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RSS FeedsRemote Sensing, Vol. 11, Pages 2972: Assessment of Canopy Porosity in Avocado Trees as a Surrogate for Restricted Transpiration Emanating from Phytophthora Root Rot (Remote Sensing)

 
 

11 december 2019 18:02:37

 
Remote Sensing, Vol. 11, Pages 2972: Assessment of Canopy Porosity in Avocado Trees as a Surrogate for Restricted Transpiration Emanating from Phytophthora Root Rot (Remote Sensing)
 


Phytophthora root rot (PRR) disease is a major threat in avocado orchards, causing extensive production loss and tree death if left unmanaged. Regular assessment of tree health is required to enable implementation of the best agronomic management practices. Visual canopy appraisal methods such as the scoring of defoliation are subjective and subject to human error and inconsistency. Quantifying canopy porosity using red, green and blue (RGB) colour imagery offers an objective alternative. However, canopy defoliation, and porosity is considered a ‘lag indicator’ of PRR disease, which, through root damage, incurs water stress. Restricted transpiration is considered a ‘lead indicator’, and this study sought to compare measured canopy porosity with the restricted transpiration resulting from PRR disease, as indicated by canopy temperature. Canopy porosity was calculated from RGB imagery acquired by a smartphone and the restricted transpiration was estimated using thermal imagery acquired by a FLIR B250 hand-held thermal camera. A sample of 85 randomly selected trees were used to obtain RGB imagery from the shaded side of the canopy and thermal imagery from both shaded and sunlit segments of the canopy; the latter were used to derive the differential values of mean canopy temperature (Δ Tmean), crop water stress index (Δ CWSI), and stomatal conductance index (Δ Ig). Canopy porosity was observed to be exponentially, inversely correlated with Δ CWSI and Δ Ig (R2 > 90%). The nature of the relationship also points to the use of canopy porosity at early stages of canopy decline, where defoliation has only just commenced and detection is often beyond the capability of subjective human assessment.


 
209 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2973: Modelling the Altitude Dependence of the Wet Path Delay for Coastal Altimetry Using 3-D Fields from ERA5 (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2980: TanDEM-X Forest Mapping Using Convolutional Neural Networks (Remote Sensing)
 
 
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