developed and applied in many studies (Singh 1989; Jensen 2005; Lu and Weng
2004). Four important aspects of monitoring changes were suggested and summarized by Macleod and Congalton (1998). They are (a) determination of whether or
not the changes happened, (b) identification of the nature of the changes,
(c) detection of the areal extent of the changes, and (d) analysis of the change
patterns. It is worth that to know selecting an appropriate change detection method
is very critical to obtain reliable results because of different purposes and objectives
of applications (Jensen 2005; Lu and Weng 2004). To illustrate and compare the
key characteristics, advantages and disadvantages of the most often used change
detection methods, Table 4.2 is depicted in terms of some previous review works
(Singh 1989; Jensen 2005; Lu and Weng 2004; Coppin et al. 2004; Alphan 2011;
Lunetta and Elvidge 1998).
The methods listed in Table 4.2 have been proved to be effective for detecting
changes in various applications. Image algebra method can be used in monitoring
forest canopy change (Hayes and Sader 2001), monitoring irrigated crops
Table 4.1 Band designations for Landsat sensors
Sensor
Spectral bands
Wavelength (μm) Resolution (m)
Landsat 1,2,3
Landsat 4,5
MSS
4 – green
1 – green
0.5–0.6
80
5 – red
2 – red
0.6–0.7
80
6 – near-IR
3 – near-IR
0.7–0.8
80
7 – near-IR
4 – near-IR
0.8–1.1
80
8 – thermal (Landsat 3)
10.4–12.6
240
TM and ETM+ 1 – blue – green
0.45–0.52
30
2 – green
0.52–0.60
30
3 – red
0.63–0.69
30
4 – near-IR
0.76–0.90
30
5 – SWIR 1
1.55–1.75
30
6 – thermal
10.40–12.5
120; 60 (ETM+)
7 – SWIR 2
0.98–2.35
30
8 – panchromatic (ETM+)
0.52–0.90
15
OLI and TIRS 1 – coastal/aerosol
0.43–0.45
30
2 – blue
0.45–0.51
30
3 – green
0.53–0.59
30
4 – red
0.64–0.67
30
5 – near IR
0.85–0.88
30
6 – SWIR 1
1.57–1.65
30
7 – SWIR 2
2.11–2.29
30
8 – panchromatic
0.50–0.68
15
9 – cirrus
1.36–1.38
30
10 – thermal 1
10.60–11.19
100
11 – thermal 2
11.50–12.51
100
4 Long-Term Change Dynamics Using Landsat Archive for the Region of Waterloo. . .
67
2004). Four important aspects of monitoring changes were suggested and summarized by Macleod and Congalton (1998). They are (a) determination of whether or
not the changes happened, (b) identification of the nature of the changes,
(c) detection of the areal extent of the changes, and (d) analysis of the change
patterns. It is worth that to know selecting an appropriate change detection method
is very critical to obtain reliable results because of different purposes and objectives
of applications (Jensen 2005; Lu and Weng 2004). To illustrate and compare the
key characteristics, advantages and disadvantages of the most often used change
detection methods, Table 4.2 is depicted in terms of some previous review works
(Singh 1989; Jensen 2005; Lu and Weng 2004; Coppin et al. 2004; Alphan 2011;
Lunetta and Elvidge 1998).
The methods listed in Table 4.2 have been proved to be effective for detecting
changes in various applications. Image algebra method can be used in monitoring
forest canopy change (Hayes and Sader 2001), monitoring irrigated crops
Table 4.1 Band designations for Landsat sensors
Sensor
Spectral bands
Wavelength (μm) Resolution (m)
Landsat 1,2,3
Landsat 4,5
MSS
4 – green
1 – green
0.5–0.6
80
5 – red
2 – red
0.6–0.7
80
6 – near-IR
3 – near-IR
0.7–0.8
80
7 – near-IR
4 – near-IR
0.8–1.1
80
8 – thermal (Landsat 3)
10.4–12.6
240
TM and ETM+ 1 – blue – green
0.45–0.52
30
2 – green
0.52–0.60
30
3 – red
0.63–0.69
30
4 – near-IR
0.76–0.90
30
5 – SWIR 1
1.55–1.75
30
6 – thermal
10.40–12.5
120; 60 (ETM+)
7 – SWIR 2
0.98–2.35
30
8 – panchromatic (ETM+)
0.52–0.90
15
OLI and TIRS 1 – coastal/aerosol
0.43–0.45
30
2 – blue
0.45–0.51
30
3 – green
0.53–0.59
30
4 – red
0.64–0.67
30
5 – near IR
0.85–0.88
30
6 – SWIR 1
1.57–1.65
30
7 – SWIR 2
2.11–2.29
30
8 – panchromatic
0.50–0.68
15
9 – cirrus
1.36–1.38
30
10 – thermal 1
10.60–11.19
100
11 – thermal 2
11.50–12.51
100
4 Long-Term Change Dynamics Using Landsat Archive for the Region of Waterloo. . .
67