Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4972883 | ISPRS Journal of Photogrammetry and Remote Sensing | 2016 | 13 Pages |
Abstract
Our results revealed strong correlations between six wavelet features and LDMC, as well as between four wavelet features and SLA. The wavelet features at 1741Â nm (scale 5) and 2281Â nm (scale 4) were the two most strongly correlated with LDMC and SLA respectively. The combination of all the identified wavelet features for LDMC yielded the most accurate prediction (R2Â =Â 0.59 and RMSEÂ =Â 4.39%). However, for SLA the most accurate prediction was obtained from the single most correlated feature: 2281Â nm, scale 4 (R2Â =Â 0.85 and RMSEÂ =Â 4.90). Our results demonstrate the applicability of Continuous Wavelet Analysis (CWA) when inverting radiative transfer models, for accurate mapping of forest leaf functional traits.
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Authors
Abebe Mohammed Ali, Andrew K. Skidmore, Roshanak Darvishzadeh, Iris van Duren, Stefanie Holzwarth, Joerg Mueller,