Fusing Landsat and SAR Data for Mapping Tropical Deforestation through Machine Learning Classification and the PVts-β Non-Seasonal Detection Approach
Tarazona Coronel, Yonatan (Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions)
Zabala Torres, Alaitz (Universitat Autònoma de Barcelona. Departament de Geografia)
Pons, Xavier (Universitat Autònoma de Barcelona. Departament de Geografia)
Broquetas, Antoni (Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions)
Nowosad, Jakub (Adam Mickiewicz University (Polònia). Institute of Geoecology and Geoinformation)
Zurqani, Hamdi A. (Clemson University (Estats Units). Department of Forestry and Environmental Conservation)

Título variante:	Fusion des donnees Landsat et RSO pour la cartographie de la déforestation tropicale grâce à la classification par apprentissage automatique et a l'approche de detection non saisonnière PVts-b
Fecha:	2021
Resumen:	This article focuses on mapping tropical deforestation using time series and machine learning algorithms. Before detecting changes in the time series, we reduced seasonality using Photosynthetic Vegetation (PV) index fractions obtained from Landsat images. Single and multi-temporal filters were used to reduce speckle noise from Synthetic Aperture Radar (SAR) images (i. e. , ALOS PALSAR and Sentinel-1B) before fusing them with optical images through Principal Component Analysis (PCA). We detected only one change in the two PV series using a non-seasonal detection approach, as well as in the fused images through five machine learning algorithms that were calibrated with Cross-Validation (CV) and Monte Carlo Cross-Validation (MCCV). In total, four categories were obtained: forest, cropland, bare soil, and water. We then compared the change map obtained with time series and that obtained with the classification algorithms with the best calibration performance, revealing an overall accuracy of 92. 91% and 91. 82%, respectively. For statistical comparisons, we used deforestation reference data. Finally, we conclude with some discussions and reflections on the advantages and disadvantages of the detections made with time series and machine learning algorithms, as well as the contribution of SAR images to the classifications, among other aspects.
Ayudas:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1690 Agencia Estatal de Investigación RTI2018-099397-B-C21
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Lengua:	Anglès
Documento:	Article ; Versió publicada
Publicado en:	Canadian Journal of Remote Sensing, Vol. 47, núm. 5 (2021) , p. 677-696, ISSN 1712-7971

DOI: 10.1080/07038992.2021.1941823

21 p, 7.8 MB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias > Methods and Applications in Remote Sensing and Geographic Information Systems Research Group (GRUMETS)
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