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 - PubMed Skip to main page content
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. 2018 Jan 30;18(2):397.
doi: 10.3390/s18020397.

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

Tomas Poblete  1 , Samuel Ortega-Farías  2   3 , Dongryeol Ryu  4
Affiliations

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

Tomas Poblete et al. Sensors (Basel). .

Abstract

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.

Keywords: UAV; crop water stress index (CWSI); midday stem water potential; multispectral and thermal automatic coregistration; shadow removal.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Comparison between the thermal and visible (490 nm) canopy shadow information for a drip-irrigated vineyard: (A) thermal image; (B) VIS (490 nm) image; and (C) VIS (490 nm) image without shadow pixels (represented in red).
Figure 2
Figure 2
Slope calculation for previously matched descriptors points as an output of the scale invariant feature transformation (SIFT) algorithm.
Figure 3
Figure 3
The RANSAC filtered points obtained at different times for a drip-irrigated vineyard: (A) initial matched points; (B) first execution; and (C) second execution.
Figure 4
Figure 4
Filtered previous matched points considering the mode of the slope as a filter for a drip-irrigated vineyard.
Figure 5
Figure 5
Six spectral band images and its distribution for a drip-irrigated vineyard: (A) 490 nm; (B) 550 nm; (C) 680 nm; (D) 720 nm; (E) 800 nm; and (F) 900 nm.
Figure 6
Figure 6
Six spectral clustered images using K-means++ algorithm for a drip-irrigated vineyard: (A) 490 nm; (B) 550 nm; (C) 680 nm; (D) 720 nm; (E) 800 nm; and (F) 900 nm.
Figure 7
Figure 7
Shadow masks applied to an RGB composition for a drip-irrigated vineyard: (A) canopy and internal shadow mask; (B) RGB composition; (C) 490 nm; (D) 550 nm; (E) 680 nm; (F) 720 nm; (G) 800 nm; and (H) 900 nm.
Figure 8
Figure 8
Final resulting thermal image of the drip-irrigated vineyard after automatic coregistration with the 490-nm image and filtered using the proposed shadow removal algorithm.
Figure 9
Figure 9
Relationships between CWSI and SWP for the vineyard: (A) center of the row temperature; and (B) temperature after coregistration with the 490-nm image and application of the proposed shadow removal algorithm.
Figure 10
Figure 10
Comparison of the effect of shadow deletion on the CWSI-SWP relationship for a Cabernet Sauvignon vineyard.
See this image and copyright information in PMC

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