Exploring BFAST to detect forest changes in Portugal

Hugo Costa, Anny Giraldo, Mário Caetano

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Landsat 8 data and Breaks For Additive Season and Trend (BFAST) were used in a region of central Portugal to detect forest clear-cuts and burnt areas. A total of 79 Landsat 8 images from 2013 to 2019 were downloaded for path/row 204/032, and the NDVI was calculated. The same data processing was done for path/row 203/032 to create a denser time series in the overlapping area, which increased to 124 images. The output of the analysis is a binary map of change (i.e., forest loss) and no-change. A probabilistic accuracy assessment based on random stratified sampling was implemented with 100 random points per stratum. Each point was interpreted as being either "no-change", "clear-cut"or "burnt area"based on reference data. Furthermore, the date of change (if any) was defined. Results show an overall accuracy of 0.85±0.02 for the binary classification with omission and commission errors of class "Change"of 0.30±0.02 and 0.19±0.02. Moreover, it is estimated that 32% of the forested area in path/row 204/032 went through at least one episode of clear-cut or fire in the period analyzed. The time lag between the date of change and detection was about 2.5 months on average, which decreased to 1.5 months in the regions of the denser time series. The results are promising but BFAST is somewhat slow and hence some concerns remain about its efficiency in operation use.

Original languageEnglish
Title of host publicationImage and Signal Processing for Remote Sensing XXVI
EditorsLorenzo Bruzzone, Francesca Bovolo, Emanuele Santi
PublisherSPIE-International Society for Optical Engineering
ISBN (Electronic)9781510638792
Publication statusPublished - 20 Sep 2020
EventImage and Signal Processing for Remote Sensing XXVI 2020 - Virtual, Online, United Kingdom
Duration: 21 Sep 202025 Sep 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceImage and Signal Processing for Remote Sensing XXVI 2020
CountryUnited Kingdom
CityVirtual, Online


  • Burnt areas
  • Change detection
  • Forest clear cuts
  • Landsat
  • NDVI time series


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