Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient

Abstract

Leaf area index (LAI) is one of the key biophysical variables required for crop modeling. Direct LAI measurements are time consuming and difficult to obtain for experimental and commercial fruit orchards. Devices used to estimate LAI have shown considerable errors when compared to ground-truth or destructive measurements, requiring tedious site-specific calibrations. The objective of this study was to test the performance of a modified digital cover photography method to estimate LAI in apple trees using conventional digital photography and instantaneous measurements of incident radiation (Io) and transmitted radiation (I) through the canopy. Leaf area of 40 single apple trees were measured destructively to obtain real leaf area index (LAID), which was compared with LAI estimated by the proposed digital photography method (LAIM). Results showed that the LAIM was able to estimate LAID with an error of 25% using a constant light extinction coefficient (k = 0.68). However, when k was estimated using an exponential function based on the fraction of foliage cover (ff) derived from images, the error was reduced to 18%. Furthermore, when measurements of light intercepted by the canopy (Ic) were used as a proxy value for k, the method presented an error of only 9%. These results have shown that by using a proxy k value, estimated by Ic, helped to increase accuracy of LAI estimates using digital cover images for apple trees with different canopy sizes and under field conditions.

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

“…An automated image acquisition and calculation method was proposed by Fuentes et al 2008 applied to Eucalyptus trees [26] and it has been successfully applied for other crops such as grapevines compared to allometric measurements and to validate NDVI calculated from satellite information (WorldView-2) [27], apple trees with increased accuracy by using a variable light extinction coefficient ( ) [28], and cherry trees improving the method by extracting nonleaf material such as branches for tall trees [29,30]. In late 2015, a computer application (App) for smartphones and tablet PCs called VitiCanopy was released for free use to assess canopy architecture parameters using the cover photography automated algorithms, which can be applied to any other tree crop by changing to a specific value [31,32].…”

Quantifying the Severity of Phytophthora Root Rot Disease in Avocado Trees Using Image Analysis

“…The method developed specifically for avocado canopies was evolved from similar analysis calculating the leaf area index (LAI) of grapevine canopies [18,32] and Eucalyptus [31]. The predominant processing steps included segmentation of canopy from sky and image gap analysis (sky/leaf ratio) following methodologies described by Poblete-Echeverría et al [16] and Fuentes et al [31].…”

“…Red, green, blue (RGB) photographs acquired by digital cameras have been successfully used to estimate canopy density, tree architecture, and growth in forest trees and grapevines [13][14][15][16]. Canopy porosity (crown porosity) or gap distribution was calculated from the proportion of sky area visible within the canopy extent [14,[17][18][19].…”

Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography

“…Digital cover photography (DCP) is an emerging indirect method to quantify canopy cover and leaf area index (Ryu et al, 2010(Ryu et al, , 2012Macfarlane, 2011;Chianucci and Cutini, 2013;Kimm and Ryu, 2015;Poblete-Echeverría et al, 2015). DCP uses a narrow field of view (FOV: 0-30•), which provides higher image resolution than digital hemispherical photography (FOV: 0-90•) (Pekin and Macfarlane, 2009).…”