LAB 7 Image analysis Tim Slivka 02 November 2009

















Answers to Questions:


1. Spatial subsetting is the act of creating an image that is only one region of a larger image (the new image covers a smaller area than the original, and is therefore a subset of the original). Spectral subsetting deals not with the physical area that an image covers, but with the spectral components of the image. The image may contain several bands (ranges of frequency of radiation e.g., blue) of spectral data spectral subsetting is the creation of a new image which is composed of only a subset of the original set of spectral bands. For example, if the original image was composed of the R,G,B, and IR bands, a spectral subset might be R, IR.


2. Georeferencing is the process by which an image or map of unknown location in physical space is located by reference to an image or map of know location in terms of map projection or coordinate system. In practice, this is usually done by assigning coordinates to locations in the un-located image that correspond to the same location in the image of know geographic location. The more locations, or points, that are linked in this way between the two images, the better georeferenced the first image becomes.


3. In hand-in 7, when we compare the images, we see the highest levels of the vegetation index (IR R) occur in areas that appear to be fields of vegetation. This is consistent with our knowledge that vegetation has a high IR reflectance relative to its reflectance of the R band. I.e., the values IR R, or vegetative index, will be high in areas covered by vegetation.


4. My second choice of vegetation index was the NDVI (Normalized Difference Vegetation Index: (IR R)/(IR + R) ). The NDVI is the vegetation index divided by the sum of IR and R. Whereas the value of IR R may be any value, the value of the normalized expression NDVI is limited to the values between -1 and +1. Areas with a high (or low) vegetative index will be the same areas with high (or low) NDVI and this is what we observe in comparing the two output images.


5. Five categories, preliminary identification:

1. seawater

2. trees/forest

3. grass

4. shrub vegetation

5. sand/bare earth