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

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 6; p. 901
• Main Authors: de Bem, Pablo, de Carvalho Junior, Osmar, Fontes Guimarães, Renato, Trancoso Gomes, Roberto
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2020
• Subjects: Algorithms; Artificial neural networks; Change detection; Classification; Climate change; Climate effects; cnn; Deep learning; Deforestation; Emissions; Greenhouse effect; Greenhouse gases; Land use; Landsat; Landsat satellites; Learning algorithms; Machine learning; Mapping; Multilayer perceptrons; Neural networks; Performance measurement; Post-production processing; Rainforests; Remote sensing; Roads & highways; Satellite imagery; Semantics; Studies; Brazil
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12060901

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.