Campagnolo, ML

Killick, R

Mota, BWC

Pereira, JMC

Area burned in tropical savannas of Brazil and Australia was mapped using SPOT-VGT daily 1km resolution imagery and a new algorithm based on change point detection techniques. Each study area covers about 250,000 km2 and the mapping exercise spans a 11-yr period (1999-2009). Our algorithm addresses each pixel as a time series and detects changes in the statistical properties (mean and variance) of NIR and SWIR reflectance values, toidentify potential burning dates. We compare the performance of binary segmentation (BinSeg) and Pruned Exact Linear Time (PELT) change point detection techniques. Mean reflectance values observed at a pixel over the week after a change point has been detected are compared with a biome-specific statistical distribution of burned area reflectance values, to assess the probability that the change point detected does correspond to a burn event. Change points corresponding to an increase in reflectance are dismissed as potential burn events, as are those occurring outside of a pre-defined fire season. In the last step of the algorithm, monthly burned area probability maps are converted to dichotomous (burned-unburned maps), using Markov Random Fields. A preliminary assessment of our results is performed by comparing them with those from the MODIS active fires and burned area products, taking into account differences in spatial resolution between the two sensors

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                       King’s Research Portal  Document VersionEarly version, also known as pre-printLink to publication record in King's Research PortalCitation for published version (APA):Pereira, JMC., Mota, BWC., Campagnolo, ML., & Killick, R. (2012). An algorithm for satellite-based burned areamapping using change point detection and Markov random fields. Geophysical Research abstracts, 14, 10598.Citing this paperPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this maydiffer from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination,volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you areagain advised to check the publisher's website for any subsequent corrections.General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyrightowners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain•You may freely distribute the URL identifying the publication in the Research PortalTake down policyIf you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access tothe work immediately and investigate your claim.Download date: 08. Oct. 2023Geophysical Research AbstractsVol. 14, EGU2012-10598, 2012EGU General Assembly 2012© Author(s) 2012An algorithm for satellite-based burned area mapping using change pointdetection and Markov random fieldsJ. M.C. Pereira (1), B.W.C. Mota (1), M.L. Campagnolo (1), and R. Killick (2)(1) Technical University of Lisbon, School of Agriculture, Forest Research Centre, Lisbon, Portugal(jmocpereira@gmail.com), (2) Department of Mathematics and Statistics, University of LancasterArea burned in tropical savannas of Brazil and Australia was mapped using SPOT-VGT daily 1km resolutionimagery and a new algorithm based on change point detection techniques. Each study area covers about 250,000km2 and the mapping exercise spans a 11-yr period (1999-2009). Our algorithm addresses each pixel as a timeseries and detects changes in the statistical properties (mean and variance) of NIR and SWIR reflectance values, toidentify potential burning dates. We compare the performance of binary segmentation (BinSeg) and Pruned ExactLinear Time (PELT) change point detection techniques. Mean reflectance values observed at a pixel over the weekafter a change point has been detected are compared with a biome-specific statistical distribution of burned areareflectance values, to assess the probability that the change point detected does correspond to a burn event. Changepoints corresponding to an increase in reflectance are dismissed as potential burn events, as are those occurringoutside of a pre-defined fire season. In the last step of the algorithm, monthly burned area probability maps areconverted to dichotomous (burned-unburned maps), using Markov Random Fields. A preliminary assessment ofour results is performed by comparing them with those from the MODIS active fires and burned area products,taking into account differences in spatial resolution between the two sensors.

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An algorithm for satellite-based burned area mapping using change point detection and Markov random fields

https://kclpure.kcl.ac.uk/portal/files/29138290/EGU2012_10598.pdf

An algorithm for satellite-based burned area mapping using change point detection and Markov random fields

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