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Remote Sens. 2013, 5(9), 4533-4550; doi:10.3390/rs5094533

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 / Revised: 20 August 2013 / Accepted: 6 September 2013 / Published: 12 September 2013
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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. View Full-Text
Keywords: Remote sensing; mixed grassland; soil line; quantile regression; PVI; ground cover Remote sensing; mixed grassland; soil line; quantile regression; PVI; ground cover

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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