Towards an Improved LAI Collection Protocol via Simulated and Field-Based PAR Sensing

Abstract

In support of NASA’s next-generation spectrometer—the Hyperspectral Infrared Imager (HyspIRI)—we are working towards assessing sub-pixel vegetation structure from imaging spectroscopy data. Of particular interest is Leaf Area Index (LAI), which is an informative, yet notoriously challenging parameter to efficiently measure in situ. While photosynthetically-active radiation (PAR) sensors have been validated for measuring crop LAI, there is limited literature on the efficacy of PAR-based LAI measurement in the forest environment. This study (i) validates PAR-based LAI measurement in forest environments, and (ii) proposes a suitable collection protocol, which balances efficiency with measurement variation, e.g., due to sun flecks and various-sized canopy gaps. A synthetic PAR sensor model was developed in the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and used to validate LAI measurement based on first-principles and explicitly-known leaf geometry. Simulated collection parameters were adjusted to empirically identify optimal collection protocols. These collection protocols were then validated in the field by correlating PAR-based LAI measurement to the normalized difference vegetation index (NDVI) extracted from the “classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) data (R2 was 0.61). The results indicate that our proposed collecting protocol is suitable for measuring the LAI of sparse forest (LAI < 3–5 (normalm2/normalm2)).

A Simulation Study Using Terrestrial LiDAR Point Cloud Data to Quantify Spectral Variability of a Broad-Leaved Forest Canopy

“…Considering that leaf reflectance in the SWIR part of the electromagnetic spectrum is dominated by liquid water absorption, it has been assumed that the NDII is correlated to the LAI by integrating individual leaf equivalent water thickness for each leaf layer to have an estimation of the total canopy equivalent water thickness [ 51 ]. Furthermore, the impact on calculated vegetation indices originated from different vertical LAD profiles can have significant effects on several scientific applications, such as on validating protocols for PAR-based LAI measurements in forest environments [ 52 ], predicting LAI on crop canopies [ 53 ], monitoring fuel moisture content [ 50 ], and estimating aboveground biomass [ 54 ].…”

The Challenge in the Management of Historic Trees in Urban Environments during Climate Change: The Case of Corso Trieste (Rome, Italy)

“…In fact, reduced ventilation in street canyons can affect heat dispersion and lead to high temperatures and thermal discomfort. In such urban geometries, the presence of aerodynamic obstacles, such as vegetation itself, can indeed lead to pollutant trapping [26,27]. For this reason, in order to deeply investigate the role of Pinus pinea L. along Corso Trieste, the morphological characterisation was carried out by acquiring measurements of the buildings' height and the street width using a telelaser LEICA DISTO D810 (Swiss Technology by Leica Geosystems, St. Gallen, Switzerland; www.leicadisto.it, accessed on 1 March 2021).…”

Exploring the Limits of Species Identification via a Convolutional Neural Network in a Complex Forest Scene through Simulated Imaging Spectroscopy

“…DIRSIG has already shown its efficiency in simulating complex remote sensing scenes and sensor design. It has been previously used to assess waveform deconvolution and preprocessing approaches [24][25][26] to evaluate the level of detail one can extract with 1064 nm waveform light detection and ranging (LiDAR) systems [27], to determine how subpixel structural variability impacts imaging spectrometers' spectral response [28], and even to optimize sub-canopy leaf area index sampling via a specific photometer [29], and more.…”