Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques.
Informatica 31:249–268
Lu D, Weng Q (2004) Spectral mixture analysis of the urban landscape in Indianapolis with
Landsat ETM+ imagery. Photogramm Eng Remote Sens 70(9):1053–1062
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving
classification performance. Int J Remote Sens 28(5):823–870
Lunetta RS, Elvidge CD (1998) Remote sensing change detection: environmental monitoring
methods and applications. Sleeping Bear Press, Ann Arbor. ISBN 1-57504-037-9
Lunetta RS, Johnson DM, Lyon JG, Crotwell J (2004) Impacts of imagery temporal frequency on
land-cover change detection monitoring. Remote Sens Environ 89:444–454
Macleod RD, Congalton RG (1998) A quantitative comparison of change-detection algorithms for
monitoring eelgrass from remotely sensed data. Photogramm Eng Remote Sens 64(3):207–216
Manavalan P, Kesavasamy K, Adiga S (1995) Irrigated crops monitoring through seasons using
digital change detection analysis of IRS-LISS 2 data. Int J Remote Sens 16(4):633–640
Mellor A, Haywood A, Stone C, Jones S (2013) The performance of random forests in an
operational setting for large area sclerophyll forest classification. Remote Sens 5(6):2838–2856
Nemmour H, Chibani Y (2011) Support vector machines for automatic multi-class change
detection in Algerian Capital using landsat TM imagery. J Indian Soc Remote Sens 38
(4):585–591
Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens
26(1):217–222
Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J
Remote Sens 26(5):1007–1011
Parra GA, Mouchot MC, Roux C (1996) A multitemporal land-cover change analysis tool using
change vector and principal components analysis. IEEE 0-7803-3068-4/96, pp 1753–1755
Patino JE, Duque JC (2013) A review of regional science applications of satellite remote sensing in
urban settings. Comput Environ Urban Syst 37:1–17
Peiman R (2011) Pre-classification and post-classification change-detection techniques to monitor
land-cover and land-use change using multi-temporal Landsat imagery: a case study on Pisa
Province in Italy. Int J Remote Sens 32(15):4365–4381
Prakash A, Gupta RP (1998) Land-use mapping and change detection in a coal mining area ─ a
case study in the Jharia coalfield, India. Int J Remote Sens 19(3):391–410
Region of Waterloo (2006a) Regional Growth Management Strategy (RGMS). Retrieved
from: http://www.regionofwaterloo.ca/en/aboutTheEnvironment/resources/RegionalGrowth
Management.pdf
Region of Waterloo (2006b) Regional Growth Management Strategy (RGMS) highlights brochure. Retrieved from: Region of Waterloo (2006) Regional Growth Management Strategy
(RGMS) highlights brochure. Retrieved from: http://www.regionofwaterloo.ca/en/
aboutTheEnvironment/resources/FINALRGMSBrochure2006.pdf
Region of Waterloo (2010) Regional Official Plan (ROP). Retrieved from: http://www.
regionofwaterloo.ca/en/regionalGovernment/PreviousROP.asp
Ridd MK, Hipple JD (2006) Remote sensing of human settlements: manual of remote sensing, 3rd
edn. American Society for Photogrammetry and Remote Sensing. ISBN 1-57083-077-0
Sexton JO, Urban DL, Donohue MJ, Song C (2013a) Long-term land cover dynamics by multitemporal classification across the Landsat-5 record. Remote Sens Environ 128:246–258
Sexton JO, Song X, Huang C, Channan S (2013b) Urban growth of the Washington, D.C.Baltimore, MD metropolitan region from 1984 to 2010 by annual, landsat-based estimates of
impervious cover. Remote Sens Environ 129:42–53
Singh A (1989) Digital change detection techniques using remotely-sensed data. Int J Remote Sens
10(6):989–1003
Sleeter BM, Wilson TS, Acevedo W (2012) Status and trends of land change in the Western United
States – 1973 to 2000: USGS professional paper 1794–A, 324 p. Retrieved from: http://pubs.
usgs.gov/pp/1794/a/
4 Long-Term Change Dynamics Using Landsat Archive for the Region of Waterloo. . .
85
Informatica 31:249–268
Lu D, Weng Q (2004) Spectral mixture analysis of the urban landscape in Indianapolis with
Landsat ETM+ imagery. Photogramm Eng Remote Sens 70(9):1053–1062
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving
classification performance. Int J Remote Sens 28(5):823–870
Lunetta RS, Elvidge CD (1998) Remote sensing change detection: environmental monitoring
methods and applications. Sleeping Bear Press, Ann Arbor. ISBN 1-57504-037-9
Lunetta RS, Johnson DM, Lyon JG, Crotwell J (2004) Impacts of imagery temporal frequency on
land-cover change detection monitoring. Remote Sens Environ 89:444–454
Macleod RD, Congalton RG (1998) A quantitative comparison of change-detection algorithms for
monitoring eelgrass from remotely sensed data. Photogramm Eng Remote Sens 64(3):207–216
Manavalan P, Kesavasamy K, Adiga S (1995) Irrigated crops monitoring through seasons using
digital change detection analysis of IRS-LISS 2 data. Int J Remote Sens 16(4):633–640
Mellor A, Haywood A, Stone C, Jones S (2013) The performance of random forests in an
operational setting for large area sclerophyll forest classification. Remote Sens 5(6):2838–2856
Nemmour H, Chibani Y (2011) Support vector machines for automatic multi-class change
detection in Algerian Capital using landsat TM imagery. J Indian Soc Remote Sens 38
(4):585–591
Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens
26(1):217–222
Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J
Remote Sens 26(5):1007–1011
Parra GA, Mouchot MC, Roux C (1996) A multitemporal land-cover change analysis tool using
change vector and principal components analysis. IEEE 0-7803-3068-4/96, pp 1753–1755
Patino JE, Duque JC (2013) A review of regional science applications of satellite remote sensing in
urban settings. Comput Environ Urban Syst 37:1–17
Peiman R (2011) Pre-classification and post-classification change-detection techniques to monitor
land-cover and land-use change using multi-temporal Landsat imagery: a case study on Pisa
Province in Italy. Int J Remote Sens 32(15):4365–4381
Prakash A, Gupta RP (1998) Land-use mapping and change detection in a coal mining area ─ a
case study in the Jharia coalfield, India. Int J Remote Sens 19(3):391–410
Region of Waterloo (2006a) Regional Growth Management Strategy (RGMS). Retrieved
from: http://www.regionofwaterloo.ca/en/aboutTheEnvironment/resources/RegionalGrowth
Management.pdf
Region of Waterloo (2006b) Regional Growth Management Strategy (RGMS) highlights brochure. Retrieved from: Region of Waterloo (2006) Regional Growth Management Strategy
(RGMS) highlights brochure. Retrieved from: http://www.regionofwaterloo.ca/en/
aboutTheEnvironment/resources/FINALRGMSBrochure2006.pdf
Region of Waterloo (2010) Regional Official Plan (ROP). Retrieved from: http://www.
regionofwaterloo.ca/en/regionalGovernment/PreviousROP.asp
Ridd MK, Hipple JD (2006) Remote sensing of human settlements: manual of remote sensing, 3rd
edn. American Society for Photogrammetry and Remote Sensing. ISBN 1-57083-077-0
Sexton JO, Urban DL, Donohue MJ, Song C (2013a) Long-term land cover dynamics by multitemporal classification across the Landsat-5 record. Remote Sens Environ 128:246–258
Sexton JO, Song X, Huang C, Channan S (2013b) Urban growth of the Washington, D.C.Baltimore, MD metropolitan region from 1984 to 2010 by annual, landsat-based estimates of
impervious cover. Remote Sens Environ 129:42–53
Singh A (1989) Digital change detection techniques using remotely-sensed data. Int J Remote Sens
10(6):989–1003
Sleeter BM, Wilson TS, Acevedo W (2012) Status and trends of land change in the Western United
States – 1973 to 2000: USGS professional paper 1794–A, 324 p. Retrieved from: http://pubs.
usgs.gov/pp/1794/a/
4 Long-Term Change Dynamics Using Landsat Archive for the Region of Waterloo. . .
85