Quantifying the Accuracy of Digital Hemispherical Photography for Leaf Area Index Estimates on Broad-Leaved Tree Species

Abstract

Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical images with dedicated tools, several steps are still manual and thus easily affected by user’s experience and sensibility. The purpose of this study was to quantify the impact of user’s subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of the method, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentation method was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained for medium and low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory results were achieved for trueness, regardless of the canopy structure. The paired t-test revealed that the effect of the segmentation method on LAI estimates was significant. Despite a non-negligible user effect, the accuracy metrics for DHP are consistent with those determined for other indirect methods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies.

Microclimatic physiological and productive effect of the overcanopy irrigation in an apple orchard

“…After the opportune calibration, the extinction coefficient (K) was derived. These outputs allow applying indirect methods to estimate LAI in orchards, or in row trees, which is a widely used parameter for plant monitoring purpose (Gilardelli et al 2018), regardless the tree species, and according to canopy structure. In particular, it can be useful for studies investigating crop and environment exchange, such as water and energy balance at larger scale, as for example irrigation district or water basin, including remote sensed data (Hirose 2005;Duchemin et al 2009).…”

“…Size, shape, position and orientation of plants, as well as the distribution of optical properties, characterize and affect the canopy structure (Weiss et al 2004). As a consequence, light environment in the fruit zone and radiation interception are indicators of crop vigor and phenological phases (Gilardelli et al 2018). In particular, Leaf Area Index (LAI), Canopy Cover (CC), and extinction fraction coefficient (K) data are key variables for describing crop and environment interactions (Ramirez-Garcia, Almendros, and Quemada 2012).…”