A Study of Soil Line Simulation from Landsat Images in Mixed Grassland

Remote Sensing

Dandan Xu¹ 

关键词: Remote sensing;    mixed grassland;    soil line;    quantile regression;    PVI;    ground cover;   

DOI  :  10.3390/rs5094533

来源: mdpi

【 摘 要 】

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, NIR_min) 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, NIR_min) method is better for soil line simulation than quantile regression to extract green biomass or green cover information.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland