Remote Sensing - ERDAS Imagine and Digital Data

Lab Objectives:

• Utilize tools and functions of ERDAS Imagine
• Interpret Layer Info of digital data in ERDAS Imagine
• Distinguish between the four types of resolution in ERDAS Imagine
• Interpret and analyze thematic rasters in ERDAS Imagine

This lab involved using ERDAS Imagine to obtain information about images, examining various types of resolution in images, examining how these resolutions were relevant to digital data being captured, stored, and displayed, and learning how to interpret and analyze a thematic (soil) raster.

In the first exercise, metadata was used to examine layer properties of specific "layers", or bands. Layer properties included file info, layer info, statistics info, map info, and projection info. Data type (bit), under layer info, represents radiometric resolution. Pixel size, under map info, represents spatial resolution.

The second exercise focused on four types of resolution: radiometric, spatial, spectral, and temporal. Spatial resolution is determined by the pixel size of the image. The smaller the pixel size, the higher the spatial resolution. However, higher resolution also means larger file sizes. Spatial resolution is the most common type of resolution: it is often referred to just as "resolution". Spatial resolution was compared among four identical images, however the spatial resolution of the images varied from high to low, showing different levels of detail.

Radiometric resolution is described as the detail of an image based on the level of contrast between objects. The radiometric resolution of an imaging system describes its ability to discriminate very slight differences in energy. The finer the radiometric resolution of a sensor, the more sensitive it is to detecting small differences in reflected or emitted energy. Max digital number (DN), under statistics info, represents the highest brightness value for all the pixels in the image. Max DN corresponds to data type, or bit. The higher the DN (max 255), the higher the bit (max 8-bit). Four images with varying radiometric resolutions (high to low) were compared. The last image had the lowest radiometric resolution, showing only black and white contrast. However, the first image had the highest resolution, showing varying contrast: white, black, but also varying levels of gray.

Spectral resolution is how well an image can be used to distinguish between different wavelengths, or bands. Two things contribute to the spectral resolution of an image: the number of bands and the wavelengths that they cover. The more bands an image has, and the narrower the bandwidths, the higher the spectral resolution. Two images were compared. One image was multispectral, having multiple bands, while the other had only one band. Thus, the multispectral image had higher spectral resolution.

Temporal resolution is how frequently an image of the same area can be taken. Temporal resolution usually deals with the orbit of a satellite. Landsat 7, for instance, passes over the same area every 16 days. The Landsat images then have a temporal resolution of 16 days.

The third exercise involved computing area and percent area coverage of different soil types. Also, soil types with high susceptibility to erosion were defined, displayed, and highlighted. An image of the final result was then saved.