Remote sensing images are used to produce land use maps. Land use maps serve a wide range of purposes. In this exercise we will slice out water, wetlands, crops, and levees/bare soil.
The image used for this exercise is the Mississippi River. All seven bands of Landsat imagery are provided. They are all co-registered and have a resolution of 30 meters/pixel.
Landsat imagery represents reflectivity values of surface areas. Different types of ground features have different reflectivity. In order to classify raw satellite data ranges of values representing ground features are taken out of the image one at a time and assigned a specific value. This process is called density slicing. this is a primitive means of classifying images because we use only one band at a time.
In this exercise Band 4 (MISS4) will be used.
The task at hand now is to group data number values (reflected light) that are similar together. For example, water has low data number values because it absorbs lights. Using the density slice tool provided in NIH Image pixels with similar values can be grouped together.
When this command is activated the group of pixels represented by the red bar, the pixels on the image, and the values on the histogram are all the same. The red band in the slicing bar is tricky to use. Using the LUT tool the band can be moved up and down and also increased or decreased. Move the bar around to get the feel of the tool. After the use of the tool becomes natural, adjust the bar and record the DN values for the following features. Identify the lower and the upper DN value.
It is annoying, but the histogram has to be displayed each time the DN bar is moved. Compare the results with the ones listed below,
In order to make a land cover map, the range of values selected above now have to be given one value per land type. For example, all values from 4-15 could be assigned a value of 20, all values of 16-30 could be assigned a value of 60, 31-47 would become 150, and 48-79 would be 200. This groups spectral values together and makes a land cover map that can be used to calculate areas from the imagery. This is an advantage not only of satellite imagery but of digital data.
For centuries maps have been used to convey information. Not only should maps be as accurate as possible, they can be considered works of art. The colors, fonts, legends, and scale bars should not only be correct they should be as attractive as possible.
The steps to make the land cover map are tedious and should be followed carefully.
The image now has the spectral values of 4-15 replaced with 20 and everything else is white.
A map must have a legend, a scale bar, and a compass before it is complete. These map be drawn on the image directly or they can be made as new images and pasted on the finished map.
In order to measure the area of different types of land cover area the scale of the image must be set.
Calculate the area of the entire image. If this image is 512 width and 512 height and 30 meter resolution, the are in square meters would be 512 x 512 x 30 x 30 sq meters.) The next step is to calculate each type of land (water, wetlands, crop1, and levees/bare soil)