Fractional vegetation cover estimation based on satellite-sensed land classification

Yunhao Chen; Xiaobing Li; Xia Li; Peijun Shi

doi:10.1117/12.466867

16 June 2003 Fractional vegetation cover estimation based on satellite-sensed land classification

Yunhao Chen, Xiaobing Li, Xia Li, Peijun Shi

Proceedings Volume 4897, Multispectral and Hyperspectral Remote Sensing Instruments and Applications; (2003) https://doi.org/10.1117/12.466867
Event: Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2002, Hangzhou, China

Abstract

Vegetation fraction, the ratio of vegetation occupying a unit area, as a significant parameter in the development of climate and ecological models, is indispensable information of many global and regional climate numerical models. It is also an important basic data of describing ecosystem. However, It is also a wasting manpower and financial resources with low-precision work to measure the vegetation fraction by fieldwork, especially in large areas. This study explores the potential of deriving vegetation fraction from normalized difference vegetation index (NDVI) using the TM data. Under the assumption that the pixel of TM image is a mosaic structure, sub-pixel models for vegetation fraction estimation have been introduced firstly. Then the idea of utility of different sub-pixel model for vegetation fraction estimation based on land cover classification is proposed. The model for vegetation fraction estimation has been established under many assumptions, and there is the complex relationship of vegetation index vegetation fraction and leaf area index, so it is unrealistic to obtain vegetation fraction with high precision. But it is helpful to improve estimation precision to some extent by probing into application of assistant information and finery parameters of model.

Citation Download Citation

Yunhao Chen, Xiaobing Li, Xia Li, and Peijun Shi "Fractional vegetation cover estimation based on satellite-sensed land classification", Proc. SPIE 4897, Multispectral and Hyperspectral Remote Sensing Instruments and Applications, (16 June 2003); https://doi.org/10.1117/12.466867

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