3.1 Introduction
Biomass is defined as the mass per unit area of live or dead plant organic matter.
Forest ecosystem covers about a third of the Earth’s land surface, and it stores about
80 % of all above-ground and 40 % of all below-ground terrestrial organic carbon
(IPCC 2001). Forest significantly affects the exchange of gases and energy between
the atmosphere and the surface, through photosynthesis and the production of woody
plant matter. During productive seasons, forests take up carbon dioxide (CO 2 ) from
the atmosphere and store as plant biomass (Losi et al. 2003; Phat et al. 2004), while
they release CO 2 to atmosphere during deforestation, decomposition, and biomass
burning (Chambers et al. 2000; Van der Werf et al. 2010; Zhang et al. 2012). Changes
of forest biomass in time can be used as an essential climate variable, because it is a
direct measure of sequestration or release of carbon between terrestrial ecosystems
and the atmosphere. Measuring the size and complexity of forest biomass over large
areas would enable scientists to better understand the environmental processes,
availability of renewable energy, and global carbon cycle.
Forest biomass consists of above-ground biomass (AGB) and below-ground
biomass. AGB represents all living biomass above the soil including stem, stump,
branches, bark, seeds, and foliage, while below-ground biomass consists of all
living roots excluding fine roots (less than 2 mm in diameter) (FAO 2004).
Because it is relatively easy to measure and it accounts for the majority of the total
accumulated biomass in forest ecosystem, AGB is usually estimated in many
studies to refer as to forest biomass (Aboal et al. 2005; Brown 1997; Kraenzel
et al. 2003; Laclau 2003; Losi et al. 2003; Segura and Kanninen 2005).
Forest biomass has been traditionally estimated at field plot scales (usually less
than one acre). To calculate tree biomass, a large number of studies have focused
on the development of species and site specific allometric models depending on
bole diameter at breast height (e.g., Paster et al. 1984; Ter-Mikaelian and Korzukin
1997). The plot estimates of national forest inventories are commonly aggregated
to represent forest biomass at national or regional scales (Brown et al. 1999;
Jenkins et al. 2001).
Recently, remote sensing has been extensively used as a robust tool in deriving
forest structure and AGB because it provides a practical means of acquiring spatially-distributed forest biomass from local, continental, to global areas (Dobson
2000; Saatchi et al. 2007a, b; Houghton et al. 2007; Baccini et al. 2004; Blackard
et al. 2008; Zhang and Kondragunta 2006; Zheng et al. 2004; Lu 2006; Le Toan et al.
2011). Three types of remote sensing data are often used, which are passive optical
remote sensing, radar (radio detection and ranging, microwave) data, and lidar (light
detection and ranging) data. Optical spectral reflectances are sensitive to vegetation
structure (leaf area index, crown size and tree density), texture and shadow, which
are strongly correlated with AGB. Radar data are related to AGB through measuring
dielectric and geometrical properties of forests (Le Toan et al. 2011). Lidar remote
sensing is promising in characterizing vegetation vertical structure and height which
are then associated to ABG (Lefsky et al. 2005; Drake et al. 2002).
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X. Zhang and W. Ni-meister
Biomass is defined as the mass per unit area of live or dead plant organic matter.
Forest ecosystem covers about a third of the Earth’s land surface, and it stores about
80 % of all above-ground and 40 % of all below-ground terrestrial organic carbon
(IPCC 2001). Forest significantly affects the exchange of gases and energy between
the atmosphere and the surface, through photosynthesis and the production of woody
plant matter. During productive seasons, forests take up carbon dioxide (CO 2 ) from
the atmosphere and store as plant biomass (Losi et al. 2003; Phat et al. 2004), while
they release CO 2 to atmosphere during deforestation, decomposition, and biomass
burning (Chambers et al. 2000; Van der Werf et al. 2010; Zhang et al. 2012). Changes
of forest biomass in time can be used as an essential climate variable, because it is a
direct measure of sequestration or release of carbon between terrestrial ecosystems
and the atmosphere. Measuring the size and complexity of forest biomass over large
areas would enable scientists to better understand the environmental processes,
availability of renewable energy, and global carbon cycle.
Forest biomass consists of above-ground biomass (AGB) and below-ground
biomass. AGB represents all living biomass above the soil including stem, stump,
branches, bark, seeds, and foliage, while below-ground biomass consists of all
living roots excluding fine roots (less than 2 mm in diameter) (FAO 2004).
Because it is relatively easy to measure and it accounts for the majority of the total
accumulated biomass in forest ecosystem, AGB is usually estimated in many
studies to refer as to forest biomass (Aboal et al. 2005; Brown 1997; Kraenzel
et al. 2003; Laclau 2003; Losi et al. 2003; Segura and Kanninen 2005).
Forest biomass has been traditionally estimated at field plot scales (usually less
than one acre). To calculate tree biomass, a large number of studies have focused
on the development of species and site specific allometric models depending on
bole diameter at breast height (e.g., Paster et al. 1984; Ter-Mikaelian and Korzukin
1997). The plot estimates of national forest inventories are commonly aggregated
to represent forest biomass at national or regional scales (Brown et al. 1999;
Jenkins et al. 2001).
Recently, remote sensing has been extensively used as a robust tool in deriving
forest structure and AGB because it provides a practical means of acquiring spatially-distributed forest biomass from local, continental, to global areas (Dobson
2000; Saatchi et al. 2007a, b; Houghton et al. 2007; Baccini et al. 2004; Blackard
et al. 2008; Zhang and Kondragunta 2006; Zheng et al. 2004; Lu 2006; Le Toan et al.
2011). Three types of remote sensing data are often used, which are passive optical
remote sensing, radar (radio detection and ranging, microwave) data, and lidar (light
detection and ranging) data. Optical spectral reflectances are sensitive to vegetation
structure (leaf area index, crown size and tree density), texture and shadow, which
are strongly correlated with AGB. Radar data are related to AGB through measuring
dielectric and geometrical properties of forests (Le Toan et al. 2011). Lidar remote
sensing is promising in characterizing vegetation vertical structure and height which
are then associated to ABG (Lefsky et al. 2005; Drake et al. 2002).
64
X. Zhang and W. Ni-meister