Exploring BFAST to detect forest changes in Portugal

Hugo Costa; Anny Giraldo; Mário Caetano

doi:10.1117/12.2566669

20 September 2020 Exploring BFAST to detect forest changes in Portugal

Hugo Costa, Anny Giraldo, Mário Caetano

Proceedings Volume 11533, Image and Signal Processing for Remote Sensing XXVI; 1153308 (2020) https://doi.org/10.1117/12.2566669
Event: SPIE Remote Sensing, 2020, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Hugo Costa, Anny Giraldo, and Mário Caetano "Exploring BFAST to detect forest changes in Portugal", Proc. SPIE 11533, Image and Signal Processing for Remote Sensing XXVI, 1153308 (20 September 2020); https://doi.org/10.1117/12.2566669

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available