Next Article in Journal
A Liquid-Surface-Based Three-Axis Inclination Sensor for Measurement of Stage Tilt Motions
Next Article in Special Issue
Experimental Study of Multispectral Characteristics of an Unmanned Aerial Vehicle at Different Observation Angles
Previous Article in Journal
A Flexible Multiring Concentric Electrode for Non-Invasive Identification of Intestinal Slow Waves
Previous Article in Special Issue
A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data
Open AccessArticle

Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard

1
Centro de Investigación y Transferencia en Riego y Agroclimatología (CITRA), Universidad de Talca, Casilla 747, Talca 3460000, Chile
2
Research Program on Adaptation of Agriculture to Climate Change (A2C2), Universidad de Talca, Casilla 747, Talca 3460000, Chile
3
Department of Infrastructure Engineering, The University of Melbourne, Parkville 3010, Australia
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(2), 397; https://doi.org/10.3390/s18020397
Received: 28 November 2017 / Revised: 4 January 2018 / Accepted: 25 January 2018 / Published: 30 January 2018
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
Water stress caused by water scarcity has a negative impact on the wine industry. Several strategies have been implemented for optimizing water application in vineyards. In this regard, midday stem water potential (SWP) and thermal infrared (TIR) imaging for crop water stress index (CWSI) have been used to assess plant water stress on a vine-by-vine basis without considering the spatial variability. Unmanned Aerial Vehicle (UAV)-borne TIR images are used to assess the canopy temperature variability within vineyards that can be related to the vine water status. Nevertheless, when aerial TIR images are captured over canopy, internal shadow canopy pixels cannot be detected, leading to mixed information that negatively impacts the relationship between CWSI and SWP. This study proposes a methodology for automatic coregistration of thermal and multispectral images (ranging between 490 and 900 nm) obtained from a UAV to remove shadow canopy pixels using a modified scale invariant feature transformation (SIFT) computer vision algorithm and Kmeans++ clustering. Our results indicate that our proposed methodology improves the relationship between CWSI and SWP when shadow canopy pixels are removed from a drip-irrigated Cabernet Sauvignon vineyard. In particular, the coefficient of determination (R2) increased from 0.64 to 0.77. In addition, values of the root mean square error (RMSE) and standard error (SE) decreased from 0.2 to 0.1 MPa and 0.24 to 0.16 MPa, respectively. Finally, this study shows that the negative effect of shadow canopy pixels was higher in those vines with water stress compared with well-watered vines. View Full-Text
Keywords: multispectral and thermal automatic coregistration; shadow removal; crop water stress index (CWSI); UAV; midday stem water potential multispectral and thermal automatic coregistration; shadow removal; crop water stress index (CWSI); UAV; midday stem water potential
Show Figures

Figure 1

MDPI and ACS Style

Poblete, T.; Ortega-Farías, S.; Ryu, D. Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard. Sensors 2018, 18, 397.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop