Our primary remote sensing platform and sensor system is the Landsat Thematic Mapper.  This system views the landscape at a 30 meter cell resolution (Thematic Mapper's predecessor, Multispectral Scanner, scanned at 79 meters) for  for its optical channels, and offers relatively large geographic coverage for each collected image or scene, roughly 185 square km. Both the grain and extent of Landsat are useful for assessing human imprints on the land indicative, for example, of population settlement patterns, land clearing for the cultivation of crops, and a host of other land transformations that have important population-environment signatures and cause and consequence implications for examining LULC dynamics.

        The image below is a 2-4-1 composite from December 28, 1985, where visible green is shown in red, near infrared (highly responsive to healthy vegetation) is shown in green, and visible blue is shown in blue. 
        This combination helps highlight upland field crops and more cleared rice growing areas in colors that closely approximate true colors.  The bright green areas in the southwest represent upland field crops, such as cassava and sugar cane.  The darker green patches to the east and north of the image indicate medium density forest.  The purple and pink regions throughout the image are areas of rice cultivation.  During the dry seasons, when the rice paddys are not inundated, the rice has been harvested, leaving a bare landscape with short grasses.  When compared to the Landsat Thematic Mapper (TM) image to the right, the Multispectral Scanner image looks blurry, making the coarseness of the MSS's 79 meter pixel is easily noticeable.

       Much like Landsat Thematic Mapper, the Enhanced Thematic Mapper sensor collects data about the Earth's surface in 6 bands: 3 visible, 1 near-infrared, and 2 middle-infrared.  As such, the information that can be gleaned and derived using ETM is very much the same as that of TM.
       The image below, from December 27, 1999, is of the same region and band combination as the TM image to the upper right.  However, the ETM image was collected 4 years later.  As is clearly visible, marked changes have occured in the intervening years.  Without the broad, regional view offered by the Landsat sensors, such information would be significantly more difficult to detect.  This is one of the many benefits of using remote sensing to track, monitor and predict changes on the landscape.

        The image below is a 7-4-2 composite from December 24, 1995, where mid-infrared (highly repsonsive to rock and geologic formations) is shown in red, near infrared (highly responsive to healthy vegetation) is shown in green, and visible green is shown in blue. 
        This combination helps highlight upland field crops and more cleared rice growing areas in colors that closely approximate true colors, with plants showing up as green, bare soil showing up as red and brown, and water showing up as blue.  The bright green areas in the southwest represent upland field crops, such as cassava and sugar cane, while the darker brownish-red area in the south-central part of the image represent harvested field crops.  The darker green patches to the east and north of the image indicate medium density forest.  Again, the extensive purple and pink regions throughout are areas of rice cultivation, which has been harvested at the start of the dry season.

       In addition to the multispectral scanning capabilities, the ETM sensor added a 15 meter panchromatic (black-and-white) band.  Given its improvement in resolution, the panchromatic band can be effectively used as a compliment to the multispectral bands, through resolution merges or band-stacking.  Alternatively, the merits to using the panchromatic band alone are myriad.
        Below, the panchromatic band from December 27, 1999 is shown.  Though the image is of a somewhat smaller region than the others on this page, the improvement in spatial resolution is apparent.  Features on the ground are more discernable owing to the increased sharpness of the image.