Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks

TLDR

Mapping deforestation is essential for managing tropical rainforests, enabling monitoring of legal and illegal activity and assessing its climate‑change implications. The study aims to evaluate convolutional neural networks for mapping deforestation between consecutive Landsat images. The authors compared three CNN architectures—SharpMask, U‑Net, and ResUnet—to random forest and multilayer perceptron classifiers on Landsat images from 2017–2018 and 2018–2019. The CNNs outperformed the classic classifiers, with ResUnet achieving the highest metrics (Kappa, F1, mIoU = 0.94) and producing cleaner deforestation maps without post‑processing.

Abstract

Mapping deforestation is an essential step in the process of managing tropical rainforests. It lets us understand and monitor both legal and illegal deforestation and its implications, which include the effect deforestation may have on climate change through greenhouse gas emissions. Given that there is ample room for improvements when it comes to mapping deforestation using satellite imagery, in this study, we aimed to test and evaluate the use of algorithms belonging to the growing field of deep learning (DL), particularly convolutional neural networks (CNNs), to this end. Although studies have been using DL algorithms for a variety of remote sensing tasks for the past few years, they are still relatively unexplored for deforestation mapping. We attempted to map the deforestation between images approximately one year apart, specifically between 2017 and 2018 and between 2018 and 2019. Three CNN architectures that are available in the literature—SharpMask, U-Net, and ResUnet—were used to classify the change between years and were then compared to two classic machine learning (ML) algorithms—random forest (RF) and multilayer perceptron (MLP)—as points of reference. After validation, we found that the DL models were better in most performance metrics including the Kappa index, F1 score, and mean intersection over union (mIoU) measure, while the ResUnet model achieved the best overall results with a value of 0.94 in all three measures in both time sequences. Visually, the DL models also provided classifications with better defined deforestation patches and did not need any sort of post-processing to remove noise, unlike the ML models, which needed some noise removal to improve results.