Faculty of Mathematics and Natural Sciences - Earth Observation Lab

Faculty of Mathematics and Natural Sciences | Geography Department | Earth Observation Lab | News | Archive | Paper on "Long-term deforestation dynamics in the Brazilian Amazon—Uncovering historic frontier development along the Cuiabá–Santarém highway"

Paper on "Long-term deforestation dynamics in the Brazilian Amazon—Uncovering historic frontier development along the Cuiabá–Santarém highway"

Hannes Müller (Doctoral researcher), Patrick Griffiths (Postdoc) and Patrick Hostert (Chair of the Geomatics Lab) have used image compositing to detect annual deforestation information from 1985-2012 along the BR-163 highway in the Brazilian Amazon. For the first time, they show historic deforestation processes in this highly dynamic deforestation frontier and discuss their findings in context of a complex socio-economic and political framework.

The paper is published in the International Journal of Applied Earth Observation and Geoinformation on ScienceDirect. Go to....

The results are available as online map and can be used for further analysis regarding carbon emissions, landscape fragmentation and land system analysis.

 

Abstract

The great success of the Brazilian deforestation programme “PRODES digital” has shown the importance of annual deforestation information for understanding and mitigating deforestation and its consequences in Brazil. However, there is a lack of similar information on deforestation for the 1990s and 1980s. Such maps are essential to understand deforestation frontier development and related carbon emissions. This study aims at extending the deforestation mapping record backwards into the 1990s and 1980s for one of the major deforestation frontiers in the Amazon. We use an image compositing approach to transform 2224 Landsat images in a spatially continuous and cloud free annual time series of Tasseled Cap Wetness metrics from 1984 to 2012. We then employ a random forest classifier to derive annual deforestation patterns. Our final deforestation map has an overall accuracy of 85% with half of the overall deforestation being detected before the year 2000. The results show for the first time detailed patterns of the expanding deforestation frontier before the 2000s. The high degree of automatization exhibits the great potential for mapping the whole Amazon biome using long-term and freely accessible remote sensing collections, such as the Landsat archive and forthcoming Sentinel-2 data.

Keywords

  • Landsat;
  • Time series;
  • Long-term deforestation;
  • Image compositing