INTRODUCTION
Remote sensing is the acquisition of
information about an object or phenomenon, without making physical contact with
the object. In modern usage, the term generally refers to the use of aerial
sensor technologies to detect and classify objects on Earth (both on the surface,
and in the atmosphere and
oceans) by
means of propagated signals (e.g. electromagnetic radiation emitted from aircraft or satellites).
TYPES OF REMOTE SENSING
There
are two main types of remote sensing: passive remote sensing and active remote
sensing. Passive sensors detect natural radiation that is emitted or reflected
by the object or surrounding area being observed. Reflected sunlight is
the most common source of radiation measured by passive sensors. Examples of
passive remote sensors include film photography, infrared, charge-coupled devices, and radiometers. Active
collection, on the other hand, 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 examples of active
remote sensing where the time delay between emission and return is measured,
establishing the location, height, speed and direction of an object.
Remote
sensing makes it possible to collect data on dangerous or inaccessible areas.
Remote sensing applications include monitoring deforestation in
areas such as the Amazon
Basin, glacial
features in Arctic and Antarctic regions, and depth sounding of
coastal and ocean depths. Military collection during the Cold War
made use of stand-off collection of data about dangerous border areas. Remote
sensing also replaces costly and slow data collection on the ground, ensuring
in the process that areas or objects are not disturbed.
Orbital
platforms collect and transmit data from different parts of the electromagnetic spectrum, which in
conjunction with larger scale aerial or ground-based sensing and analysis,
provides researchers with enough information to monitor trends such as El Niño and
other natural long and short term phenomena. Other uses include different areas
of the earth
sciences such as natural resource management, agricultural fields
such as land usage and conservation, and national security and overhead,
ground-based and stand-off collection on border areas.
By satellite, aircraft,
spacecraft, buoy, ship, and helicopter images, data is created to analyze and
compare things like vegetation rates, erosion, pollution, forestry, weather,
and land use. These things can be mapped, imaged, tracked and observed. The
process of remote sensing is also helpful for city planning, archaeological
investigations, military observation and geomorphological surveying.
SATELLITE
REMOTE SENSING AND CLIMATE PREDICTION
What
can we understand and do with the application of satellite remote sensing? Some
familiar examples are the distribution of sea surface temperature and the
estimated depletion of the ozone hole, which we often see and hear about in our
daily lives. Satellite remote sensing does not only furnish present ground
surface and atmospheric conditions. For example, it is necessary to understand
the mechanism of changes occurring in the atmosphere, ocean, and ground surface
in order to create a model, when we attempt to project future Earth climates. Satellite
remote sensing plays an important role in the creation of the model.
First,
let us introduce one recent topic on climate prediction. Attention is now being
paid to the interaction between clouds and aerosols as a factor in making
climate prediction difficult. An aerosol is a generic term for minute particles
floating in the atmosphere. These particles cool or warm the atmosphere by
breaking up sunlight, or absorbing it.
In
addition, aerosol acts as the cloud condensation nuclei. When aerosol behaves
as the cloud condensation nuclei, the cloud particles are split up into smaller
pieces. It has been noted that this phenomenon has various potential effects on
the climate.
It
is well known that carbon dioxide emitted by human activity causes the temperature
of the atmosphere to increase. On the other hand, recent research suggests that
aerosols generated by human activity transform cloud microphysical
characteristics, thereby canceling temperature increases due to carbon dioxide.
Satellite remote sensing is proving to be useful in accurately understanding
and evaluating these effects.
UNDERSTANDING THE EARTH
We have addressed rainfall, vegetation, and oceanic primary
production in the previous articles of this series. Like clouds and aerosol,
these elements are also important to predict climate. Efforts are being made to
improve estimation accuracy and to model the phenomena. When these phenomena
are accurately modeled and integrated with a climate model, prediction accuracy
is expected to gradually improve.
APPLICATION OF REMOTE SENSING ON LAND DEGRADATION
Land is the
basic natural resource that provides habitant and sustenance for living
organism as well as being a major focus of economic activities. Due to the
negative impact on the environment and quality of life, land degradation is an
important global issue (Eswaran et al., 2001).
There is no
single, readily identifiable definition of land degradation, but all of them
describe how one or more of the land resources (soil, water, vegetation, rocks,
air, climate, relief) has changed from better to worse.
The use of
Geographic Information System (GIS) and Remote Sensing techniques has been seen
as one way of monitoring land degradation. Land degradation has been assessed
using image classification techniques. Five classes were produced namely; dense
vegetation; moderate vegetation; grasslands; stressed grasslands and
bare-ground. Field verification was conducted to assess the accuracy of
classification of these areas. The photographs bare-ground areas were captured.
Bare-ground from remote sensing images of different years was then assessed and
analysed. The results showed an increase in areas of bare-ground which highly
represented soil erosion. This show one way of using remote sensing towards
land degradation monitoring
Land
degradation caused by deforestation, overgrazing, and inappropriate irrigation
practices affects about 16% of Latin America and the Caribbean (LAC). This
paper addresses issues related to the application of remote sensing technologies
for the identification and mapping of land degradation features, with special
attention to the LAC region. The contribution of remote sensing to mapping land
degradation is analyzed from the compilation of a large set of research papers
published between the 1980s and 2009, dealing with water and wind erosion,
salinization, and changes of vegetation cover. The analysis undertaken found
that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49%
of the papers report their use), followed by aerial photographs (39%), and
microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works
analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping
degraded areas, with a combination of visual interpretation and advanced image
processing techniques. The use of more expensive hyperspectral and/or very high
spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to
be limited to small surface areas. The key issue of indicators that can
directly or indirectly help recognize land degradation features in the visible,
infrared, and microwave regions of the electromagnetic spectrum are discussed.
Factors considered when selecting indicators for establishing land degradation
baselines include, among others, the mapping scale, the spectral
characteristics of the sensors, and the time of image acquisition. The
validation methods used to assess the accuracy of maps produced with satellite
data are discussed as well.
APPLICATION OF REMOTE SENSING ON ROAD
NETWORKS
Remote
Sensing contributes most significantly to highway engineering during the
reconnaissance and feasibility stage of route planning when general information
is to be analyzed about large areas of terrain, rather than specific
information about a small area, as would be required for example for the final
alignment . Aerial photographs are one of the most popular Remote Sensing
techniques owing to their supporting small scale as well as large scale
surveys. Aerial photography has also been used to produce contoured topographic
maps. Satellite imagery and landsat may also provide the source of Remote
Sensing imagery. The scale of Landsat and IRS are ideal for reconnaissance
surveys and can be appropriate for preliminary interpretation as a part of more
detailed surveys. At reconnaissance and feasibility levels of survey individual
geotechnical aspects of road construction are subordinate to the main aim which
is to identify a route corridor. The reconnaissance or pre feasibility study
helps to examine the entire area lying between the end points of a road and to
identify route corridors within it. It would help to establish the relief and
geology of an area, the main soil types, climatic and hydrological conditions.
Remote Sensing in the form of photographic, scanning and processing systems is
one of the most appropriate means of recording ground conditions and assessing
their potential for engineering projects land also to evaluating the effects of
subsequent construction on the environment. Remote sensing is only an aid to
engineering investigations providing information which is complementary to
field measurements /site visits and existing sources of data such as maps and
project reports.
M.R
Wigan in 1992, discovered that Image Processing techniques can successfully be
used in road problems. The road texture characteristics, defect classification,
automatic classification of speed and shape classification, vehicle number
recognition are the various problems which can be addressed. Chester G.Wilmot
et. al in 1998 applied Remote Sensing successfully to the traffic study.
Through Remote Sensing the vehicle counts could be made with the help of
various vehicle emissions. It has been observed that the observations taken by
Remote Sensing on two-way two-lane roads are 800/hour which is approximately
80% of the observations made on one lane roads. Volume, Directional split
(Percentage of traffic in the dominant direction), and traffic composition are
the factors affecting the observations.
David
K.Loukes and John McLaughlin were the pioneer Canadians in the field of GIS
applications to transportation using various thematic data. The GIS offered the
capability of linking graphic entities (discrete points, roadway links, right
of way parcels, etc) to many existing attribute data base that contain
information about the transportation infrastructure. The linkage mechanism is
enabled through the use of carefully chosen location keys. The resultant
product would be suitable as a base of subsequent GIS-T applications.
REFERENCES
- Chester G. Wilmot et al, 1998: Validity of Remote Sensing On two Lane Roads, Journal of Transportation Engineering. Vol.1 24, No 1, pp 35-43.3.
- David K. Loukes and John McLaughlin, 1991: GIS and Transportation: Canadian Perspective, Journal of Surveying Engineering. Vol. 117 no. 3 pp 123-133.4.
- David McClung and Peter Schaerer, 1993: The Avalanche Handbook.
- Franz W. Leberl, 1982: Raster Scanning For Operational Digitizing of Graphical Data, Photogrammetric Engineering and Remote Sensing, Vol.48, pp 615-626.
- M. R. Wigan, 1992: Image Processing Techniques Applied to Road Problems. Journal of Transportation Engineering Vol.118, pp 62-81.7.
- Peter A. Bracken et al, Remote Sensing Software Systems, Manual of Remote Sensing Vol. I, pp 807-808.8.
- Peter A. Burrough and Rachael A. McDonell, 1998: Principles of GIS 9. T.J.M. Kenne et al, 1985: Remote Sensing in Civil Engineering10.Government of India, Department of Space; Annual report 2000-2001
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