Abstract
Abstract. Deforestation is one of the main causes of biodiversity reduction, climate change among other destructive phenomena. Thus, early detection of deforestation processes is of paramount importance. Motivated by this scenario, this work presents an evaluation of methods for automatic deforestation detection, specifically Early Fusion (EF) Convolutional Network, Siamese Convolutional Network (S-CNN) and the well-known Support Vector Machine (SVM), taken as the baseline. These methods were evaluated in a region of the Brazilian Legal Amazon (BLA). Two Landsat 8 images acquired in 2016 and 2017 were used in our experiments. The impact of training set size was also investigated. The Deep Learning-based approaches clearly outperformed the SVM baseline in our approaches, both in terms of F1-score and Overall Accuracy, with a superiority of S-CNN over EF.
Highlights
The Amazon Rainforest accommodates a large biodiversity
When just one tile was used for training, we recorded F1-scores equal to 46%, 44% and 48%, for Support Vector Machine (SVM), Early Fusion (EF) and Siamese Convolutional Network (S-Convolutional Neural Networks (CNNs)) respectively
This work reported an evaluation of recently proposed deep learning based methods for detection of deforestation in the Amazon forest
Summary
It is home to a large number of species, including endemic and endangered flora and fauna. It contains 20% of the fresh water of the planet (Assuncao , Rocha, 2019) and produces more than 20% of the world oxygen (Butler, 2008). It is imperative to promote sustainable development to achieve an ecological balance and to contribute to the mitigation of climate change (Sathler et al, 2018). Controlling and monitoring this ecosystem is fundamental to enforce public policies and to avoid illegal activities in the region. Remote sensing has proven to be a cost-effective information source to attain such objectives
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