Thermal Infrared Camera Research Articles

Cross-Spectral Navigation with Sensor Handover for Enhanced Proximity Operations with Uncooperative Space Objects

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target’s illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains.

Time series of alpine snow surface radiative-temperature maps from high-precision thermal-infrared imaging

Abstract. The surface temperature of snow cover is a key variable, as it provides information about the current state of the snowpack, helps predict its future evolution, and enhances estimations of the snow water equivalent. Although satellites are often used to measure the surface temperature despite the difficulty of retrieving accurate surface temperatures from space, calibration–validation datasets over snow-covered areas are scarce. We present a dataset of extensive measurements of the surface radiative temperature of snow acquired with an uncooled thermal-infrared (TIR) camera. The set accuracy goal is 0.7 K, which is the radiometric accuracy of the TIR sensor of the future CNES/ISRO TRISHNA mission. TIR images have been acquired over two winter seasons, November 2021 to May 2022 and February to May 2023, at the Col du Lautaret, 2057 m a.s.l. in the French Alps. During the first season, the camera operated in the off-the-shelf configuration with rough thermal regulation (7–39 °C). An improved setup with a stabilized internal temperature was developed for the second campaign, and comprehensive laboratory experiments were carried out in order to characterize the physical properties of the components of the TIR camera and its calibration. Thorough processing, including radiometric processing, orthorectification, and a filter for poor-visibility conditions due to fog or snowfall, was performed. The result is two winter season time series of 130 019 maps of the surface radiative temperature of snow with meter-scale resolution over an area of 0.5 km2. The validation was performed against precision TIR radiometers. We found an absolute accuracy (mean absolute error, MAE) of 1.28 K during winter 2021–2022 and 0.67 K for spring 2023. The efforts to stabilize the internal temperature of the TIR camera therefore led to a notable improvement of the accuracy. Although some uncertainties persist, particularly the temperature overestimation during melt, this dataset represents a major advance in the capacity to monitor and map surface temperature in mountainous areas and to calibrate–validate satellite measurements over snow-covered areas of complex topography. The complete dataset is provided at https://doi.org/10.57932/8ed8f0b2-e6ae-4d64-97e5-1ae23e8b97b1 (Arioli et al., 2024a) and https://doi.org/10.57932/1e9ff61f-1f06-48ae-92d9-6e1f7df8ad8c (Arioli et al., 2024b).

StarDICE II: Calibration of an Uncooled Infrared Thermal Camera for Atmospheric Gray Extinction Characterization.

The StarDICE experiment strives to establish an instrumental metrology chain with a targeted accuracy of 1 mmag in griz bandpasses to meet the calibration requirements of next-generation cosmological surveys. Atmospheric transmission is a significant source of systematic uncertainty. We propose a solution relying on an uncooled infrared thermal camera to evaluate gray extinction variations. However, achieving accurate measurements with thermal imaging systems necessitates prior calibration due to temperature-induced effects, compromising their spatial and temporal precision. Moreover, these systems cannot provide scene radiance in physical units by default. This study introduces a new calibration process utilizing a tailored forward modeling approach. The method incorporates sensor, housing, flat-field support, and ambient temperatures, along with raw digital response, as input data. Experimental measurements were conducted inside a climatic chamber, with a FLIR Tau2 camera imaging a thermoregulated blackbody source. The results demonstrate the calibration effectiveness, achieving precise radiance measurements with a temporal pixel dispersion of 0.09 W m-2 sr-1 and residual spatial noise of 0.03 W m-2 sr-1. We emphasize that the accuracy of scene radiance retrieval can be systematically affected by the camera's close thermal environment, especially when the ambient temperature exceeds that of the scene.

An Experimental and Clinical Study of Flap Monitoring with an Analysis of the Clinical Course of the Flap Using an Infrared Thermal Camera.

Flap surgery is a common method used to cover defects following tumor ablation, trauma, or infection. However, insufficient vascularity in the transferred flap can lead to flap necrosis and failure. Proper postoperative monitoring is essential to prevent these complications. Recently, research has explored the use of infrared thermal imaging in plastic surgery, leading to its clinical application. This study comprises two separate parts: an in vivo experimental study and a clinical study. In this study, 28 rats underwent reverse McFarlane flap surgery, and their flaps were analyzed using a FLIR thermal imaging camera seven days post-surgery. Additionally, thermal images of flaps were taken on postoperative days 0, 1, 2, 3, and 7 in 22 patients. This study focused on temperature differences between normal skin and the perforator compared to the average flap temperature. Results showed that the temperature difference was higher in the necrosis group and increased over time in cases of total necrosis. A lower perforator temperature compared to the flap's average indicated vascular compromise, potentially leading to flap failure. The FLIR camera, being contact-free and convenient, shows promise for understanding and inferring the clinical progression of flaps in postoperative monitoring.

Enhancing point cloud data fusion through 2D thermal infrared camera and 2D lidar scanning

Sensor data fusion from multiple sources plays a pivotal role in advancing our understanding of real-world phenomena. This paper focuses on the integration of a thermal imager and a lidar to simultaneously capture four-dimensional (XYZT) space coordinates and temperatures. The fusion of data relies on establishing correspondence between overlapping lines in the lidar’s 3D point cloud and the infrared (IR) camera’s 2D thermal map. The presented combination method offers several key advantages. Firstly, it simplifies the calibration process. Furthermore, it can achieve near real-time performance, enabling prompt data analysis. Moreover, the method demonstrates high accuracy in capturing spatial information and temperature measurements. The thermal IR camera used in the experiment features a 1280 × 1024 pixel cooled indium antimonide (InSb) detector, operating in the short wavelength infrared (MWIR) spectral range of 1.5 to 5.5 µm. It provides temperature readings with ± 1 K accuracy and sub-30 mK resolution. On the other hand, the lidar system delivers centimeter-level precision within a range of approximately 1 to 12 m, even in well-lit outdoor environments. The experimental results validate the effectiveness of our combined system, successfully generating high-quality four-dimensional (4D) point clouds. The integration of the thermal IR camera and profilometer presents an intriguing tool with significant potential in various industrial applications. For instance, it can be employed to accurately assess gas leaks, enhance metallurgical processes, and improve safety on construction sites.

Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis

The nitrogen nutrition index (NNI) has been extensively applied for the diagnosis of crop nitrogen status, providing insights into efficient nitrogen utilization and plant growth. In this study, we utilized a low-altitude unmanned aerial vehicle (UAV) platform, equipped with multispectral (MS), red–green–blue (RGB), and thermal infrared (TIR) cameras, to comprehensively capture wheat spectral information. The analysis of the relationship between NNI and relative yield revealed an initially linear relationship, which saturated for high NNI values. To enhance accuracy and minimize complexity, we employed a random forest (RF) – recursive feature elimination (RFE) method to select features as inputs for four machine learning (ML) models: back propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR), and Gaussian process regression (GPR). After feature selection, the prediction accuracies of single-sensor models were ranked as: MS > RGB > TIR. The R2 values for the four ML models were in the range of 0.54–0.75. Among multi-sensor combinations, the GPR with MS + RGB + TIR input features achieved the best results with R2 = 0.89 and RPD = 2.52. Further, the dataset was partitioned into six subsets based on location and cultivar variety to evaluate model transferability. The results showed that the transferability largely suffered during the bivariate conditions of different varieties at different locations; the transferability of the model was average improved by 11 % when GPR was combined with transfer component analysis (TCA). The accuracy and transferability of the NNI estimation models significantly improved, offering valuable guidance and methodological support for diagnosing the nitrogen nutrient status of wheat.

Effect of Incidence Angle on Temperature Measurement of Solar Panel with Unmanned Aerial Vehicle-Based Thermal Infrared Camera

This study utilizes Thermal Infrared (TIR) imaging technology to detect hotspots in photovoltaic (PV) modules of solar power plants. Unmanned aerial vehicle (UAV)-based TIR imagery is crucial for efficiently analyzing fault detection in solar power plants. This research explores optimal operational parameters for generating high-quality TIR images using UAV technology. In addition to existing variables such as humidity, emissivity, height, wind speed, irradiance, and ambient temperature, newly considered variables including the angle of incidence between the target object and the thermal infrared camera are analyzed for their impact on TIR images. Based on the solar power plant’s tilt (20°) and the location coordinate data of the hotspot modules, the inner and outer products of the vectors were used to obtain the normal vector and angle of incidence of the solar power plant. It was discovered that the difference between measured TIR temperature data and Land Surface Temperature (LST) data varies with changes in the angle of incidence. The analysis presented in this study was conducted using multiple regression analysis to explore the relationships between dependent and independent variables. The Ordinary Least Squares (OLS) regression model employed was able to explain 63.6% of the variability in the dependent variable. Further, the use of the Condition Number (Cond. No.) and the Variance Inflation Factor (VIF) revealed that the multicollinearity among all variables was below 10, ensuring that the independence among variables was well-preserved while maintaining statistically significant correlations. Furthermore, a positive correlation was observed with the actual measured temperature values, while a negative correlation was observed between the TIR image data values and the angle of incidence. Moreover, it was found that an angle of incidence between 15° and 20° yields the closest similarity to LST temperature data. In conclusion, our research emphasizes the importance of adjusting the angle of incidence to 15–20° to enhance the accuracy of TIR imaging by mitigating overestimated TIR temperature values.

Detection of Foot Rot Disease in Sweet Potatoes Using a Thermal Infrared Camera

Detection of Foot Rot Disease in Sweet Potatoes Using a Thermal Infrared Camera

A Model for Detecting the Presence of Pesticide Residues in Edible Parts of Tomatoes, Cabbages, Carrots, and Green Pepper Vegetables

With increased resistant pests and low crop yields, farmers especially in sub-Saharan Africa have greatly embraced usage of chemicals. These chemicals include pesticides used in gardens for better yields and also in the stalls for longer shelf life by sellers of farm products especially fresh perishables like tomatoes, cabbages, carrots, and green pepper vegetables. This, if not checked, may expose humans and animals to pesticide residues. In this research, a model for detecting the presence of pesticide residues in edible parts of vegetables (tomatoes, cabbages, carrots, and green pepper) was developed. A dataset consisting of 1094 images of both contaminated and uncontaminated vegetables including tomatoes, cabbages, carrots, and green pepper with a scale magnification of 800 × 1276 pixels taken using InfiRay P2 pro Night Vision Go Mini Infrared Thermal camera with a thermal module was taken from different daily markets in Mbarara city, South Western Uganda. Image preprocessing was done by noise removal and grayscale conversion. Both the neural network and median filter were applied on the images. A python script was used to cluster the dataset based on chemical concentrations rates of 0.1–0.8 mg/kg, 0.9–1.3 mg/kg, and 1.4–1.7 mg/kg, and this was done for both training and testing dataset. Feature extraction was done to detect the presence of mancozeb, dioxacarb, and methidathion residues from the cleaned images. To test the developed model, convolutional neural networks transfer learning models, Inception V3, VGG16, VGG19, ResNet50, and the scratch model were used. From the results obtained, Inception V3 achieved better performance compared to other transfer learning models with 96.77% followed by VGG16 at 86.98%, VGG19 at 87.56%, and ResNet50 at 82.11%, whereas the developed scratch model achieved 89.13% classification accuracy.

TIR‐YOLO‐ADAS: A thermal infrared object detection framework for advanced driver assistance systems

AbstractAn object detection framework using thermal infrared (TIR) cameras is proposed to meet the needs of an advanced driver assistance system (ADAS) operating at night‐time and in low‐visibility conditions. The proposed detection framework, referred to as TIR‐YOLO‐ADAS, is an improvement of YOLOX for TIR object detection in ADAS. First, to address the disadvantages of TIR objects, the part of the attention mechanism is designed to enhance the discriminative ability of feature maps in the spatial and channel dimensions. Second, a focal loss function is used as the confidence loss function to enable the framework to focus on detection tasks of difficult, misclassified targets in the process of network training. The results of the ablation experiment on the Forward‐looking infrared (FLIR) thermal ADAS dataset indicate that the proposed framework significantly improves the performance of TIR object detection. Comparative experimental results further show that TIR‐YOLO‐ADAS performs favourably when compared with three representative detection algorithms. To evaluate the practicality and feasibility of the proposed framework in various applications, a qualitative assessment in real road scenarios was conducted. The experimental results confirm that the proposed framework performs promisingly and could be integrated into vehicle platforms as an ADAS module.

Drone with Mounted Thermal Infrared Cameras for Monitoring Terrestrial Mammals

This study investigates the use of a drone equipped with a thermal camera for recognizing wild mammal species in open areas and to determine the sex and age of red deer (Cervus elaphus) and roe deer (Capreolus capreoulus) in a 13 km2 moor in Denmark. Two separate surveys were conducted: (1) To achieve drone images for the identification of mammals, the drone was tested around a bait place with a live wildlife camera that was often visited by European badger (Meles meles), stone marten (Martes foina), European hare (Lepus europaeus), roe deer and cattle (Bos taurus). The thermal images of wild animal species could be distinguished by their body measures when the drone filmed with the camera pointed perpendicular to the ground in an altitude range of 50–120 m. A PCA ordination showed nonoverlapping body characteristics and MANOVA showed that the combined body measures used were significantly distinctive F = 6.8, p < 0.001. The reactions and behavioral responses of the different species to the altitude and noise of the drone were also tested in this place. (2) On a 13 km2 moor, a drone was used for a population study of deer. Red deer and roe deer were counted and separated by body measures. Red deer individuals could, at the right altitude, be separated into adults and calves, and males and females. Body length was the most conclusive body measure, and therefore a reference measurement in the field is recommended. The frame thermal images were effective in species recognition and for use in population studies of deer, and are thought to be more time-efficient and less invasive than traditional methods. In autumn, the number of stags and the life stage of red deer, as well as the distribution of deer in different types of vegetation, could be determined.

Quantifying Within-Flight Variation in Land Surface Temperature from a UAV-Based Thermal Infrared Camera

Quantifying Within-Flight Variation in Land Surface Temperature from a UAV-Based Thermal Infrared Camera

Real-time multispectral transmission of hard tooth tissues and dental composites with their heating.

The composites tested were: Filtek Universal Restorative (FUR), Filtek One Bulk Fill (FBF), Tetric EvoCeram (TEC), Tetric Bulk Fill (TBF) and Tetric PowerFill (TPF). The new LCU Pinkwave (a four-wavelength source manufactured by Vista Apex, USA) was placed either centrally or eccentrically for 3mm above the sample. A Fiber spectrometer detected irradiation and Infrared Thermal camera polymerization temperatures. All eccentric LCU positions significantly weaken transmitted spectra for all composites in both thickness, jeopardizing Blue light. The LCU position did not affect transmitted irradiation for tooth tissues. The reduction in wavelength intensity when penetrating through thicker compared to thinner composite samples was 62%, 50% and 31% for Blue, NIR and Red, respectively, and 90%, 50% and 35% for tooth tissue samples, respectively. The temperature of bulk fill composites with additional photoinitiators rises faster. Eccentric LCU positions cause a significant decrease in both speed and the maximal value of temperature rise. Red and NIR irradiations contribute to the polymerization temperature. Tested LCU source cause considerable inhomogeneity in the emitted and transmitted spectra. Tooth tissues homogenize irradiation, but drastically attenuates it. Red light has better potential than Blue light concerning penetration and could be further investigated as the wavelength for activation of an adjusted photoinitiator.

Case study of detection and monitoring of wildlife by UAVs equipped with RGB camera and TIR camera

Purpose Observation of the animal world is an important component of nature surveys. It provides a number of different information concerning aspects such as population sizes, migration directions, feeding sites and many other data. The paper below presents the results from the flights of an unmanned aerial vehicle (UAV) aimed at detecting animals in their natural environment. Design/methodology/approach The drone used in the research was equipped with RGB and thermal infrared (TIR) cameras. Both cameras, which were mounted on the UAV, were used to take pictures showing the concentration of animals (deer). The overview flights were carried out in the villages of Podlaskie Voivodeship: Szerokie Laki, Bialousy and Sloja. Research flights were made in Bialousy and Sloja. A concentration of deer was photographed during research flights in Sloja. A Durango unmanned platform, equipped with a thermal imaging camera and a Canon RGB camera, was used for research flights. The pictures taken during the flights were used to create orthomaps. A multicopter, equipped with a GoPro camera, was used for overview flights to film the flight locations. A flight control station was also used, consisting of a laptop with MissionPlanner software. Findings Analysis of the collected images has indicated that environmental, organisational and technical factors influence the quality of the information. Sophisticated observation precision is ensured by the use of high-resolution RGB and TIR cameras. A proper platform for the cameras is an UAV provided with advanced positioning systems, which makes it possible to create high-quality orthomaps of the area. When observing animals, the time of day (temperature contrast), year season (leaf ascent) or flight parameters is important. Originality/value The paper introduces the conclusions of the research flights, pointing out useful information for animal observation using UAVs.

Experimental investigation of the influence of nanoparticles on droplet spreading dynamics and heat transfer during early stage cooling

With the increasing interest in improving high-performance cooling systems, research on droplet cooling requires more complex methods for enhancing heat transfer. Hence, we investigate the effect of nanoparticle presence and concentration on droplet spreading dynamics and heat transfer during the early stages of spreading on a heated surface. For this purpose, water and three water-based TiO2 nanofluid droplets (0.2, 0.5, and 1 wt%) are tested at four release heights impinging on a sapphire substrate below boiling point (80 °C). With an emissive TiAlN coating in the infrared (IR) domain, the surface temperature of the substrate is measured via a high-speed IR thermal camera. Droplet spreading is simultaneously monitored with two high-speed black/white cameras. The thermal and rheological properties are experimentally characterized for accurate results and to investigate only the nanoparticle presence in the fluid. The captured temperature fields are analyzed by solving the inverse heat conduction problem. We observe that all nanofluid droplets spread on a heated surface marginally less than water droplets. To the best of the authors’ knowledge, this study is the first to reveal that during the early stages of droplet spreading, the presence of nanoparticles and the resulting change in viscosity primarily influence heat transfer through the modification of the droplet spreading diameter, rather than changes in thermal properties.

Multi-modal image matching to colorize a SLAM based point cloud with arbitrary data from a thermal camera

Thermal mapping of buildings can be one approach to assess the insulation, which is important in regard to upgrade buildings to increase energy efficiency and for climate change adaptation. Personal laser scanning (PLS) is a fast and flexible option that has become increasingly popular to efficiently map building facades. However, some measurement systems do not include sufficient colorization of the point cloud. In order to detect, map and reference any damages to building facades, it is of great interest to transfer images from RGB and thermal infrared (TIR) cameras to the point cloud. This study aims to answer the research question if a flexible tool can be developed, which enable such measurements with high spatial resolution and flexibility. Therefore, an image-to-geometry registration approach for rendered point clouds is combined with a deep learning (DL)-based image feature matcher to estimate the camera pose of arbitrary images in relation to the geometry, i.e. the point cloud, to map color information. We developed a research design for multi-modal image matching to investigate the alignment of RGB and TIR camera images to a PLS point cloud with intensity information using calibrated and un-calibrated images. The accuracies of the estimated pose parameters reveal the best performance of the registration for pre-calibrated, i.e. undistorted, RGB camera images. The alignment of un-calibrated RGB and TIR camera images to a point cloud is possible if sufficient and well-distributed 2D-3D feature matches between image and point cloud are available. Our workflow enables the colorization of point clouds with high accuracy using images with very different radiometric characteristics and image resolutions. Only a rough approximation of the camera pose is required and hence the approach reliefs strict sensor synchronization requirements.

In Situ Analysis of Curling Defects in Powder Bed Fusion of Polyamide by Simultaneous Application of Laser Profilometry and Thermal Imaging

Additive manufacturing (AM) is one of the key technologies in the global manufacturing market within various application sectors. The unique capabilities of AM enable high structural and part complexity, low material waste, and benefits in productivity by reducing design cycles and time to market. Efficient real-time quality control is still an important challenge in AM. In this paper, a real-time and in situ approach for monitoring the process in powder bed fusion of polyamide (PBF-LB/P/PA12) is proposed using the simultaneous application of two individual sensors, enabling the overlay and direct comparison of independent output data. An industrial grade laser profilometer and a thermal infrared (IR) camera were successfully integrated into a commercial system for PBF-LB/P. Artificially created curling defects were induced in a reproducible way by the manipulation of process parameters. The radiometric data was evaluated and processed into 3D topology and profile measurements to highlight peaks and curling progression. The results measured using different powder bed conditions were contrasted with corresponding thermographic data to prove the thermal visibility of curling and the influence of inhomogeneous temperature distribution on geometrical powder surface defects. The experimental setup enables the measuring of the entire powder bed surface inside the machine, with no limitations to sub-areas. Results indicate the measurable presence of curling and related temperature influences. When curling reached maximum values, inverted warpage into the negative z-direction was detected at part center as a further effect. These results can be used for improving real-time quality control in AM.

Micro-thermography for imaging ice crystal growth and nucleation inside non-transparent materials

Ice crystal growth and nucleation rate measurements are usually done using light microscopy in liquid and transparent samples. Yet, the understanding of important practical problems depends on monitoring ice growth inside solid materials, for example how rapid ice growth leads to structural damage of food, or how the final structure of cementitious materials is affected by ice during curing. Imaging crystal growth inside solid materials cannot be done with visible light and is intrinsically more challenging than visible light imaging. Thermography is a technique that uses thermal (infrared) cameras to monitor temperature changes in a material, and it has been used to provide a qualitative description of ice propagation with a low spatial resolution. Here, we describe a method that uses a novel micro-thermography system to image ice nucleation and growth inside non-transparent samples. This method relies on two major components: a cold stage with accurate temperature control (±0.001 °C) and a thermal camera with high spatial and temperature resolution. Our experiments include imaging of ice formation and growth in pure water first and then inside plant leaves used as a model for a non-transparent material. An ice growth rate of 2.2 mm/s was measured inside a plant leaf at -12 °C, and ice nucleation in single plant cells was observed as a hotspot having a diameter of 160 µm. The results presented here provide an experimental proof that high-quality imaging of ice growth is achievable, thus paving the way for quantitative measurements of ice growth kinetics and ice nucleation in solid materials.

Spatial corn canopy temperature extraction: How focal length and sUAS flying altitude influence thermal infrared sensing accuracy

Irrigation water management starts with quantifying irrigation prescriptions based on crop water requirements at a spatial scale. The most accepted and effective method for estimating this water need is to use the canopy temperature of the plants. The use of small unmanned aerial systems (sUAS) with a thermal infrared camera has long been established as an effective method of measuring plant canopy temperatures at a spatial scale. However, concerns still exist about the accuracy of canopy temperatures collected by these systems and how imagery collection altitude or camera viewing angle affects the temperature estimation. To address these concerns, this study was designed to evaluate the effects of flying altitude of airborne thermal infrared (TIR) imagery and camera view angle on corn-canopy temperature sensing accuracy and image quality. Three different thermal cameras with focal lengths of 9 mm, 13 mm, and 19 mm were used, and each camera was flown at an altitude of 30 m, 50 m, and 70 m. Thermal cameras were mounted on an sUAS and flown over a 6000 m2 cornfield. The sUAS was flown at a speed of 3 m/s on autopilot mode guided by pre-planned missions. The thermal camera was triggered once per second, and imagery was geotagged using an external GPS unit. Images obtained using different combinations of focal lengths and flight altitudes were processed and converted into orthomosaics. Calibration was conducted using ground-based temperature reference panels and temperature maps were created from the orthomosaics. The orthomosaics were examined for the accuracy of the corn-canopy temperature sensing, ability to differentiate between hot and cold surfaces, ease of image stitching/developing orthomosaics, geometric accuracy, image quality, and spatial resolution. The results indicated that with the combination of appropriate camera focal length, altitude, and image calibration techniques, a canopy temperature map of crops with a temperature error of less than 2 °C from the actual canopy temperature can be produced. A narrow-angle thermal camera flying at low altitudes (<50 m) was found to be the least suitable combination for corn canopy temperature sensing. The most appropriate combination for temperature estimation of corn canopies was with a 13 mm focal length camera flying at an altitude of 50 m above ground level.

UAV-based RGB and TIR imaging for geothermal monitoring: a case study at Kestanbol geothermal field, Northwestern Turkey.

Kestanbol is one of the most important geothermal fields in NW Turkey. This study conducted the first-ever surveys over a 10ha reach of the Kestanbol geothermal field using an unmanned aerial vehicle (UAV) equipped with visible (RGB) and thermal infrared (TIR) cameras. Low-altitude flights below 40m above the ground were operated above the Kestanbol geothermal field. Approximately 3500 RGB and TIR images were captured using the UAV. We recorded high-resolution RGB and TIR data of the Kestanbol geothermal field and applied the structure from motion (SfM) algorithm to identify the distribution of geothermal springs and seeps. The Kestanbol geothermal field was monitored to create a georeferenced RGB orthophoto, RGB 3D surface model, thermal anomaly map, and digital surface model (DSM) of the area with centimeter-level accuracy. In the TIR orthophoto, the surface temperature in the geothermal field was found to be between 15 and 75°C. All the thermal anomalies revealed by the survey were verified by field observations. The geothermal springs and seeps were parallel to the NE-SW regional tectonic trends. The results of this study demonstrate an effective technique for monitoring and assessing geothermal water using UAV-based RGB and TIR imaging and provide an accurate basis for geothermal development projects. RGB and TIR imaging using UAVs are considered promising methods for improving the assessment of the effects of geothermal water on the environment.