%0 Journal Article
%T A Non-Reference Temperature Histogram Method for Determining Tc from Ground-Based Thermal Imagery of Orchard Tree Canopies
%J Remote Sensing | An Open Access Journal from MDPI
%D 2019
%R https://doi.org/10.3390/rs11060714
%X Obtaining average canopy temperature (T c) by thresholding canopy pixels from on-ground thermal imagery has historically been undertaken using ¡®wet¡¯ and ¡®dry¡¯ reference surfaces in the field (reference temperature thresholding). However, this method is extremely time inefficient and can suffer inaccuracies if the surfaces are non-standardised or unable to stabilise with the environment. The research presented in this paper evaluates non-reference techniques to obtain average canopy temperature (T c) from thermal imagery of avocado trees, both for the shaded side and sunlit side, without the need of reference temperature values. A sample of 510 thermal images (from 130 avocado trees) were acquired with a FLIR B250 handheld thermal imaging camera. Two methods based on temperature histograms were evaluated for removing non-canopy-related pixel information from the analysis, enabling T c to be determined. These approaches included: 1) Histogram gradient thresholding based on temperature intensity changes (HG); and 2) histogram thresholding at one or more standard deviation (SD) above and below the mean. The HG method was found to be more accurate (R 2 > 0.95) than the SD method in defining canopy pixels and calculating T c from each thermal image (shaded and sunlit) when compared to the standard reference temperature thresholding method. The results from this study present an alternative non-reference method for determining T c from ground-based thermal imagery without the need of calibration surfaces. As such, it offers a more efficient and computationally autonomous method that will ultimately support the greater adoption of non-invasive thermal technologies within a precision agricultural system. View Full-Tex
%U https://www.mdpi.com/2072-4292/11/6/714