A Robust Dual-Mode Machine Learning Framework for Classifying Deforestation Patterns in Amazon Native Lands

Abstract

The integrated use of remote sensing and machine learning stands out as a powerful and well-established approach for dealing with various environmental monitoring tasks, including deforestation detection. In this paper, we present a tunable, data-driven methodology for assessing deforestation in the Amazon biome, with a particular focus on protected conservation reserves. In contrast to most existing works from the specialized literature that typically target vast forest regions or privately used lands, our investigation concentrates on evaluating deforestation in particular, legally protected areas, including indigenous lands. By integrating the open data and resources available through the Google Earth Engine, our framework is designed to be adaptable, employing either anomaly detection methods or artificial neural networks for classifying deforestation patterns. A comprehensive analysis of the classifiers’ accuracy, generalization capabilities, and practical usage is provided, with a numerical assessment based on a case study in the Amazon rainforest regions of São Félix do Xingu and the Kayapó indigenous reserve.

Keywords

• anomaly detection
• Google Earth Engine
• machine learning
• neural networks