Fusing Landsat and SAR Data for Mapping Tropical Deforestation through Machine Learning Classification and the PVts-β Non-Seasonal Detection Approach

Abstract

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.