Deforestation dynamics in Mato Grosso in the southern Brazilian Amazon using GIS and NOAA/AVHRR data

Abstract

Mato Grosso has emerged as the Brazilian state with the highest deforestation rate, and with the most dynamic changes in vegetation and land cover. In this article, we focus on the following two main objectives: (i) to quantitatively assess the extent of vegetation change over the past two decades for more accurate eco-climatic impact analysis; and (ii) to clarify the causes to the changes in this state, with special focus to agro-pastoral activities and roads networks. A set of four 5-year digital vegetation model maps was created for the 1981–2001 period, using the first components of the principal components analysis of the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) multispectral data (Channels 1, 2 and 4). Vegetation and land cover changes are characterized by destruction of primary forests in the north and large-scale broad areas with intensive human activity and vast grasslands expanding from the south. Large-scale soybean production spreading over the central areas and vast herds of cattle in the north can be pointed to as the main causes of vegetation change in these areas. It is shown that 76.1% of the changes in land cover types occur within 30 km of paved and unpaved roads. This emphasizes the role not only of road building, but also of navigable rivers, in accelerating deforestation, especially over the sparsely populated and road-less north.

Acknowledgements

We are very grateful to the members of the Eco-Climate Joint Seminar, Graduate School of Bioresources, Mie University, especially to Professor K. Fukuyama, Professor Y. Kuzuha, Professor N. Matsumura, Professor Y. Sekine and Professor Y. Tachibana, for their continuous remarks and suggestions during the elaboration of this work. Special thanks from Ms. Yoshikawa to Dr. A. Nonomura, Dr. Aung Than Oo, Dr. D. E. Musiega, Dr. Kobayashi and to the other members of the Laboratory of Climate and Ecosystems Dynamics (LCED) for their kind collaboration. Last but not least, our special thanks go to the Ritsumeikan Asia Pacific University (APU) for making available its facilities during our long period of research at the Laboratory of Environmental Geoscience.