scholarly journals Species composition and carbon stock estimation in Pulau Sembilan secondary mangrove forests, North Sumatra, Indonesia

2021 ◽  
Vol 713 (1) ◽  
pp. 012014
Author(s):  
M Basyuni ◽  
E O Simanjutak
Keyword(s):  
Species Composition ◽  
Carbon Stock ◽  
Mangrove Forests ◽  
Stock Estimation ◽  
North Sumatra

scholarly journals Carbon Stock Estimation of Mangrove Forest in Village Margasari Sub-District Labuhan Maringgai District East Lampung

2018 ◽  
Vol 6 (1) ◽  
pp. 66 ◽  
Author(s):  
Cahyaning Windarni ◽  
Agus Setiawan ◽  
Rusita Rusita
Keyword(s):  
Mangrove Forest ◽  
Carbon Stock ◽  
Dry Weight ◽  
Co2 Concentration ◽  
Line Transect ◽  
Mangrove Forests ◽  
First Line ◽  
Average Biomass ◽  
Stock Estimation ◽  
Total Dry Weight

Increasing CO2 in the atmosphere and decreasing amount of forest as absorb CO2are factors which was the underlying repercussion of climate change. One of solutions for decreasing CO2 concentration through the forest vegetation’s development and emendation. Mangrove forest estimated that effectively absorb carbon through photosynthesis. The purpose of the studyis to estimate the stand and litter carbon stock of mangrove forest. The research used line transectmethod. The first line and plot determined randomly then the next lineand plots was sistematically. The observation plots had measurement with amount of 20m x 20m with spacing between plot in line 20 m with total 20 plots. Each plot was measured diameter just  ≥ 5 cm. Each plot made observations litter sub plots with amount of 0,5 m x 0,5 m. Carbon estimation of stand biomass using allometric equations B = 0,1848D2.3624 and litter biomass using total dry weight. Carbon concentration of organic material typically contains around 46% thus multiplying the biomass by 46%. The average biomass of mangrove forests amounted to 431,78 tons/ha. Carbon estimated of mangrove stand was 197,36 ton/ha and litter carbon was 1,25 ton/ha, based on the research total of carbon mangrove forest was198,61 ton/ha. Keywords:carbon above ground,line transect, mangrove forest

The use of CO2 flux time series for parameter and carbon stock estimation in carbon cycle research

2011 ◽  
Vol 18 (1) ◽  
pp. 179-193 ◽  
Author(s):  
Timothy Charles Hill ◽  
Edmund Ryan ◽  
Mathew Williams
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Carbon Stock ◽  
Co2 Flux ◽  
Stock Estimation

Biomass and vegetation carbon stock in mangrove forests of the Andaman Islands, India

Hydrobiologia ◽  
2021 ◽  
Author(s):  
P. Ragavan ◽  
Sanjeev Kumar ◽  
K. Kathiresan ◽  
P. M. Mohan ◽  
R. S. C. Jayaraj ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Mangrove Forests ◽  
Andaman Islands ◽  
Vegetation Carbon

scholarly journals Carbon stock estimation of Shree Rabutar Forest of Gaurishankar Conservation Area, Dolakha, Nepal

2018 ◽  
Vol 6 ◽  
pp. 61-67
Author(s):  
Karishma Gubhaju ◽  
Dipesh Raj Pant ◽  
Ramesh Prasad Sapkota
Keyword(s):  
Carbon Stock ◽  
Shannon Index ◽  
Tree Biomass ◽  
Carbon Dioxide Absorption ◽  
Pinus Roxburghii ◽  
Conservation Area ◽  
Rhododendron Arboreum ◽  
Alnus Nepalensis ◽  
Below Ground ◽  
Stock Estimation

Forests store significant amount of atmospheric carbon in the form of above and below ground biomass and the amount of carbon stored in forests differs along spatial continuum which provides important information regarding forest quality. This study was carried out to estimate the carbon stock of Shree Rabutar Forest of Gaurishankar Conservation Area, Dolakha, Nepal. In total, 20 circular sampling plots with an area 250 m2 were randomly laid in the study area. Ten tree species were observed in the sampling plots laid in the forest. The higher values of density, frequency, abundance and basal area were observed for Rhododendron arboreum, Alnus nepalensis, Pinus roxburghii and Pinus wallichiana. On the basis of Important Value Index, the dominant tree in the forest was Alnus nepalensis followed by Rhododendron arboreum and Pinus roxburghii. Shannon Index of general diversity of trees in the forest was 0.74 with equal value of Evenness Index, whereas the index of dominance was low (0.22) in the forest. Mean biomass of the forest was 464.01±66.71 tonha-1 contributed by above ground tree biomass (384.44 tonha-1), leaf litter, herbs and grasses biomass (2.69±0.196 tonha-1) and below ground tree biomass (76.88±11.13 tonha-1). Mean carbon stock was 262.77±30.79 tonha-1 including soil carbon stock 44.69±2.25 tonha-1. Individuals of trees with 20-30 cm DBH class were observed in maximum number, which shows that the forest has high potential to sequester carbon over time. Carbon stock estimation and forest management can be one of the potential strategies for climate change mitigation especially through carbon dioxide absorption by the forests.

scholarly journals Remote Estimation of Mangrove Aboveground Carbon Stock at the Species Level Using a Low-Cost Unmanned Aerial Vehicle System

2019 ◽  
Vol 11 (9) ◽  
pp. 1018 ◽  
Author(s):  
Zhen Li ◽  
Qijie Zan ◽  
Qiong Yang ◽  
Dehuang Zhu ◽  
Youjun Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Low Cost ◽  
Important Variable ◽  
Species Level ◽  
Canopy Height ◽  
Individual Species ◽  
Support Vector ◽  
Mangrove Forests ◽  
Aboveground Carbon

There is ongoing interest in developing remote sensing technology to map and monitor the spatial distribution and carbon stock of mangrove forests. Previous research has demonstrated that the relationship between remote sensing derived parameters and aboveground carbon (AGC) stock varies for different species types. However, the coarse spatial resolution of satellite images has restricted the estimated AGC accuracy, especially at the individual species level. Recently, the availability of unmanned aerial vehicles (UAVs) has provided an operationally efficient approach to map the distribution of species and accurately estimate AGC stock at a fine scale in mangrove areas. In this study, we estimated mangrove AGC in the core area of northern Shenzhen Bay, South China, using four kinds of variables, including species type, canopy height metrics, vegetation indices, and texture features, derived from a low-cost UAV system. Three machine-learning algorithm models, including Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were compared in this study, where a 10-fold cross-validation was used to evaluate each model’s effectiveness. The results showed that a model that used all four type of variables, which were based on the RF algorithm, provided better AGC estimates (R2 = 0.81, relative RMSE (rRMSE) = 0.20, relative MAE (rMAE) = 0.14). The average predicted AGC from this model was 93.0 ± 24.3 Mg C ha−1, and the total estimated AGC was 7903.2 Mg for the mangrove forests. The species-based model had better performance than the considered canopy-height-based model for AGC estimation, and mangrove species was the most important variable among all the considered input variables; the mean height (Hmean) the second most important variable. Additionally, the RF algorithms showed better performance in terms of mangrove AGC estimation than the SVR and ANN algorithms. Overall, a low-cost UAV system with a digital camera has the potential to enable satisfactory predictions of AGC in areas of homogenous mangrove forests.

Ecosystem Carbon Stock of Selected Mangrove Forests of Vypin – Cochin Region, Southwest Coast of India

Wetlands ◽  
2020 ◽  
Vol 40 (6) ◽  
pp. 2263-2273
Author(s):  
M. S. ShyleshChandran ◽  
Arun Ravi ◽  
Sheffy Molly John ◽  
Silpa Sivan ◽  
M. S. Asha ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Mangrove Forests ◽  
Southwest Coast Of India ◽  
Southwest Coast ◽  
Ecosystem Carbon

scholarly journals Keragaman Kondisi Salinitas Pada Lingkungan Tempat Tumbuh Mangrove di Teluk Kupang,NTT

2019 ◽  
Vol 17 (3) ◽  
pp. 425
Author(s):  
Jeriels - Matatula
Keyword(s):  
Mangrove Forest ◽  
Interpolation Method ◽  
Systematic Sampling ◽  
Mangrove Forests ◽  
Transect Line ◽  
Straight Line ◽  
Measurement Results ◽  
The Government ◽  
Systematic Sampling Method ◽  
North Sumatra

Mangrove forest ecosystems are  habitat of various types of microorganisms, but now, problems of mangrove forests experience are in quality and wealth. Efforts to rehabilitate activities  mangrove forests againts had been carried out but the results shown had not been maximized, so it is need the correct strategies  to achieve the success for rehabilitation activities. The studied was conducted in Teluk Kupang, East Nusa Tenggara, covered coastal areas of Kupang's mangrove forests and coastal mangrove forests in Kupang district. The method used in this studied was a systematic sampling method that was systematically distributed across all the mangrove forest areas. The method used for salinity measurement was  method of transmission and Transect Line Plots. Measurements was made in straight line and the size of the plot is 10 m x 10 m, the distanced between lines was 50 meters so that  total lane is 547 with the number of plots of observation 1641 plots. The value of  measurement results was made into the spread of salinity used the interpolation method. The condition of the crossed of  mangrove forest located on  coastal panoramas of  Kupang city shows a salinity value of 10,26 - 26.33%, while  salinity conditions was on the coast of  Kupang ten district 10-42.33 ‰. The salinity condition was formulation of the environmental conditions  mangrove forest on  coast of the island as well as attempts to support  management of mangrove forest activities. The distribution of different mangrove forest conditions along the coastal zone of North Sumatra shows an environmental condition that supports mangrove growth even though in some places the salinity values are high. Results of this study of salinity conditions is a study of the environment of mangrove growth so that it can provide an overview for the government in conducting various activities to rehabilitate mangrove forests.   

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