Chapter 3
Remote Sensing of Forest Biomass
Xiaoyang Zhang and Wenge Ni-meister
Abstract Forest biomass reflects sequestration or release of carbon between terrestrial ecosystems and the atmosphere. Measuring the size and complexity of
forest biomass over large areas can enable us to better understand the environmental processes, availability of renewable energy, and global carbon cycle. This
chapter reviews recent progress in measuring forest biomass from remote sensing.
In quantifying forest biomass, forest properties are often characterized from three
types of remote sensing data. Passive optical spectral reflectances are sensitive to
vegetation structure (leaf area index, crown size and tree density), texture and
shadow. Radar data measure dielectric and geometrical properties of forests. Lidar
data characterize vegetation vertical structure and height. Because these instruments have their advantages and disadvantages in reflecting forest properties, data
fusion techniques can combine data from multiple sensors and related information
from associated databases to achieve improved accuracy in biomass estimation.
The remote sensing data or derived forest attributes are commonly correlated to
forest biomass using empirical regression models, non-parametric methods, and
physically-based allometric models. Although forest biomass is widely estimated
at various scales from remote sensing data, models tend to underestimate large
biomass densities and overestimate small ones because of saturation issues.
Finally, the assessment and validation of forest biomass obtained from remote
sensing is critical because current biomass estimates at large area are of large
uncertainties.
X. Zhang (&)
Geospatial Sciences Center of Excellence, South Dakota State University,
Brookings, South Dakota, USA
e-mail: xiaoyang.zhang@sdstate.edu
W. Ni-meister
Department of Geography, Hunter College City University of New York,
New York, NY, USA
e-mail: wnimeist@hunter.cuny.edu
J. M. Hanes (ed.), Biophysical Applications of Satellite Remote Sensing,
Springer Remote Sensing/Photogrammetry, DOI: 10.1007/978-3-642-25047-7_3,
Ó Springer-Verlag Berlin Heidelberg 2014
63
Remote Sensing of Forest Biomass
Xiaoyang Zhang and Wenge Ni-meister
Abstract Forest biomass reflects sequestration or release of carbon between terrestrial ecosystems and the atmosphere. Measuring the size and complexity of
forest biomass over large areas can enable us to better understand the environmental processes, availability of renewable energy, and global carbon cycle. This
chapter reviews recent progress in measuring forest biomass from remote sensing.
In quantifying forest biomass, forest properties are often characterized from three
types of remote sensing data. Passive optical spectral reflectances are sensitive to
vegetation structure (leaf area index, crown size and tree density), texture and
shadow. Radar data measure dielectric and geometrical properties of forests. Lidar
data characterize vegetation vertical structure and height. Because these instruments have their advantages and disadvantages in reflecting forest properties, data
fusion techniques can combine data from multiple sensors and related information
from associated databases to achieve improved accuracy in biomass estimation.
The remote sensing data or derived forest attributes are commonly correlated to
forest biomass using empirical regression models, non-parametric methods, and
physically-based allometric models. Although forest biomass is widely estimated
at various scales from remote sensing data, models tend to underestimate large
biomass densities and overestimate small ones because of saturation issues.
Finally, the assessment and validation of forest biomass obtained from remote
sensing is critical because current biomass estimates at large area are of large
uncertainties.
X. Zhang (&)
Geospatial Sciences Center of Excellence, South Dakota State University,
Brookings, South Dakota, USA
e-mail: xiaoyang.zhang@sdstate.edu
W. Ni-meister
Department of Geography, Hunter College City University of New York,
New York, NY, USA
e-mail: wnimeist@hunter.cuny.edu
J. M. Hanes (ed.), Biophysical Applications of Satellite Remote Sensing,
Springer Remote Sensing/Photogrammetry, DOI: 10.1007/978-3-642-25047-7_3,
Ó Springer-Verlag Berlin Heidelberg 2014
63