Exploring BFAST to detect forest changes in Portugal

Hugo Costa, Anny Giraldo, Mário Caetano, Francesca Bovolo, Lorenzo Bruzzone, Nazzareno Pierdicca, Claudia Notarnicola, Fabio Bovenga, Emanuele Santi, Jon Atli Benediktsson
2020 Image and Signal Processing for Remote Sensing XXVI  
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
more » ... ) 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.
doi:10.1117/12.2566669 fatcat:breooggr4jhrzh7cupodwyb3jm