Thermal Infrared Images Research Articles

Automated leakage detection method of pipeline networks under complicated backgrounds by combining infrared thermography and Faster R-CNN technique

Leakage detection is essential to process safety and loss prevention of pipeline networks. As one of the attractive methods for detecting leakages in single pipelines, infrared thermography (IRT) faces new challenges for leakage detection of pipeline networks. Compared with the single pipeline, the pipeline network has a complicated structure with higher topology features and diverse material composition. In addition, the environment of the pipeline network is complex and changeable. These factors increase the complexity of the infrared (IR) thermal images of pipeline networks, which makes the leakage features too weak to be detected via naked-eye observation in an efficient and precise manner. To address this issue, this study develops an automated detection method for pipeline network leakages under complicated background, which is achieved by combining IRT with the Faster Region-based Convolutional Neural Network (Faster R-CNN) technique. First, the pipeline network system in the complicated background is designed for IR thermal image acquisition and data analysis. Then, an automated leakage detection model is built based on Faster R-CNN, which uses a modified VGG16 network for feature extraction for its outstanding performance in the feature extraction of small objects under complicated backgrounds. Finally, the high efficiency and precision of the proposed method for automated detection of pipeline network leakages in complicated backgrounds are verified by extensive experiments. Meanwhile, the generalization ability of the proposed method is verified by conducting experiments under various experimental conditions (different moments all-day, different viewing angles of IR camera, pedestrian disturbing). In general, the proposed method provides a promising way to detect pipeline network leakages in complicated backgrounds.

MUGAN: Thermal Infrared Image Colorization Using Mixed-Skipping UNet and Generative Adversarial Network

Common cameras cannot capture high quality image in the night or extreme weather conditions that without enough light, while thermal infrared(TIR) cameras are not limited in this situation. Hence, TIR imaging technique is widely used in military, surveillance, nighttime traffic and other scenarios. However, TIR images are monochromatic, and the majority of details of such images are lost, which are difficult for human or computer system to analyze. Translating TIR images into visible images is beneficial to subsequent observation or further processing. Though there are some advances to realize the transformation from TIR images to color visible images, edge distortion and semantic confusion remain to be solved. Therefore, we propose a Mixed-Skipping UNet(MS-UNet) based image colorization model joint Generative Adversarial Network, which is denoted by MUGAN. Firstly, the dense skip connections of UNet++ and full-scale skip connections of UNet 3+ are combined to form the MS-UNet, which is regarded as the generator. In addition, we design a feature extraction module in MS-UNet to effectively capture the multi-scale features in source image. Then, a novel attention mechanism module is designed for decoding stage which can help decoder of MS-UNet to focus on the important information. Moreover, we explore the effect of different loss functions in TIR image colorization task, and the loss function with excellent performance is selected to further optimize the training process of the model. Extensive experiments prove the superiority of our method in the task of TIR image colorization. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HangyingLiao/MUGAN</uri> .

Near-Surface Soil Moisture Characterization in Mississippi’s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were &gt;0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.

Possibility of infrared (IR) thermography camera to investigate properties of injected plastic product and optimisation injection process parameter using a design of experiment

ABSTRACT The injection processing parameters have an important role in product quality. The purpose of this research is to estimate the usage of infrared (IR) thermal camera in automatic quality control of polymer parts in the production lines compared to simulation. The evaluation of injected part properties monitored from an IR camera is studied in terms of the defect and mechanical properties. The optimisation of the processing parameters to increase mechanical properties and decrease injection defects is examined. There are four processing factors with four levels namely, melt temperature, mould temperature, injection pressure, and flow rate designed by using Taguchi’s experimental design. The effect of average temperature of injection moulded parts monitored by IR camera on defect and mechanical properties is determined. The variance (ANOVA) method is used to evaluate the influence of each factors. The parameter factors of all experiments are definitely different because of P-values less than 0.05. The optimum processing parameters of PP were a melting temperature of 193°C, mould temperature of 30°C, injection pressure of 50 bar, and flow rate of 40 cm3/s. The suitable processing conditions for ABS are melting temperature, mould temperature, injection pressure, and flow rate of 216°C, 50°C, 90 bar, and 20 cm3/s respectively. IR thermograph accurately monitors short shot, sink mark, and large flash in moulding part; moreover, IR camera can estimate the mechanical properties of ABS by average temperature but cannot accurately measure the mechanical properties of high glossy PP. It is meant that IR thermal imaging technique can automatically control the product quality in the injection moulding process.

Attention and Pixel Matching in RGB-T Object Tracking

Visual object tracking using visible light images and thermal infrared images, named RGB-T tracking, has recently attracted increasing attention in the tracking community. Deep neural network-based methods becoming the most popular RGB-T trackers, still have to balance the robustness and the speed of calculation. A novel tracker with Siamese architecture is proposed to obtain the accurate object location and meet the real-time requirements. Firstly, a multi-modal weight penalty module is designed to assign different weights to the RGB and thermal infrared features. Secondly, a new pixel matching module is proposed to calculate the similarity between each pixel on the search and the template features, which can avoid bringing excessive background information versus the regular cross-correlation operation. Finally, an improved anchor-free bounding box prediction network is put forward to further reduce the interference of the background information. The experimental results on the standard RGB-T tracking benchmark datasets show that the proposed method achieves better precision and success rate with a speed of over 34 frames per second which satisfies the real-time tracking.

Generation of fused visible and thermal-infrared images for uncooperative spacecraft proximity navigation

State-of-the-art techniques for vision-based relative navigation rely on images acquired in the visible spectral band. Consequently, the accuracy and robustness of the navigation is strongly influenced by the illumination conditions. The exploitation of thermal-infrared images for navigation purposes is studied in the present paper, as a possible solution to improve navigation in close proximity with a target spacecraft. Thermal-infrared images depend on the thermal radiation emitted by the target, hence, they are independent from light conditions; however, they suffer from a poorer texture and a lower contrast with respect to visible ones. This paper proposes pixel-level image fusion to overcome the limitations of the two types of images. The two source images are merged into a more informative one, retaining the strengths of the distinguished sensing modalities. The contribution of this work is twofold: firstly, a realistic thermal infrared images rendering tool for artificial targets is implemented; secondly, different pixel-level visible-thermal infrared images fusion techniques are assessed through qualitative and quantitative performance metrics to ease and improve the subsequent image processing step. The work presents a comprehensive evaluation of the best fusion techniques for on-board implementation, paving the way to the development of a multispectral end-to-end navigation chain.

Digital infrared thermal imaging system based breast cancer diagnosis using 4D U-Net segmentation

Medical Research field has been taken continuous efforts to develop an efficient method for detecting breast cancer, but the goal has still not yet achieved. To overcome this issue, a 4D U-Net segmentation using digital infrared (IR) thermal imaging system is proposed in this manuscript for the diagnosis of breast cancer (DBC-4D U-Net-DITI). Initially, the digital infrared thermal images are taken from DMR-IR data set as input, and the imageries are pre-processed to maintain local features and compress the dynamic range of image based upon Altered Phase Preserving Dynamic Range Compression (APPDRC) approach by removing the speckle noise. Then, the image segmentation is carried out with the help of 4D U-Net for obtaining the segmented digital infrared thermal image. The 4D U-Net weight parameters are optimized with Glowworm Swarm Optimization Algorithm (GSOA). The segmented regions of digital infrared thermal images are fed to Binarized Spiking Neural Network (BSNN) for classifying the pathology stage as No spread, Early Stage, Localized, Regional and Distant. The proposed approach is executed in MATLAB. The performance of proposed approach attains better accuracy of 39.01%, 28.34%, and 37.45%, better precision of 17.12%, 24.12% and 32.07% when compared to existing approaches like chaotic salp swarm algorithm (CSSA) based segmentation of thermal images for breast cancer identification (DBC-CSSA-DITI), marine-predators-algorithm based segmentation of thermal images for the diagnosis of breast cancer (DBC-MPA-DITI) and diagnosis of breast cancer based upon CNN using thermal imageries (DBC-CNN-DITI) respectively.

π‐Conjugated Carbon‐Based Materials for Infrared Thermal Imaging

AbstractInfrared (IR) thermal imaging is receiving a great deal of attention due to its wide range of applications. Given multiple issues (like cost and availability) with the inorganic materials currently exploited for IR imaging, there is nowadays a great push of developing organic imaging materials. Carbon‐based materials are known to have significant transparency in the visible and IR regions and some are used as transparent conductors. Here, whether π‐conjugated carbon‐based materials are suitable for long‐wave (LW) and mid‐wave (MW) IR imaging applications is computationally assessed. Using density functional theory calculations, the IR‐vibrational properties of molecules from acenes to coronenes and fullerenes, and of periodic systems like graphene and carbon nanotubes are characterized. Fullerenes, graphenes, and double‐walled carbon nanotubes are found to be very attractive as they are transparent in both the LWIR and MWIR regions, a feature resulting from the absence of hydrogen atoms. Also, it is found that replacing hydrogen atoms in a molecule with deuterium or sulfur atoms can be an efficient way to improve their LWIR or MWIR transparency, respectively. For fused‐ring systems having hydrogen atoms on the periphery, designing molecules with trio CH‐units is another way to enhance the transparency in the LWIR region.

LANDSAT 8 LST Pan sharpening using novel principal component based downscaling model

The panchromatic (PAN) band is accessible at a much higher spatial resolution than the thermal infrared (TIR) band in remote sensing satellites. To mitigate this issue, current research proposes a fusion method based on principal components of PAN and TIR band pairs to enhance the spatial resolution of Land surface temperature (LST) images. In this research, LANDSAT-8 LST images of 100 m resolution have been downscaled to 15 m resolution by the Novel principal component (NPC) based downscaling model. The downscaled LST image's thermal values have been cross-verified by in situ thermal points collected by the thermal data logger and image quality indices. The results have shown the root mean square error (RMSE) of 0.51–0.92 for downscaled LST image from 100m to 15m resolution. The correlation between downscaled LST and ground truth points is between 0.54 and 0.83. The proposed NPC-based LST-PAN combination technique collects spatial details of the Earth's surface from PAN data as well as indulges thermal information of TIR image. The proposed fusion technique has upgraded the spatial goal and peculiarity of TIR images data set. The higher spatial resolution of thermal images can increase the level of precision in various remote sensing applications. These improved data sets can be used in a variety of applications, including urban heat island mapping, agricultural crop monitoring, forest fire detection, and hydrological modeling, to name a few. The increased spatial resolution provided by NPC fusion results in more accurate and consistent temperature readings, leading to improved decision-making and management in these and other applications.

Classification of Micromobility Vehicles in Thermal-Infrared Images Based on Combined Image and Contour Features Using Neuromorphic Processing

Trends of environmental awareness, combined with a focus on personal fitness and health, motivate many people to switch from cars and public transport to micromobility solutions, namely bicycles, electric bicycles, cargo bikes, or scooters. To accommodate urban planning for these changes, cities and communities need to know how many micromobility vehicles are on the road. In a previous work, we proposed a concept for a compact, mobile, and energy-efficient system to classify and count micromobility vehicles utilizing uncooled long-wave infrared (LWIR) image sensors and a neuromorphic co-processor. In this work, we elaborate on this concept by focusing on the feature extraction process with the goal to increase the classification accuracy. We demonstrate that even with a reduced feature list compared with our early concept, we manage to increase the detection precision to more than 90%. This is achieved by reducing the images of 160 × 120 pixels to only 12 × 18 pixels and combining them with contour moments to a feature vector of only 247 bytes.

Machine Learning-Based Pain Severity Classification of Lumbosacral Radiculopathy Using Infrared Thermal Imaging

Pain is subjective and varies among individuals. Doctors determine pain severity based on a patient’s self-reported symptoms. In such situations, a language barrier may prevent patients from expressing their pain accurately, which may cause doctors to underestimate their pain degree. Moreover, patients’ subjective descriptions of pain can determine their eligibility for secondary benefits, as in the case of compensation for traffic or industrial accidents. Therefore, to perform a multiclass prediction of the severity of lumbar radiculopathy, the authors applied digital infrared thermographic imaging (DITI) to a machine-learning (ML) algorithm. The DITI dataset included data from a healthy population and patients with radiculopathy with herniated lumbar discs at the L3/4, L4/5, and L5/S1 levels. The dataset of 1000 patients was split into training and test datasets in a 7:3 ratio to evaluate the model’s performance. For the training dataset, the average accuracy, precision, recall, and F1 score were 0.82, 0.76, 0.72, and 0.74, respectively. For the test dataset, these values were 0.77, 0.71, 0.75, and 0.73, respectively. Applying the ML algorithm to a pain-severity classification using thermographic images will aid in the treatment of lumbosacral radiculopathy and allow providers to monitor the therapeutic effect of interventions through an assessment of physiological evidence.

Clinical Retrospective Analysis of Cold and Heat Diagnosis of Traditional Chinese Medicine by Application of Infrared Thermal Imaging Technology

Objective: Exploring the application of infrared thermal imaging technology in Traditional Chinese Medicine (TCM) diagnosis of cold and heat by comparing the cold and heat of TCM clinical dialectics with the cold and heat objectively measured by infrared thermal imaging technology. Methods: First, there were 77 patients from the Prevention and Treatment Center of Guangdong Hospital of TCM selected to analyze their clinical information retrospectively. Then, transforming the temperature index measured by infrared thermal imaging technology into the cold and heat deviation index, which can reflect the cold and heat of traditional Chinese medicine. Finally, a Correlation analysis was made between patients' cold and heat deviation index, and clinical symptoms, the type of cold and heat syndrome diagnosed by doctors. Results: The cold and heat deviation index of the outer, upper outer, and lower outer parts of the patients was significantly lower than that of the inner, upper inner, and lower inner parts of the patients and the difference in the mean values of the three groups was 0.06, 0.07 and 0.03, respectively. In all parts of the subjects, the coincidence rate of clinical dialectics of cold and cold and heat deviation index less than 0 (belonging to cold) was 70.17%. In patients with fatigue symptoms, the cold and heat deviation index of the lower inner part, the upper inner part, and the inner part was significantly higher than that of the lower outer part, the upper outer part, and the outer part. Conclusions: The pattern of human torso region division combined with the cold and heat deviation index can be used to reflect the quantitative understanding of cold and heat in the TCM clinic. Quantifying the variation of cold and heat according to the trend of torso regional temperature deviation can explain the pathogenesis of some elementary symptoms closely related to cold and heat. As a quantitative index of TCM cold and heat, the offset index can be proof for visualization, objectification, and quantification, combined with doctors' judgment, which can an effective auxiliary diagnostic means of TCM to a certain extent.

Fusion of UAV-based infrared and visible images for thermal leakage map generation of building facades

To make the best use of available energy resources and reduce costs, improving the energy efficiency of buildings has become a critical issue for the construction industry. Today, developing a three-dimensional model of the energy consumption rates in buildings based on thermal infrared images is essential to visualize, identify and increase energy efficiency. The purpose of this study is to suggest a methodology for generating a thermal leakage map of building facades utilizing the fusion of thermal infrared and visible images captured by Unmanned Aerial Vehicles (UAVs). In general, the proposed method involves three basic steps: the generation of thermal infrared and visible dense point clouds from the building’s facade using Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms; the fusion of visible and thermal infrared dense point clouds using the Iterative Closest Point (ICP) algorithm to overcome thermal infrared point cloud constraints; the use of edge extraction and region-based segmentation methods to determine the location of the thermal leakage of building facade’s. To that end, two datasets obtained for separate building facades are used to assess the proposed strategy. The results of the data analyses for the extraction of the desired components and determination of thermal leakage locations on the building facets provided a Precision and Recall score of 87 and 90% for the first dataset and 87 and 88 for the second dataset. Examining the outcomes of calculating thermal leakage zones indicates improving Precision and Recall.

RGB-T Semantic Segmentation With Location, Activation, and Sharpening

Semantic segmentation is important for scene understanding. To address the scenes of adverse illumination conditions of natural images, thermal infrared (TIR) images are introduced. Most existing RGB-T semantic segmentation methods follow three cross-modal fusion paradigms, i.e. encoder fusion, decoder fusion, and feature fusion. Some methods, unfortunately, ignore the properties of RGB and TIR features or the properties of features at different levels. In this paper, we propose a novel feature fusion-based network for RGB-T semantic segmentation, named \emph{LASNet}, which follows three steps of location, activation, and sharpening. The highlight of LASNet is that we fully consider the characteristics of cross-modal features at different levels, and accordingly propose three specific modules for better segmentation. Concretely, we propose a Collaborative Location Module (CLM) for high-level semantic features, aiming to locate all potential objects. We propose a Complementary Activation Module for middle-level features, aiming to activate exact regions of different objects. We propose an Edge Sharpening Module (ESM) for low-level texture features, aiming to sharpen the edges of objects. Furthermore, in the training phase, we attach a location supervision and an edge supervision after CLM and ESM, respectively, and impose two semantic supervisions in the decoder part to facilitate network convergence. Experimental results on two public datasets demonstrate that the superiority of our LASNet over relevant state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/LASNet.

Developing thermal infrared de-ghost and multi-level nested conglutinated segmentation algorithm for detection of rice seed setting rate

The seed setting rate (SSR) of rice is not only a key component of yield, but also an important parameter in rice phenotypic analysis. Fast and accurate detection of SSR is of great significance for yield prediction. The purpose of this research was to detect the SSR of rice quickly and automatically. A thermal infrared–visible light dual imaging system was built to obtain thermal infrared images and RGB images of rice grains. This paper proposed image registration method, thermal infrared de-ghost method and multi-layer nested conglutinated segmentation algorithm to detect SSR. Compared with the detection accuracy of three deep learning models (Faster RCNN 96.43%, SSD 81.84%, YOLO V3 96.75%) and image registration methods (80.83%), the highest SSR detection accuracy (97.66%) was achieved by fusing thermal infrared de-ghost and multi-layer nested conglutinated segmentation algorithm. This method has the advantages of simple structure, high efficiency and competitive results, and has great potential in detecting seed setting rate.

The Thermal Manifestation Monitoring Program at Muara Labuh Case Studies

The thermal manifestations at the Muara Labuh field are monitored regularly to see their behaviors in response to the field production. The monitoring results are then used to identify and mitigate any potential hazards associated with the thermal area changes. This paper presents the monitoring results on the main thermal features Idung Mancung (IM) fumaroles complex, located in the vicinity of the steam cap production zone in the Northeast (NE) Sector of the field. Due to its proximity to the main production area, this thermal complex is considered to be susceptible to any reservoir changes. The thermal manifestations characteristics monitored are areal extent, numbers of the thermal funnels, the activities, physical and chemical characteristics, and geophysical signatures. Baseline data were taken from previous remote sensing studies, geology maps, and thermal manifestation geochemistry surveys prior to production periods. Remote sensing studies using true color composite (TCC) and NDVI suggest that the IM fumarole area covers the wider area prior to stage-1 project development in 2017. After production, periodic aerial photo analysis suggests no changes in IM fumarole thermal area growth. The fact is supported by the periodic Thermal Infra-Red Image (TIR), which shows no sign of thermal anomaly still contained inside the IM fumarole baseline area. IM fumarole field checks show that lack of thermal area changes and no sign that the manifestations have become more aggressive. Temperature and flow rate observation on several manifestation funnels indicates that rainfall rate variation affects the manifestation activities and temperature. As for the fumarole gas chemistry, the IM main fumarole shows a decreasing trend in NCG and CO2 contents from 2016 to 2021 and increasing in H2S, which indicates the return of degassing fluid. After one year of field operation, the microgravity and leveling survey in the IM fumarole area show slight changes from -0.1 to -0.14 mGal. The changes correspond with the area subsidence rate of -10 to -15 mm/yr. These changes are still considered low compared to other operating geothermal fields and did not directly affect the thermal manifestation activity.

Target Area Extraction Algorithm of Infrared Thermal Image Combining Target Detection with Matching Correction

Target Area Extraction Algorithm of Infrared Thermal Image Combining Target Detection with Matching Correction

Analyzing a series of thermal infrared images to identify defects using a hybrid approach that combines robust principal component analysis and image segmentation

Thermography is widely used to identify defects; however, analyzing just the images containing important information instead of analyzing each individual image from a series of thermal infrared images is an important capability. Principal component analysis (PCA) is the traditionally used dimensionality reduction approach for projecting a given dataset onto a low-dimensional space. In doing so, several images can be extracted from this low-dimensional space that contain significant information about the given data. The problem with this is that PCA is sensitive to noise and shadows. Robust PCA (RPCA), Sparse PCA (SPCA), and Kernel PCA (KPCA) have therefore been developed and employed for defect detection. The question then arises as to which method can preserve the most information from a series of thermal infrared images after the data are projected onto the low-dimensional space. This study analyzed a series of thermal infrared images covered with shadows using different PCAs. By comparing the results, it was found that RPCA can preserve the most information of the given data. Image segmentation was employed to segment the extracted images such that the thermal patterns could be easily identified from the segmented regions. The shadow locations could be identified by selecting the areas whose shadow effects estimated using image segmentation were less than 1.0. The results were encouraging and the proposed scheme can be feasibly employed for defect detection.

Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies

The Harmony satellite mission was recently approved as the next European Space Agency (ESA) Earth Explorer 10. The mission science objectives cover several applications related to solid earth, the cryosphere, upper-ocean dynamics and air–sea interactions. The mission consists of a constellation of two satellites, flying with the Copernicus Sentinel 1 (C or D) spacecraft, each hosting a C-band receive-only radar and a thermal infrared (TIR) payload. From an ocean dynamics/air–sea interaction perspective, the mission will provide the unique opportunity to observe simultaneously the signature of submesoscale upper-ocean processes via synthetic aperture radar and TIR imagery. The TIR imager is based on microbolometer technology and its acquisitions will rely on four channels: three narrow-band channels yielding observations at a ≃1 km spatial sampling distance (SSD) and a panchromatic (PAN, 8–12 μm) channel characterized by a ≃300 m SSD. Our study investigates the potential of Harmony in retrieving spatial features related to sea surface temperature (SST) gradients from the high-resolution PAN channel, relying on top-of-atmosphere (TOA) observations. Compared to a standard SST gradient retrieval, our approach does not require atmospheric correction, thus avoiding uncertainties due to inter-channel co-registration and radiometric consistency, with the possibility of exploiting the higher resolution of the PAN channel. The investigations were carried out simulating the future Harmony TOA radiances (TARs), as well as relying on existing state-of-the-art level 1 satellite products. Our approach enables the correct description of SST features at the sea surface avoiding the generation of spurious features due to atmospheric correction and/or instrumental issues. In addition, analyses based on existing satellite products suggest that the clear-sky TOA observations, in a typical mid-latitude scene, allow the reconstruction of up to 85% of the gradient magnitudes found at the sea-surface level. The methodology is less efficient in tropical areas, suffering from smoothing effects due to the high concentrations of water vapor.

Assessment of the accuracy of counting large ungulate species (red deer Cervus elaphus) with UAV‐mounted thermal infrared cameras during night flights

Unmanned aerial vehicles (UAVs) are increasingly used in wildlife surveying, including estimation of population densities. It is essential that we evaluate and test new survey methods to guide optimal sampling strategies. This study aimed to assess the accuracy of using a UAV‐mounted thermal infrared (TIR) camera to count red deer Cervus elaphus populations, and how this was influenced by flight season, height and velocity, in order to help guide future census design. We flew 57 flights across a captive population of red deer in a 13 ha deer park enclosure of semi‐natural habitat, representative of the species' range in northern Germany. Flights and image assessments were performed with no prior knowledge of actual population size. Accuracy was quantified by comparing real population size (known only to deer park staff) and independently estimated population sizes from UAV TIR images. Accuracy was significantly influenced by ecological season (early and late winter, spring and early summer) and height. Across all seasons, lower flights (100 m) performed better than higher ones (120 m), with lower flights in early winter and early summer being on average accurate to within 1% of actual population counts. For the season where we had the largest range of temperatures between flights (late winter) we found that accuracy was highest when temperatures were lowest. Flights were also able to identify all five stags (defined as a male deer ≥ 2 years old) present in early summer, but not in spring. Deer appeared to avoid the landing/take‐off area, but there were no noted behavioural responses to drones flying over animals when at constant height and velocity during surveys. Our results indicate that UAV‐mounted TIR camera have the potential to accurately count populations of large ungulate species, but that flight season, height and potentially temperature need to be taken into account to maximise accuracy. This approach has the potential to be scaled up to more accurately estimate densities of wild populations compared to existing approaches.