Evapotranspiration estimation of Urmia Lake Basin using GCOM-C thermal imagery

Basin-scale monthly and annual evapotranspiration (ET) is estimated for Urmia Lake Basin by applying the Global Change Observation Mission for Climate (GCOM-C) global ETindex estimation algorithm to thermal imagery observed by the GCOM-C satellite. In total, 297 satellite images acquired during 2018-2019 were used in this study. ET estimation accuracy was examined for an area dominated by apple fields using traditional surface irrigation. The estimated ET was 15% lower than the standard crop ET, which was computed using a procedure suggested by the Food and Agriculture Organization of the United Nations on a monthly timescale, and was 8% lower on an annual timescale. Comparison of estimated ET with a satellite-based ET map derived by using the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model showed a similar difference. The 8%–15% differences among the different sources of ET were small, given that a similar or wider range of uncertainty is frequently available even in ground-based ET measurements. Comparison between the estimated ET and the MODIS ET Product (MOD16) revealed a greater difference in the evaluated area of the apple fields. Given the climatic ET demands and the irrigation practices of the area, ET estimation accuracy is more likely to be higher using the dataset derived from this study than using MOD16. The GCOM-C satellite started routine surface observations in January 2018. Its contribution to agricultural water management, such as by estimating ET as presented in this study, will increase as the amount of historical data stored continues to accumulate.

Temperature Measurement and Control Application in a Laser Plastic Surgery Real Temperature Detection System

The purpose of this study was to grasp the development process of thermal image temperature measurement technology. It provides directional support for the optimization development of the thermal imagery and laser plastic surgery and laser treatment. This paper uses the infrared thermal image temperature measurement principle and performs infrared thermal image precise temperature measurement technology and its application research. The results showed that there was a correlation between 595 nm pulse dye laser, age, laser energy density, and skin temperature ( P < 0.05 ). There is a significant difference in the average ( P < 0.05 ). The infrared thermal imagery temperature monitoring system is a simple and relatively accurate temperature detection system that can be widely used in temperature measurement and control of laser plastic surgery.

Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy

The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area.

Advancing Cave Detection Using Terrain Analysis and Thermal Imagery

Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) Determine the efficacy of methods designed for terrain analysis and applied to thermal imagery; (2) evaluate the usefulness of predawn and midday imagery for detecting caves; and (3) ascertain which imagery type (predawn, midday, or the difference between those two times) was most informative. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses for model selection, and a thermal imagery dataset acquired from the Mojave Desert, California, we examined the efficacy of three well-known terrain descriptors (i.e., slope, topographic position index (TPI), and curvature) on thermal imagery for cave detection. We also included the actual, untransformed thermal DN values (hereafter “unenhanced thermal”) as a fourth dataset. Thereafter, we compared the thermal signatures of known cave entrances to all non-cave surface locations. We determined these terrain-based analytical methods, which described the “shape” of the thermal landscape, hold significant promise for cave detection. All imagery types produced similar results. Down-selected covariates per imagery type, based upon the FSL models, were: Predawn— slope, TPI, curvature at 0 m from cave entrance, as well as slope at 1 m from cave entrance; midday— slope, TPI, and unenhanced thermal at 0 m from cave entrance; and difference— TPI and slope at 0 m from cave entrance, as well as unenhanced thermal and TPI at 3.5 m from cave entrance. We provide recommendations for future research directions in terrestrial and planetary cave detection using thermal imagery.

What Can Thermal Imagery Tell Us About Glacier Melt Below Rock Debris?

Rock debris on the surface of a glacier can dramatically reduce the local melt rate, where the primary factor governing melt reduction is debris layer thickness. Relating surface temperature to debris thickness is a recurring approach in the literature, yet demonstrations of reproducibility have been limited. Here, I present the results of a field experiment conducted on the Canwell Glacier, Alaska, United States to constrain how thermal data can be used in glaciology. These datasets include, 1) a measured sub-daily “Østrem curve” time-series; 2) a time-series of high resolution thermal images capturing several segments of different debris thicknesses including the measurements from 1); 3) a thermal profile through a 38 cm debris cover; and 4) two Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite thermal images acquired within 2 and 3 min of a field-based thermal camera image. I show that, while clear sky conditions are when space-borne thermal sensors can image a glacier, this is an unfavorable time, limiting the likelihood that different thicknesses of debris will have a unique thermal signature. I then propose an empirical approach to estimate debris thickness and compare it to two recently published methods. I demonstrate that instantaneous calibration is essential in the previously published methods, where model parameters calibrated only 1 h prior to a repeat thermal image return diminished debris thickness estimates, while the method proposed here remains robust through time and does not appear to require re-calibration. I then propose a method that uses a time-series of surface temperature at one location and debris thickness to estimate bare-ice and sub-debris melt. Results show comparable cumulative melt estimates to a recently published method that requires an explicit/external estimate of bare ice melt. Finally, I show that sub-pixel corrections to ASTER thermal imagery can enable a close resemblance to high resolution, field-based thermal imagery. These results offer a deeper insight into what thermal data can and cannot tell us about surface debris properties and glacier melt.

Automated Detection of Animals in Low-Resolution Airborne Thermal Imagery

Detecting animals to estimate abundance can be difficult, particularly when the habitat is dense or the target animals are fossorial. The recent surge in the use of thermal imagers in ecology and their use in animal detections can increase the accuracy of population estimates and improve the subsequent implementation of management programs. However, the use of thermal imagers results in many hours of captured flight videos which require manual review for confirmation of species detection and identification. Therefore, the perceived cost and efficiency trade-off often restricts the use of these systems. Additionally, for many off-the-shelf systems, the exported imagery can be quite low resolution (<9 Hz), increasing the difficulty of using automated detections algorithms to streamline the review process. This paper presents an animal species detection system that utilises the cost-effectiveness of these lower resolution thermal imagers while harnessing the power of transfer learning and an enhanced small object detection algorithm. We have proposed a distant object detection algorithm named Distant-YOLO (D-YOLO) that utilises YOLO (You Only Look Once) and improves its training and structure for the automated detection of target objects in thermal imagery. We trained our system on thermal imaging data of rabbits, their active warrens, feral pigs, and kangaroos collected by thermal imaging researchers in New South Wales and Western Australia. This work will enhance the visual analysis of animal species while performing well on low, medium and high-resolution thermal imagery.