Washington, Oct 23 (Inditop.com) An omniscient eye in the sky can spot natural and man-made disasters, give advance warning about forest fires, water contamination or an oil slick.
A new Tel Aviv University (TAU) technology combines sophisticated sensors in orbit with ground based sensors to create a “Hyperspectral Remote Sensor” (HRS).
Eyal Ben-Dor, geography professor at TAU, describes his team’s HRS technology as a combination of physical, chemical and optical disciplines.
Today, it can take years before authorities can detect chemicals that can compromise our health. For example, about 90 percent of all petrol stations leak contaminants into the soil, says Ben-Dor.
The HRS simultaneously acquires hundreds of optical images, each from a different frequency, that enable a “spectral assessment” from distances high in the air via airplanes and in orbit using satellites.
This raw data is then processed by Ben Dor and his team to yield sophisticated thematic maps. “These are not regular maps at all,” says Ben-Dor.
“We are combining properties from the physical, chemical and optical worlds, using all the latest technologies available from these fields. Ours is one of a few leading teams in the world exploring this novel way of mapping earth.”
His new HRS can monitor gas stations and identify problematic areas. “Our space sensors combined with ground measurements and GPS data will be able to detect and map hydrocarbon contamination in real time. Within a year, we’ll be able to identify these problematic areas far more quickly than with traditional methods,” he says.
It can also indicate where water runoff should be directed and what minerals may be lacking in a given parcel of land, says a TAU release.
“Water is an expensive commodity today,” says Ben Dor. “Knowing how to better manage water resources is a top priority for states like California, and our new tool could help them do that.”
Details were published in journals like Soil Science Society of America Journals, Soil Science Journal and the International Journal of Remote Sensing.
