Images from satellites help scientists, engineers, government officials, medical professionals, and others determine the extent of flood and fire damage, predict hurricanes, estimate crop yields and atmospheric pollution, and even monitor human rights.
For remote sensing to fully reap these benefits to humanity, the geospatial imagery has to be extracted, classified, interpreted and analyzed, usually with sophisticated software.
Assessing the Burn
After the June 2002 Hayman Fire in Colorado (USA), which began in the Pike-San Isabel National Forest, land managers needed to map the severity of the burn and assess its damage to various ecosystems. The fire burned approximately 138,170 acres.
Using software from ITT Visual Information Solutions, which provides spectral processing tools and integrated software solutions to analyze and visualize data obtained from satellites, the U.S. Geological Survey and the National Park Service developed a burn severity index based on data and images from Landsat, a series of satellites that observe the Earth in seven spectral bands. Landsat's Thematic Mapper and Enhanced Thematic Mapper (TM/ETM) bands 4 (near-infrared) and 7 (mid-infrared) helped establish a Normalized Burn Ratio (NBR) that showed land managers and fire ecologists how to evaluate and compare burn severity within individual fires and between fires across various ecosystems.
ITT's ENVI software was used with Landsat data to map the severe burn areas. The perimeter of the fire can be automatically delineated using ENVI classification tools, and the severity of the burned region is evaluated using specific thresholds.
Figure 1. The first image depicts the region prior to the fire. The center image represents a false color composite of the site after the fire. A band combination of 7, 4, and 3 was used for red, green, and blue respectively. Using this band combination greatly highlights burned areas because of the reflective properties of burned vegetation in the infrared. Calculating the Normalized Burn Ratio (NBR) and using the result with ENVI's density slice capabilities, the image at right shows areas of no fire damage (black), moderate fire damage (yellow), and severe damage (red).
An unprecedented infestation by mountain pine beetles across the western United States is threatening to wipe out lodgepole pine trees in northern Colorado and the Front Range within five years. Based on recent aerial surveys revealed by the U.S. Forest Service in the region, more than one million acres of the state's high-latitude forests had been destroyed, including half a million acres last year, since the infestation started in 1996. Affected counties are Boulder, Larimer, Gilpin, Chaffee, Clear Creek and Lake.
One symptom of trees infested with pine beetles is foliage that turns yellowish to reddish throughout the entire tree crown. This usually occurs eight to 10 months after a successful MPB attack. Using ENVI and Quickbird data, this effect can be readily identified and quantified.
Quickbird data provides the necessary spatial and spectral resolution and ENVI's full suite of vegetation analysis and feature identification tools can process the imagery into meaningful information.
Figure 2. A topographical map layered with remote sensing data shows the extensive damage created by the Mountain Pine Beetle in national forest areas in Colorado.
The 2004 Indian Ocean earthquake was an undersea earthquake that occurred at 00:58:53 UTC on December 26, 2004, with an epicentre off the west coast of Sumatra, Indonesia. The earthquake triggered a series of devastating tsunamis along the coasts of most landmasses bordering the Indian Ocean, killing more than 225,000 people in eleven countries, and inundating coastal communities with waves up to 30 meters (100 feet) high. It was one of the deadliest natural disasters in history. Indonesia, Sri Lanka, India, and Thailand were hardest hit.
Using Digital Globe Quickbird imagery taken before and after the tsunami, ENVI can rapidly process the areas that were changed by the tsunami giving rapid measurement of the affected area and automatically delineating those regions from areas unaffected by the tsunami.
Figure 3. By combining before and after images of the coastal areas affected by the 2004 Indian Ocean Tsunami, researchers can quickly determine the extent of damage done and how the geography of the coastline has been changed.
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Posted October 2008