Dar Roberts Awarded Outstanding Contributions Award in Remote Sensing


Professor Dar Roberts has just been awarded the Outstanding Contributions Award in Remote Sensing for 2013. “Sponsored by the Remote Sensing Specialty Group (RSSG) of the Association of American Geographers (AAG) since 1992, the award seeks to recognize individuals who have made outstanding contributions to the field of remote sensing and to the geographic community through their remote sensing research, teaching, and/or outreach” (award notification letter to Dar from Jane M. Read, Chair, RSSG Awards Committee, 15 February, 2013).

The RSSG is a diverse, international blend of professionals from academia, industry, and government, as well as students, whose mission is “to foster an understanding of remote sensing science. Emphasis is placed on developing a meaningful dialogue among geographers interested in understanding and applying remote sensing technology in research, instruction, public service, and private enterprise” (source).

Past UCSB Geography winners of this prestigious award include David Simonett (1992) and Jack Estes (1994). The award will be presented during the RSSG Business meeting at the upcoming AAG Annual Meeting in Los Angeles (April 9-13). Professor Keith Clarke will accept the award on Dr. Roberts’ behalf because Dar had previously committed to teaching a two day short course for the Brazilian Remote Sensing Conference in Foz do Iguassu in April, something he does every two years.

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Dar is a Professor in the UCSB Department of Geography where he started in January 1994. He is the author of over 70 refereed publications, over 10 books/book chapters, and over 100 abstracts and non-refereed articles. Research interests include sensor fusion, change identification using multitemporal remote sensing, imaging spectrometry, spectroscopy, and wildfire hazard assessment. He is the UCSB Principal Investigator of the Southern California Wildfire Hazard Center and leads the group in developing wildfire fuels maps and mapping fuel moisture using remote sensing.

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