2006•Shuitu baochi yanjiuRequires access

Estimation of Vegetation Fraction in Beiyunhe District by Remote Sensing

Meng Jian

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Abstract

Vegetation fraction is a very important index to score the vegetation coverage on the land surface and is used in many aspects as one of the input parameters.Based on the dimidiate pixel model and the normalized difference vegetation index,the vegetation fraction of study region can be calculated by using the model of quantifying vegetation fraction from normalized difference vegetation.The vegetation fraction in Beiyunhe valley of remote sensing was calculated based on this model.The authors used 1994 and 2004 Landsat TM data to analyze vegetation changes in Beiyunhe valley.

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What this paper is about

Vegetation fraction is a very important index to score the vegetation coverage on the land surface and is used in many aspects as one of the input parameters.Based on the dimidiate pixel model and the normalized difference vegetation index,the vegetation fraction of study region can be calculated by using the model of quantifying vegetation fraction from normalized difference vegetation.The vegetation fraction in Beiyunhe valley of remote sensing was calculated based on this model.The authors used 1994 and 2004 Landsat TM data to analyze vegetation changes in Beiyunhe valley.

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Available abstract

Vegetation fraction is a very important index to score the vegetation coverage on the land surface and is used in many aspects as one of the input parameters.Based on the dimidiate pixel model and the normalized difference vegetation index,the vegetation fraction of study region can be calculated by using the model of quantifying vegetation fraction from normalized difference vegetation.The vegetation fraction in Beiyunhe valley of remote sensing was calculated based on this model.The authors used 1994 and 2004 Landsat TM data to analyze vegetation changes in Beiyunhe valley.

Key concepts: Vegetation (pathology), Enhanced vegetation index, Fraction (chemistry), Normalized Difference Vegetation Index, Vegetation Index, Remote sensing, Environmental science, Hydrology (agriculture)

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