27 March 2015 Modified enhanced vegetation index for reducing topographic effects
Zhanmang Liao, Binbin He, Xingwen Quan
Author Affiliations +
Abstract
Monitoring the environmental status of mountainous or hilly areas is very important for their great influence on the global ecosystem and humanity. The enhanced vegetation index (EVI) has been widely used in environmental monitoring. It can reduce background and atmospheric noise via its feedback-based format. However, the application of EVI in mountainous areas will be limited, because EVI is greatly affected by topographic effects as its soil adjustment index is not in a band ratio format. To moderate the topographic effects on EVI, we modified the EVI by changing the soil adjustment index from a constant to a variable related to the incidence angle. In the evaluation of the modified EVI, three other well-known topographic correction methods, Sun-canopy-sensor (SCS), SCS with C-correction (SCS+C), and modified Minnaert (MM), were used for comparison. The results indicated that the modified EVI and SCS+C perform better than MM and SCS by visual comparison. Quantitatively, modified EVI, which has an effect similar to SCS+C in the low incidence angle regions, largely decreased the standard deviation of the same land features and the correlation between EVI and the cosine of the incidence angle. When the incidence angle exceeds 90 deg, SCS+C and other two topographic correction methods caused overcorrections. However, modified EVI solved this problem well due to its smaller increasing curvature than other three topographic correction methods. Moreover, compared to SCS+C, modified EVI better preserved the characters of land surface features.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2015/$25.00 © 2015 SPIE
Zhanmang Liao, Binbin He, and Xingwen Quan "Modified enhanced vegetation index for reducing topographic effects," Journal of Applied Remote Sensing 9(1), 096068 (27 March 2015). https://doi.org/10.1117/1.JRS.9.096068
Published: 27 March 2015
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CITATIONS
Cited by 24 scholarly publications.
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KEYWORDS
Vegetation

Near infrared

Reflectivity

Visualization

Earth observing sensors

Landsat

Environmental monitoring

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