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GIS and Remote Sensing Research

Whats is Remote Sensing :

Remote Sensing is the science of collection and interpretation of a target area without being in physical contact with the object.  In this manual the term remote sensing is being referred to as the gathering and processing of information about Earth’s environment, natural & cultural resources through usage of  photos and scanned data acquired from aircraft or satellites.

Remote Sensing Data Acquisition Systems :

Sensors :

There is no single sensor which can detect the signal from the entire range of the electromagnetic spectrum. Each sensor can only gather information form a certain band of the spectrum. The camera lens-film system is one such sensor system which has proved to be the most versatile for recording signals in the visible spectrum. The negative emulsion here acts as a sensor.

Platforms:

There are three main categories of platforms namely ground borne, air borne and space borne.The ground borne platforms consists of the photographic cameras that are in common use in photography.The airborne platforms consists of the balloon based and aircraft based platforms.The space borne platforms for remote sensing have now come in use and are rendering very useful service. The main advantages of space borne platforms are that they cannot be affected by atmosphere  and hence the orbits can be well defined.The entire earth or any part of the earth can be covered at specified intervals and imagery obtained can be in real time. Polar orbiting satellites which can cover the entire earth through successive orbits are mainly utilized for remote sensing of the earth’s terrain, the geo-stationary satellites can be utilized to continuously monitor the position of earth.The ceiling heights of the space borne platforms vary widely form 220 km in the case space shuttle to 36,000 km for the geostationary satellite. The orbital height of the polar orbiting remote sensing satellites is around 900 km.

Multi-spectral Remote Sensing:

Normal aerial cameras can take pictures in black and white,color, black and white infra red or false color by using suitable films. In the case of this system, the sensing is done in the visible and photo infrared parts of the spectrum of a single sensing unit and the data recorded is information form the entire band. However, if data in different sub-regions of this band can be obtained separately for a comparative study, more useful information can be extracted than can be obtained from a single photograph covering the entire band. This is achieved in multi-spectral photography of multi-spectral scanning

Image Processing Concepts :

The term digital image processing refers to the use of a computer to manipulate image data stored in a digital format. The goal of image processing for earth science applications is to enhance geographic data to make it more meaningful to the user, extract quantitative information, and solve problems.

 

A digital image is stored as a two-dimensional array (or grid) of small areas called pixels (picture elements), and each pixel corresponds spatially to an area on the earth’s surface. This array or grid structure is also called a raster , so image data is often referred to as raster data. The raster data is arranged in horizontal rows called lines, and vertical columns called samples. Each pixel in the image raster is represented by a Digital Number (or DN).

 

These image DNs can represent many different types of data depending on the data source. For satellite data such as Landsat and SPOT, the DNs represent the intensity of reflected light in the visible, infrared, or other wavelengths. For imaging radar (SAR) data, the DNs represent the strength of a radar pulse returned to the antenna. For digital terrain models (DTMs), the DNs represent terrain elevation. No matter what the source, all these types of data can be stored in a raster format.

By applying mathematical transformations to the digital numbers, remote sensing softwares can enhance image data to highlight and extract very subtle information that would be impossible using traditional manual interpretation techniques. This is why image processing has become such a powerful tool for all types of earth science applications. The exercises in this manual provide many examples that illustrate how image processing is typically used to enhance image data and extract information.

 

Many image datasets have multiple bands (or layers) of data covering the same geographic area, each containing a different type of information. For example, a SPOT HRV-XS satellite image has three bands of data, each recording reflectance from the earth’s surface in a different wavelength of light. Since each band records reflectance in a different part of the spectrum, this type of data is often called  multispectral data. Many powerful image processing techniques have been developed to combine various bands from multispectral images to highlight specific types of earth science information such as vegetation abundance, water quality parameters, or the types of minerals present at the earth’s surface.

 

Image processing applications :

Image processing has become an important tool for a wide range of earth science mapping, analysis, and modelling applications. Following are just a few of the many applications for which image processing is commonly used:

 

  • land use/land cover mapping and change detection
  • agricultural assessment and monitoring
  • coastal and marine resource management
  • mineral exploration
  • oil & gas exploration
  • forest resource management
  • urban planning and change detection
  • telecommunications siting and planning
  • physical oceanography
  • geology and topographic mapping
  • sea ice detection and mapping