Record ID

UAMef9cac752c5a4febb58793ad1f9c29a1

Author

Yonatan Tarazona Yonatan Tarazona,, Undefined Affiliation

Alaitz Zabala Alaitz Zabala,, Undefined Affiliation

Xavier Pons Xavier Pons,, Undefined Affiliation

Antoni Broquetas Antoni Broquetas,, Undefined Affiliation

Jakub Nowosad Jakub Nowosad,, Zakład Geoinformacji [nowa struktura organizacyjna] (SNP/WNGiG/JWNGiG)Szkoła Nauk Przyrodniczych [nowa struktura organizacyjna] (SNP)

Hamdi A. Zurqani Hamdi A. Zurqani,, Undefined Affiliation

Other language title versions

Fusion des données Landsat et RSO pour la cartographie de la déforestation tropicale grâce à la classification par apprentissage automatique et à l’approche de détection non saisonnière PVts-β

Journal series

Canadian Journal of Remote Sensing, ISSN 0703-8992, e-ISSN 1712-7971

Issue year

2021

Vol

47

No

5

Pages

677-696

Abstract in original language

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.

DOI

DOI:10.1080/07038992.2021.1941823 Opening in a new tab

Language

eng (en) English

Score (nominal)

100

Score source

journalList

Score

Ministerial score = 100.0, 11-02-2022, ArticleFromJournal

Publication indicators

WoS Citations = 0; WoS Impact Factor: 2019 (2 years) = 2.126 - 2019 (5 years) =2.826

Citation count

1

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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.