What is an image? What is an image and which image bands are “best” for visual interpretation?

An image is made up of individual elements called pixels that are arranged in a grid of rows and columns. Each pixel represents an area on the ground defined by its resolution. The resolution determines the detail that can be seen in an image. For example, a 1 meter pixel will provide more detail than a 30 meter pixel

The sensor acquires several images (bands) at once, each recording a specific color or range of colors. When viewed, each individual band looks like a black and white photograph Landsat band 2 - (wavelength range = µm = blue light)

For each band the amount of energy (intensity) for a specific range of wavelengths (colors) is measured

Spectral signatures

Spectral signatures with TM bands

RGB Band Composite

At the pixel level digital numbers from three bands are converted to grayscale colors, passed through a color filter, and then composited to make a color pixel

Certain bands or band combinations are better than others for identifying specific land cover features. Landsat TM Red=band 3 Green=band 2 Blue = band 1 Landsat TM Red=band 4 Green=band 5 Blue = band 3

Landsat ETM+ band 1 ( µm, blue-green) Penetrates water better than the other bands so it is often the band of choice for aquatic ecosystems Penetrates water better than the other bands so it is often the band of choice for aquatic ecosystems Used to monitor sediment in water, mapping coral reefs, and water depth Used to monitor sediment in water, mapping coral reefs, and water depth The “noisiest” of the Landsat bands since short wavelength blue light is scattered more than the other bands The “noisiest” of the Landsat bands since short wavelength blue light is scattered more than the other bands Rarely used for "pretty picture" type images Rarely used for "pretty picture" type images

Landsat ETM+ band 2 ( µm, green) Similar qualities to band 1 but not as noisy Similar qualities to band 1 but not as noisy Matches the wavelength for the color green Matches the wavelength for the color green

Landsat ETM+ band 3 ( µm, red) Since vegetation absorbs nearly all red light (it is sometimes called the chlorophyll absorption band) this band can be useful for distinguishing between vegetation and soil and in monitoring vegetation health Since vegetation absorbs nearly all red light (it is sometimes called the chlorophyll absorption band) this band can be useful for distinguishing between vegetation and soil and in monitoring vegetation health

Landsat ETM+ band 4 ( µm, near infrared) Since water absorbs nearly all light at this wavelength water bodies appear very dark. This contrasts with bright reflectance for soil and vegetation so it is a good band for defining the water/land interface Since water absorbs nearly all light at this wavelength water bodies appear very dark. This contrasts with bright reflectance for soil and vegetation so it is a good band for defining the water/land interface Sensitive to vegetation cover Sensitive to vegetation cover Less affected by atmospheric contamination Less affected by atmospheric contamination

Landsat ETM+ band 5 ( µm, mid-infrared) Very sensitive to moisture and is therefore used to monitor vegetation water stress and soil moisture Very sensitive to moisture and is therefore used to monitor vegetation water stress and soil moisture Useful to differentiate between clouds and snow Useful to differentiate between clouds and snow

Landsat ETM+ band 6 ( µm, thermal infrared) Measures surface temperature Measures surface temperature Geological applications Geological applications Differentiate clouds from bright soils since clouds tend to be very cold Differentiate clouds from bright soils since clouds tend to be very cold The resolution is twice as course as the other bands (60 m instead of 30 m) The resolution is twice as course as the other bands (60 m instead of 30 m)

Landsat ETM+ band 7 ( µm mid-infrared) Can detect high surface temperatures Can detect high surface temperatures Also used for vegetation moisture although generally band 5 is preferred for that application Also used for vegetation moisture although generally band 5 is preferred for that application Commonly used in geology Commonly used in geology Good at detecting burn scars Good at detecting burn scars

Landsat ETM+ bands 3,2,1 Penetrates shallow water and shows submerged shelf, water turbidity Landsat ETM+ bands 4,3,2 Peak chlorophyll, land/water boundary, urban areas

Landsat ETM+ bands 4,5,3 Land/water boundary, Vegetation type and condition, soil moisture Landsat ETM+ bands 7,4,2 Moisture content in vegetation and soils, geological mapping, vegetation mapping

“Raw” valuesAfter enhancement Image colors need to be enhanced to highlight specific features or simply to look nice.

Image enhancement Common enhancement tools include: Brightness/contrast, Linear histogram stretching, Histogram equalization, Customized histogram stretching, and Many more options Experience with different tools provides a sense for what works best for different situations. Software tools with a graphical display of the input (before) and output (after) histogram are helpful to understand how the enhancement process works. Enhancements can focus on a specific feature although this often produces an image that is less visually pleasing.

Image fusion Image sharpening combines lower resolution multispectral images with a higher spatial resolution panchromatic (single, broad band) image to create a finer resolution multispectral image. Image sharpening combines lower resolution multispectral images with a higher spatial resolution panchromatic (single, broad band) image to create a finer resolution multispectral image. There are several algorithms for doing this and it is best to experiment with different ones to see what works best. There are several algorithms for doing this and it is best to experiment with different ones to see what works best. The sharpened image is visually more pleasing but the pixel values have been altered from the original images so automated classification methods might produce unexpected results. The sharpened image is visually more pleasing but the pixel values have been altered from the original images so automated classification methods might produce unexpected results.

Effects of image fusion Multispectral Landsat ETM+ 30 meter image before sharpening Image after sharpening using Landsat ETM+ 15 meter panchromatic image