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Abstract

This article describes the design of a multidimensional environmental datacube to support the creation of deforestation risk prediction models. By integrating a comprehensive set of variables from diverse data sources, such as land tenure, accessibility, connectivity, and biophysical conditions, the datacube enables researchers and policymakers to develop more accurate and informed predictive models. This supports conservation strategies and sustainable forest management in the Amazon. The strategic aggregation of these variables enhances the understanding of deforestation dynamics and empowers decision-makers with the insights needed to implement effective interventions. By facilitating a nuanced analysis of deforestation's socio-economic and environmental drivers, the datacube aids in identifying high-risk areas and formulating targeted policies that balance economic development with environmental preservation

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Institutions
  • 1 Instituto Nacional de Pesquisas Espaciais (INPE)
  • 2 INPE
  • 3 Instituto Nacional de Pesquisas Espaciais
  • 4 Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis (IBAMA)
  • 5 Instituto Nacional de Pesquisas Espaciais - SJC
  • 6 Instituto Nacional de Pesquisa Espaciais (INPE)
  • 7 Pontifícia Universidade Católica do Rio de Janeiro – PUC-Rio
  • 8 Instituto Militar de Engenharia (IME)
  • 9 Pontifícia Universidade Católica do Rio de Janeiro
Track
  • 19. Environmental monitoring and modeling
Keywords
deforestation risk
datacube
spatiotemporal data
modelling