Abstract
Canopy temperature (Tc) is used to characterize plant water physiology, and thermal infrared (TIR) remote sensing is a convenient technology for measuring Tc in forest ecosystems. However, the images produced through this method contain background pixels of forest gaps, thereby reducing the accuracy of Tc observations. Extracting Tc data from TIR images is of great significance for understanding changes in ecosystem water status. In this study, a temperature threshold method was developed to rapidly, accurately, and automatically extract forest canopy pixels for Tc data obtention. Specifically, this method takes the temperature corresponding to the point with a slope of 0.5 in the curve composed of the normalized average temperature and the normalized cumulative number of pixels as the segmentation threshold to separate the forest gap pixels from the forest canopy pixels in the TIR images and extract the separated forest canopy pixels based on the pixel coordinates for Tc data obtention. Taking the Tc values, measured using a thermocouple, as the standard, Tc extraction using the new temperature threshold method and traditional methods (the Otsu algorithm and direct extraction) was compared in cork oak plantations. The results showed that the temperature threshold method offered the highest extraction accuracy, followed by the direct extraction method and the Otsu algorithm. The temperature threshold method was determined to be the most suitable for extracting Tc data from the TIR images of cork oak plantations.
Highlights
Canopy temperature (Tc ) is an important parameter that characterizes plant physiological and ecological processes and energy balances and can accurately reflect a plant’s water status [1,2,3]
We developed a new threshold-based method for extracting canopy temperatures from thermal infrared (TIR) images based on 3the principle of 15 of the Otsu algorithm and combined with the advantages of visual interpretation, the edge detection algorithm
This study found that the difference in the Tc values extracted using the temperature threshold method and the direct extraction method was greater than zero (Figure 6b), demonstrating that the temperature threshold method had a positive effect on segmentation and the elimination of forest gap pixels
Summary
Canopy temperature (Tc ) is an important parameter that characterizes plant physiological and ecological processes and energy balances and can accurately reflect a plant’s water status [1,2,3]. Zhang et al [18] extracted the canopy pixels of corn via the fusion of RGB and TIR imagery This method offers a high accuracy level but requires additional equipment to obtain reference images, which is more expensive. We developed a new threshold-based method (the temperature threshold method) for extracting canopy temperatures from TIR images based on 3the principle of 15 of the Otsu algorithm and combined with the advantages of visual interpretation, the edge detection algorithm. The specific objectives of this study were (1) to find the segmentation point between the forest canopy and forest gap pixels in the TIR imagery in order to esthe Otsu algorithm and combined with the advantages of visual interpretation, the edge tablish the temperature threshold method and (2) to clarify the applicability of the temdetection algorithm.
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