scholarly journals MATERIAL CYCLING IN A MANGROVE AREA

2004 ◽  
pp. 193-208 ◽  
Author(s):  
Yoshihisa AKAMATSU ◽  
Syunsuke IKEDA
Keyword(s):  
Mangrove Area ◽  
Material Cycling

scholarly journals NUMERICAL SIMULATION OF MATERIAL CYCLING IN A MANGROVE AREA

2003 ◽  
Vol 47 ◽  
pp. 1015-1020
Author(s):  
Yoshihisa AKAMATSU ◽  
Syunsuke IKEDA
Keyword(s):  
Numerical Simulation ◽  
Mangrove Area ◽  
Material Cycling

STRUKTUR DAN FISIOGNOMI VEGETASI MANGROVE DI REMPANG CATE KOTA BATAM

SIMBIOSA ◽  
2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Yarsi Efendi ◽  
Dahrul Aman Harahap
Keyword(s):  
Data Collection ◽  
Coastal Area ◽  
Growth Zone ◽  
Mangrove Area ◽  
Rhizophora Apiculata ◽  
The Difference ◽  
Mangrove Zonation ◽  
Vertical Transect ◽  
Zone Growth ◽  
Vegetation Physiognomy

Structure and physiognomy of mangrove strongly influenced by the zonation that occurred in the area of mangroves growth. The differences of zona growth will effect  to differences in the structure and composition of vegetation. There are three zones in the mangrove area, which is caused by the difference of flooding which also resulted in the difference to the salinity. The differences of growth zone will performed to the type vegetation performance (Physiognomy). This study is aims to prove the mangrove’s physiognomy that taken in the coastal area of Rempang Cate  Batam, on March 2014 to June 2014. This study was a survey with data collection using a vertical transect plots 100 m. Based on the research that has been done obtained difference vegetation physiognomy stands for every level of growth in each zone growth. Proximally found 13 species of mangroves in 8 families. The results of the analysis of the vegetation on the trees growth level are, Ceriops decandra have the greatest significance important value 167.55% on sapling (juvenille ) level is dominated by Rhizophora apiculata 120%, and seedling growth level dominated by Rhizophora apiculata  186.80%. Keywords: Structure and physiognomy, mangrove zonation

scholarly journals Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

2021 ◽  
Vol 13 (8) ◽  
pp. 1427
Author(s):  
Kasturi Devi Kanniah ◽  
Chuen Siang Kang ◽  
Sahadev Sharma ◽  
A. Aldrie Amir
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Peninsular Malaysia ◽  
Area Index ◽  
Mangrove Area ◽  
The Mean ◽  
Shape Complexity ◽  
The Impact

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 (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

Methanotrophic Community Detected by DNA-SIP at Bertioga’s Mangrove Area, Southeast Brazil

2021 ◽  
Author(s):  
Débora do Carmo Linhares ◽  
Flávia Talarico Saia ◽  
Rubens Tadeu Delgado Duarte ◽  
Cristina Rossi Nakayama ◽  
Itamar Soares de Melo ◽  
...  
Keyword(s):  
Mangrove Area ◽  
Southeast Brazil

An updated national-scale mangrove forest map in China Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine

2021 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Yan Huang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Mangrove Area ◽  
Google Earth Engine ◽  
Forest Maps ◽  
Sentinel 2

&lt;p&gt;A high resolution mangrove map (e.g., 10-m), which can identify mangrove patches with small size (&lt; 1 ha), is a central component to quantify ecosystem functions and help government take effective steps to protect mangroves, because the increasing small mangrove patches, due to artificial destruction and plantation of new mangrove trees, are vulnerable to climate change and sea level rise, and important for estimating mangrove habitat connectivity with adjacent coastal ecosystems as well as reducing the uncertainty of carbon storage estimation. However, latest national scale mangrove forest maps mainly derived from Landsat imagery with 30-m resolution are relatively coarse to accurately characterize the distribution of mangrove forests, especially those of small size (area &lt; 1 ha). Sentinel imagery with 10-m resolution provide the opportunity for identifying these small mangrove patches and generating high-resolution mangrove forest maps. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features for random forest to classify mangroves in China. We found that Sentinel-2 imagery is more effective than Sentinel-1 in mangrove extraction, and a combination of SAR and MSI imagery can get a better accuracy (F1-score of 0.94) than using them separately (F1-score of 0.88 using Sentinel-1 only and 0.895 using Sentinel-2 only). The 10-m mangrove map derived by combining SAR and MSI data identified 20,003 ha mangroves in China and the areas of small mangrove patches (&lt; 1 ha) was 1741 ha, occupying 8.7% of the whole mangrove area. The largest area (819 ha) of small mangrove patches is located in Guangdong Province, and in Fujian the percentage of small mangrove patches in total mangrove area is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest maps are expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of mangrove forest.&lt;/p&gt;

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