Day: August 13, 2010

Sensors More Accurately Map the Chesapeake Bay’s Forested Wetlands

/ Matt Artz

Two U.S. Department of Agriculture (USDA) scientists have created new maps of Chesapeake Bay forested wetlands that are about 30 percent more accurate than existing maps.

Agricultural Research Service (ARS) soil scientist Greg McCarty and USDA Forest Service ecologist Megan Lang did this by merging two remote sensing devices: an airborne LiDAR (light detection and ranging) laser sensor with an advanced “synthetic aperture radar” (SAR) satellite sensor. McCarty is at the ARS Hydrology and Remote Sensing Laboratory in Beltsville, Md., where Lang is a visiting scientist. ARS is the chief intramural scientific research agency of USDA.

Wetlands are critical to the health of bodies of water like the Chesapeake Bay. But many wetlands are forested, and it can be hard to see the wetlands on aerial photography because the view is blocked by the trees. Also, maps drawn from aerial photographs are subjective, causing more loss of accuracy.

With the combined data from the two types of remote sensing devices, the scientists can see whether water flows without filtration into the Bay, or whether it flows first through a forested wetland that might filter out possible pollutants.

The maps show previously unknown connections between some wetlands, drainage ditches, intermittent streams and ponds. Because forested wetlands had been thought to be isolated from each other and the Bay, the Clean Water Act did not offer them the same regulatory protections as other wetlands.

The maps also show changes in wetlands caused by drainage ditches, other construction, farming and weather. The maps can be used to predict flooding and effects of climate change.

The scientists use advanced computer software and models to process data from the latest versions of the two different sensors.

With recent advances in the technologies and the spreading of LiDAR topographic mapping from state to state, it won’t be long before this research adds much more information vital to Chesapeake Bay cleanup efforts and state and national wetland conservation debates.

A paper on this research appeared in Wetlands, an international journal of the Society of Wetland Scientists.

Read more about this research in the August 2010 issue of Agricultural Research magazine.

[Source: United States Department of Agriculture-Research, Education, and Economics press release]

The Application of Open Standards to Enhance the Interoperability of Geoscience Information

/ Matt Artz / 1 Comment

International Journal of Digital Earth, Volume 3, Issue S1 April 2010 , pages 14 – 30

G. Percivall

“We can adequately study broad global issues and policies only by taking geosciences into account. Our research and decision-making must share and make effective use of interdisciplinary data sources, models, and processes. Non-interoperability impedes sharing of data and computing resources. Standards from the Open Geospatial Consortium (OGC) and other organizations are the basis for successfully deploying a seamless, distributed information infrastructure for the geosciences. Several specifications now adopted by the OGC consensus process are the result of OGC interoperability initiatives. The OGC standards, deployment architectures, and interoperability initiatives are described showing how the OGC standards baseline has been developed and applies to the geosciences.”

Linked Sensor Data

/ Matt Artz

International Symposium on Collaborative Technologies and Systems (CTS), Chicago, Illinois, 17-21 May 2010

Patni, H.; Henson, C.; and Sheth, A.

“A number of government, corporate, and academic organizations are collecting enormous amounts of data provided by environmental sensors. However, this data is too often locked within organizations and underutilized by the greater community. In this paper, we present a framework to make this sensor data openly accessible by publishing it on the Linked Open Data (LOD) Cloud. This is accomplished by converting raw sensor observations to RDF and linking with other datasets on LOD. With such a framework, organizations can make large amounts of sensor data openly accessible, thus allowing greater opportunity for utilization and analysis.”