Pendugaan Cadangan Karbon Above Ground Biomass (AGB) Pada Tegakan Agroforestri Di Kabupaten Langkat (the Estimate of Carbon Stocks Above Ground Biomass (AGB) on Agroforestry Stands in Langkat)

Agroforestry system is estimate to have a high potential for carbon sequestration in the atmosphere. Agroforestry systems contributed to reducing the increase in atmospheric CO2 and other green house gases by increasing carbon in the soil and reduce the pressure for forest clearing where the carbon comes from CO2 is taken up by plants and stored in the form of biomass. This study aimed to quantify the carbon content. Calculation of carbon stocks was done with non-destructive by using allometric method. The results indicated that the types of vegetation in agroforestry in Langkat was are sengon, mindi, sungkai, coklat, mangga, mahoni, nangka, durian, karet, kemiri, jati, jengkol, petai, and duku. The amount of carbon stocks in agroforestry stands in Sei Bingai sub-district, Bahorok sub-district, and Wampu sub-district was 58,438 ton/ha, 63,005 ton/ha, and 56,76 ton/ha. Differences in carbon content acquisition was influenced by vegetation density, diversity of size, and distribution of vegetation density

Field assessments of above ground biomass (AGB) of mangrove stand in Merbok, Malaysia

Mangroves are considered as unique and important ecosystems that occupy an intertidal zone of protected shorelines. The halophytic plants present in mangroves provide support not only for social economic needs but also for ecological roles which include carbon sinks. Above ground biomass (AGB) of mangroves was estimated in mangrove stands in Merbok, Kedah. Field data collection was conducted from January 2013 to May 2013. A total of 25 sites measuring 100 m x 100 m were surveyed in the study area. Within randomly selected plots, diameter at breast height (DBH), tree height and crown width were measured. Mangrove trees were identified at the species level. Published allometric functions were used to compute the AGB of mangroves. Rhizophoraapiculata was found to be the most abundant species followed by Bruguieraparviflora, Bruguieragymnorrhiza and Avicennia marina. An overall mean for AGB in study area was estimated to be 176 Mg/ha. From the analysis of variance (ANOVA), it was found that there is a significant different in the means of all mangrove variables measured between four mangrove species (p <0.0001). Positive relationships were found between DBH, height and crown width and AGB with r values of 0.88, 0.43 and 0.81 respectively. The subsequent analysis will involve a study of relationships between mangrove stand attributes with spectral radiance recorded from remote sensing

Floristics and above-ground biomass (AGB) in Peatlands in Peruvian Lowland Amazonia, Loreto – Peru

Amazonian forests comprise almost 10% of stored carbon (C) in the world’s land ecosystems. This C is held both in above-ground biomass (AGB) and in the soil. AGB in an individual plant depends on plant size, often measured in trees as height (H) and diameter (D), and the density of plant tissues, often approximated in trees by wood density (WD). Soil C storage depends on the balance between inputs from AGB due to mortality and senescence and outputs due to decay and erosion. Peatlands, wetlands recently described in northern Peruvian Amazonia, show unusually high rates of soil C accumulation. For these habitats information on C budget contributions from peatland plants is unavailable. In this study I estimated AGB in various peatlands of northern Peruvian Amazonia, and asked why some of these peatlands store more AGB than others. I first set out to estimate the relative contribution of inter- and intra-specific variation to variation in AGB among individual peatland trees. I found that 80% of the variation in AGB among individual trees was due to inter-specific variation. Then I assessed the extent to which the three traits that determine AGB (i.e., D, H and WD) contribute to inter- and intra-specific variation in AGB among peatland trees. I found variation in D and the interaction between D and H contributed most to inter- and intra-specific variation in AGB among trees. Last, I estimated the extent to which variation in AGB among peatland locations was due to variation in species composition, stem density and intra-specific variation in AGB. I found that species composition and intra-specific variation, but not stem density, explained nearly equal amounts of variation in AGB among peatland locations. In summary, detailed knowledge of tree size can provide good estimates of species level biomass estimates in the peatlands of northern Peruvian Amazonia. Additionally, what species are present, as well as how their biomass varies (intra-specifically) from site to site drives AGB variation among peatland locations

Pendugaan Cadangan Karbon Above Ground Biomass (AGB) Pada Tegakan Sawit (Elaeis Guineensis Jacq.) Di Kabupaten Langkat / (the Estimate of Carbon Stocks Above Ground Biomass (AGB) on Palm (Elaeis Guineensis Jacq.) Stands in Langkat District)

Palm Oil Plant a CO2 absorber as well as other crops such as forest plants. Langkat District is one of the areas that have a high oil commodity. Research on AGB estimates carbon stocks in standing palm (Elaeis guineensis Jacq) in Langkat conducted in July through September 2012. This study uses Allometric that calculate carbon stocks in standing palm (Elaeis guineensis Jacq.) Associated with the value of Normalized Difference Vegetation Index (NDVI).Largest carbon stocks contained in the 14-year-old stands of oil reserves by 68.84 tonnes carbon / ha and the smallest carbon stocks contained in the 3-year old palm stands by the number of carbon reserves 19.20 tonnes / ha. Vegetation index can be calculated by the model equation Y = 38.39 + 26.24 x NDVI on Landsat imagery. NDVI has a positive relationship with the value of the field in which the R ² of the resulting equation is equal to 49% of the stands of palm oil (Elaeis guineensis Jacq.)

Hedgerow agroforestry in England and Wales: increasing width to sequester additional carbon

N/

The design of a Space-borne multispectral canopy LiDAR to estimate global carbon stock and gross primary productivity

Understanding the dynamics of the global carbon cycle is one of the most challenging issues for the scientific community. The ability to measure the magnitude of terrestrial carbon sinks as well as monitoring the short and long term changes is vital for environmental decision making. Forests form a significant part of the terrestrial biosystem and understanding the global carbon cycle, Above Ground Biomass (AGB) and Gross Primary Productivity (GPP) are critical parameters. Current estimates of AGB and GPP are not adequate to support models of the global carbon cycle and more accurate estimates would improve predictions of the future and estimates of the likely behaviour of these sinks. Various vegetation indices have been proposed for the characterisation of forests including canopy height, canopy area, Normalised Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI). Both NDVI and PRI are obtained from a measure of reflectivity at specific wavelengths and have been estimated from passive measurements. The use of multi-spectral LiDAR to measure NDVI and PRI and their vertical distribution within the forest represents a significant improvement over current techniques. This paper describes an approach to the design of an advanced Multi-Spectral Canopy LiDAR, using four wavelengths for measuring the vertical profile of the canopy simultaneously. It is proposed that the instrument be placed on a satellite orbiting the Earth on a sun synchronous polar orbit to provide samples on a rectangular grid at an approximate separation of 1km with a suitable revisit frequency. The systems engineering concept design will be presented

The Use of Remote Sensing and Geographic Information System to Determine the Spatial Distribution of Melaleuca cajuputi as a Major Bee Plant in Marang, Terengganu

In Malaysia, honey is chiefly obtained from species of honeybees known as Apis dorsata and to a lesser extent Apis cerana. Honey from Apis dorsata is a supplementary source of income to many rural poor in the district of Marang, Terengganu. The colonies of A. dorsata are found to nest in aggregates on tall bee trees (tree emergent) in the open, as well as, nesting singly in concealed locations when nesting low, especially in the submerged forest of Melaleuca cajuputi as in the vast hectare (> 200,000 hectares) of Melalueca forest along the coastal areas of Terengganu. So, Melaleuca forest mapping and flower mapping can be reliable methods for determining this species distribution as the main source of nectar and pollen for these aforementioned honey bees. In ecology, biomass can be defined as accumulation of living matter which is useful as a biophysical index for mapping of flower in forest. In this study, we used SPOT-5 and RADARSAT-1 for inventory of Melaleuca forest in Marang and developed Above Ground Biomass (AGB) estimation model as indirect index for obtaining and producing distribution of Melaleuca cajuputi flowers. Also, Apis dorsata colonies distribution and motorbike parking points of honey hunters were collected using GPS in field survey to determine distribution of colonies and improve searching ability in Apis dorsata colonies harvesting by honey hunters in the study area. The Melaleuca forest, located in Marang, Terengganu, Malaysia which is lying in upper left latitude 5°17'15.473"N, and longitude 103°05'25.021"E and lower right latitude 4°37'55.236" N, longitude 103°45'47.568"E was chosen for this research. SPOT-5 was enhanced, classified and vectorized using image processing software for the purpose of Melaleuca forest mapping. Based on the image analysis of the SPOT-5 image the Melaleuca forest were classified as five classes Melaleuca Cajuputi, Acacia auriculiformis, non-vegetation, water bodies and Cloud/haze/Shadow. The analysis showed that Melaleuca cajuputi covered 76,061.73ha (61.72%), Acacia auriculiformis 24,484.32ha (19.88%), non-vegetation 9,991.76ha (8.11%), water bodies 2,203.47ha (1.79%) and Cloud/Haze/Shadow 10,491.86ha (8.51%) with an overall classification accuracy of 91.79% while the statistics value obtained from kappa coefficient was more than 0.86 which is relatively quite good results for image processing. Based on Melaleuca forest inventory, 10 plots of 10