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Remote Sensing and Image Processing - Essay Example

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Report - Remote Sensing and Image Processing [Instructor Name] Remote Sensing and Image Processing Introduction This report demonstrates the semantics related to events that were accomplished in practical classes associated with the domain of remote sensing and image processing…
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Remote Sensing and Image Processing
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Download file to see previous pages Moreover, these events also include images and pictures that are captured via satellites that are powered with remote sensing technology. The advantage of remote sensing is that it can detect objects in real time along with providing coverage of an expanded area within a short period of time. However, remote sensing can be divided in to two types i.e. active remote sensing and passive remote sensing. The active remote sensing provides opportunity to be utilized at any time throughout the day and regardless of any season. Moreover, active remote sensing also facilitates the inspection of wavelengths that are not effectively illustrated by the Sun. In addition, it also takes charge of the illumination on the targeted object. On the other hand, passive remote sensing requires the Sun, in order to brighten the objective or the target. Moreover, this type of remote sensing uses reflected waves to measure the distance. As (Smith 2010) states that remote sensing based on active methodology needs a significant amount of energy source for illuminating the target. The data for the process of task that was associated with the vegetation estimation exposure was gathered from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR). In order to achieve a detection of vegetation, band rationing of the value of Infra-Red (700-1300nm) by Visible Light (400-700nm) is utilized. By using these values and methods, the albedo effects will be eliminated along with issues related to shadows that emerge from the images that are processed. Moreover, this method will also facilitate high quality visibility for vegetation in images. Moreover, a task that is related to classification of information classes, Churn Farm image is the best option. Likewise, data available in this image is gathered by an airplane in the year 1984 from the NERC ATM scanner. Likewise, the image includes four bands and sketches the sites that are associated with agricultural land use. Moreover, integer represents the cover type for specific land cover type, in this way; probable training sites can be marked for each type of land cover. Task 1 - Vegetation Index Methodology This task utilized a methodology including calculation of Ratio Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI). The NDVI will be used for the image of Lake Nakuru Thermatic Mapper (TM). Likewise, for data associated with TM, two bands are utilized i.e. band 3 and band 4. Band 3 calculates the red light and band four calculates the red infra light. Outside the scope of the town named as Nakuru, a small salt ware lake named as ‘lake Nakuru’ is located. Likewise, the lake is famed for the spectacle, as approximately one million flamingos comes to it for feeding themselves with green algae located in the warm water areas of the lake. Moreover, pelicans often come here to feed themselves with cormorants that are also available deep in the lake (Smith 2010). Steps Implemented Steps are demonstrated in points below: Considering as a Raster Layer, open the image file named as nakuru.img. In the available options, select the option to view the image with channel 4 that will be associated with green gun and red gun. However, channel 2 will demonstrate blue gun. From the main menu options tab, select the option ...Download file to see next pages Read More
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