RESUMEN

Deforestation rates in the Amazon have markedly increased in the last few years, affecting non-protected and protected areas (PAs). Brazil is a hotspot of Protected Area Downgrading, Downsizing, and Degazettement (PADDD) events, with most events associated with infrastructure projects. Despite the threats dams impose on PAs, there is a knowledge gap in assessing deforestation in PAs around large dams in the Amazon. This study investigates how deforestation affects Biodiversity Protection Areas (BioPAs) and Indigenous Lands around the Jirau and Santo Antônio (JSA) dams (Madeira River, Rondônia) and Belo Monte dam (Xingu River, Pará) in the Brazilian Amazon. We compared clear-cutting between PAs and control areas and the annual rates of forest change between pre-dam and post-dam periods. We discussed deforestation-related factors (e.g., PADDD events and the presence of management plans or councils). Our results show an increase in deforestation after the operation of the dams when environmental control from licensing agencies decreases and other political and economic factors are in practice. Indigenous Lands experienced a significant increase in deforestation around the Belo Monte dam, which is associated with the demarcation process and land conflicts. Surrounding the JSA dams, sustainable use BioPAs showed high deforestation rates, and 27 PADDD events were reported, four directly related to dams. In addition to dams, deforestation was associated with the crisis of Brazilian democracy and the weakening of environmental policies. In conclusion, the weak environmental control from environmental licensing agencies during dam operation and PADDD events have contributed to increased deforestation rates and additional stresses in the Amazon.

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RESUMEN

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance.