Abstract
The construction of the Belo Monte hydroelectric dam began in 2011, resulting in rapidly increased population from less than 80,000 persons before 2010 to more than 150,000 persons in 2012 in Altamira, Para State, Brazil. This rapid urbanization has produced many problems in urban planning and management, as well as challenging environmental conditions, requiring monitoring of urban land-cover change at high temporal and spatial resolutions. However, the frequent cloud cover in the moist tropical region is a big problem, impeding the acquisition of cloud-free optical sensor data. Thanks to the availability of different kinds of high spatial resolution satellite images in recent decades, RapidEye imagery in 2011 and 2012, Pleiades imagery in 2013 and 2014, SPOT 6 imagery in 2015, and CBERS imagery in 2016 with spatial resolutions from 0.5 m to 10 m were collected for this research. Because of the difference in spectral and spatial resolutions among these satellite images, directly conducting urban land-cover change using conventional change detection techniques, such as image differencing and principal component analysis, was not feasible. Therefore, a hybrid approach was proposed based on integration of spectral and spatial features to classify the high spatial resolution satellite images into six land-cover classes: impervious surface area (ISA), bare soil, building demolition, water, pasture, and forest/plantation. A post-classification comparison approach was then used to detect urban land-cover change annually for the periods between 2011 and 2016. The focus was on the analysis of ISA expansion, the dynamic change between pasture and bare soil, and the changes in forest/plantation. This study indicates that the hybrid approach can effectively extract six land-cover types with overall accuracy of over 90%. ISA increased continuously through conversion from pasture and bare soil. The Belo Monte dam construction resulted in building demolition in 2015 in low-lying areas along the rivers and an increase in water bodies in 2016. Because of the dam construction, forest/plantation and pasture decreased much faster, while ISA and water increased much faster in 2011–2016 than they had between 1991 and 2011. About 50% of the increased annual deforestation area can be attributed to the dam construction between 2011 and 2016. The spatial patterns of annual urban land-cover distribution and rates of dynamic change provided important data sources for making better decisions for urban management and planning in this city and others experiencing such explosive demographic change.
Highlights
The construction of the Belo Monte hydroelectric dam near Altamira, Pará State, Brazil, has attracted a large population to this region, resulting in unprecedented land-cover change in the past five years (2011–2016)
The bare soil class in 2011 and 2012 has especially low accuracies with user’s accuracies of only 56.3% and 60.9%, probably because of its relatively low spatial resolution in RapidEye imagery compared to Pleiades, resulting in more confusion of spectral signatures among bare soil, impervious surface area (ISA), and pasture
This research has shown that the incorporation of spatial features into multispectral data from high spatial resolution satellite images is needed for improving land-cover classification in urban landscapes
Summary
The construction of the Belo Monte hydroelectric dam near Altamira, Pará State, Brazil, has attracted a large population to this region, resulting in unprecedented land-cover change in the past five years (2011–2016). It is an urgent task to map urban land-cover distribution and its dynamic change at high temporal and spatial resolutions to provide scientific data for effectively planning and managing urban expansion and construction This situation will be common in Brazilian moist tropical regions due to many planned dams along major rivers such as Xingu, Tapajos, and Madeira [1]. The availability of many satellite images such as IKONOS, QuickBird, Worldview, Pleiades, RapidEye, and SPOT 6 with high spatial resolutions make it possible to examine land-cover change using different sensor data This situation brings new challenges in conducting change detection analysis because the current techniques are mainly based on the same sensor data with a multitemporal scale [9,10] to reduce the influences of different spatial and spectral resolutions on the change detection results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
