@Article{FengLuMoDuCaOl:2017:ExSpDi,
author = "Feng, Yunyun and Lu, Dengsheng and Moran, Emilio F. and Dutra,
Luciano Vieira and Calvi, Miqu{\'e}ias Freitas and Oliveira,
Maria Antonia Falc{\~a}o de",
affiliation = "{Zhejiang Agriculture and Forestry University} and {Zhejiang
Agriculture and Forestry University} and {Michigan State
University} and {Instituto Nacional de Pesquisas Espaciais (INPE)}
and {Universidade Federal do Par{\'a} (UFPA)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Examining spatial distribution and dynamic change of urban Llnd
covers in the Brazilian Amazon using multitemporal multisensor
high spatial resolution satellite imagery",
journal = "Remote Sensing",
year = "2017",
volume = "9",
number = "4",
month = "Apr.",
keywords = "urban land-cover change, high spatial resolution satellite images,
multisensor data, change detection technique, moist tropical
region, Belo Monte hydroelectric dam construction.",
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.",
doi = "10.3390/rs9040381",
url = "http://dx.doi.org/10.3390/rs9040381",
issn = "2072-4292",
language = "en",
targetfile = "feng_examining.pdf",
urlaccessdate = "28 mar. 2024"
}