Selecting Canopy Zones and Thresholding Approaches to Assess Grapevine Water Status by Using Aerial and Ground-Based Thermal Imaging

Abstract

Aerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower baseline temperature values). During the 2014-2015 growing season, an experimental study under different water conditions (slight, mild, moderate, and severe water stress) was carried out in a commercial vineyard (Vitis vinifera L., cv. Carménè). In this study thermal images were obtained from different canopy zones by using both aerial (>60 m height) and ground-based (sunlit, shadow and nadir views) thermography. Using customized code that was written specifically for this research, three different thresholding approaches were applied to each image: (i) the standard deviation technique (SDT); (ii) the energy balance technique (EBT); and (iii) the field reference temperature technique (FRT). Results obtained from three different approaches showed that the EBT had the best performance. The EBT was able to discriminate over 95% of the leaf material, while SDT and FRT were able to detect around 70% and 40% of the leaf material, respectively. In the case of canopy zone analysis, ground-based nadir images presented the best correlations with stomatal conductance (g s ) and stem water potential (Ψ stem ), reaching determination coefficients (r 2 ) of 0.73 and 0.82, respectively. The best relationships between thermal indices and plant-based variables were registered during the period of maximum atmospheric demand (near veraison) with significant correlations for all methods.

Optimizing the Processing of UAV-Based Thermal Imagery

“…In agriculture, UAV-based thermal remote sensing has been successfully applied for disease detection [20], high-throughput phenotyping in plant breeding [21], or for mapping drought stressed areas e.g., [22][23][24]. New developments also include the estimation of transpiration [25][26][27] or stomatal conductance [28]. In forestry and in ecological studies, the applications so far are less numerous, although the first studies indicate the usefulness of UAV-based thermal remote sensing in areas including fire detection [29], wildlife monitoring [30,31], assessing within-ecosystem variability in water availability [32], or in estimating plant growth and productivity [14].…”

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

“…The increasing prevalence of UAS along with low-cost camera systems has brought about much interest in the characterisation of crop water status/stress during the growing season to inform orchard or farm management decisions, in particular, irrigation scheduling [272,273]. Traditional methodologies to assess crop water stress are constrained by limitations relating to large farm sizes and accompanying spatial variability, high labour costs to collect data, and access to instrumentation that is both inexpensive and portable [272].…”

Assessment of Canopy Porosity in Avocado Trees as a Surrogate for Restricted Transpiration Emanating from Phytophthora Root Rot

“…The CWSI and I g values were therefore calculated by substituting T canopy , T dry and T wet with T mean , T max and T min . CWSI gives values of between 0 to 1, where I g provide values that can range from 1 to infinity [39]. The higher the CWSI values, the greater the plant stress, whilst the opposite is the case for I g [28,31].…”

“…Thermal imaging sensors are able to capture non-visible radiation in the wavelength range 8-12 µm and illustrate, in easily discernible 'pseudo color' format, images where each image pixel contains the temperature value of each surface element, governed by the spatial resolution of the image [33]. These cameras may be hand-held [36,37], including as a smartphone plug-in (FLIR ONE) [38], or can be deployed in 'unmanned aerial vehicles' (UAVs) [39,40] and aircraft [41,42]. There is a limited amount of satellite-based thermal sensors available, such as Landsat [17,43], but their low spatial resolution (30 m, obtained by resampling from the original 100 m resolution) with the corresponding thermal band severely limits their usefulness in the detection of individual trees.…”