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

Abstract

Abstract: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 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.

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

“…LAI inversion from a canopy reflectance model is usually ill‐posed, meaning that the numerical solution does not depend continuously on the data and, thus, may result in unstable and inaccurate inversion performance (Jacquemoud, ; Kimes et al, ). Various regularization strategies have been proposed to increase the robustness of the estimates, including the use of alternative cost functions, prior parameter constraints, multiple best solutions, and added noise for measurements and models (Banskota et al, ; Leonenko et al, ; Rivera et al, ; Verrelst et al, ). There is a high degree of flexibility in selecting the most robust optimization functions (Leonenko et al, , ; Rivera et al, ).…”

Discrete Anisotropic Radiative Transfer (DART 5) for Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of Natural and Urban Landscapes

“…DART forward simulations of vegetation reflectance were successfully verified by real measurements [32] and also cross-compared against a number of independently designed 3D reflectance models (e.g., FLIGHT [26], Sprint [33], Raytran [27]) in the context of the RAdiation transfer Model Intercomparison (RAMI) experiment [34][35][36][37][38]. To date, DART has been successfully employed in various scientific applications, including development of inversion techniques for airborne and satellite reflectance images [39,40], design of satellite sensors (e.g., NASA DESDynl, CNES Pleiades, CNES LIDAR mission project [41]), impact studies of canopy structure on satellite image texture [42] and reflectance [32], modeling of 3D distribution of photosynthesis and primary production rates in vegetation canopies [43], investigation of influence of Norway spruce forest structure and woody elements on canopy reflectance [44], design of a new chlorophyll estimating vegetation index for a conifer forest canopy [45], and studies of tropical forest texture [46][47][48], among others. DART creates and manages 3D landscapes independently from the RT modeling (e.g., visible and thermal infrared IS, LIDAR, radiative budget).…”

Calibration of urban canopies albedo and 3D shortwave radiative budget using remote-sensing data and the DART model

“…FLIGHT [North, 1996], Sprint [Thompson & Goel, 1998], Raytran [Govaerts & Verstraete, 1998]) in the context of the RAdiation transfer Model Intercomparison experiment (Pinty et al, 2001(Pinty et al, , 2004Widlowski et al, 2013Widlowski et al, , 2008Widlowski et al, , 2007. To date, DART has been successfully employed in various scientific applications, including development of inversion techniques for airborne and satellite reflectance images (Banskota et al, 2015(Banskota et al, , 2013 Gascon, Gastellu-Etchegorry, Lefevre-Fonollosa, & Dufrene, 2004), simulation of airborne sensor images of vegetation and urban landscapes , design of satellite sensors [e.g. NASA DESDynl, CNES Pleiades, CNES LIDAR mission project (Durrieu et al, 2013)], impact studies of canopy structure on satellite image texture (Bruniquel-Pinel & Gastellu-Etchegorry, 1998), modelling of 3D distribution of photosynthesis and primary production rates in vegetation canopies , investigation of influence of Norway spruce forest structure and woody elements on canopy reflectance (Malenovsky et al, 2008), design of a new chlorophyll estimating vegetation index for a conifer forest canopy (Malenovský et al, 2013) and studies of tropical forest texture (Barbier, Couteron, Gastelly-Etchegorry, & Proisy, 2012;Barbier, Couteron, Proisy, Malhi, & Gastellu-Etchegorry, 2010;Proisy et al, 2011), among others.…”