Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions

Abstract

Abstract:The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was OPEN ACCESSRemote Sens. 2015, 7 13411 evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products.

Influence of Leaf Specular Reflection on Canopy Radiative Regime Using an Improved Version of the Stochastic Radiative Transfer Model

“…Note that canopy BRF at red and NIR bands and NDVI have been used in operational algorithm for generating global LAI/FPAR products from the MODIS, VIIRIS, and EPIC datasets [13,[27][28][29]. Neglecting leaf specular reflection in the photon-canopy interactions causes underestimations of canopy BRF at red and NIR bands and overestimation of canopy NDVI which, in turn, will result in overestimation of canopy LAI/FPAR [28,29,[32][33][34].…”

Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion

“…In situ LAI measurements, required in the calibration of the empirical methods, are usually derived from optical instruments [20][21][22], including digital hemispheric photographs [23,24], TRAC [25], LAI-2000 Plant Canopy Analyzer [26], or its successor LAI-2200. The manual nature of the optical instruments makes the traditional in situ measurements time-consuming and labor-intensive.…”

Assessment of Leaf Area Index of Rice for a Growing Cycle Using Multi-Temporal C-Band PolSAR Datasets

“…The leaf area index (LAI) of the crop is crucial in the quantitative description of the canopy structure and photosynthetic processes [2,3]. However, the ground LAI measurements using digital photography [4,5] or integrated sensors [6,7] are time-consuming and laborious, restricting their practical application over a large region. Spaceborne or airborne remote sensing techniques and datasets are a possible alternative for the LAI monitoring at a regional scale [8][9][10].…”