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

Abstract

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.

Socio-Economic Impact Assessment of Small Dams Based on T-Paired Sample Test Using SPSS Software

“…2-tailed). The statistical results of the parameters obtained through test are mentioned in Tables (5)(6)(7)(8), which clearly indicate that variations have occurred among the parameters in positive way and have strong correlation except one after the execution of dams.…”

“…The largest infrastructure project provoked the largest resettlement of people and produced the greatest negative environmental impacts [6]. It led to rapid population increase [7], expansion of the urban area, and changes in the landscape [8]. The largest projects met strong opposition from indigenous groups and from members of the regional population who were concerned with the negative social and environmental impacts of a large dam [9].…”

A Review of Earth Observation-Based Analyses for Major River Basins

“…On the other hand, ∼79% of these studies performed regional and subbasin scale land cover and land use change analyses. Here, dam induced land cover change was analyzed particularly often [81][82][83][84][85]. For example, Feng et al [85] investigated regional land cover change induced by the construction of the Belo Monte dam in the Amazon river basin.…”

“…Here, dam induced land cover change was analyzed particularly often [81][82][83][84][85]. For example, Feng et al [85] investigated regional land cover change induced by the construction of the Belo Monte dam in the Amazon river basin. In this study, the authors used high and very high spatial resolution multispectral imagery from 4 different sensors, acquired between 2011 to 2016.…”

Examining Spatial Patterns of Urban Distribution and Impacts of Physical Conditions on Urbanization in Coastal and Inland Metropoles

“…The migration of populations from rural to urban regions and improved economic conditions in developing countries have resulted in rapid urban expansion [1,2], requiring timely updates of urban spatial distribution and expansion datasets. Urban distribution and dynamic change products are often obtained from remotely sensed data using classification-based approaches, such as maximum likelihood, decision tree classifier, support vector machine, and object-oriented classifier [3][4][5]. However, directly extracting urban features from remotely sensed data is often difficult due to the complex composition of urban land covers and their spectral confusions, such as among buildings, Remote Sens.…”