Remote Sensing is the science and art of obtaining information about an object/phenomena or area through the analysis of data acquired by a device that is not in contact with the object under investigation. The technology involves both satellite and aerial remote sensing. The basic source for this technology is electromagnetic radiation and this energy from the sun reaches the earth surface and again reflected or transmitted or absorbed by the objects which is collected by the satellite sensors. The reflectance/remittance/absorption of energy by an object forms the base for the brightness or darkness in an image or photographs. This is further interpreted for the identification of the features. Once a satellite image is obtained, it is first interpreted to identify the features in the image. The interpretation may be both visual or digital or combination of both. The entire process of visual interpretation can be divided into following few steps namely detection of an object, interpretation, recognition and identification, analysis, classification, deduction and idealisation and based on this identifying an object conclusion. Hence interpretation is the combined result of identification of feature through photo recognition elements, field verification and preparation of final thematic maps. It also requires the process of observation coupled with imagination and great deal of patience.  Figure 13: (Sonitpur District of Assam, LANDSAT ETM+ (December, 2001) 1. Tea Patches 2. Barren Land 3. River 4. Forest 5. River Bed 6. Settlements In this study the remote sensing images used are the LISS III, LANDSAT ETM+ and ASTER. All the three images are taken at three different dates to see the variations in the plantation areas. After correcting the images both geometrically and radiometrically the next step that was carried out was masking out the study area. Then the false coloured composite was generated and the interpretation of the images were carried out using the various interpretation keys like the shape, size, pattern, tone, texture, shadows, location, association and resolution. Using the shape man-made features as well as the natural features like the river, hills, river beds were identified from the image. Using pattern, the airstrip, railway tracks as well as the road networks were identified. Though roads and railway tracks looks linear but both can be distinguished from each other as the major roads are associated with steep curves and many intersections with the minor roads. Shadow shows the height of the terrain. Taller features will show larger shadows than the shorter features. Tone shows the brightness of the object based on the reflection, emittance, transmission or absorption character of an object. Using the tonal variations the vegetation status of the area was identified. The tea patches were categorized into healthy, moderately healthy and diseased tea patches based on the smooth and rough surfaces showing the higher and lower reflectance respectively. Similarly scrubs, forest and barren land were identified. Healthy vegetation reflects infrared radiation much stronger than green energy and appears very bright in the image. Using the texture, the tonal change was observed. It gives the smoothness and roughness of the area. Texture directly depends upon the shape, size, pattern and shadow. The texture images were generated for each band on the basis of different texture parameters. |