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3 results for “BFAST”
Disturbances in vegetation detected with BFAST in the Purapel fluvial catchment
<p>This dataset contains the results (69 TIFF files) of seasonal disturbances detected in vegetation in the Purapel catchment (southern Chile) for the period from 2002 to 2019. These disturbances were obtained by applying the Breaks for Additive Season and Trend (BFAST, Verbesselt et al., 2010) algorithm to 745 Landsat 5, 7 and 8 imagery. We used Collection 2 Level 2 surface reflectance products and applied the CFMask algorithm (Foga et al., 2017) for cloud masking before utilizing the BFAST algorithm.</p> <p>The BFAST algorithm detects changes in the NDVI time series of each pixel. To determine which event was considered a disturbance, we used the same intensity thresholds as in Cabezas and Fassnacht (2018). We then filtered the results to keep just the disturbances with areas greater than 1 hectare, eliminating noisy data.</p> <p>Except for 2 big wildfires (2015 and 2017) it was assumed that all of the disturbances were clear cuts, since forestry is the main productive activity in the region. This was confirmed by validating the data with 35 manually drawn polygons that were randomly distributed across the catchment. Then, we performed an accuracy assessment, obtaining a confusion matrix with a balanced accuracy of 0,86 and a F1 score of 0,69.</p> <p>Each TIFF file is a binary grid with “zeros” representing no disturbance and “ones” representing a disturbance in the season that the name of the file indicates.</p> <p>A GIF file is also included, which contains the time series of the disturbances for easier graphical purposes.</p> <p>References</p> <p>J. Cabezas and F. E. Fassnacht. Reconstructing the Vegetation Disturbance History of a Biodiversity Hotspot in Central Chile Using Landsat, Bfast and Landtrendr. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 7636–7639. IEEE, 7 2018. ISBN 978-1-5386-7150-4. doi: 10.1109/IGARSS.2018.8518863.</p> <p>S. Foga, P. L. Scaramuzza, S. Guo, Z. Zhu, R. D. Dilley, T. Beckmann, G. L. Schmidt, J. L. Dwyer, M. Joseph Hughes, and B. Laue. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194:379–390, 6 2017. ISSN 00344257. doi: 10.1016/j.rse.2017.03.026.</p> <p>J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1):106–115, 1 2010. ISSN 00344257. doi: 10.1016/ j.rse.2009.08.014. URL http://linkinghub.elsevier.com/retrieve/pii/S003442570900265X.</p>
Results from monitoring changes in NDVI time series over southwest Ethiopia based on 11 years of Landsat 7 imagery and BFAST
<p>(see README.md)</p>
Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm
<p>This package supplements the following paper entitled “Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm” published with Science China Earth Sciences.</p>
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