Writing Scientific Report - Coursework Example

Different techniques such as RVI and NDVI will be utilised. The various sensors used will be Landsat TM, Lansat MSS and NOAA AVHRR. The Landsat TM is used for thematic imaging only and can reveal only a limited amount of information such as the availability or non avialblity of vegetation. On the other hand the Landsat MSS (multi spectral surveillance) can be used to distinguish between various kinds of vegetation too. The NOAA AVHRR sensor is used to estimate the thermal emission or cooling of the surface.

The presence of vegetation modifies thermal emission rates by lowering them while the absence of vegetation speeds them up. A complete treatment of the ideas presented above is outlined below to delineate the relative strengths and weaknesses of each system. Part One Vegetation Analysis of Lake Nakuru This section deals with the vegetation analysis of Lake Nakuru using simple and advanced models to discern the patterns of vegetation. The first analysis will utilise RVI (Ratio Vegetation Index) to attempt to explain how vegetation is interpreted from an image.

The RVI is a ratio between the NIR (Near Infra Red) and R (Red) from each pixel in an image. Vegetation in general tends to reflect NIR as much as possible because NIR does not contribute significantly to plant nourishment and is speculated to cause plants to overheat. (Tucker, 1979) Red is reflected far less than NIR. Water, soil and manmade features have a far more static response to both NIR and R throughout the year. (Banman, 2001) A walk through of the methodology is presented below to enhance understanding.

The image supplied is for Lake Nakuru and is sized at 500 rows and 640 columns and possesses 4 bands. The red, green and blue bands have been set at 4, 4 and 2 respectively. Spectral enhancement has then been utilised to analyse the image. The ratio of NIR to R is a ratio of channel 4 to channel 3. The output sensor has been selected as Landsat TM. The output from this process is shown below (on the left) in comparison to the actual image (on the right). The image presented above is then re-coloured using a pseudo colour system with brown and green as limits.

This produces the image presented below. A simple comparison of the images presented above reveals that the first above tends to coalesce human structures and certain rocks with vegetation. The resulting image cannot be used to classify vegetation with reliability. Instead the second image produced can delineate vegetation much better as can be seen. The second image has excluded vegetation near the centre especially and around it where human made structures exist. This image may be used to delineate a few factors that control vegetation distribution but expecting a complete appraisal of factors is not possible.

The NDVI method has been used next to analyse the image because it tends to produce an empirical scale between +1 and -1 which appraises vegetation cover better. NDVI is the ratio of (NIR – R) to (NIR + R). All other settings utilised are the same as the ones used for the RVI analysis. However, the sensor used here is the Landsat MSS because it is far more fitting. The output is shown below. The RVI analysed image is shown on the left while the NDVI analysed image is shown on the right. It can be clearly seen that the NDVI image is far more detailed in terms of description of vegetation.

The RVI image is less descriptive and tends to combine the various bands of vegetation while the NDVI image tends to differentiate the various bands of vegetation. Next the image data for Tunisia will be analysed to determine if desertification is occurring. The analysis is NDVI while the sensor employed is the NOAA AVHRR that can compensate for time effects far better than other types of sensors. (Holben, 1986) The various