Changes in the land cover and land use of the Itacaiunas River watershed, arc of deforestation, Carajas, southeastern Amazon
- 1Vale Institute of Technology (ITV), Belém, Pará, Brazil
- 2Intelligence Planning and Environment, Vale S.A., Nova Lima, Minas Gerais, Brazil
- 3Geoscience Institute, Universidade Federal do Pará, Belém, Pará, Brazil
Keywords: Landsat, change detection, object based image analysis, GEOBIA, multitemporal image analysis
Abstract. Human actions are changing the Amazon’s landscape by clearing tropical forest and replacing it mainly by pasturelands. The focus of this work is to assess the changes in the Itacaiúnas River watershed; an area located in the southeastern Amazon region, near Carajás, one of the largest mining provinces of the World. We used a Landsat imagery dataset to map and detect land covers (forest and montane savanna) and land use (pasturelands, mining and urban) changes from 1984 to 2013. We employed standard image processing techniques in conjunction with visual interpretation and geographic object-based classification. Land covers and land use (LCLU) “from-to” change detection approach was carried out to recognize the trajectories of LCLU classes based on object change detection analysis. We observed that ~47% (~1.9 million ha) of forest kept unchanged; almost 41% (~1.7 million ha) of changes was associated to conversion from forest to pasture, while 8% (~333,000 ha) remained unchanged pasture. The conversion of forest and montane savannah to mining area represents only 0.24% (~9,000 ha). We can conclude that synergy of visual interpretation to discriminate fine level objects with low contrast associated to urban, mining and savanna classes; and automatic classification of coarse level objects related to forest and pastureland classes is most successfully than use these methods individually. In essence, this approach combines the advantages of the human quality interpretation and quantitative computing capacity.