Multi‐temporal assessment of selective logging in the Brazilian Amazon using Landsat data

Abstract

Large‐scale selective logging is a relatively new activity in the Amazon and its full consequences have yet to be evaluated. Impacts by selective logging alone have been estimated to increase approximately 4–7% of the annual carbon release from deforestation. In this research, visual interpretation and semi‐automated remote sensing techniques were applied to identify and map areas of selective logging in tropical terra firme (upland) forests together with the correlated multi‐annual measurement results for 1992, 1996, and 1999, for the Brazilian Amazon. The research results indicate that selective logging is rapidly increasing in both intensity (regional) and area (basin‐wide). By 1992, at least 5980 km² of forest had been logged. During the 1992–1996 and 1996–1999 periods the area impacted expanded by an additional 10 064 km², and 26 085 km², respectively. Selective logging within protected areas increased more than twofold between 1992 and 1996, and more than fivefold between 1996 and 1999 in that region. We also estimated that at least 3689 km² had been actively logged in 1992, an additional 5107 km², and 11 638 km², had been logged in 1996 and 1999, and at least 10% of total logged forests detected in 1999 were previously logged in the period of analysis.

Acknowledgements

We acknowledge that the work was supported by grants from NASA's REAS.N Program (NNG046677A) and Land Cover and Land Use Change Program (NNG04GN67G). We would like to thank Daniel Gomes, Mauro Lucio Matricardi, Wilson Soares Abdala, Vilmar Ferreira, Luiz Claudio Fernandes, Antonio Lisboa, Marcelo Gama, Professor Israel Baptista Xavier from the Federal University of Rondônia, and Professor Wagner Matricardi, from the Federal University of Mato Grosso, for all help, friendship, and information provided during the fieldwork in Brazil. We also would like to express our gratitude to Dr Dina Franceschi and Emily Clifton for carefully reviewing early versions of the manuscript, and Dr Michael Weir and the anonymous reviewers of International Journal of Remote Sensing for providing further editorial improvements and helping to improve the scientific quality of this article.