MAPPING REMOTE PLANTS THROUGH REMOTE SENSING TECHNOLOGY AND GIS

International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 2, Issue 2 (March-April 2014), PP. 77-80
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MAPPING REMOTE PLANTS THROUGH
REMOTE SENSING TECHNOLOGY AND GIS
SABA HASAN
Amity Institute of Biotechnology, Amity University, Lucknow
U.P.) – India 226010
Abstract: Remote sensing implies to the collection of data
about an object from a distance. With the use of LiDAR
remote sensing technology, the three dimensional
distribution of plant canopies and vegetation structural
attributes can be accurately estimated. The measure of
difference in reflectance from leaves of plants, due to the
presence of chlorophyll pigments in different ratios, can be
helpful to locate and characterize the plant remote
location through remote sensing technology and
Geographic Information System (GIS). The present study
highlights the importance of remote sensing technology and
GIS in detection of herbs located distantly.
Key Words — Remote sensing, GIS, LiDAR, Spatial
resolution
I. INTRODUCTION
A. Remote Sensing Technology
Remote sensing is the technique of deriving information about
objects on the surface of the earth without physically coming
into contact with them. This process involves making
observations using sensors mounted on platforms (aircraft and
satellites), recording the observations on a suitable medium
(images on photographic films and digital data on magnetic
tapes). When electromagnetic radiation falls upon a surface,
some of energy is absorbed, some transmitted through the
surface, and the rest is reflected. Surfaces also naturally emit
radiation, mostly in the form of heat. This reflected and
emitted radiation is recorded either on the photographic film or
digital sensor. Since the intensity and wavelengths of this
radiation are a function of the surface, each surface is
described as processing a characteristic ʺSpectral
Signatureʺ[1]. If an instrument can identify and distinguish
between different spectral signatures, then it will be possible to
map the extent of surfaces (Figure 1).
The two main types of remote sensing: Passive (film
photography, radiometers etc.) detect natural radiation that is
emitted or reflected by the object or surrounding area being
observed, while Active emits energy in order to scan objects
and areas whereupon a sensor then detects and measures the
radiation that is reflected or backscattered from the target.
RADAR
and LiDAR are active remote sensors where the time delay
between emission and return is measured, establishing the
locations, height, speed and direction of an object [2].
The mode of satellite can be geostationary, permitting
continuous sensing of a portion of the earth or sun,
synchronous with polar orbit covering the entire earth at the
same equator crossing time. Sensors installed on satellites,
used for making observations, consist of mechanisms usually
sophisticated lenses with filter coatings to focus the area
observed onto a plane in which detectors are placed. These
detectors are sensitive to a particular region in which the
sensor is designed to operate and produce outputs, which either
represent observed area as in the case of the camera or produce
electrical signals proportionate to radiation intensity.
B. Use of LiDAR sensor
Light detection and ranging is an optical remote sensing
technology that can measure the distance or other properties of
a target by illuminating the target with light, often using pulses
from a laser. Wavelengths from a range about 10µm to UV
range are used to suit the target. Typically light is reflected via
back scattering. Different types of scattering are used for
different LiDAR applications. Most common are Rayleigh
scattering, Raman scattering, as well as fluorescence, and thus
the LiDAR can be accordingly called as Rayleigh LiDAR,
Raman LiDAR and Na / Fe / K Fluorescence LiDAR and so
on [3]. Suitable combinations of wavelengths can allow for
remote mapping of atmospheric contents by looking for
wavelength dependent changes in the intensity of the returned
signal.
Figure 1. Schema of remote sensing technology
The quality of remote sensing data by LIDAR sensor
consists of its spatial, spectral, radiometric and temporal
resolutions.
Spatial resolution: It is a measure of the smallest angular or
linear separation between two objects that can be resolved by
the sensor. The greater the sensor’s resolution, the greater the
data volume and smaller the area covered. In fact, the area
coverage and resolution are interdependent and these factors
determine the scale of the imagery. The size of a pixel that is

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recorded in a faster image may correspond to square areas
ranging in size length from 1 to 1,000 m.
Spectral resolution: It refers to the dimension and number of
specific wavelength intervals in the electromagnetic spectrum
to which a sensor is sensitive. Narrow bandwidths in certain
regions of the electromagnetic spectrum allow the
discrimination of various features more easily.
Temporal resolution: It refers to how often a given sensor
obtains imagery of a particular area. Ideally, the sensor obtains
data repetitively to capture unique discriminating
characteristics of the phenomena of interest.
Radiometric sensitivity: It is the capability to differentiate
spectral reflectance from various targets. This depends on the
number of quantization levels within the spectral band. The
number of bits of digital data in the spectral band will decide
the sensitivity of the sensor.
3. How LiDAR sensor can help in locating herbs at distinct
location?
Plants are green because they have a substance called
chlorophyll in them:
Chlorophyll a: The most abundant pigment in plants, and
absorbs light with wavelengths of 430 nm (blue) and 662 nm
(red). It reflects green light strongly so it appears green to us.
Chlorophyll b: This molecule has a structure similar to that of
chlorophyll a. It absorbs light of 453 nm and 642 nm
maximally. It helps increase the range of light a plant can use
for energy.
Carotenoids: This is a class of accessory pigments that occur in
all photosynthetic organisms, & absorb light maximally
between 460 nm and 550 nm and appear red, orange, or yellow
to us.
Key differences among lidar sensors are related to the laser’s
wavelength, power, pulse duration and repetition rate, beam
size and divergence angle, the specifics of the scanning
mechanism (if any), and the information recorded for each
reflected pulse. Lasers for terrestrial applications generally
have wavelengths in range of 900–1064 nm, where vegetation
reflectance is high. In the visible wavelengths, vegetation
absorbance is high and only a small amount of energy would
be returned to the sensor (Figure 2). One drawback of working
in this range of wavelengths is absorption by clouds, which
impedes the use of these devices during overcast conditions.
Bathymetric lidar systems (used to measure elevations under
shallow water bodies) make use of wavelengths near 532 nm
for better penetration of water
The power of the laser and size of the receiver aperture
determine the maximum flying height, which limits the width
of the swath that can be collected in one pass [4]. The intensity
or power of the return signal depends on several factors : the
total power of the transmitted pulse, the fraction of the laser
pulse that is intercepted by a surface, the reflectance of the
intercepted surface at the laser’s wavelength, and the fraction
of reflected illumination that travels in the direction of the
sensor. The laser pulse returned after intercepting a
morphologically complex surface, such as a vegetation canopy,
will be a complex combination of energy returned from
surfaces at numerous distances, the distant surfaces
represented later in the reflected signal. The type of
information collected from this return signal distinguishes two
broad categories of sensors. Discrete-return lidar devices
measure either one (single-return systems) or a small number
(multiple-return systems) of heights by identifying, in the
return signal, major peaks that represent discrete objects in the
path of the laser illumination. The distance corresponding to
the time elapsed before the leading edge of the peak(s), and
sometimes the power of each peak, are typical values recorded
system [4]. Waveform recording devices record the time-
varying intensity of the returned energy from each laser pulse,
providing a record of the height distribution of the surfaces
illuminated by the laser pulse [5][6][7]. By analogy to
chromatography, the discrete-return systems identify, while
receiving the return signal, the retention times and heights of
major peaks; the waveform-recording systems capture the
entire signal trace for later processing (Figure 3.)
Figure 3. Illustration of the conceptual differences between
waveform recording and discrete-return lidar devices. At the
left is the intersection of the laser illumination area, with a
portion of a simplified tree crown. In the center of the figure is
a hypothetical return signal (the lidar waveform) that would be

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collected by a waveform-recording sensor over the same area.
To the right of the waveform, the heights recorded by three
varieties of discrete-return lidar sensors are indicated. First-
return lidar devices record only the position of the first object
in the path of the laser illumination, whereas last-return lidar
devices record the height of the last object in the path of
illumination and are especially useful for topographic
mapping. Multiple-return lidar, a recent advance, records the
height of a small number (generally five or fewer) of objects in
the path of illumination.
Both discrete-return and waveform sampling sensors are
typically used in combination with instruments for locating the
source of the return signal in three dimensions. These include
Global Positioning System (GPS) receivers to obtain the
position of the platform, Inertial Navigation Systems (INS) to
measure the attitude (roll, pitch, and yaw) of the lidar sensor,
and angle encoders for the orientation of the scanning
mirror(s). Combining this information with accurate time
referencing of each source of data yields the absolute position
of the reflecting surface, or surfaces, for each laser pulse.
There are advantages to both discrete-return and waveform
recording lidar sensors. For example, discrete-return systems
feature high spatial resolution, made possible by the small
diameter of their footprint and the high repetition rates of these
systems (as high as 33,000 points per second), which together
can yield dense distributions of sampled points. Thus, discrete-
return systems are preferred for detailed mapping of ground [8]
and canopy surface topography.
An additional advantage made possible by this high spatial
resolution is the ability to measure the reflectance from leaves
of different herb species, and aggregate the data over areas and
scales specified during data analysis, so that specific locations
on the ground and the specific specie can be distinguished
from other species due to presence of chlorophyll in different
ratio among plants, such as a particular forest inventory plot or
even a single tree crown, can be characterized. Finally,
discrete-return systems are readily and widely available, with
ongoing and rapid development, especially for surveying and
photogrammetric applications [8]. The primary users of these
systems are surveyors serving public and private clients, and
natural resource managers seeking a cheaper source of high-
resolution topographic maps and digital terrain models
(DTMs). A potential drawback is that proprietary data-
processing algorithms and established sensor configurations
designed for commercial use may not coincide with scientific
objectives.
4. Analysis of Data generated through GIS Software
Figure 4. Flow chart for studying the ecosystem changes using
satellite data and GIS [9]
.
A Geographical Information System (GIS) is a system of
hardware, software and procedures to facilitate the
management, manipulation, analysis, modeling, representation
and display of geo referenced data to solve complex problems
regarding planning and management of resources. Functions of
GIS include data entry, data display, data management,
information retrieval and analysis. The applications of GIS
include mapping locations, quantities and densities, finding
distances and mapping and monitoring change. Esri's ArcGIS
is software for working with maps and geographic information.
It is used for: creating and using maps; compiling geographic
data; analyzing mapped information; sharing and discovering
geographic information; using maps and geographic
information in a range of applications; and managing
geographic information in a database. This software can be
helpful to analyze the data in image format and help in
characterizing the specific herb species from other species at
remote locations.
REFERENCES
[1] S. Ramachandran, S. Ramesh, And R. Krishnamoorthy,
“Application of Remote Se nsing and GIS in Coastal Lagoonal
Ecosystem: A Case Study from Pulicat Lake, Southern India”,
In: Marine Remote Sensing Applications. Institute for Ocean
Management, Anna University, 2000b, pp. 333-343.
[2] Liu, Jian Guo & Mason, J. Philippa, “ Essential image
processing for GIS and Remote sensing”. Wiley - Blackwell,
2009, p.4. ISBN978-0-470-51032-2.
[3] Cracknell, P. Arthur, Hayes, Ladson, “Introduction to
Remote Sensing (2 ed.)”. London: Taylor and Francis 2007,
ISBN 0-8493-9255-1 OCLC 70765252.

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[4] A Wehr, U Lohr, “Airborne laser scanning—an
Introduction and overview”. ISPRS Journal of
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[5] D.J. Harding, J.B. Blair, J.B. Garvin, W.T. Lawrence,
“Laser altimetry waveform measurement of vegetation canopy
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[6] D.J. Harding, M.A. Lefsky, G.G. Parker, J.B. Blair,” Lidar
altimeter measurements of canopy structure: Methods and
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[7] R. Dubayah, R. Knox, M. Hofton, J.B. Blair, J. Drake,
“Land surface characterization using lidar remote sensing”.
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[8] M. Flood, B. Gutelis, “Commercial implications of
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radar”. Photogrammetric Engineering and Remote Sensing,
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[9] Y.A. Hussin, M. Mahfud, and Zuhair Michael Weir,
“Monitoring Mangrove Forests using Remote Sensing and
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MAPPING REMOTE PLANTS THROUGH REMOTE SENSING TECHNOLOGY AND GIS

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