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Forests sequester a large
amount of carbon and plays a crucial role in the global agenda of climate
change.  Forest can act as both source
and sink of carbon. When the forest is healthy and growing, carbon is sequestrated
from atmosphere; but when the forests are destroyed, overharvested, or burned,
they no longer contribute in sequestration and become a source of CO2
which increase climate change (Hussin et al., 2014). Hence, quantification of forest
biomass is of vital importance to assess productivity – a critical information
for carbon budget accounting, carbon flux monitoring and for understanding the
forest ecosystem response to climate change (Watham et al., 2016; Nandy et al., 2017). Meanwhile, refo+restation,
afforestation and avoiding deforestation are mechanisms of tackling climate
change (Hunt, 2009; Luong et al., 2015). 
In addition, estimation of the forest carbon stocks not only contributes
in reducing emissions from deforestation and forest degradation (REDD); but
also in sustainable management of the forest (Hussin et al., 2014).

The quantification of
biomass and carbon sequestration in tropical forests is particularly relevant
within the United Nations Framework Convention on Climate Change (UNFCC). The
UNFCC adopted Kyoto Protocol which sets binding targets to industrialized
countries for reducing greenhouse gases emissions (Breidenich et al., 1998; Protocol, 2011; Hussin et al., 2014). The Bali Action Plan Conference of
the Parties (COP-13) in 2007 opened an avenue for developing countries to
participate in forest carbon financing through the mechanism of reducing
emissions by reducing emissions from deforestation and forest degradation (REDD)
(Hussin et al., 2014;
Luong et al., 2015). Under the REDD mechanism, countries
will need to measure and monitor the emissions of CO2 resulting from
deforestation and degradation within their borders (Luong et al., 2015). Emissions are converted to carbon credits in the carbon
trade. All the greenhouse gas inventories and emissions reduction programs
require scientifically robust methods to quantify forest carbon storage over
time across extensive landscapes (Gonzalez et al., 2010). Vietnam has been participating in UN-REDD as a potential
member of carbon trade, which requires estimation of biomass/carbon stock in
the country to be prepared for REDD implementation.

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Remotely sensed data integrated with
forest inventories has been becoming an effective approach used to estimate above
ground biomass (AGB) and hence ultimately carbon stocks. Remote sensing-based
studies relate reflectance recorded at the sensor with ground-based
measurements to estimate biomass (Tucker et al., 1985; Sader et al., 1989; Gibbs et al., 2007; Kumar et al., 2015). Recently, many studies in different
regions have found strong correlations between biomass and reflectance at
different wavelengths (Kumar et al., 2015). Kumar et al. (2015) also concluded that for regional level
where field data are scarce or difficult to collect, remote sensing is the superlative
method to project biomass since its enhanced spatial, spectral, and
radiometric characteristics (Delegido
et al., 2011; Irons et al., 2012; Chrysafis et al.,
2017) can further
contribute to accurate, spatially explicit estimations of forest inventory
parameters, and
improved update frequency with a lower cost for monitoring forests and
measuring variables (Andersson et al., 2009; Dube & Mutanga, 2015; Yadav & Nandy, 2015). Therefore, this method to become a
popular method and widely used for biomass estimation.

 

 

1.1.        
Statement
of problem

The quantification, mapping
and monitoring of biomass are now key issues due to the importance of forest biomass
in ecosystem and biomass role as a renewable energy source in many countries around
the world. However, detailed ground-based information of total biomass are scarce
(Sierra et al.,
2007;
Hussin et al., 2014). AGB estiation for the
tropical and sub-tropical area is still a challenging task and requires
accurate and consistent measurement methods because these forest areas are
characteristed with complex stands and varying environmental conditions (Lu, 2005; Kumar et al., 2015). A shortage of information of global biomass due to
uncertainties in accuracy and cost is still remaining as a matter of further
exploration (Nguyen, 2010; Hussin et al., 2014). According to Lu (2006), it is essential to integrate
remotely sensed data and forest inventory data, so as to develop appropriate approach
for AGB estimation. Zians & Mencuccini, (2004) also emphasize on need of
rapid and easily implemented methods to assess above ground woody biomass for
carbon estimation which can be used to track changes in carbon stocks
(Ketterings, et al., 2001). 

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