Chapter 4
Long-Term Change Dynamics Using Landsat
Archive for the Region of Waterloo
in Ontario, Canada
Anqi Fu, Jonathan Li, and Saied Pirasteh
Abstract Urban land use and land cover classification have always been crucial
due to the ability and to link many elements of human and physical environments.
Timely, accurate, and detailed knowledge of the urban land cover information
derived from remote sensing data is increasingly required among a wide variety
of communities. This chapter presents a surge of interest that has predominately
driven from the recent innovations in data, theories in urban remote sensing, and
technologies. The Region of Waterloo was chosen for land use and land cover
classification by applying remote sensing techniques to satellite images from 1984
to 2013.
4.1 Introduction
To date, the entire world is continuously experiencing rapid urbanization (Ridd and
Hipple 2006). Urban growth is mainly caused by population growth, economic
growth, environmental condition, availability of technologies and frequent human
activities such as industrialization and migration from rural to urban area and
resettlement (Bhatta 2010; Ridd and Hipple 2006). It is obvious that the aforementioned will inevitably lead to land use changes and landscape pattern alteration at
local and regional scale (Yin et al. 2011; Tan et al. 2009; Deng et al. 2009;
Sundarakumar et al. 2012). Those changes include losses of agriculture fields,
water bodies, forest and other vegetated green spaces and non-vegetated fields
(Yang 2002; Sexton et al. 2013a, b; Sundarakumar et al. 2012; Yin et al. 2011).
Disturbance of natural environment by urban growth can bring various urban
A. Fu • J. Li (*) • S. Pirasteh
Department of Geography and Environmental Management, University of Waterloo,
200 University AVE W., Waterloo, ON N2L3G1, Canada
e-mail: a3fu@uwaterloo.ca; junli@uwaterloo.ca; s2pirast@uwaterloo.ca
© Springer Science+Business Media Dordrecht 2015
J. Li, X. Yang (eds.), Monitoring and Modeling of Global Changes:
A Geomatics Perspective, Springer Remote Sensing/Photogrammetry,
DOI 10.1007/978-94-017-9813-6_4
63
Long-Term Change Dynamics Using Landsat
Archive for the Region of Waterloo
in Ontario, Canada
Anqi Fu, Jonathan Li, and Saied Pirasteh
Abstract Urban land use and land cover classification have always been crucial
due to the ability and to link many elements of human and physical environments.
Timely, accurate, and detailed knowledge of the urban land cover information
derived from remote sensing data is increasingly required among a wide variety
of communities. This chapter presents a surge of interest that has predominately
driven from the recent innovations in data, theories in urban remote sensing, and
technologies. The Region of Waterloo was chosen for land use and land cover
classification by applying remote sensing techniques to satellite images from 1984
to 2013.
4.1 Introduction
To date, the entire world is continuously experiencing rapid urbanization (Ridd and
Hipple 2006). Urban growth is mainly caused by population growth, economic
growth, environmental condition, availability of technologies and frequent human
activities such as industrialization and migration from rural to urban area and
resettlement (Bhatta 2010; Ridd and Hipple 2006). It is obvious that the aforementioned will inevitably lead to land use changes and landscape pattern alteration at
local and regional scale (Yin et al. 2011; Tan et al. 2009; Deng et al. 2009;
Sundarakumar et al. 2012). Those changes include losses of agriculture fields,
water bodies, forest and other vegetated green spaces and non-vegetated fields
(Yang 2002; Sexton et al. 2013a, b; Sundarakumar et al. 2012; Yin et al. 2011).
Disturbance of natural environment by urban growth can bring various urban
A. Fu • J. Li (*) • S. Pirasteh
Department of Geography and Environmental Management, University of Waterloo,
200 University AVE W., Waterloo, ON N2L3G1, Canada
e-mail: a3fu@uwaterloo.ca; junli@uwaterloo.ca; s2pirast@uwaterloo.ca
© Springer Science+Business Media Dordrecht 2015
J. Li, X. Yang (eds.), Monitoring and Modeling of Global Changes:
A Geomatics Perspective, Springer Remote Sensing/Photogrammetry,
DOI 10.1007/978-94-017-9813-6_4
63