Preharvest Monitoring of Biomass Production - Engineering and Science of Biomass Feedstock Production and Provision - page 59

4.1 I ntroduction

Remote sensing technology has been recognized as the key technology to enable
site-specifi c management of crop production. Researchers have developed precision
agricultural technologies and processes that have enhanced agricultural production
in traditional crops like corn and soybeans. The same methodology has been applied
to the production of energy crops to maximize biomass feedstock production (BFP)
throughout the world. “BFP is a critical subsystem within the overall bio-based
energy production and utilization system. It provides necessary materials input to
the conversion process of biomass into fuel, power, and value-added materials. This
subsystem includes the operations of agronomic production of energy crops and
physical handling/delivery of biomass, as well as other enabling logistics [ 1 ].” As
concerns over energy security and environmental degradation have risen, ensuring
sustainable biomass and biofuel production has become critical. Here, there are
potentially important applications of remote sensing in ensuring that the desired
objectives are met, which will ultimately lead to a sustainable bioenergy system.
The agronomic production depends on tracking the yield variability over the
growing season and utilizing the optimum harvesting window to meet quantity and
quality targets. The measurement of yield variability of biomass is needed for devel-
oping and evaluating site-specifi c crop management (SSCM) practices. In different
growth stages, fi eld spectroscopy has the fundamental importance for assessing
spectral response of plant canopies and photosynthetically active radiation for bio-
mass conversion. Therefore, multispectral imagery of preharvesting monitoring is
the key point to understand crop response in remote sensing applications. Field
spectroscopy involves the study of interrelationships between the spectral character-
istics of objects and their biophysical attributes in the fi eld environment. Firstly, it
acts as a bridge between the laboratory measurements of spectral refl ectance and the
fi eld situation and is useful in calibration of airborne and satellite sensors. Secondly,
it is useful in predicting the optimum spectral bands viewing confi guration and time
to perform a particular remote sensing task. Thirdly, it provides a tool for the devel-
opment, refi nement, and testing of models relating biophysical attributes to remotely
sensed data [ 2 ]. The multispectral imagery refers to images that capture data at
specifi c wavelengths across the electromagnetic spectrum. The wavelengths may be
separated by fi lters or by the use of instruments that are sensitive to particular wave-
lengths, including light from frequencies beyond the visible light range, such as
infrared. Multispectral imagery can allow extraction of information from spectral
response that the human eye fails to capture with its receptors for red, green, and
blue. The relationships between crop refl ectance in the visible and near-infrared
wavelength are closely correlated with the amount of photosynthetically active tis-
sue in the crop [ 3 ]. Currently, aerial hyperspectral and multispectral images are
available for agricultural remote sensing to fi nd nitrogen stress and mapping [ 4 - 7 ].
The most widely accepted method for describing vegetative growth using refl ec-
tance spectra is band ratio or vegetation indices. Vegetation indices are spectrally
based values generated through the mathematical manipulation of refl ectance mea-
surements from two or more spectral wavelengths [ 8 ]. The vegetation index is used