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Forests sequester a largeamount of carbon and plays a crucial role in the global agenda of climatechange.

  Forest can act as both sourceand sink of carbon. When the forest is healthy and growing, carbon is sequestratedfrom atmosphere; but when the forests are destroyed, overharvested, or burned,they no longer contribute in sequestration and become a source of CO2which increase climate change (Hussin et al., 2014). Hence, quantification of forestbiomass is of vital importance to assess productivity – a critical informationfor carbon budget accounting, carbon flux monitoring and for understanding theforest ecosystem response to climate change (Watham et al., 2016; Nandy et al., 2017).

Meanwhile, refo+restation,afforestation and avoiding deforestation are mechanisms of tackling climatechange (Hunt, 2009; Luong et al., 2015). In addition, estimation of the forest carbon stocks not only contributesin reducing emissions from deforestation and forest degradation (REDD); butalso in sustainable management of the forest (Hussin et al., 2014).The quantification ofbiomass and carbon sequestration in tropical forests is particularly relevantwithin the United Nations Framework Convention on Climate Change (UNFCC). TheUNFCC adopted Kyoto Protocol which sets binding targets to industrializedcountries for reducing greenhouse gases emissions (Breidenich et al., 1998; Protocol, 2011; Hussin et al.

, 2014). The Bali Action Plan Conference ofthe Parties (COP-13) in 2007 opened an avenue for developing countries toparticipate in forest carbon financing through the mechanism of reducingemissions by reducing emissions from deforestation and forest degradation (REDD)(Hussin et al., 2014;Luong et al.

, 2015). Under the REDD mechanism, countrieswill need to measure and monitor the emissions of CO2 resulting fromdeforestation and degradation within their borders (Luong et al., 2015). Emissions are converted to carbon credits in the carbontrade. All the greenhouse gas inventories and emissions reduction programsrequire scientifically robust methods to quantify forest carbon storage overtime across extensive landscapes (Gonzalez et al., 2010).

Vietnam has been participating in UN-REDD as a potentialmember of carbon trade, which requires estimation of biomass/carbon stock inthe country to be prepared for REDD implementation.Remotely sensed data integrated withforest inventories has been becoming an effective approach used to estimate aboveground biomass (AGB) and hence ultimately carbon stocks. Remote sensing-basedstudies relate reflectance recorded at the sensor with ground-basedmeasurements to estimate biomass (Tucker et al.

, 1985; Sader et al., 1989; Gibbs et al., 2007; Kumar et al., 2015). Recently, many studies in differentregions have found strong correlations between biomass and reflectance atdifferent wavelengths (Kumar et al., 2015). Kumar et al.

(2015) also concluded that for regional levelwhere field data are scarce or difficult to collect, remote sensing is the superlativemethod to project biomass since its enhanced spatial, spectral, andradiometric characteristics (Delegidoet al., 2011; Irons et al., 2012; Chrysafis et al.,2017) can furthercontribute to accurate, spatially explicit estimations of forest inventoryparameters, andimproved update frequency with a lower cost for monitoring forests andmeasuring variables (Andersson et al., 2009; Dube & Mutanga, 2015; Yadav & Nandy, 2015). Therefore, this method to become apopular method and widely used for biomass estimation.   1.1.

        Statementof problemThe quantification, mappingand monitoring of biomass are now key issues due to the importance of forest biomassin ecosystem and biomass role as a renewable energy source in many countries aroundthe world. However, detailed ground-based information of total biomass are scarce(Sierra et al.,2007;Hussin et al.

, 2014). AGB estiation for thetropical and sub-tropical area is still a challenging task and requiresaccurate and consistent measurement methods because these forest areas arecharacteristed with complex stands and varying environmental conditions (Lu, 2005; Kumar et al., 2015). A shortage of information of global biomass due touncertainties in accuracy and cost is still remaining as a matter of furtherexploration (Nguyen, 2010; Hussin et al., 2014). According to Lu (2006), it is essential to integrateremotely sensed data and forest inventory data, so as to develop appropriate approachfor AGB estimation.

Zians & Mencuccini, (2004) also emphasize on need ofrapid and easily implemented methods to assess above ground woody biomass forcarbon estimation which can be used to track changes in carbon stocks(Ketterings, et al., 2001). 

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