Intro to ERDAS Imagine and Digital Data

This is a subset of an image we processed using ERDAS Imagine.  First we had to learn a bit about how to use this new program, which seems to have a huge range of capabilities.  After loading a raster image into the Viewer and making sure the display was set to Pseudo Color, we spent some time learning how to navigate around the image and comparing one image at a lower level of detail (AVHRR) with one a LANDSAT Thematic Mapper satellite image.  The first was classified, while the second was continuous - raw data from the sensor.  We set the preferences to enable Fit to Frame and Background Transparent, making sure the Clear Display option was off, and experimented with options in the Multispectral tab to see what combination of Bands enhanced which types of features, such as vegetation, rivers, urban areas, etc.  The third part of this lab was the creation of a map which we began in ERDAS Imagine, where we created an attribute to show the area of each class in the image, and then exported a subset of the map to make a map in ArcMap.  This was done using the Inquire Box and then creating a subset image.  In ArcMap we made Class_Name the value field, with Unique Values chosen so that the hectares for each class would be listed.  I chose to edit the description so that the information would remain with the data rather than being temporary. 

Clearly the ERDAS Imagine program is very useful and has huge capabilities, but is limited by a few bugs which is unfortunate.

Truthing for Accuracy

The red and green dots represent 30 sample points that were randomly selected in order to check the accuracy of how they were classified in last week's lab.  I located them in a way to reduce bias, by creating and locating them in roughly equal proportions according to each classification type. Then, using Street View in Google Maps, I examined each site and recorded whether it was accurate or not, and if it was not, what classification it should have been.  Finally, I calculated the % accuracy of the sample points.  Because we generalized in making the polygons and classifying them originally, the accuracy was not very high.  Sometimes the point landed on a structure that was commercial rather than residential, although overall the polygon seemed to be the correct type.  In one case, what had been a sandy area when the photograph was taken had been built up with several houses.  What I had thought might have been an academic complex turned out to be several businesses and the Jackson State Fairgrounds.  Several areas that seemed forested were actually residential.  A closer reading of the classification descriptions also prompted me to change one or two classifications.

Level II LULC Classification

 

 

Classification of land use and land cover was the focus of this week's lab.  An aerial photograph of an area of Pascagoula, MS was digitized to create a land use/land cover map.  Features were identified using tone, pattern, shadows, size and shape, and associations, the subject of last week's lab, and then polygons were drawn around those features and classified.  The USGS Standard Land Use/Land Cover Classification System we used has several levels - we worked at Level II.  To begin, Level I classifications were identified, such as Urban or Built-up Land, Forest Land, and Water.  Within each of those, Level II categories were located, such as Residential areas, Lakes, and Deciduous Forest Land.  To create the map, the polygons were labeled, a logical color scheme was chosen to highlight the various categories, and the other essential map elements were added.

 

From this map, it is clear that Level II classification is quite general, and that by generalizing, many specific features and land types are incorrectly identified.  Level III classification provides far better identification, but is obviously more time consuming.

Visual Interpretation of Aerial Photographs

This week we produced 2 maps showing some of the ways that aerial images can be interpreted.  The first map shows variations in tone and texture.  There are 5 variations for each scale, with each polygon enclosing one variation.

 

 

The second map uses 4 criteria to identify features: Shape and Size, Shadows, Patterns, and Association.