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A Study of Soil Line Simulation from Landsat Images in Mixed Grassland
Department of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, SK S7N5C8, Canada
* Author to whom correspondence should be addressed.
Received: 26 June 2013; in revised form: 20 August 2013 / Accepted: 6 September 2013 / Published: 12 September 2013
Abstract: The mixed grassland in Canada is characterized by low to medium green vegetation cover, with a large amount of canopy background, such as non-photosynthetic vegetation residuals (litter), bare soil, and ground level biological crust. It is a challenge to extract the canopy information from satellite images because of the influence of canopy background. Therefore, this study aims to extract a soil line, a representation of bare soil with litter and soil crust in the surface, from Landsat images to reduce the background effect. Field work was conducted in the West Block of Grasslands National Park (GNP) in Canada, which represents the northern mixed grassland from late June to early July 2005. Six TM images with either no or only a small amount of cloud content were collected in 2005. In this study, soil lines were extracted directly from images by quantile regression and the (R, NIRmin) method. The results show that, (1) both cloud and cloud shadow have obvious influence on simulating soil line automatically from images; (2) green up and late senescence seasons are relatively better for soil line simulation; (3) the (R, NIRmin) method is better for soil line simulation than quantile regression to extract green biomass or green cover information.
Keywords: Remote sensing; mixed grassland; soil line; quantile regression; PVI; ground cover
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MDPI and ACS Style
Xu, D.; Guo, X. A Study of Soil Line Simulation from Landsat Images in Mixed Grassland. Remote Sens. 2013, 5, 4533-4550.
Xu D, Guo X. A Study of Soil Line Simulation from Landsat Images in Mixed Grassland. Remote Sensing. 2013; 5(9):4533-4550.
Xu, Dandan; Guo, Xulin. 2013. "A Study of Soil Line Simulation from Landsat Images in Mixed Grassland." Remote Sens. 5, no. 9: 4533-4550.