Mangrove Extent Research Articles

Uncovering Dynamics of Global Mangrove Gains and Losses

Supporting successful global mangrove conservation and policy requires accurate identification of anthropogenic and biophysical drivers of mangrove extent, yet such studies are scarce. We apply a hybrid methodology, combining existing remote sensing mangrove maps with local expert knowledge of vegetation and land use dynamics. We conducted stratified random sampling in eight subregions, and local experts visually interpreted over 20,900 plots using high-resolution imagery in Collect Earth Online. Similar to previous estimates, we found 147,771 km2 (±1.4%) of mangroves globally in 2020 and that rates of mangrove loss have decreased from 2000–2010 to 2010–2020, largely driven by South and Southeast Asia. Anthropogenic drivers of loss have shifted across subregions, with oil palm cultivation emerging in South and Southeast Asia and aquaculture in South America and Western and Central Africa, highlighting the need for ongoing monitoring and adaptable conservation efforts. Natural expansion outpaced natural retraction in both periods. This is the first global study uncovering land use drivers of mangrove decline and recovery, only made possible by collaboration with local experts. Key breakthroughs include successfully discerning spectrally similar anthropogenic from biophysical drivers, such as aquaculture from natural retraction, and creating data collection approaches that streamline visual interpretation efforts.

Spatio-Temporal Mapping and Monitoring of Mangrove Forests Changes from 1991 to 2021 in Panay Island, Philippines using Machine Learning

Mangrove forests hold a crucial role in our social, economic, and ecological activities. Despite this immense importance, they are constantly threatened by reclamation, deforestation, and climate change. In order to forward conservation and restoration efforts, accurate and cost-effective mangrove mapping and monitoring must be done. This paper explores the use of a supervised learning algorithm called Random Forest (RF) in mapping mangrove extent in Panay Island, Philippines from 1991-to 2021. Using land cover data from Landsat, maps of the mangrove extent from 1991to 2021 were developed. Results revealed that there has been an 8% decline from 1991 to 1996; 24% decrease in 1996 to 2001; 6% increase in 2001 to 2006; 21% decline from 2006 to 2011; 17% increase in 2011-2016; and 16% increase in 2016 to 2021. Over the past three (3) decades, the Philippines has lost 20% of its mangrove forests. From 31713 ha in 1991 to only 25313 ha in 2021. Through a confusion matrix, the model was evaluated and it showed a specificity, sensitivity, and AUC(Area Under the ROC Curve) above 70%. This suggests that, machine learning, when integrated with remote sensing, can provide an effective yet low-cost approach to mapping mangrove extent at a large-scale.

A regional map of mangrove extent for Myanmar, Thailand, and Cambodia shows losses of 44% by 1996

Southeast Asia is home to some of the planet’s most carbon-dense and biodiverse mangrove ecosystems. There is still much uncertainty with regards to the timing and magnitude of changes in mangrove cover over the past 50 years. While there are several regional to global maps of mangrove extent in Southeast Asia over the past two decades, data prior to the mid-1990s is limited due to the scarcity of Earth Observation (EO) data of sufficient quality and the historical limitations to publicly available EO. Due to this literature gap and research demand in Southeast Asia, we conducted a classification of mangrove extent using Landsat 1-2 MSS Tier 2 data from 1972 to 1977 for three Southeast Asian countries: Myanmar, Thailand, and Cambodia. Mangrove extent land cover maps were generated using a Random Forest machine learning algorithm that effectively mapped a total of 15,420.51 km2. Accuracy assessments indicated that the classification for the mangrove and non-mangrove class had a producer’s accuracy of 80% and 98% user’s accuracy of 90% and 96%, and an overall accuracy of 95%. We found a decline of 6,830 km2 between the 1970s and 2020, showing that 44% of the mangrove area in these countries has been lost in the past 48 years. Most of this loss occurred between the 1970s and 1996; rates of deforestation declined dramatically after 1996. This study also elaborated on the nature of mangrove change within the context of the social and political ecology of each case study country. We urge the remote sensing community to empathetically consider the local need of those who depend on mangrove resources when discussing mangrove loss drivers.

Marine macroinvertebrate ecosystem services under changing conditions of seagrasses and mangroves

This study aimed to investigate the impact of changing environmental conditions on MMI ES in seagrasses and mangroves. We used data from satellite and biodiversity platforms combined with field data to explore the links between ecosystem pressures (habitat conversion, overexploitation, climate change), conditions (environmental quality, ecosystem attributes), and MMI ES (provisioning, regulation, cultural). Both seagrass and mangrove extents increased significantly since 2016. While sea surface temperature showed no significant annual variation, sea surface partial pressure CO2, height above sea level and pH presented significant changes. Among the environmental quality variables only silicate, PO4 and phytoplankton showed significant annual varying trends. The MMI food provisioning increased significantly, indicating overexploitation that needs urgent attention. MMI regulation and cultural ES did not show significant trends overtime. Our results show that MMI ES are affected by multiple factors and their interactions can be complex and non-linear. We identified key research gaps and suggested future directions for research. We also provided relevant data that can support future ES assessments.

Monitoring of Coastal Geo-Environment for Hazard Mitigation: A Case Study of Machilipatnam Region, Andhra Pradesh, India

The coastal zone is highly dynamic, which responds in various ways to human interventions and extreme weather events. Tropical storms cause enormous damage to the coastal region and its communities during landfall. Many of the world’s largest deltas are densely populated and are increasingly vulnerable to natural disasters. The Krishna district within the river delta, is amongst the few districts with very high proneness to cyclone hazards. Machilipatnam in Krishna district is one of the oldest coastal towns in Andhra Pradesh, situated on a series of ancient beach ridges. Two major storms struck this region in the last half-century causing large scale devastation. The extent of inundation from multiple flood events can be a very good indicator for flood hazard zonation. The lateral expansion of Machilipatnam town is analysed in GIS environment based on time series satellite data. The developmental activities in the near shore zone are modifying the pristine coastline configuration. This is evident from shoreline monitoring. It is observed that there is a net increase in the extent of mangroves, indicating a better protection to the town against tropical storms. The unplanned growth of aquaculture may be an amplifying factor for coastal flooding, particularly if it is interfering with the drainage system. In addition to the existing flood protection measures, the areas suitable for shelter belt plantation and mangrove regeneration are identified for coastal vulnerability reduction.

Global Mangrove Watch: Monthly Alerts of Mangrove Loss for Africa

Current mangrove mapping efforts, such as the Global Mangrove Watch (GMW), have focused on providing one-off or annual maps of mangrove forests, while such maps may be most useful for reporting regional, national and sub-national extent of mangrove forests, they may be of more limited use for the day-to-day management of mangroves and for supporting the Global Mangrove Alliance (GMA) goal of halting global mangrove loss. To this end, a prototype change mangrove loss alert system has been developed to identify mangrove losses on a monthly basis. Implemented on the Microsoft Planetary Computer, the Global Mangrove Watch v3.0 mangrove baseline extent map for 2018 was refined and used to define the mangrove extent mask under which potential losses would be identified. The study period was from 2018 to 2022 due to the availability of Sentinel-2 imagery used for the study. The mangrove loss alert system is based on optimised normalised difference vegetation index (NDVI) thresholds used to identify mangrove losses and a temporal scoring system to filter false positives. The mangrove loss alert system was found to have an estimated overall accuracy of 92.1%, with the alert commission and omission estimated to be 10.4% and 20.6%, respectively. Africa was selected for the mangrove loss alert system prototype, where significant losses were identified in the study period, with 90% of the mangrove loss alerts identified in Nigeria, Guinea-Bissau, Madagascar, Mozambique and Guinea. The primary drivers of these losses ranged from economic activities that dominated West Africa and Northern East Africa (mainly agricultural conversion and infrastructure development) to climatic in Southern East Africa (primarily storm frequency and intensity). The production of the monthly mangrove loss alerts for Africa will be continued as part of the wider Global Mangrove Watch project, and the spatial coverage is expected to be expanded to other regions over the coming months and years. The mangrove loss alerts will be published on the Global Mangrove Watch online portal and updated monthly.

Changes in mangrove coverage classification criteria could impact the conservation of mangroves in Mexico

Accurate estimates of habitat extent and rates of change are crucial inputs for the global, regional, and national assessments that guide policy-making and prioritize strategies. This can contribute to an understanding of ecosystems in the landscape for their use, management, and preservation. Mangroves are one of the types of ecosystems in which estimation discrepancies have been analyzed to determine the impacts of data quality on conservation and policy-making. We identify significant discrepancies in the extent of the last map of Mexican mangroves (i.e., 2020) produced by the Mexican Mangrove Monitoring System (MMMS). We performed a comparative assessment between the 2020 and 2015 maps by using geographical information systems to analyze the spatial extent across these years and estimate the accuracy of map changes with airborne data. We observed a spurious gain of 129,531 ha between 2015 and 2020, including 102,610 ha (79% of total changes) in the Sian Ka'an Biosphere Reserve and its surroundings. Furthermore, the mangrove definition changed, causing the MMMS to map other coastal wetlands with the presence of Rhizophora mangle scrubs dispersed in the landscape. The analysis of MMMS airborne data demonstrates that this significant increase is due to changes in mangrove mapping criteria and definitions. The definition and spatial delimitation of "mangrove" (i.e., mangrove community or stands forest) has implications relevant to the conservation policy of these coastal wetlands/coastal resources. MMMS discrepancies in mangrove extent could generate misleading perspectives on different sectors. A cartographic solution is to separate the other coastal wetland areas with the presence of R. mangle from all MMMS products and reclassify them as "other wetlands with the presence of R. mangle." Robust ecosystem extent data is crucial for the design and implementation of efficient land use and conservation policies.

SPATIO-TEMPORAL ANALYSIS OF DECADAL MANGROVE COVER AND FRAGMENTATION IN REGION IV-B MIMAROPA, PHILIPPINES

Abstract. Mangroves are important vegetation that provides coastal protection and basic life necessities to humans and different marine organisms. Studies have observed that mangrove deforestation is occurring in the Philippines. Thus, monitoring of mangrove extent and fragmentation for rehabilitation planning is necessary to prevent further mangrove cover loss. The utilization of free satellite imagery in mangrove monitoring helps in this enterprise as it reduces the need for expensive field activities. Furthermore, it allows researchers to perform spatio-temporal analyses to observe patterns with respect to time and location. This study analyzed the mangrove loss and growth patterns from 2000 to 2020 in Region IV-B MIMAROPA, Philippines – known to house large amounts of mangrove cover – using Landsat 5, 6, 8. Since fragmentation is associated with deforestation, it was also analyzed using the ZonalMetrics toolbox for ArcGIS Pro 2.8. The results showed that Region IV-B experienced mangrove cover increase with only a few cities/towns that suffered significant deforestation. The change pattern analysis showed that most cities/towns were able to strongly preserve the mangroves, with more mangrove gain than loss. The cities/towns are then categorized for their increasing/decreasing mangrove area and fragmentation in the form of a quadrant. Many of the cities/towns experienced an increase in fragmentation metric values over time. However, the increase in mangrove areas in these cities/towns, especially in areas that previously did not have them, suggests that there is little fragmentation of existing mangrove patches, but rather conversion from non-mangrove land cover to mangrove cover. Still, there are a few towns that do not belong to this category that show signs of deforestation and fragmentation, which needs urgent action.

ASSESSMENT OF MANGROVE EXTENT EXTRACTION ACCURACY OF THRESHOLD SEGMENTATION-BASED INDICES USING SENTINEL IMAGERY

Abstract. Mangroves have been protecting coastlines, nourishing wildlife, and capturing carbon for climate regulation. The decline of mangroves calls for action to rapidly and accurately monitor them. Remote Sensing makes it possible to remotely monitor mangroves from images captured from space. Sentinel-1 and Sentinel-2 are examples of remote sensing satellites and there is extensive research on their land cover mapping capabilities, including mangrove mapping. While machine learning is a popular methodology for mangrove mapping (e.g., the use of Random Forest) there exist simpler techniques, i.e., utilizing threshold segmentation-based indices that only use a formula and a specific threshold to extract mangrove extents from satellite imagery (mostly Sentinel imagery). This study compared the products and the accuracy of different threshold segmentation-based mangrove mapping indices in four study areas in the Philippines and one in Indonesia. Results showed that the Mangrove Vegetation Index (MVI), Automatic Mangrove Map and Index (AMMI), and the Optical and SAR images Combined Mangrove Index (OSCMI) subindex SWIRB (full name of this subindex here) were the superior indices with overall accuracies (OA) greater than 80% in all study areas and reaching a maximum of 90%, 91% and 96%, respectively. By McNemar’s test showed that their results have insignificant differences. MVI, SWIRB, and AMMI only used Sentinel-2 optical imagery, which means the addition of Sentinel-1 SAR imagery was unnecessary. Since the validation data is a product of machine-learning classification, this shows that using threshold segmentation-based indices is promising as it is simpler, faster, and requires little skill compared to using classification techniques.

Prediction of the joint impacts of sea level rise and land development on distribution patterns of mangrove communities

Mangrove distribution along shorelines shows distinct zonation patterns; thus, different communities may face various influences from sea level rise (SLR) and land use. However, long-term change predictions are usually based only on the total extent of mangroves. Few studies have revealed how SLR and land development such as agriculture, aquaculture, and urbanization jointly affect different intertidal mangrove communities. This study proposed a novel framework combining SLAMM (Sea Level Affecting Marshes Model) and the CLUE-S (Conversion of Land Use and its Effect at Small regional extent) model to assess the potential impacts on upper and lower intertidal mangrove communities. Maoweihai in Guangxi, China, was selected as the study area and the potential impacts from the squeeze effect and mangrove expansion potential were evaluated. We established three scenarios combining SLR and land use patterns to predict mangrove coverage projections by 2070. The results showed that, under a single SLR driver, the upper intertidal mangroves would be more adaptive to rapid SLR than the lower intertidal mangroves. However, under the combined influence of the two drivers, the upper intertidal mangroves would experience larger squeeze effects than the lower intertidal mangroves, with up to 80.5% of suitable habitat lost. Moreover, the expansion potential of upper intertidal mangroves would be considerably more limited than that of lower intertidal mangroves. The length of the expandable habitat patch boundary of upper intertidal mangroves only reached 1.4–1.8 ​km, while that of the lower intertidal mangroves reached up to 99.2–111.2 ​km. Further, we found that aquaculture ponds and cropland are the top two land development types that could occupy suitable habitat and restrict the mangrove expansion potential. Our results highlight that timely improvement of land use policies to create available landward accommodation space for mangrove migration is essential to maintain the coverage and diversity of mangrove communities under SLR. The proposed method can be a helpful tool for adaptive mangrove conservation and management under climate change.

Multi-spatiotemporal analysis of changes in mangrove forests in Palawan, Philippines: predicting future trends using a support vector machine algorithm and the Markov chain model.

Multi-temporal remote sensing imagery can be used to explore how mangrove assemblages are changing over time and facilitate critical interventions for ecological sustainability and effective management. This study aims to explore the spatial dynamics of mangrove extents in Palawan, Philippines, specifically in Puerto Princesa City, Taytay and Aborlan, and facilitate future predictions for Palawan using the Markov Chain model. The multi-date Landsat imageries during the period 1988-2020 were used for this research. The support vector machine algorithm was sufficiently effective for mangrove feature extraction to generate satisfactory accuracy results (>70% kappa coefficient values; 91% average overall accuracies). In Palawan, a 5.2% (2693 ha) decrease was recorded during 1988-1998 and an 8.6% increase in 2013-2020 to 4371 ha. In Puerto Princesa City, a 95.9% (2758 ha) increase was observed during 1988-1998 and 2.0% (136 ha) decrease during 2013-2020. The mangroves in Taytay and Aborlan both gained an additional 2138 ha (55.3%) and 228 ha (16.8%) during 1988-1998 but also decreased from 2013 to 2020 by 3.4% (247 ha) and 0.2% (3 ha), respectively. However, projected results suggest that the mangrove areas in Palawan will likely increase in 2030 (to 64,946 ha) and 2050 (to 66,972 ha). This study demonstrated the capability of the Markov chain model in the context of ecological sustainability involving policy intervention. However, as this research did not capture the environmental factors that may have influenced the changes in mangrove patterns, it is suggested adding cellular automata in future Markovian mangrove modelling.

Quantifying Mangrove Extent Using a Combination of Optical and Radar Images in a Wetland Complex, Western Region, Ghana

The classification of mangrove forests in tropical coastal zones, based only on passive remote sensing methods, is hampered by mangrove complexities, topographical considerations, and cloud cover effects, among others. This paper reports on a study that combines optical and radar data to address the challenges of distinguishing mangrove stands in cloud-prone regions. The Google Earth Engine geospatial processing platform was used to extract multiple scenes of Landsat surface reflectance Tier 1 and synthetic aperture radar (C-band and L-band). The images were enhanced by creating a feature that removes clouds from the optical data and using speckle filters to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Classification was evaluated using three scenarios: classification of optical data only, classification of radar data only, and combination of optical and radar data. Our results revealed that the scenario that combines optical and radar data performed better. Further analysis showed that about 16.9% and 21% of mangrove and other vegetation/wetland cover were lost between 2009 and 2019. Whereas water body and bare land/built-up areas increased by 7% and 45%, respectively. Accuracy was evaluated based on the three scenarios. The overall accuracy of the 2019 classification was 98.9% (kappa coefficient = 0.979), 84.6% (kappa coefficient = 0.718), and 99.1% (kappa coefficient = 0.984), for classification of optical data only, classification of radar data only, and combination of optical and radar data, respectively. This study has revealed the potential to map mangroves correctly, enabling on-site conservation practices in the climate change environment.

A comparison of global mangrove maps: Assessing spatial and bioclimatic discrepancies at poleward range limits

Mangrove distribution maps are used for a variety of applications, ranging from estimates of mangrove extent, deforestation rates, quantify carbon stocks, to modelling response to climate change. There are multiple mangrove distribution datasets, which were derived from different remote sensing data and classification methods, and so there are some discrepancies among these datasets, especially with respect to the locations of their range limits. We investigate the latitudinal discrepancies in poleward mangrove range limits represented by these datasets and how these differences translate climatologically considering factors known to control mangrove distributions. We compare four widely used global mangrove distribution maps - the World Atlas of Mangroves, the World Atlas of Mangroves 2, the Global Distribution of Mangroves, the Global Mangrove Watch. We examine differences in climate among 21 range limit positions by analysing a set of bioclimatic variables that have been commonly related to the distribution of mangroves. Global mangrove maps show important discrepancies in the position of poleward range limits. Latitudinal differences between mangrove range limits in the datasets exceed 5°, 7° and 10° in western North America, western Australia and northern West Africa, respectively. In some range limit areas, such as Japan, discrepancies in the position of mangrove range limits in different datasets correspond to differences exceeding 600 mm in annual precipitation and > 10 °C in the minimum temperature of the coldest month. We conclude that dissimilarities in mapping mangrove range limits in different parts of the world can jeopardise inferences of climatic thresholds. We expect that global mapping efforts should prioritise the position of range limits with greater accuracy, ideally combining data from field-based surveys and very high-resolution remote sensing data. An accurate representation of range limits will contribute to better predicting mangrove range dynamics and shifts in response to climate change.

Applying the DPSIR framework to qualitatively assess the globally important mangrove ecosystems of Indonesia: A review towards evidence-based policymaking approaches

Indonesia has the largest extent of mangroves globally, supporting communities at the local scale while contributing to climate change mitigation at the global scale. Yet, they are highly threatened by multiple anthropogenic and natural stressors. Thus, in this study, we aim to qualitatively assess Indonesia’s mangrove ecosystems by conducting a literature review applying the Driver-Pressure-State-Impact-Response (DPSIR) framework. By using the framework, we identified multiple stressors and existing management efforts in 27 provinces of Indonesia. For instance, we identified aquaculture expansion as the most frequented driver of mangrove loss leading to the conversion of mangroves to shrimp farms in 20 provinces. In terms of responses, mangrove rehabilitation and community-based management have a long history in Indonesia while economic valuation and payment of ecosystem services are yet to be established across the country. The identification of multiple stressors and/or threats is instrumental to addressing the causes of degradation of globally-important mangroves in Indonesia if effectively translated into policies, which remains a future challenge.

Mapping mangrove sustainability in the face of sea level rise and land use: A case study on Leizhou Peninsula, China

Habitat loss and degradation of mangrove forests can be caused by both sea level rise (SLR) and unsustainable land practices. Current long-term change projections are often based on changes to mangrove extent; however, this may overlook fragmentation and the associated habitat resilience decline and therefore fail to adequately reveal the risks to mangrove habitats. A mangrove sustainability index (MSI) was proposed in this study to assess the impact of SLR and land use on mangrove habitats. The index consists of four components: habitat area change, habitat quality, landscape pattern, and protection ratio. Ecological models and landscape models were combined to calculate the MSI. Considering the SLR under RCP4.5 and RCP8.5 and land use strategies, four scenarios were set with prediction periods of base year (2020) to 2050 and 2100. The Leizhou Peninsula, China was used as the case study. The results showed that dual stressors would reduce the extent of mangroves by 16.6%–56.2%. Habitat quality was sensitive to land use change but was not affected by SLR. Landscape pattern and protection ratio were influenced by SLR but less effected by land use. In all scenarios, mangroves tended to migrate out of the protected areas, with protection ratio decreasing from 37% to 16.9%–29.9%. Newly expanding habitats may suffer from patch fragmentation and low connectivity. Unsustainable mangrove distribution sites on Leizhou Peninsula were identified as hotspots for management. Projections under different scenarios showed that some unsustainable sites could be reversed to sustainable sites through improvements in land use policies. The proposed approach could provide essential tools for the formulation of mangrove conservation and restoration strategies adapted to climate change.

A Review of Spectral Indices for Mangrove Remote Sensing

Mangrove ecosystems provide critical goods and ecosystem services to coastal communities and contribute to climate change mitigation. Over four decades, remote sensing has proved its usefulness in monitoring mangrove ecosystems on a broad scale, over time, and at a lower cost than field observation. The increasing use of spectral indices has led to an expansion of the geographical context of mangrove studies from local-scale studies to intercontinental and global analyses over the past 20 years. In remote sensing, numerous spectral indices derived from multiple spectral bands of remotely sensed data have been developed and used for multiple studies on mangroves. In this paper, we review the range of spectral indices produced and utilised in mangrove remote sensing between 1996 and 2021. Our findings reveal that spectral indices have been used for a variety of mangrove aspects but excluded identification of mangrove species. The included aspects are mangrove extent, distribution, mangrove above ground parameters (e.g., carbon density, biomass, canopy height, and estimations of LAI), and changes to the aforementioned aspects over time. Normalised Difference Vegetation Index (NDVI) was found to be the most widely applied index in mangroves, used in 82% of the studies reviewed, followed by the Enhanced Vegetation Index (EVI) used in 28% of the studies. Development and application of potential indices for mangrove cover characterisation has increased (currently 6 indices are published), but NDVI remains the most popular index for mangrove remote sensing. Ultimately, we identify the limitations and gaps of current studies and suggest some future directions under the topic of spectral index application in connection to time series imagery and the fusion of optical sensors for mangrove studies in the digital era.

Assessment of land use, land cover change in the mangrove forest of Ghogha area, Gulf of Khambhat, Gujarat

The most important biological ecosystems on the earth are mangroves, palms, shrubs, and trees that have adjusted to the challenging environments of high salinity, warm air and temperatures, severe tides, murky, sediment-hampered waterways, and oxygen-depleted soils make up mangroves that are mostly found in the intertidal zone. It is a sanctuary for a variety of aquatic animals and provide protection to coastal land. It acts as “Kalpvruksh” for human being and aquatic culture. Despite the importance of the mangrove ecosystem, the area of mangroves has been declining for several decades. The multispectral data and image processing techniques are frequently used to map and monitor mangrove changes. In this study, the changes of mangroves were accomplished along the Ghogha coast, Bhavnagar district of Gujarat coast, India using the Landsat 8 OLI and Sentinel–2A satellite data for 2016 and 2018, respectively. The image analysis and change detection studies were carried out using QGIS (3.10.14) and ArcMap (10.7.1) software. Using high-resolution satellite data and the Normalized Difference Vegetation Index (NDVI), this research attempts to determine the geographical extent of mangroves. The result shows that the total mangrove cover decreased from 2016 to 2018 and Sentinel-2A images shows a better performance in comparison to Landsat 8 OLI, because of its higher spatial resolution.

Economic valuation of ecosystem services for application of circular economy approach in conservation of Kundapura mangroves of Karnataka, India

AbstractCoastal areas are overwhelmingly rich in resources and support the livelihoods of the local communities. Coastal mangrove ecosystem provides a wide range of goods and services for human needs and wellbeing. To value conservation benefits of 252.29 ha extent of Kundapura mangrove ecosystem's goods and services, an economic valuation study was conducted using Systems of Environmental Economics Accounting (SEEA) framework. In total, seven ecosystem benefits were identified under provisioning, regulating and cultural services categories of SEEA. Identified baskets of benefits are fisheries, timber, fuelwood, protection against storms and floods, water quality maintenance, carbon sequestration, recreation and tourism. Out of seven benefits, the present study has found that the disturbance regulation benefit's contribution, which is about 92.65% of overall benefits with its economic value, US$1.786 million per year (17.86 lakh). Disturbance regulation benefit has significant role in reducing the impact of vulnerability from coastal hazards and sheltering 4354 individuals of population occupying 1088 houses. Despite that fact, Kundapura mangrove's assets are being degraded by various negative externalities. Particularly reclamation for aquaculture purpose and consequent discharging of the effluents, and dumping of sludge and municipal solid wastes (MSWs). These externalities would exacerbate the environmental problems and impacts on the flow of benefits. Hence, circular economic approach as pioneering to renovate the current linear system into circular one through 3R's strategy (reduce, reuse, and recycle) in the process of production, distribution, and consumption of ecosystem goods and services. This research paper explains about the application of circular economic approach in conservation management by assessing economic valuation of Kundapura mangrove's specific services. This approach is expected to finally result in aiding decision‐making process to limit the quantity of polluting residuals and control the degradation activities thereby sustain the mangroves in good health. Further the regeneration and expansion of mangrove extent would improve the mangrove‐dependent fishery, promote sustainable tourism, and maximize the coastal protection and economic benefits in this region. This will go a long way in sustaining the health of the otherwise fragile mangrove ecosystem of Kundapura coastal stretch, which is under the category of Critically Vulnerable Coastal Areas (CCVAs), identified under CRZ of India.

Modelling spatial-temporal changes in carbon sequestration by mangroves in an urban coastal landscape

Mangrove deforestation by land use conversions is the greatest threat to the conservation of coastal ecosystems, ranking Brazil as a country with high gross annual CO2 emissions. Despite the recognized socio-ecological importance in providing essential ecosystem services (e.g., erosion prevention, protection against extreme weather events, provision of habitats for estuarine species, and blue carbon storage), mangroves have been converted from blue carbon sinks to sources. Here, we modelled and evaluated the spatial-temporal changes in blue carbon stocks and net sequestration potential in mangrove forests in Santos and São Vicente, Brazil, from 1988 to 2050. We used classified images obtained from MapBiomas and the InVEST Coastal Blue Carbon model, which quantifies the potential carbon sequestered in the study area based on changes in the land use and land cover over the determined study period. We found an increased trend in carbon stocks and net sequestration of mangroves in both municipalities. Over the analysis period, carbon stocks in the mangroves of the city of Santos increased 29% in total, while we found a smaller rise (14%) in São Vicente. We found that land use changed substantially during 1988 until 2018, with mangrove extent varying in the region from 3,375 ha in 1988 to 3,764 ha in 2018 within the entire study region. Overall, the net carbon sequestration was approximately 925,393 Mg CO2e and 287,130 Mg CO2e, in Santos and São Vicente, respectively, over a period from 1988 to 2050. To our knowledge, this study is the first to quantify soil carbon stock and accumulation in mangroves along an urban coastal landscape in Brazil and emphasizes the current and future role of mangroves in climate change mitigation. Our findings can support the development of public policies for mangrove conservation and restoration actions to mitigate climate impacts.

Is aquaculture development responsible for mangrove conversion in India? - A geospatial study to assess the influence of natural and anthropogenic factors on mangroves in the last three decades

Aquaculture was cited as the primary factor for the reduction of mangrove area worldwide. But, there is no data available on the extent of conversion of mangroves for aquaculture in India, the second largest country in global aquaculture production. The present study assessed the factors of changes in mangroves area between the pre aquaculture period and recent years. Landsat TM images of 1988, Sentinel 2 A images of 2018, geospatial analysis, ground truth verification, post-classification approach, and accuracy assessment were used for the assessment. Comparing the extent of mangroves area in 1988 with 2018, the gain and loss was 278% and 30% in Kerala, 859% and 19% in Karnataka, 204% and 18% in Maharashtra, and 4100 and 20% in Daman and Diu respectively. Mangrove extent has increased due to restoration activities, while lost due to coastal erosion and other developmental activities. Combining mangrove statistics drawn from this study with available data from earlier study, indicate that the aquaculture has converted 2052 ha of mangroves, which represents 0.66% of India's mangrove area. The extent of mangroves increased by 20.72% in India between 1988 and 2018. There is a possibility that part of the mangrove - converted lands are privately owned. Besides, Coastal Aquaculture Authority Act of 2005 prohibits the use of mangrove lands to aquaculture. The present study proves that shrimp aquaculture is not accountable for the large-scale changes in the mangrove lands of India, except in Andhra Pradesh State. However, it demands the land use policy to protect the mangrove lands in private owned lands by allocating alternate resource space, and monitoring periodically.