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Remote Sens. 2017, 9(4), 381; doi:10.3390/rs9040381

Examining Spatial Distribution and Dynamic Change of Urban Land Covers in the Brazilian Amazon Using Multitemporal Multisensor High Spatial Resolution Satellite Imagery

1
The Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, School of Environmental & Resource Sciences, Zhejiang Agriculture and Forestry University, Lin An 311300, China
2
Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, USA
3
National Institute for Space Research, Av. dos Astronautas, 1758, São Jose dos Campos, SP 12245-010, Brazil
4
Forestry Faculty, Federal University of Pará, Altamira, PA 68372-040, Brazil
*
Author to whom correspondence should be addressed.
Academic Editors: Weiqi Zhou, Junxiang Li, Conghe Song, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 5 February 2017 / Revised: 9 April 2017 / Accepted: 17 April 2017 / Published: 19 April 2017
(This article belongs to the Special Issue Remote Sensing of Urban Ecology)
View Full-Text   |   Download PDF [5701 KB, uploaded 19 April 2017]   |  

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, Pará 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. View Full-Text
Keywords: urban land-cover change; high spatial resolution satellite images; multisensor data; change detection technique; moist tropical region; Belo Monte hydroelectric dam construction urban land-cover change; high spatial resolution satellite images; multisensor data; change detection technique; moist tropical region; Belo Monte hydroelectric dam construction
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MDPI and ACS Style

Feng, Y.; Lu, D.; Moran, E.F.; Dutra, L.V.; Calvi, M.F.; de Oliveira, M.A.F. Examining Spatial Distribution and Dynamic Change of Urban Land Covers in the Brazilian Amazon Using Multitemporal Multisensor High Spatial Resolution Satellite Imagery. Remote Sens. 2017, 9, 381.

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