Day: August 16, 2010

Utility Improves Outage Response with Mobile GIS

/ Matt Artz

Northeastern Rural Electric Membership Corporation (REMC) is able to better serve the energy needs of its growing customer base by using mobile geographic information system (GIS) technology from Esri. The consumer-owned utility supplies electric power to more than 26,000 households and businesses in northeastern Indiana.

Northeastern REMC utility linemen once navigated the service territories by memory, armed with a handful of paper maps. Now field crews use laptops equipped with ArcGIS software from Esri to instantly update information related to work orders, regular maintenance, and customers. The GIS data is accessible throughout the company, with approximately 43 users on different devices in several departments.

Steven Weber, Northeastern REMC GIS technician, said, “Using GIS technology, we can deliver the right information to the right hands at the right time. Field crews can view, search, and revise GIS data and use GPS for facility locating and routing. This improves the flow of accurate information from the field to the office to the customers.”

Bill Meehan, Esri director of utility solutions, said, “Utilities have struggled for years to move information into and out of the field. It is wonderful to see how Northeastern REMC addressed this problem by empowering field-workers with mobile GIS.”

For more information on GIS for electric utilities, visit www.esri.com/electric.

[Source: Esri press release]

GBIF Secretariat Launches New Social Site to Foster Communication and Collaboration in the Biodiversity Informatics Community

/ Matt Artz

After a successful trial period that has involved more than 200 users from all over the world, the final version of the GBIF Community Site is officially launched today – 13th August 2010:

community.gbif.org

This site is a free online social platform for professional interaction in the scope of GBIF: collaborative projects, discussions, sharing of information and expertise, announcements, mentoring, etc.

The site is open to GBIF delegations, Nodes, technicians, biodiversity data publishers and users, and everyone interested in Biodiversity Informatics.

Visit the site now, create your account and start networking! Invite your colleagues and contacts to also join the site and benefit from the collaboration tools available in the site: work groups, (micro)blogs, community news, online chat, file and image sharing and much more!

You can also check the GBIF website for a description of the site and to access a quick guide with an overview of its features.

NOTE: The site interface is available in English, Spanish and French.

With best regards,

The GBIF Community Site Team

[Source: GBIF email announcement]

Urban Road Tracking by Fusion of SVDD and Region Adjacency Graphs from VHR Imagery

/ Matt Artz / 1 Comment

ISPRS Technical Commission VII Symposium: 100 Years ISPRS – Advancing Remote Sensing Science, 05-07 July 2010, Vienna, Austria

Zhengjun Liu, Xiangguo Lin, Jixian Zhang, and Pengxian Pu

“Road surfaces are seriously disturbed by a variety of noises on the very high resolution (VHR) remotely sensed imagery in urban areas, e.g., abrupt geometric deformation and radiometric changes caused by sharp turning, shadows of tall buildings, and appearance of vehicles, which leads to frequent failures for most of current road tracking methods. In this paper, a semi-automatic method is proposed for urban road tracking on VHR imagery. Initially, a human operator inputs three seed points on a selected road, and then necessary information, such as road direction, road width, start point, and a reference template, is automatically derived. The automatic tracking is consequently triggered. During the process, the reference template is moved to generate several target templates. For each target template, a binary template is derived by classifying the target template using support vector data description (SVDD). Subsequently, region adjacency graphs (RAG) is used to eliminate the small disturbing features on the road surfaces in each binary template, which is helpful to search the optimal road centerline points. The above tracking process is repeated until a whole road is completed. Two VHR images were used for the test. The preliminary results show that our method can extract roads more robustly than existing least-squares template matching method in urban areas.”

Using Remote Sensing and GIS Technologies to Estimate Grass Yield and Livestock Carrying Capacity of Alpine Grasslands in Golog Prefecture, China

/ Matt Artz

Pedosphere, Volume 20, Issue 3, June 2010, Pages 342-351

Long YU, Li ZHOU, Wei LIU, and Hua-Kun ZHOU

“Remote sensing data from the Terra Moderate-Resolution Imaging Spectroradiometer (MODIS) and geospatial data were used to estimate grass yield and livestock carrying capacity in the Tibetan Autonomous Prefecture of Golog, Qinghai, China. The MODIS-derived normalized difference vegetation index (MODIS-NDVI) data were correlated with the aboveground green biomass (AGGB) data from the aboveground harvest method. Regional regression model between the MODIS-NDVI and the common logarithm (LOG10) of the AGGB was significant (r2 =0.51, P < 0.001), it was, therefore, used to calculate the maximum carrying capacity in sheep-unit year per hectare. The maximum livestock carrying capacity was then adjusted to the theoretical livestock carrying capacity by the reduction factors (slope, distance to water, and soil erosion). Results indicated that the grassland conditions became worse, with lower aboveground palatable grass yield, plant height, and cover compared with the results obtained in 1981. At the same time, although the actual livestock numbers decreased, they still exceeded the proper theoretical livestock carrying capacity, and overgrazing rates ranged from 27.27% in Darlag County to 293.99% in Baima County. Integrating remote sensing and geographical information system technologies, the spatial and temporal conditions of the alpine grassland, trend, and projected stocking rates could be forecasted for decision making.”