the limitations and uncertainties of the change dynamics analysis method will be
discussed. In addition, some recommendations will be considered for future studies.
4.2 Satellite Data and Change Detection Methods
4.2.1 Satellite Data for Monitoring LULC Change
The objects on the earth surface and sometimes subsurface can be interpreted from
remotely sensed data such as Landsat ETM+, Quick Bird etc. The various techniques can be applied to extract the information from the satellite images. Basically,
objects are interpreted based on their reflected or emitted electromagnetic radiation
(Patino and Duque 2013; Jensen 2005). Thus land use and land cover, urban
morphology, vegetation distribution, and some other biophysical information can
be extracted for planners and environmental scientists to analyze the environment
disturbance (Patino and Duque 2013). Since the late 1950s, aerial images have been
used for LULC change analyses. However, with the launch of several earth-orbiting
satellites, the focus of studies has shifted from using aerial images to satellite-based
images because of their lower costs, wider area coverage, and frequent image
updates (Patino and Duque 2013). Since the earliest satellite Landsat 1 with Multispectral Scanner (MSS) was launched in 1972, many satellites with various
sensors in different spectral, spatial and temporal resolutions were launched in
the past four decades (Patino and Duque 2013; Jensen 2005). Patino and Duque
(2013) reviewed that Earth-orbiting satellite sensors such as Landsat 5 Thematic
Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), SPOT 1–5,
QuickBird, IKONOS, NASA Terra Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER) and Indian Remote Sensing (IRS-1C) are the most
often used earth observation (EO) systems in LULC change detection studies. They
also stated that the moderate spatial resolution images are appropriate for detecting
LULC change. It is because historical images can be used dating back to 1970s. To
illustrate the lengths of the archives of listed remote sensing systems, a time scale
figure, showed in Fig. 4.1 was generated by Patino and Duque (2013).
A long-term record of global landscape information has been acquired since the
first Landsat satellite launched in 1972 (USGS 2013a, b, c, d, e, f). Landsat project
is the oldest satellite project in the United States for land-surface observation
(Jensen 2007). Landsat Project which was initiated by National Aeronautics and
Space Administration (NASA) and USGS has launched eight satellites to collect
data from the earth surface. This provides the resources for people who manage
regional development, manage natural resources, and those who do research in
various environmental fields throughout the United States and worldwide (USGS
2013a, b, c, d, e, f). Based on Landsat Project Statistics on USGS website (2013) the
most primary use of Landsat data is detecting LULC change. To consider establishment of routinely gather earth resource information from space, the Landsat
4 Long-Term Change Dynamics Using Landsat Archive for the Region of Waterloo. . .
65
discussed. In addition, some recommendations will be considered for future studies.
4.2 Satellite Data and Change Detection Methods
4.2.1 Satellite Data for Monitoring LULC Change
The objects on the earth surface and sometimes subsurface can be interpreted from
remotely sensed data such as Landsat ETM+, Quick Bird etc. The various techniques can be applied to extract the information from the satellite images. Basically,
objects are interpreted based on their reflected or emitted electromagnetic radiation
(Patino and Duque 2013; Jensen 2005). Thus land use and land cover, urban
morphology, vegetation distribution, and some other biophysical information can
be extracted for planners and environmental scientists to analyze the environment
disturbance (Patino and Duque 2013). Since the late 1950s, aerial images have been
used for LULC change analyses. However, with the launch of several earth-orbiting
satellites, the focus of studies has shifted from using aerial images to satellite-based
images because of their lower costs, wider area coverage, and frequent image
updates (Patino and Duque 2013). Since the earliest satellite Landsat 1 with Multispectral Scanner (MSS) was launched in 1972, many satellites with various
sensors in different spectral, spatial and temporal resolutions were launched in
the past four decades (Patino and Duque 2013; Jensen 2005). Patino and Duque
(2013) reviewed that Earth-orbiting satellite sensors such as Landsat 5 Thematic
Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), SPOT 1–5,
QuickBird, IKONOS, NASA Terra Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER) and Indian Remote Sensing (IRS-1C) are the most
often used earth observation (EO) systems in LULC change detection studies. They
also stated that the moderate spatial resolution images are appropriate for detecting
LULC change. It is because historical images can be used dating back to 1970s. To
illustrate the lengths of the archives of listed remote sensing systems, a time scale
figure, showed in Fig. 4.1 was generated by Patino and Duque (2013).
A long-term record of global landscape information has been acquired since the
first Landsat satellite launched in 1972 (USGS 2013a, b, c, d, e, f). Landsat project
is the oldest satellite project in the United States for land-surface observation
(Jensen 2007). Landsat Project which was initiated by National Aeronautics and
Space Administration (NASA) and USGS has launched eight satellites to collect
data from the earth surface. This provides the resources for people who manage
regional development, manage natural resources, and those who do research in
various environmental fields throughout the United States and worldwide (USGS
2013a, b, c, d, e, f). Based on Landsat Project Statistics on USGS website (2013) the
most primary use of Landsat data is detecting LULC change. To consider establishment of routinely gather earth resource information from space, the Landsat
4 Long-Term Change Dynamics Using Landsat Archive for the Region of Waterloo. . .
65