Preharvest Monitoring of Biomass Production - Engineering and Science of Biomass Feedstock Production and Provision - page 61

feedstock. Furthermore, the preharvest monitoring systems need to be able to fulfi ll
data collection in different traffi c conditions with high maneuverability, stability,
and mobility for either high plants and short plants or different bioenergy crop
plants in all growing seasons. There is a need to develop optimized instrumentations
for stand-alone remote sensing applications to monitor perennial growth of biomass
feedstock over the growing season as well as a specially designed close proximity
data collection vehicle and an unmanned aerial vehicle (UAV)-based near-real-time
remote sensing system. However, preharvest monitoring of biomass crops has not
been widely done. This chapter emphasizes the description of three platforms
recently developed specifi cally for monitoring the production of energy crops.

4.2 R emote Sensing and I ts A pplication

Remote sensing is the science and art of obtaining information about an object, area,
or phenomenon through the analysis of data acquired by a device that is not in contact
with the object, area, or phenomenon under investigation [ 15 ]. Herein, the art refers to
technology, instruments, methods, software, skill, personal knowledge, and expertise.
There are three broad categories of applications: (1) Photogrammetric analyses
use remote-sensed data to provide spatial measurements of a feature or a phenom-
ena (e.g., distance, area, volume), (2) classifi cation analyses identify and map areas
with similar characteristics (e.g., classify land cover into categories using image
analysis software tools), and (3) quantitative analyses provide estimates of earth
surface properties (e.g., vegetation index to measure plant biomass). There are many
ways remote sensing systems are used, some of which are mentioned here: (1) car-
tography and mapping; (2) natural resource management; (3) disaster management
(fi re, earthquakes, etc.); (3) geostationary weather monitoring; (4) sea ice, oil spill,
sea surface temperature monitoring; (5) atmospheric (water vapor, ozone, etc.)
monitoring; and (6) data for Geographic Information Systems (GIS) [ 16 - 18 ].
In precision agriculture, NDVI is widely used to predict crop leaf area index, crop
growth and disease control, biomass productivity, economic yield, etc. NDVI is a very
useful application of spectral ratio. This index relies on the spectral absorption and
refl ectance characteristics of living (i.e., green) vegetation in primarily the red and
NIR wavelength bands. As illustrated in Fig. 4.2 , NDVI is calculated as follows [ 19 ]:

( ) - ( )
( ) + ( )

r NIR r Red

NDVI = ( 4.1 )

r NIR r Red

where ρ (NIR) = brightness values (or digital number) of near-infrared band and

ρ (Red) = brightness values (or digital number) of red band in a remote sensing dataset.

The remote sensing technology has been widely used to provide image, informa-
tion, as well as decision support for precision agriculture (PA) or SSCM since the
fi rst aerial photos were used as a basis for soil mapping, which began in the late
1930s with the advent of aerial photography [ 20 - 22 ]. SSCM is the management of