10 As most geographic information systems in the developing countries are
regional and resource and environment based, they are especially useful for implementing the sustainable development strategy.
2.5. Remote Sensing
2.5.1. Definition of Remote Sensing
According to Juppenlantz and Tian 1996, remote sensing is technology that collects data relating to the earth’s surface without contacting with it, through
a sensor mounted in a satellite or high-flying aircraft. The Earth’s surface and atmosphere emit individual characteristic signatures
within the visible light and electromagnetic radiation spectrum. The spectrum is divided into spectral bands ranging from short gamma rays to long radio waves.
The Earth Resources Technology Satellite ERS-1, later renamed Landsat- 1, was the first unmanned satellite designed top provide systematic global
coverage of earth resources. Launched by the United States on July 23, 1972. It was designed as an experimental system to test the feasibility of collecting earth
resource data from satellites Aronoff, 1991. The kind of Landsat that are useful for image interpretation for a much
wider range of applications is Landsat Thematic Mapper TM. The characteristic of Landsat Thematic Mapper TM which first loaded on Landsat 4 in 1982 was
designed to provide improved spectral and spatial resolution over the Multi Spectral Scanner MSS instrument. Landsat TM is designed to capture
electromagnetic in 7 spectral bands. It has three bands in visible region band 1, 2, and 3, one band in near infra red band 4, two bands in mid infrared band 5
11 and 7, and one in thermal infra red band 6. Geometrically, TM data are
collected using a 30 m IFOV Instantaneous Field of View for all but thermal band which has a 120 m IFOV Lillesand and Kiefer, 1987.
2.5.2. Digital Image Processing
Digital image processing involves the manipulation and interpretation of digital images with the aid of a computer. The central idea behind digital image
processing is quite simple. The digital image is fed into a computer one pixel at a time. The computer is programmed to insert these data into an equation, or series
of equations, and then store the result of computation for each pixel Lillesand and Kiefer, 1987.
The procedures of digital image processing are following four broad types of computer assisted operations: image rectification and restoration, image
enhancement, image classification, and data merging. Image rectification and restoration are operations aiming at correcting
distorted or degraded image data, which stem from image acquisition; to create a more faithful representation of original scene. The procedures of image
rectification and restoration consist of geometric correction, radiometric correction, and noise removal.
Image enhancement is procedures that are applied to image data in order to effectively display or record the data for subsequent visual interpretation. Steps
that most commonly applied digital enhancement technique can be categorized as contrast manipulation, spatial features manipulation, or multi-image manipulation.
12 The objective of image classification is to replace visual analysis of the
image data with quantitative technique for automating the identification of features in a scene.
2.5.3. Geometric Correction
Raw digital images usually contain geometric distortions so significant that they cannot be used as maps. The geometric correction process is normally
implemented as two-step procedure. First, those distortions that are systematic, or predictable, are considered. Second, those distortions that are essentially random,
or unpredictable, are considered Lillesand and Kiefer, 1987. As systematic distortions are constant and predicable they do not constitute a
problem to the user of satellite imagery. The agencies that supply the imagery do the corrections. The main systematic distortions are: panoramic or scanner
distortion, scan skew, and change in scanning velocity Meijerink, et.al., 1994. Systematic distortion are well understood and easily corrected by applying
formulas derived by modeling the sources of the distortions mathematically. Random distortions and residual unknown systematic distortions are corrected by
analyzing well-distributed ground control point s GCPs occurring in an image. As with their counterparts on aerial photographs, GCPs are features of known
ground location that can be accurately located on digital imagery. Some features that make good control points are highway intersections and distinct shoreline
features Lillesand and Kiefer, 1987.
13
2.5.4. Radiometric Correction
As with geometric correction, the type of radiometric correction applied to any given digital image data set varies widely among sensors. Other things being
equal, the radiance measured by any given system over a given object is influenced by such factors as changes in scene illumination, atmospheric
conditions, viewing geometry, and instrument response characteristics.
2.5.5. Supervised Classification
In image classification there are two classification technique kinds that commonly known, Supervised classification and Unsupervised classification. The
fundamental difference between these techniques is that supervised classification involves a training step followed by classification step. In the unsupervised
approach the image data are first classified by aggregating them into natural groupings or clusters present in the scene Lillesand and Kiefer, 1987.
In supervised classification this is realized by an operator who defines the spectral characteristics of the classes by identifying sample areas training areas.
Supervised classification requires that the operator be familiar with the areas of interest. The operator needs to know where to find the classes of interest in the
area covered by the image. This information can be derived from general area knowledge or from dedicated field observations Janssen and Goerte, 2000.
Supervised classification is the procedure most often used for quantitative analysis of remote sensing data. It rest upon using suitable algorithm to label the
pixel in an image as representing particular ground cover types, or classes. A variety of algorithms is available for this, ranging from those based upon
14 probability distribution of models for the classes of interest to those in which the
multi spectral space in partitioned into class-specific using optimally located surfaces Richards, 1993.
2.6. Role of GIS and Remote Sensing
