential application of “blue carbon” within real-world carbon markets has focused on blue carbon as a mitigation strategy in the context of avoided deforestation (e.g., REDD+). Here, we report structural dynamics and carbon storage gains from mangrove sites that have undergone rehabilitation to ascertain whether reforestation can complement conservation activities and warrant project investment. Replicated sites at two locations with contrasting geomorphic conditions were selected, Tiwoho and Tanakeke on the island of Sulawesi, Indonesia. These locations are representative of high (Tiwoho, deep muds and silty substrates) and low (Tanakeke, shallow, coralline sands) productivity mangrove ecosystems. They share a similar management history of clearing and conversion for aquaculture before restorative activities were undertaken using the practice of Ecological Mangrove Rehabilitation (EMR). Species diversity and mean biomass carbon storage gains after 10 yr of regrowth from the high productivity sites of Tiwoho (49.2 ± 9.1 Mg C·ha-1·yr-1) are already almost of one-third of mean biomass stocks exhibited by mature forests (167.8 ± 30.3 Mg C·ha-1·yr-1). Tiwoho’s EMR sites, on average, will have offset all biomass C that was initially lost through conversion within the next 11 yr, a finding in marked contrast to the minimal carbon gains observed on the low productivity, low diversity, coral atoll EMR sites of Tanakeke (1.1 ± 0.4 Mg C·ha-1·yr-1). These findings highlight the importance of geomorphic and biophysical site selection if the primary purpose of EMR is intended to maximize carbon sequestration gains.

For the period 1996-2010, we provide the first indication of the drivers behind mangrove land cover and land use change across the (pan-)tropics using time-series Japanese Earth Resources Satellite (JERS-1) Synthetic Aperture Radar (SAR) and Advanced Land Observing Satellite (ALOS) Phased Array-type L-band SAR (PALSAR) data. Multi-temporal radar mosaics were manually interpreted for evidence of loss and gain in forest extent and its associated driver. Mangrove loss as a consequence of human activities was observed across their entire range. Between 1996-2010 12% of the 1168 1°x1° radar mosaic tiles examined contained evidence of mangrove loss, as a consequence of anthropogenic degradation, with this increasing to 38% when combined with evidence of anthropogenic activity prior to 1996. The greatest proportion of loss was observed in Southeast Asia, whereby approximately 50% of the tiles in the region contained evidence of mangrove loss, corresponding to 18.4% of the global mangrove forest tiles. Southeast Asia contained the greatest proportion (33.8%) of global mangrove forest. The primary driver of anthropogenic mangrove loss was found to be the conversion of mangrove to aquaculture/agriculture, although substantial advance of mangroves was also evident in many regions.

The global significance of carbon storage in Indonesia’s coastal wetlands was assessed based on published and unpublished measurements of the organic carbon content of living seagrass and mangrove biomass and soil pools. For seagrasses, median above- and below-ground biomass was 0.29 and 1.13 Mg C ha⁻¹ respectively; the median soil pool was 118.1 Mg C ha⁻¹. Combining plant biomass and soil, median carbon storage in an Indonesian seagrass meadow is 119.5 Mg C ha⁻¹. Extrapolated to the estimated total seagrass area of 30,000 km², the national storage value is 368.5 Tg C. For mangroves, median above- and below-ground biomass was 159.1 and 16.7 Mg C ha⁻¹, respectively; the median soil pool was 774.7 Mg C ha⁻¹. The median carbon storage in an Indonesian mangrove forest is 950.5 Mg C ha⁻¹. Extrapolated to the total estimated mangrove area of 31,894 km², the national storage value is 3.0 Pg C, a likely underestimate if these habitats sequester carbon at soil depths >1 m and/or sequester inorganic carbon. Together, Indonesia’s seagrasses and mangroves conservatively account for 3.4 Pg C, roughly 17 % of the world’s blue carbon reservoir. Continued degradation and destruction of these wetlands has important consequences for CO₂ emissions and dissolved carbon exchange with adjacent coastal waters. We estimate that roughly 29,040 Gg CO₂ (eq.) is returned annually to the atmosphere–ocean pool. This amount is equivalent to about 3.2 % of Indonesia’s annual emissions associated with forest and peat land conversion. These results highlight the urgent need for blue carbon and REDD+ projects as a means to stem the decline in wetland area and to mitigate the release of a significant fraction of the world’s coastal carbon stores.

This study was conducted to analyze the biodiversity of mangrove species and fauna in Indonesia as well as the management strategies for its preservation. The results showed that the total number of mangrove species was 240, consisting of 48 true and 192 associated mangrove. This number also comprised 74 trees, 36 shrubs, 52 herbs, six palms, 43 epiphytes, 23 lianas, three ferns, and three parasite species. Aglaia mackiana was identified as a new record in the Papua region attributed to the New Guinea Coastal Current (NGCC), while Ceriops australis was newly found in regions of Papua, Bali—Nusa Tenggara (Timor, Flores, Sumbawa), Java, and Sumatra (Pulau Bilinton). The diversity of marine fauna in the mangrove area consisted of 125 fish species from 47 families and 169 macrozoobenthos from 52 families. In addition, there were 161 terrestrial faunas, consisting of 80 birds, 38 squamata, four crocodiles, six amphibians, 11 testudinate, and 21 mammal species. This high level of biodiversity was influenced by the commitment of the Indonesian government to managing mangrove ecosystems through conservation. These efforts were carried out to preserve and improve ecosystem services such as mangrove biodiversity, carbon stock potential, coastal protection, and the unique biodiversity of marine and terrestrial fauna. Based on the results, incredibly unique fauna included Crocodilus found in Papua, Kalimantan, Java, and Sumatra region, Halcyon sp. in Papua and Java region, Anhinga sp. in Kalimantan and Java region, as well as Nasalis larvatus in Kalimantan.