Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App

Estimating leaf area index (LAI) of using indirect methods involves some critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI measurements. Data were collected during...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2016-11, Vol.16 (12), p.2004-2004
Main Authors: Orlando, Francesca, Movedi, Ermes, Coduto, Davide, Parisi, Simone, Brancadoro, Lucio, Pagani, Valentina, Guarneri, Tommaso, Confalonieri, Roberto
Format: Article
Language:English
Subjects:
Canopies
Destructive testing
Error correction
Estimating
hemispherical photography
Heterogeneity
Leaf area index
Monitors
Photography
Photography - methods
Plant Leaves - physiology
plant vigour
Pruning
Remote sensing
Remote Sensing Technology - methods
smart-app
Vineyards
Vitis - physiology
Vitis vinifera
Wineries & vineyards
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Estimating leaf area index (LAI) of using indirect methods involves some critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI measurements. Data were collected during six surveys in an experimental site characterized by a high level of heterogeneity among plants, allowing us to explore a wide range of LAI values. During the last survey, the possibility to combine remote sensing data and in-situ PocketLAI estimates (smart scouting) was evaluated. Results showed a good agreement between PocketLAI data and direct measurements, especially for LAI ranging from 0.13 to 1.41 ( ² = 0.94, RRMSE = 17.27%), whereas the accuracy decreased when an outlying value (vineyard LAI = 2.84) was included ( ² = 0.77, RRMSE = 43.00%), due to the saturation effect in case of very dense canopies arising from lack of green pruning. The hemispherical photography showed very high values of ², even in presence of the outlying value ( ² = 0.94), although it showed a marked and quite constant overestimation error (RRMSE = 99.46%), suggesting the need to introduce a correction factor specific for vineyards. During the smart scouting, PocketLAI showed its reliability to monitor the spatial-temporal variability of vine vigor in cordon-trained systems, and showed a potential for a wide range of applications, also in combination with remote sensing.
ISSN:1424-8220
1424-8220
DOI:10.3390/S16122004