Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI

Remote Sensing

Asim Banskota⁴  Randolph H. Wynne²  Valerie A. Thomas²  Shawn P. Serbin¹  Nilam Kayastha⁴  Jean P. Gastellu-Etchegorry³ 

[1] Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53706, USA; E-Mails:;Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA; E-Mails:;Centre d’études spatiales de la biosphère (CESBIO), 18 Avenue Edouard Belin, F-31055 Toulouse Cedex, France; E-Mail:;Geospatial and Environmental Analysis Program, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA; E-Mail:

关键词: leaf area index;    hyperspectral;    imaging spectrometer;    radiative transfer;    DART;    LUT;    inversion;    discrete wavelet transform;   

DOI  :  10.3390/rs5062639

来源: mdpi

【 摘 要 】

The need for an efficient and standard technique for optimal spectral sampling of hyperspectral data during the inversion of canopy reflectance models has been the subject of many studies. The objective of this study was to investigate the utility of the discrete wavelet transform (DWT) for extracting useful features from hyperspectral data with which forest LAI can be estimated through inversion of a three dimensional radiative transfer model, the Discrete Anisotropy Radiative Transfer (DART) model. DART, coupled with the leaf optical properties model PROSPECT, was inverted with AVIRIS data using a look-up-table (LUT)-based inversion approach. We used AVIRIS data and in situ LAI measurements from two different hardwood forested sites in Wisconsin, USA. Prior to inversion, model-simulated and AVIRIS hyperspectral data were transformed into discrete wavelet coefficients using Haar wavelets. The LUT inversion was performed with three different datasets, the original reflectance bands, the full set of wavelet extracted features, and two wavelet subsets containing 99.99% and 99.0% of the cumulative energy of the original signal. The energy subset containing 99.99% of the cumulative signal energy provided better estimates of LAI (RMSE = 0.46, R² = 0.77) than the original spectral bands (RMSE = 0.60, R² = 0.47). The results indicate that the discrete wavelet transform can increase the accuracy of LAI estimates by improving the LUT-based inversion of DART (and, potentially, by implication, other terrestrial radiative transfer models) using hyperspectral data. The improvement in accuracy of LAI estimates is potentially due to different properties of wavelet analysis such as multi-scale representation, dimensionality reduction, and noise removal.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland