Aerial Thermal Images Research Articles

Estimation of apparent thermal inertia of roofing materials from aerial thermal imagery

The rapid expansion of urban areas and soil sealing is enhancing the Urban Heat Island (UHI) phenomenon, especially during heat waves. The different thermal inertia of the building materials compared to natural surfaces is one of the major driving factors of UHI. The present contribution aims to test a methodology for mapping the Apparent Thermal Inertia (ATI)—a proxy that can be derived from remote sensing data—of roofing surfaces at the scale of an entire city and with a high spatial resolution. Day and night aerial thermal images with the resolution of 0.5 m were acquired over two test areas in Bologna (Italy), together with satellite multispectral data. Statistics on the buildings in the test areas are computed considering different classes of roofing materials (e.g. bituminous sheath, clay tiles, metal sheet, gravel tiles). Observed median ATI values for each class range from 0.03 to 0.09 K-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} with interquartile ranges between 0.02 and 0.14 K-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}, so the intra-class variability in some cases appears higher than the variability among different material classes, proving the importance of ATI mapping for UHI investigations.

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Multi-sensor drone survey of ancestral agricultural landscapes at Picuris Pueblo, New Mexico

Although aircraft-acquired lidar has proven to be a transformative technology for archaeology in forested regions around the world, drone-acquired lidar systems that could potentially offer higher-resolution imagery at much lower cost remain difficult to deploy in field settings, while the data they produce are prone to large errors and are challenging to process. This paper presents results of a study that employs a new ultra-compact drone-based lidar system, alongside aerial thermal and visible light imaging, to document ancient agricultural landscapes at Picuris Pueblo, New Mexico. We discuss the advantages of this new instrumentation, various approaches to field data collection, and as well as our novel data processing and filtering methods, which collectively offer key methodological advances for archaeological investigations using drone-based lidar. Results reveal extensive remains of a vast system of terraces and stone-built field systems, preserved below the modern piñon-juniper-ponderosa forests of the Picuris Reservation and surrounding areas. These findings offer new perspectives on the scale and intensity of past agricultural activities in this highland region of the American Southwest, while demonstrating the power of combining multiple drone-based remote sensing datasets with detailed surface surveys to aid in the discovery, mapping, and interpretation of archaeological landscapes in forested regions.

Reply to Comments by Kale and Pande on: “Machine-learning based re-classification of the stratigraphy of the Rajahmundry Traps, India”

Hasandağ is a double-peaked long-dormant volcano, where associated volcanic risk is poorly known and needs to be assessed. Weak fumaroles and water vapor emissions have been observed at several vents, on the western flank of the Greater Hasandağ summit, between 3000 and 3100 m (a.s.l.). The highest temperature and CO2 values measured in those vents were ~ 70 °C and > 100,000 ppm respectively. Multisource and wide-period thermal remote sensing analyses were performed to investigate the thermal state and to test the possibilities of remote thermal monitoring of the volcano.MODIS daily surface temperature data were analyzed for a period of 18 years. We used a statistical approach using the Seasonal Decomposition of Time-series procedure to decompose the temperature data into the trend, seasonal, and remainder components. The trend component was used to analyze temporal thermal anomalies in comparison to the decomposed meteorological temperature data. 56 nighttime ASTER TIR imagery acquired between 2001 and 2018 had been used to derive and analyze the Surface temperature, Surface temperature anomaly and Relative radiative heat flux. High-resolution ground-based and aerial thermal mosaic images were built to investigate the thermal radiation over the summit fumarole zone and its structural dependence.A baseline reference value for thermal monitoring of the fumarole zone was achieved using remotely sensed data and time-series decomposition of long-term TIR data. The resolution of the thermal imagery positively affects the mapping and monitoring capability. A permanent ground-based thermal observation station should be established for direct and remote thermal monitoring of the fumarole zone.

Internet of Things Platform for Photovoltaic Maintenance Management: Combination of Supervisory Control and Data Acquisition System and Aerial Thermal Images

Suitable maintenance management plants of solar photovoltaic plants are required for global energy demands. The volume and variety of data acquired by thermographic cameras carried by unmanned aerial vehicles and Supervisory Control and Data Acquisition Systems increase the complexity of fault detection and diagnosis. The maintenance industry is requiring novel fault detection techniques that can be implemented in Internet of Thing platforms to automate the analysis and increase the suitability and reliability of the results. This paper presents a novel platform built with PHP, HTML, CSS and JavaScript for the combined analysis of data from Supervisory Control and Data Acquisition Systems and thermal images. The platform is designed. A real case study with thermal images and time series data from the same photovoltaic plant is presented to test the viability of the platform. The analysis of thermal images showed a 97% of accuracy for panel detection and 87% for hot spot detection. Shapelets algorithm is selected for time series analysis, providing an 84% of accuracy for the pattern selected by user. The platform has proven to be a flexible tool that can be applied for different solar plants through data upload by users.

Drone Aided Thermal Mapping for Selective Irrigation of Localized Dry Spots

We report a smart irrigation system that allows selective irrigation of localized dry spots in an agricultural field. The proposed irrigation system uses a quadcopter drone equipped with a Thermal Infrared (TIR) camera and a GPS module to generate georeferenced thermal images that indicate the area and location of the dry spots in a survey area. Drones navigate and acquire aerial thermal images, which are then processed by an onboard edge intelligence module along with flight data (GPS coordinates, altitude, and drone direction). Smart sprinklers deployed on the field are able to wirelessly receive the coordinates of dry spots so they can be irrigated selectively. A terrestrial edge unit generates an irrigation pattern for the smart sprinklers using a pre-trained machine learning (ML) model to generate an irrigation pattern by varying the head rotation angle ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta$ </tex-math></inline-formula> ) and the water flow control valve rotation angle( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\emptyset$ </tex-math></inline-formula> ) of the smart sprinkler.

Using Aerial Thermal Imagery to Evaluate Water Status in Vitis vinifera cv. Loureiro.

The crop water stress index (CWSI) is a widely used analytical tool based on portable thermography. This method can be useful in replacing the traditional stem water potential method obtained with a Scholander chamber (PMS Model 600) because the latter is not feasible for large-scale studies due to the time involved and the fact that it is invasive and can cause damage to the plant. The present work had three objectives: (i) to understand if CWSI estimated using an aerial sensor can estimate the water status of the plant; (ii) to compare CWSI from aerial-thermographic and portable thermal cameras with stem water potential; (iii) to estimate the capacity of an unmanned aerial vehicle (UAV) to calculate and spatialize CWSI. Monitoring of CWSI (CWSIP) using a portable device was performed directly in the canopy, by measuring reference temperatures (Tdry, Twet, and canopy temperature (Tc)). Aerial CWSI calculation was performed using two models: (i) a simplified CWSI model (CWSIS), where the Tdry and Twet were estimated as the average of 1% of the extreme temperature, and (ii) an air temperature model (CWSITair) where air temperatures (Tair + 7 °C) were recorded as Tdry and in the Twet, considering the average of the lowest 33% of histogram values. In these two models, the Tc value corresponded to the temperature value in each pixel of the aerial thermal image. The results show that it was possible to estimate CWSI by calculating canopy temperatures and spatializing CWSI using aerial thermography. Of the two models, it was found that for CWSITair, CWSIS (R2 = 0.55) evaluated crop water stress better than stem water potential. The CWSIS had good correlation compared with the portable sensor (R2 = 0.58), and its application in field measurements is possible.

Multisource and temporal thermal infrared remote sensing of Hasandağ Stratovolcano (Central Anatolia, Turkey)

Hasandağ is a double-peaked long-dormant volcano, where associated volcanic risk is poorly known and needs to be assessed. Weak fumaroles and water vapor emissions have been observed at several vents, on the western flank of the Greater Hasandağ summit, between 3000 and 3100 m (a.s.l.). The highest temperature and CO2 values measured in those vents were ~ 70 °C and > 100,000 ppm respectively. Multisource and wide-period thermal remote sensing analyses were performed to investigate the thermal state and to test the possibilities of remote thermal monitoring of the volcano.MODIS daily surface temperature data were analyzed for a period of 18 years. We used a statistical approach using the Seasonal Decomposition of Time-series procedure to decompose the temperature data into the trend, seasonal, and remainder components. The trend component was used to analyze temporal thermal anomalies in comparison to the decomposed meteorological temperature data. 56 nighttime ASTER TIR imagery acquired between 2001 and 2018 had been used to derive and analyze the Surface temperature, Surface temperature anomaly and Relative radiative heat flux. High-resolution ground-based and aerial thermal mosaic images were built to investigate the thermal radiation over the summit fumarole zone and its structural dependence.A baseline reference value for thermal monitoring of the fumarole zone was achieved using remotely sensed data and time-series decomposition of long-term TIR data. The resolution of the thermal imagery positively affects the mapping and monitoring capability. A permanent ground-based thermal observation station should be established for direct and remote thermal monitoring of the fumarole zone.

Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal images that cause poor performance for various state-of-the-art object detection algorithms. Though many deep-learning-based object detection algorithms have shown impressive performance for generic object detection tasks, their ability to detect smaller objects in the aerial thermal images is analyzed through this study. This work carried out the performance evaluation of Faster R-CNN and single-shot multi-box detector (SSD) algorithms with different backbone networks to detect human targets in aerial view thermal images. For this purpose, two standard aerial thermal datasets having human objects of varying scale are considered with different backbone networks, such as ResNet50, Inception-v2, and MobileNet-v1. The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respectively. SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU. Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset. The experimental results demonstrated the application of Faster R-CNN and SSD algorithms for human detection in aerial view thermal images, and the impact of varying backbone network and anchor parameters on the performance improvement of these algorithms.

Characteristics of optical flow from aerial thermal imaging, “thermal flow”

AbstractThis study explores the utility of optical flow calculated from thermal imaging cameras, “thermal flow,” mounted on an aircraft for localization in day and night conditions. Our sensor implementation utilizes a long wave infrared (LWIR) micro sensor to capture sequences of thermal images and an on‐board computer to compute an optical flow estimate. We compared the performance of optical flow from the LWIR camera with the output of visible spectrum optical flow sensor. Flights were conducted spanning a 24 h window to explore how thermal flow performs relative to optical flow as the ground heats and cools. Agreement between optical and thermal flow was found during daylight when both sensors were functional. Additionally, thermal flow results were reliable in the middle of the day through to late evening, gradually degrading until shortly after sunrise.

Predicting Soil Water Content on Rainfed Maize through Aerial Thermal Imaging

Restrictions on soil water supply can dramatically reduce crop yields by affecting the growth and development of plants. For this reason, screening tools that can detect crop water stress early have been long investigated, with canopy temperature (CT) being widely used for this purpose. In this study, we investigated the relationship between canopy temperature retrieved from unmanned aerial vehicles (UAV) based thermal imagery with soil and plant attributes, using a rainfed maize field as the area of study. The flight mission was conducted during the late vegetative stage and at solar noon, when a considerable soil water deficit was detected according to the soil water balance model used. While the images were being taken, soil sampling was conducted to determine the soil water content across the field. The sampling results demonstrated the spatial variability of soil water status, with soil volumetric water content (SVWC) presenting 10.4% of variation and values close to the permanent wilting point (PWP), reflecting CT readings that ranged from 32.8 to 40.6 °C among the sampling locations. Although CT correlated well with many of the physical attributes of soil that are related to water dynamics, the simple linear regression between CT and soil water content variables yielded coefficients of determination (R2) = 0.42, indicating that CT alone might not be sufficient to predict soil water status. Nonetheless, when CT was combined with some soil physical attributes in a multiple linear regression, the prediction capacity was significantly increased, achieving an R2 value = 0.88. This result indicates the potential use of CT along with certain soil physical variables to predict crop water status, making it a useful tool for studies exploring the spatial variability of in-season drought stress.

Artificial intelligence-enabled Internet of Things-based system for COVID-19 screening using aerial thermal imaging

Internet of Things (IoT) has recently brought an influential research and analysis platform in a broad diversity of academic and industrial disciplines, particularly in healthcare. The IoT revolution is reshaping current healthcare practices by consolidating technological, economic, and social views. Since December 2019, the spreading of COVID-19 across the world has impacted the world’s economy. IoT technology integrated with Artificial Intelligence (AI) can help to address COVID-19. UAVs equipped with IoT devices can collect raw data that demands computing and analysis to make intelligent decision without human intervention. To mitigate the effect of COVID-19, in this paper, we propose an IoT-UAV-based scheme to collect raw data using onboard thermal sensors. The thermal image captured from the thermal camera is used to determine the potential people in the image (of the massive crowd in a city), which may have COVID-19, based on the temperature recorded. An efficient hybrid approach for a face recognition system is proposed to detect the people in the image having high body temperature from infrared images captured in a real-time scenario. Also, a face mask detection scheme is introduced, which detects whether a person has a mask on the face or not. The schemes’ performance evaluation is done using various machine learning and deep learning classifiers. We use the edge computing infrastructure (onboard sensors and actuators) for data processing to reduce the response time for real-time analytics and prediction. The proposed scheme has an average accuracy of 99.5% using various performance evaluation metrics indicating its practical applicability in real-time scenarios.

A Posteriori Outlier Rejection Approach Owing to the Well-ordering Property of a Sample Consensus Method for the Stitching of Drone-based Thermal Aerial Images

Abstract In this study, the authors generate panoramic images using feature-based registration for drone-based aerial thermal images. In the case of drone aerial images, the distortion of the photographing angle due to the unstableness in the shooting altitude deteriorates the performance of the stitching. Furthermore, for the thermal aerial images, the same objects photographed at the same time zone may have different colors due to the relative temperature, which may lead to a more severe condition to be stitched. Applying the scale-invariant feature transform descriptor, they propose a posteriori outlier rejection scheme to estimate the hypothesis of the mapping function for the stitching of consecutive thermal aerial images. By extension of the method of optimal choice of initial candidate inliers (OCICI) and a posteriori outlier rejection scheme using cross-correlation calculus, the authors obtained elaborate stitching of thermal aerial images. Their proposed method is numerically verified for its quality by comparing it with other possible approaches of post-outlier rejection treatments employed of OCICI. Also, after the Poisson blending using the finite difference method is conducted, the stitching performance is compared with some benchmark software such as Matlab-toolbox, OpenCV, Autopano Giga, Hugin, and PTGui.

A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images

Multi-sensor imagery data has been used by researchers for the image semantic segmentation of buildings and outdoor scenes. Due to multi-sensor data hunger, researchers have implemented many simulation approaches to create synthetic datasets, and they have also synthesized thermal images because such thermal information can potentially improve segmentation accuracy. However, current approaches are mostly based on the laws of physics and are limited to geometric models’ level of detail (LOD), which describes the overall planning or modeling state. Another issue in current physics-based approaches is that thermal images cannot be aligned to RGB images because the configurations of a virtual camera used for rendering thermal images are difficult to synchronize with the configurations of a real camera used for capturing RGB images, which is important for segmentation. In this study, we propose an image translation approach to directly convert RGB images to simulated thermal images for expanding segmentation datasets. We aim to investigate the benefits of using an image translation approach for generating synthetic aerial thermal images and compare those approaches with physics-based approaches. Our datasets for generating thermal images are from a city center and a university campus in Karlsruhe, Germany. We found that using the generating model established by the city center to generate thermal images for campus datasets performed better than using the latter to generate thermal images for the former. We also found that using a generating model established by one building style to generate thermal images for datasets with the same building styles performed well. Therefore, we suggest using training datasets with richer and more diverse building architectural information, more complex envelope structures, and similar building styles to testing datasets for an image translation approach.

Calibration and Image Processing of Aerial Thermal Image for UAV Application in Crop Water Stress Estimation

Crop monitoring is a very important area of precision agriculture and smart farming. Through an accurate monitoring, it is possible to more efficiently manage the irrigation, fertilization, and pest control. In this study, we propose aerial thermal image calibration method and thermal image processing techniques to analyze the water stress level of fruit trees under different irrigation conditions. The calibration was performed using Gaussian process regression, and it was demonstrated as an appropriate regression method because it satisfied all requirements including the residuals’ normality, independence, and homoscedasticity. In addition, an appropriate image processing technique was necessary to selectively extract only the canopy temperature from the aerial thermal images, while excluding irrelevant elements such as the soil and other objects. For the image processing techniques, three methods (Gaussian mixture model, Otsu binarization algorithm, and Otsu binarization algorithm after Gaussian blurring) were employed. The Gaussian mixture model provided the highest accuracy and stable results for the extraction of the canopy temperature. After the aerial thermal images were subjected to calibration and image processing, the degree above nonstressed canopy (DANS) water stress index was calculated for the fruit trees under different water supply conditions. The distribution of the DANS water stress index was similar to the distribution of the canopy temperature and inversely proportional to the amount of supplied water content. Therefore, we expect that the DANS water stress index, calculated using the calibration and image processing techniques proposed in this study, can be a reliable measure for the estimation of the water stress of crops for the application of aerial infrared techniques to remote sensing.

Assessing the Performance of a Low-Cost Thermal Camera in Proximal and Aerial Conditions

The development of low-cost miniaturized thermal cameras has expanded the use of remotely sensed surface temperature and promoted advances in applications involving proximal and aerial data acquisition. However, deriving accurate temperature readings from these cameras is often challenging due to the sensitivity of the sensor, which changes according to the internal temperature. Moreover, the photogrammetry processing required to produce orthomosaics from aerial images can also be problematic and introduce errors to the temperature readings. In this study, we assessed the performance of the FLIR Lepton 3.5 camera in both proximal and aerial conditions based on precision and accuracy indices derived from reference temperature measurements. The aerial analysis was conducted using three flight altitudes replicated along the day, exploring the effect of the distance between the camera and the target, and the blending mode configuration used to create orthomosaics. During the tests, the camera was able to deliver results within the accuracy reported by the manufacturer when using factory calibration, with a root mean square error (RMSE) of 1.08 °C for proximal condition and ≤3.18 °C during aerial missions. Results among different flight altitudes revealed that the overall precision remained stable (R² = 0.94–0.96), contrasting with the accuracy results, decreasing towards higher flight altitudes due to atmospheric attenuation, which is not accounted by factory calibration (RMSE = 2.63–3.18 °C). The blending modes tested also influenced the final accuracy, with the best results obtained with the average (RMSE = 3.14 °C) and disabled mode (RMSE = 3.08 °C). Furthermore, empirical line calibration models using ground reference targets were tested, reducing the errors on temperature measurements by up to 1.83 °C, with a final accuracy better than 2 °C. Other important results include a simplified co-registering method developed to overcome alignment issues encountered during orthomosaic creation using non-geotagged thermal images, and a set of insights and recommendations to reduce errors when deriving temperature readings from aerial thermal imaging.

A Preliminary Study on Leakage Detection of Deteriorated Underground Sewer Pipes Using Aerial Thermal Imaging

Due to less visibility, underground facilities such as sewer pipelines are subjected to degradation until a major failure occurs. So, leakage detection of deteriorated underground sewer pipes is necessary to prevent expensive rehabilitation costs. The major causes of the frequent occurrence of ground subsidence and sewer leakage are excessive use of groundwater, reckless exploitation of natural resources, etc. In this study, the aerial thermal imaging (ATI) technique has been used to identify the leakage points in sewer pipelines by analyzing surface thermal diffusion behavior of the ground surface. Leakage is suspected at points of anomalous temperature variation. Then, ground-penetrating radar and closed-circuit television surveys have been carried out to strengthen the suspicion of sewer leakage. Irregular, erratic parabolic-shaped lines are observed at the points where the leakage is suspected through ATI. Finally, the test-pit excavation and simple cone penetration tests have been carried out which confirms the sewer leakage points. The leakage points are confirmed by a greater depth of penetration. The results of the cone penetration test carried out in E-21 and 23-24 sewer pipelines show that the depth of penetration at the leakage points is about 35 cm, which is greater than the depth of penetration at other points. So, it can be concluded that the aerial thermal imaging technique is an effective method to predict leakage in deteriorated underground sewer pipelines.

Detection and quantification of low submarine groundwater discharge flows by radionuclides to support conceptual hydrogeological model of porous aquifers

Submarine groundwater discharge (SGD) is considered as a good indicator of the natural behavior of coastal aquifers especially in the case of intense groundwater withdrawal or when the aquifer recharge conditions are limited. Indeed, a decrease in SGD flows would result in saline intrusion compromising the groundwater reservoir use. The need for SGD studies in Mediterranean and semi-arid environments have long been recognized and little is known on low and diffuse flows. For this reason, the detection and quantification of low and diffuse SGD flows have been attempted in a Mediterranean porous aquifer with low potential of recharge and high touristic pressure from using a multi-tracing approach combining aerial thermal infrared images (TIR), temperature (T), electric conductivity (E.C.), major ions, selected trace elements, 2H, 18O, 222Rn, 223Ra and 224Ra. The TIR images allowed the detection of slight temperature variations at the seawater surface. Contrasted lithology and strong water-rock interactions due to groundwater residence time of many decades favor the 222Rn, 223Ra and 224Ra contents in groundwater that reach the seashores. Low temperature and E.C, as well as high radon and radium activities, evidenced SGD flows in agreement with the known flow conditions within the aquifer. The quantification of SGD flows revealed in very good agreement with the known recharge rates of the aquifer that validates its conceptual hydrogeological model. For the first time in the Mediterranean or in semi-arid context, a natural tracing experiment based on geochemistry and isotope hydrology tools allowed the quantification of SGD flows to validate the hydrogeological conceptual model and to solve the water balance at the whole aquifer scale. Such a methodology could be applied worldwide to other similar coastal aquifers with low and diffuse SGD flow conditions.

Small Object Bird Detection in Infrared Drone Videos Using Mask R-CNN Deep Learning

A reliable method to estimate population sizes of wild turkeys (Meleagris gallopavo) using unmanned aerial vehicles and thermal video imaging data collected at several field sites in Texas is described. Automating the data processing of airborne survey videos provides a fast and reproducible way to count wild turkeys for wildlife management and conservation. A deep learning semantic segmentation pipeline is developed to detect and count roosting Rio-Grande wild turkeys (M.g. intermedia) which appear as small faint objects in drone-based thermal IR videos. The proposed approach to detect roosting turkeys that appear as small objects, relies on Mask R-CNN, a deep architecture semantic segmentation algorithm. This is followed by a post-processing data association and filtering (DAF) process for counting the number of roosting birds. DAF was used to eliminate false positives like rocks and other small bright objects, which often have noisy detections across temporally adjacent video frames, that can be filtered using appearance association and distance-based gating across time. Transfer learning was used to train the Mask R-CNN network by initializing using ImageNet weights. Drone-based thermal IR videos are extremely challenging due to the complexity of the natural environment including weather effects, occlusion of birds, terrain, trees, complex tree shapes, rocks, water and thermal inversion. The transect videos were collected at night at several times and altitudes to optimize data collection opportunities without disturbing the roosting turkeys. Preliminary performance evaluation using 280 video frames is promising.

Evaluating Remotely-Sensed Grapevine (Vitis vinifera L.) Water Stress Responses Across a Viticultural Region

The evolving spatial and temporal knowledge about vineyard performance through the use of remote sensing offers new perspectives for vine water status studies. This paper describes the application of aerial thermal imaging to evaluate vine water status to improve irrigation scheduling decisions, water use efficiency, and overall winegrape quality in the Coonawarra viticultural region of South Australia. Airborne infrared images were acquired during the 2016 and 2017 growing seasons in the region of Coonawarra, South Australia. Several thermal indices of crop water status (CWSI, Ig, (Tc-Ta)) were calculated that correlated with conventional soil and vine water status measures (Ψpd, Ψs, gs). CWSI and Ig could discriminate between the two cultivars used in this study, Cabernet Sauvignon (CAS) and Shiraz (SHI), as did the conventional water stress measures. The relationship between conventional vine water status measures appeared stronger with CWSI in the warmer and drier season (2016) compared to the cooler and wetter season (2017), where Ig and (Tc-Ta) showed stronger correlations. The study identified CWSI, Ig and (Tc-Ta) to be reliable indicators of vine water status under a variety of environmental conditions. This is the first study to report on high resolution vine water status at a regional scale in Australia using a combination of remote and direct sensing methods. This methodology is promising for aerial surveillance of vine water status across multiple blocks and cultivars to inform irrigation scheduling.