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Do flowers affect biomass estimate accuracy from NDVI and EVI?

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Abstract:

The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are vegetation indices widely used in remote sensing of above-ground biomass. Because both indexes are based on spectral features of plant canopy, NDVI and EVI may suffer reduced accuracy in estimating above-ground biomass when flower signals are mixed in the plant canopy. This paper addresses how flowers influence the estimation of above-ground biomass using NDVI and EVI for an alpine meadow with mixed yellow flowers of Halerpestes tricuspis (Ranunculaceae). Field spectral measurements were used in combination with simulated reflectance spectra with precisely controlled flower coverage by applying a linear spectral mixture model. Using the reflectance spectrum for the in-situ canopy with H. tricuspis flowers, we found no significant correlation between above-ground biomass and EVI (p = 0.17) or between above-ground biomass and NDVI (p = 0.78). However, both NDVI and EVI showed very good prediction of above-ground biomass with low root mean square errors (RMSE = 43 g m-2 for NDVI and RMSE = 43 g m-2 for EVI, both p < 0.01) when all the flowers were removed from the canopies. Simulation analysis based on the in-situ measurements further indicated that high variation in flower coverage among different quadrats could produce more noise in the relationship between above-ground biomass and NDVI, or EVI, which results in an evident decline in the accuracy of above-ground biomass estimation. Therefore, the study suggests that attention should be paid both to the flower fraction and the heterogeneity of flower distribution in the above-ground biomass estimation via NDVI and EVI.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160903578812

Affiliations: 1: Key Laboratory of Resources Remote Sensing and Digital Agriculture, Beijing, China,State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing, China 2: Key Laboratory of Resources Remote Sensing and Digital Agriculture, Beijing, China 3: National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan

Publication date: March 1, 2010

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