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.

摘要

本文重点介绍使用时间序列和机器学习算法绘制热带森林砍伐图。在检测时间序列的变化之前，我们使用从 Landsat 图像获得的光合植被 (PV) 指数分数降低了季节性。在通过主成分分析 (PCA) 将合成孔径雷达 (SAR) 图像（即 ALOS PALSAR 和 Sentinel-1B）与光学图像融合之前，使用单时域和多时域滤波器来减少散斑噪声。我们使用非季节性检测方法仅检测到两个 PV 系列中的一个变化，以及通过使用交叉验证 (CV) 和蒙特卡罗交叉验证 (MCCV) 校准的五种机器学习算法在融合图像中检测到一个变化。总共获得了四个类别：森林、农田、裸土和水。然后，我们比较了用时间序列获得的变化图和用具有最佳校准性能的分类算法获得的变化图，分别揭示了 92.91% 和 91.82% 的总体准确度。为了进行统计比较，我们使用了森林砍伐参考数据。最后，我们对使用时间序列和机器学习算法进行检测的优缺点进行了一些讨论和反思，以及 SAR 图像对分类的贡献等。