Search

This study aims to elucidate the carrying capacity of estuarine mangroves in Southeast Sulawesi, to maintain environmental health of its coastal cities. The concentrations of mercury (Hg), Copper (Cu), Manganese (Mn), Lead (Pb), Zinc (Zn), and Nickel (Ni) were analyzed in sediments and mangrove tissues samples from Kendari bay and Tinanggea estuaries. This was followed by the determination of translocation and bioaccumulation factors, as well as the total accumulation of heavy metals on a landscape level. The results showed that the sediment in Kendari Bay had higher heavy metal concentrations than in Tinanggea, indicating the anthropogenic impact of the highly populated city of Kendari. Moreover, the roots and stems of mangroves had higher heavy metal concentrations than the leaves. Overall, the Bioconcentration Factors (BCF) of Hg, Cu, Mn, Pb and Zn metals were >1, indicating that estuarine mangroves have a higher phytoremediation capacity for heavy metals. Estuarine mangrove biomass also enables the high accumulation of heavy metals in both Kendari bay and Tinanggea, signifying the greater phytoremediation potential of urban estuarine mangroves. Based on these findings, the study supports the conservation and management of mangroves in urban estuaries to maintain the environmental health of coastal cities.

Mangrove forest plays a very important role for both ecosystem services and biodiversity conservation. In Vietnam, mangrove is mainly distributed in the Mekong delta. Recently, mangrove areas in this region decreased rapidly in both quality and quantity. The forest became bare, divided and scattered into many small patches, which was a major driver of ecosystem degradation. Without a quantitative method for effectively assessing mangrove health in the regional scale, the sustainably conserving mangrove is the challenge for the local governments. Remote sensing data has been widely used for monitoring mangrove distributions, while the characterization of spatial metrics is important to understand the underlying processes of mangrove change. The objectives of this study were to develop an approach to monitor mangrove health in Mui Ca Mau, Ca Mau province of Vietnam by utilizing satellite image textures to assess the mangrove patterns. The research result showed that mangrove areas increased double by 2015, but the forest had become more fragmented. We can be seen those changes in land use mainly come from land conversion from forest to shrimp farms, settlements areas and public constructions. The conserving existing mangrove forest in Mui Ca Mau should consider the relations between mangrove health and influencing factors indicated in the manuscript.

Mangrove forest plays a key role in regulating climate change, earth carbon cycling and other biogeochemical processes within blue carbon ecosystems. Therefore, mangrove forests should be incorporated into Earth system climate models with the aim of understanding future climate change. Despite multiple carbon stock and flux assessments taking place over the past couple of decades, concrete knowledge of carbon source/sink patterns is largely lacking, particularly in the biodiversity-rich Asia-Pacific (AP) region with its 68 493 km ^2 of mangrove area. Thus, to understand the gaps in mangrove blue carbon research in the AP region, we summarize a recent decade-long inventory of carbon stock pools (aboveground, belowground and soil) and biogeochemical flux components (burial, export/import, soil-air and water-air CO _2 flux) across 25 AP countries to understand the current knowledge and gaps. While carbon stock assessments of individual components are available for all 25 countries, whole ecosystem carbon stocks—including live and standing dead aboveground and belowground, downed woody debris and soil carbon stocks—are often lacking, even in highly researched countries like Indonesia. There is restricted knowledge around biogeochemical carbon fluxes in 55% of the countries, suggesting poor carbon flux research across the region. Focusing on flux components, reports on sediment-to-sea carbon exports are extremely limited (coming from just nine countries in the AP region). There is notable scarcity of data on carbon export fluxes in Indonesian mangroves. Given the key role AP mangroves play in climate change mitigation worldwide, more detailed and methodologically comparable investigation of biogeochemical source/sink processes is required to better understand the role of this large carbon source in global carbon stocks and fluxes, and hence, global climate.

Moderate to high resolution satellite imageries are commonly used in mapping mangrove cover from local to global scales. In addition to extent information, studies such as mangrove composition, ecology, and distribution analysis require further information on mangrove zonation. Mangrove zonation refers to unique sections within a mangrove forest being dominated by a similar family, genus, or species. This can be observed both in natural and planted mangrove forests. In this study, a mapping workflow was developed to detect zonation in test mangrove forest sites in Katunggan-It Ibajay (KII) Ecopark (Aklan), Bintuan (Coron), Bogtong, and Sagrada (Busuanga) in the Philippines and Fukido Mangrove Park (Ishigaki, Japan) using Sentinel-2 imagery. The methodology was then applied to generate a nationwide mangrove zonation map of the Philippines for year 2020. Combination of biophysical products, water, and vegetation indices were used as classification inputs including leaf area index (LAI), fractional vegetation cover (FVC), fraction of photosynthetically-active radiation (FAPAR), Canopy chlorophyll content (Cab), canopy water content (Cw), Normalized Difference Vegetation Index (NDVI), modified normalized difference water index (MNDWI), modified chlorophyll absorption in reflectance index (MCARI), and red-edge inflection point (REIP). Mangrove extents were first mapped using either the Maximum Likelihood Classification (MLC) algorithm or the Mangrove Vegetation Index (MVI)-based methodology. The biophysical and vegetation indices within these areas were stacked and transformed through Principal Component Analysis (PCA). Regions of Interest (ROIs) were selected on the PCA bands as training input to the MLC. Results show that mangrove zonation maps can highlight the major mangrove zones in the study sites, commonly limited up to genera level only except for genera with only one known species thriving in the area. Four zones were detected in KII Ecopark: Avicennia zone, Nypa zone, Avicennia mixed with Nypa zone, and mixed mangroves zones. For Coron and Busuanga, the mapped mangrove zones are mixed mangroves, Rhizophora zone and sparse/damaged zones. Three zones were detected in Fukido site: Rhizophora stylosa-dominant zone, Bruguiera gymnorrhiza-dominant zone, and mixed mangrove zones. The zonation maps were validated using field plot data and orthophotos generated from Unmanned Aerial System (UAS) surveys, with accuracies ranging from 75 to 100%.

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (

Estimating baseline carbon stocks is a key step in designing forest carbon programs. While field inventories are resource-demanding, advances in predictive modeling are now providing globally coterminous datasets of carbon stocks at high spatial resolutions that may meet this data need. However, it remains unknown how well baseline carbon stock estimates derived from model data compare against conventional estimation approaches such as field inventories. Furthermore, it is unclear whether site-level management actions can be designed using predictive model data in place of field measurements. We examined these issues for the case of mangroves, which are among the most carbon dense ecosystems globally and are popular candidates for forest carbon programs. We compared baseline carbon stock estimates derived from predictive model outputs against estimates produced using the Intergovernmental Panel on Climate Change’s (IPCC) three-tier methodological guidelines. We found that the predictive model estimates out-performed the IPCC’s Tier 1 estimation approaches but were significantly different from estimates based on field inventories. Our findings help inform the use of predictive model data for designing mangrove forest policy and management actions.

Background Progress of forest production in response to the environment requires a quantitative understanding of leaf area development. Therefore, it is necessary to investigate the dynamics of seasonal crown foliage in order to understand the productivity of mangroves, which play an important role in the subtropical and tropical coastlines of the world. Method Crown foliage dynamics of the mangrove Rhizophora stylosa were studies to reveal patterns of leaf recruitment, survival and seasonal leaf area growth. Results Flushing of leaves occurred throughout the year, but both flushing and leaf area growth pattern of leaves varied with season. Maximum flushing occurred in summer, but leaf areas did not differ significantly with season. The half-expansion period is longer, and the intrinsic rate of increase was lower in winter. Summer flushed leaves grew faster at their initial stage and reached their maximum area over a shorter period of time. The difference in temperature and air vapor pressure deficit (VPD) between summer and winter contributed to the present dynamics of foliage patterns. The mean leaf longevity was estimated to be 13.1 month. The crown foliage area was almost stable throughout the year. Conclusions Homeostatic control of the crown foliage area may be accompanied by the existence of ecophysiological mechanisms in R. stylosa. Integrating crown foliage dynamics into forest models represents an important step towards incorporating physiological mechanisms into the models for predicting growth responses to environmental changes and for understanding the complex responses of tree growth and litter production.