2005
DOI: 10.1080/10106040508542340
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Multi‐level Land Cover Mapping of the Twin Cities (Minnesota) Metropolitan Area with Multi‐seasonal Landsat TM/ETM+ Data

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Cited by 64 publications
(35 citation statements)
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References 12 publications
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“…The study acquired and utilised a series of multi-temporal (cloud cover 10%) Landsat images (30 m resolution) downloaded from the Glovis website (http://glovis.usgs.gov/) for periods 1987, 1999, 2005 and 2014 to characterise land use/cover changes within L. Bunyonyi catchment. Several studies have demonstrated the usefulness of Landsat images in the characterising landscape, land use and cover types (Alberti et al, 2004;Yuan et al, 2005). The downloaded images were taken in the month of January, which is normally a dry month as observed from the precipitation records for the last 30 years in the catchment (The World Bank Group, 2017).…”
Section: Methodsmentioning
confidence: 99%
“…The study acquired and utilised a series of multi-temporal (cloud cover 10%) Landsat images (30 m resolution) downloaded from the Glovis website (http://glovis.usgs.gov/) for periods 1987, 1999, 2005 and 2014 to characterise land use/cover changes within L. Bunyonyi catchment. Several studies have demonstrated the usefulness of Landsat images in the characterising landscape, land use and cover types (Alberti et al, 2004;Yuan et al, 2005). The downloaded images were taken in the month of January, which is normally a dry month as observed from the precipitation records for the last 30 years in the catchment (The World Bank Group, 2017).…”
Section: Methodsmentioning
confidence: 99%
“…The classification of multi-spectral images has been a successful application that is used for classification of land cover maps (Lunetta and Balogh, 1999;Oettera et al, 2000;Yuan et al, 2005), urban growth (Yeh and Li, 1997;Zhang et al, 2002), forest change (Vogelmann and Rock, 1988;Hall et al, 1991;Coppin and Bauer, 1994), monitoring change in ecosystem condition (Lambin, 1998;Weng, 2002), monitoring assessing deforestation and burned forest areas (Potapov et al, 2008), agricultural expansion (Woodcock et al, 1993;Pax-Lenney et al, 1996), mapping corn (Maxwell et al, 2004), real time fire detection (Dennison and Roberts, 2009), estimating tornado damage areas (Myint et al, 2008), estimating water quality characteristics of lakes (Lillesand et al, 1983;Lathrop et al, 1991;Dekker and Peters, 1993), geological mapping (Mostafa and Bishta, 2004;Bishta, 2010), estimating crop acreage and production (Liu et al, 2005), monitoring of environmental pollution (Zhu and Basir, 2005), monitoring and mapping mangrove ecosystems (Kuenzer et al, 2011).…”
Section: Introductionmentioning
confidence: 99%
“…For image, enhancement and classification the most common nonlinear Histogram Equalize Stretch and Supervised Maximum likelihood Classification were used, respectively [24][25][26][27][28]. Thematic image accuracy has been also evaluated how well the class name on the map correspond to what is really on the ground [29,30].…”
Section: Introductionmentioning
confidence: 99%