Vegetation dynamics from NDVI time series analysis using the wavelet transform
By Beatriz Martínez, M. Gilabert. 2009
403 citations recorded by the source corpus.
Subfields: Atmospheric Science, Ecology, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/4872ab5fcf7f578cbcfe3a005ca7fcf461f3ecbf
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
In Ecology
- Frédéric Baret
- Marie Weiss
- Aleixandre Verger
- Craig T. Simmons
- Huade Guan
- Xinping Zhang
- Xinguang He
- Philippe Neveux
In Global and Planetary Change
- Craig T. Simmons
- Huade Guan
- Xinping Zhang
- Xinguang He
- Philippe Neveux
- Yanfang Sang
- Frédéric Baret
- Sivasathivel Kandasamy
Citations in the corpus
- Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, methodology): “Martínez and Gilabert [34] used the Meyer wavelet into the MRA (introduced in Section 2.”
- A wavelet‐based multiple linear regression model for forecasting monthly rainfall (2014, methodology): “In this study, DWT is performed using the Meyer orthogonal discrete wavelet function, as suggested by Martı́nez and Gilabert (2009).”
- A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, methodology): “Fourier transforms (Azzali and Menenti, 1999), or wavelet decomposition (Martinez and Gilabert, 2009) have also been used to characterize the phenology of vegetation from medium resolution observations.”