Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft). Special cameras collect remotely sensed images, which help researchers "sense" things about the Earth.

1. Remote Sensing
Submitted by :
Nandita Purohit
(M.Sc. Zoology)

2. contents
1. Introduction
2. Physical basis of RS
3. Remote Sensing process
– Platforms and sensors for RS
– RS satellites
– Systems for Data Collection
– Data analysis
4. Application
5. Advantages
6. Disadvantages

3. Introduction
• Remote sensing is the process of detecting and
monitoring the physical characteristics of an area by
measuring its reflected and emitted radiation at a distance
(typically from satellite or aircraft).
• Special cameras collect remotely sensed images, which
help researchers "sense" things about the Earth.
• These systems may be configured to collect data in just
one band of the electromagnetic spectrum, in
multispectral remote sensing radiant energy is recorded
in multiple bands and hyperspectral remote sensing
instruments acquire data in hundreds of contiguous
spectral bands.

4. A LiDAR (Light Detection and Ranging) image created with data
collected by NOAA's National Geodetic Survey.

5. Physical basis for remote sensing
• In remote sensing observations of the target
(the physical object or geographic area) are
made from a distance, using specialized
instruments called sensors.
• Reflected radiation from the objects are
recorded by sensors such as photographic
camera, scanners or radiometers which are
mounted on suitable platforms

6. Remote sensing process
The remote sensing process involves data collection and analysis
in a systematic way which can be seen in the figure

7. Platforms and sensors for remote sensing
• For remote sensing data acquisition and the platforms
could be ground based (hydraulic lift), aerial (balloon
helicopter and aircraft), and space borne satellites.
• Scanners are most commonly used in satellites to
overcome the problem of returning photographic film
on earth.
• The two most important components of sensors are
their spatial and spectral resolution
• The spatial resolution is defined by the smallest feature
that can be SEEN by the sensors, where as the spectral
resolution is the range and number of bands within the
spectrum to which a sensor is sensitive.

8. Remote sensing satellites
1. LANDSAT
• NASA launched a series of LANDSAT satellites over the period of more
than three decades the landsat missions have used various devices for
collecting the information like the return beam vidicon camera (RBVC)
2. SPOT
• The earth resource satellite of France was launched in February 1986. It
has highly sensitive sensors, which can collect information from different
directions. It can detect details as small as 10m and resolve heights with
an aggregation of less than 5 km.
3. IKONOS
• IKONOS was the first commercial high resolution satellite launched in
1999, with optical sensor assembly (OSA) having four spectral bands (1-
4). The data are used for small to medium scale topographic mapping and
also for updating the existing topographic maps.

9. 4. Indian remote sensing satellites
• Indian remote sensing satellites have been of immense
use in hydrology, forestry, urban and land use planning
and geology to identify and monitor various coastal
zone features, also it has helped to study the natural
resource management.
5. NOAA
• The National oceanic and atmospheric administration
polar orbiting satellites (NOAA 10 and NOAA11) carry
the advanced high resolution radiometer, a passage
system that is sensitive to visible and infrared
wavelengths.
6. GOES
• Geostationary operational environmental satellites
known as GOES-8 and GOES-9 provide the daily
infrared and visible images of the weather.

10. Systems for data collection
• Remote sensing data are collected using either
passive or active remote systems.
• Remote sensing systems collect analogue and
digital data.
• The satellite or aerial scanner data are processed
on board, and related down to the ground
receiving station.
• The data are stored temporarily on high density
data tapes (HDDT). The two types of system for
collecting information are

12. Passive system
• Passive system consists of an array of small sensors for
detectors which record the amount of electromagnetic
radiation reflected and/or emitted from the Earth
surface in the visible on near infrared and far red
regions.
• For receiving the emission in the visible light various
types of camera and vidicon cameras are used.
• Several types of photographic films used in the cameras
are: panchromatic, black and white - topography;
infrared - land and water distribution; false color -
vegetation; thermal infrared - microwave emission and
x and gamma rays.

13. Active Systems
• An active system uses its own source of electromagnetic
radiation and measures the intensity of the return signal as
digital numbers.
• The system includes radar pulse, photoflash beams and
laser beams and use the artificial source of energy
resources for remote sensing.
• Electromagnetic energy is propagated near the sensor and
bound towards the sensor system.
• RADAR (radio detection and ranging), SAR (synthetic
aperture radar), SONAR (sound navigation and ranging),
LiDAR (light detection and ranging) are all examples of
active systems for data collection

14. Data analysis
• The analysis of remotely sensed data includes
a variety of image processing techniques that
include analog (visual) image processing of
image data, the digital image processing of
digital data.
• The remote sensing data interpretation can be
applied for a number of purposes in geology,
forestry, agriculture, soil mapping, ecology,
water resources and wildlife management.

16. Analogue (visual) image processing
• The principles of photo interpretation was developed
for aerial photography and are not applicable to all
remote sensing visual data products.
• The visual image analysis includes image interpretation
and photogrammetric measurement on the basis of the
size, shape, shadow, color, pattern, texture, and site and
precise measurement of object.
• This depends on the ability of a person to draw
conclusions either by direct and spontaneous
recognitions of object or by using the reasoning
process.

17. Digital image processing of digital data
• The major type of digital image processing involve
statistical and syntactical pattern , photogrammetric
image processing of the stereoscopic imagery,
hyperspectral data analysis, expert system and neural
network image analysis.
• In digital image processing instead of human vision
computer is instructed to perform and interpretation
according to certain conditions.
• The goal of image classification is to convert image
data into thematic data (thematic characteristics include
land use, land cover, soil type, etc).

18. Applications
• Remote sensing has a wide range of applications
in many different fields:
• Coastal applications: Monitor shoreline changes,
track sediment transport, and map coastal
features. Data can be used for coastal mapping
and erosion prevention.
• Ocean applications: Monitor ocean circulation
and current systems, measure ocean temperature
and wave heights, and track sea ice. Data can be
used to better understand the oceans and how to
best manage ocean resources.

19. contd.
• Hazard assessment: Track hurricanes,
earthquakes, erosion, and flooding. Data can be
used to assess the impacts of a natural disaster and
create preparedness strategies to be used before
and after a hazardous event.
• Natural resource management: Monitor land
use, map wetlands, and chart wildlife habitats.
Data can be used to minimize the damage that
urban growth has on the environment and help
decide how to best protect natural resources.

21. Advantages of remote sensing
• Synoptic view: a large area can be covered by a
single image as produced from different sensors
and satellites enabling us to see all the features of
the area together
• Repetivity: the data for any area can be obtained
repeatedly at regular interval of time which help
to monitor changes over time
• Mapping of different terrain: accurate data can
be collected for the inaccessible areas like
mountains, swampy areas, and thick forests.

22. contd.
• Management planning: the digital data in
different spectral regions can be processed and
analyzed using a computer to get baseline
information for a suitable management of an
area.
• Cost effective and time effective: if used
efficiently, remote sensing could be cost
effective and less time consuming for
monitoring large areas.

23. Disadvantages of remote sensing
• Expensive to build and operate.
• Measurement uncertainty can be large.
• Resolution is often coarse.
• Data interpretation can be difficult.
• One needs to understand theoretically how the
instrument is processing the measurements.
• Knowledge of the phenomena should be well
addressed while sampling.