Denoising and wavelet-based feature extraction of MODIS multi-temporal vegetation signatures

Abstract

Temporal vegetation signatures (i.e., vegetation indices as functions of time) generated using the MODIS imagery poses many challenges, primarily due to signal-to-noise-related issues. This article describes the use of MODIS time-series data for the detection of specific tropical invasive species vegetation types. Due to challenges with the MODIS quality assurance data, a significant level of noise was present in the temporal signatures. This study investigated methods for denoising the vegetation temporal signatures, followed by a comparative analysis of three denoising methods to generate signatures for vegetation target detection. The analytical approach focused on the use of wavelet-based versus Fourier-based feature extraction methods. Methods included the development of a novel wavelet-based feature extraction method that quantifies the fundamental shape of the temporal signatures.