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Assigning dates and identifying areas affected by fires in Portugal based on MODIS data

Overview of attention for article published in Anais da Academia Brasileira de Ciências, September 2017
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Title
Assigning dates and identifying areas affected by fires in Portugal based on MODIS data
Published in
Anais da Academia Brasileira de Ciências, September 2017
DOI 10.1590/0001-3765201720160707
Pubmed ID
Authors

Jessica Panisset, Carlos C Dacamara, Renata Libonati, Leonardo F Peres, Teresa J Calado, Ana Barros

Abstract

An automated procedure is here presented that allows identifying and dating burned areas in Portugal using values of daily reflectance from near-infrared and middle-infrared bands, as obtained from the MODIS instrument. The algorithm detects persistent changes in monthly composites of the so-called (V,W) Burn-Sensitive Index and the day of maximum change in daily time series of W is in turn identified as the day of the burning event. The procedure is tested for 2005, the second worst fire season ever recorded in Portugal. Comparison between the obtained burned area map and the reference derived from Landsat imagery resulted in a Proportion Correct of 95.6%. Despite being applied only to the months of August and September, the algorithm is able to identify almost two-thirds of all scars that have occurred during the entire year of 2005. An assessment of the temporal accuracy of the dating procedure was also conducted, showing that 75% of estimated dates presented deviations between -5 and 5 days from dates of hotspots derived from the MODIS instrument. Information about location and date of burning events as provided by the proposed procedure may be viewed as complementary to the currently available official maps based on end-of-season Landsat imagery.

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Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 21%
Student > Master 4 14%
Student > Ph. D. Student 3 11%
Professor 2 7%
Professor > Associate Professor 2 7%
Other 5 18%
Unknown 6 21%
Readers by discipline Count As %
Environmental Science 10 36%
Earth and Planetary Sciences 5 18%
Agricultural and Biological Sciences 4 14%
Computer Science 1 4%
Physics and Astronomy 1 4%
Other 1 4%
Unknown 6 21%