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

Julia Rodrigues, Mauricio Araújo Dias (), Rogério Negri, Sardar Muhammad Hussain and Wallace Casaca
Additional contact information
Julia Rodrigues: São Paulo State University (UNESP), Institute of Biosciences, Humanities and Exact Sciences (IBILCE), São José do Rio Preto 15054-000, Brazil
Mauricio Araújo Dias: São Paulo State University (UNESP), Faculty of Science and Technology (FCT), Presidente Prudente 19060-900, Brazil
Rogério Negri: São Paulo State University (UNESP), Science and Technology Institute (ICT), São José dos Campos 12245-000, Brazil
Sardar Muhammad Hussain: Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Faculty of Basic Sciences (FBS), Quetta 87300, Pakistan
Wallace Casaca: São Paulo State University (UNESP), Institute of Biosciences, Humanities and Exact Sciences (IBILCE), São José do Rio Preto 15054-000, Brazil

Land, 2024, vol. 13, issue 9, 1-19

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 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/9/1427/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/9/1427/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:9:p:1427-:d:1471021

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().