High-resolution Thermal Imaging Research Articles

Operational Mapping of Submarine Groundwater Discharge into Coral Reefs: Application to West Hawai‘i Island

Submarine groundwater discharge (SGD) is a recognized contributor to the hydrological and biogeochemical functioning of coral reef ecosystems located along coastlines. However, the distribution, size, and thermal properties of SGD remain poorly understood at most land–reef margins. We developed, deployed, and demonstrated an operational method for airborne detection and mapping of SGD using the 200 km coastline of western Hawai‘i Island as a testing and analysis environment. Airborne high spatial resolution (1 m) thermal imaging produced relative sea surface temperature (SST) maps that aligned geospatially with boat-based transects of SGD presence–absence. Boat-based SST anomaly measurements were highly correlated with airborne SST anomaly measurements (R2 = 0.85; RMSE = 0.04 °C). Resulting maps of the relative difference in SST inside and outside of SGD plumes, called delta-SST, revealed 749 SGD plumes in 200 km of coastline, with nearly half of the SGD plumes smaller than 0.1 ha in size. Only 9% of SGD plumes were ≥1 ha in size, and just 1% were larger than 10 ha. Our findings indicate that small SGD is omnipresent in the nearshore environment. Furthermore, we found that the infrequent, large SGD plumes (>10 ha) displayed the weakest delta-SST values, suggesting that large discharge plumes are not likely to provide cooling refugia to warming coral reefs. Our operational approach can be applied frequently over time to generate SGD information relative to terrestrial substrate, topography, and pollutants. This operational approach will yield new insights into the role that land-to-reef interactions have on the composition and condition of coral reefs along coastlines.

Thermal imaging from UAS for estimating crop water status in a Merlot vineyard in semi-arid conditions

AbstractThermal remote sensing indicators of crop water status can help to optimize irrigation across time and space. The Crop Water Stress Index (CWSI), calculated from thermal data, has been widely used in irrigation management as it has a proven association with evapotranspiration ratios. However, different approaches can be used to calculate the CWSI. The aim of this study is to identify the most robust method for estimating the CWSI in a commercial Merlot vineyard using high-resolution thermal imaging from Unoccupied Aerial Systems (UAS). To that end, three different methods were used to estimate the CWSI: Jackson’s model (CWSIj), Wet Artificial Reference Surface (WARS) method (CWSIw), and the Bellvert approach (CWSIb). A simpler indicator calculated as the difference between canopy and air temperature (Tc–Ta) was the benchmark to beat. The water status of a vine cultivar with anisohydric behavior (Merlot) in a vineyard in central Spain was assessed for two years with different agroclimatic conditions. Canopy temperature (Tc) was obtained from UAS flights at 9:00 h and 12:00 h solar hour over eight days during the irrigation period (June–August), and from vines under five different irrigation treatments. Stem water potential (SWP), stomatal conductance (gs), and leaf temperature (TL) were recorded at the time of the flights and compared with the thermal indices (CWSIj, CWSIw, CWSIb) and the benchmark indicator (Tc–Ta). Results show that the simpler indicator of water stress, Tc–Ta, performed better at identifying varying levels of crop hydration than CWSIb or CWSIw at 12:00 h. Under conditions of extreme aridity, the latter indices were less accurate than the physically-based CWSIj at 12:00 h, which had the highest correlation with SWP (r = 0.84), followed by the benchmark index Tc–Ta (r = 0.70 at 12:00). Considering the current climatic trends towards aridification, the CWSIj emerges as a useful operational tool, with robust performance across days and times of day. These results are important for irrigation management and could contribute to improving water use efficiency in agriculture.

Spatially Resolved Quantum Sensing with High-Density Bubble-Printed Nanodiamonds.

Nitrogen-vacancy (NV-) centers in nanodiamonds have emerged as a versatile platform for a wide range of applications, including bioimaging, photonics, and quantum sensing. However, the widespread adoption of nanodiamonds in practical applications has been hindered by the challenges associated with patterning them into high-resolution features with sufficient throughput. In this work, we overcome these limitations by introducing a direct laser-writing bubble printing technique that enables the precise fabrication of two-dimensional nanodiamond patterns. The printed nanodiamonds exhibit a high packing density and strong photoluminescence emission, as well as robust optically detected magnetic resonance (ODMR) signals. We further harness the spatially resolved ODMR of the nanodiamond patterns to demonstrate the mapping of two-dimensional temperature gradients using high frame rate widefield lock-in fluorescence imaging. This capability paves the way for integrating nanodiamond-based quantum sensors into practical devices and systems, opening new possibilities for applications involving high-resolution thermal imaging and biosensing.

High-resolution thermal infrared contrails images identification and classification method based on SDGSAT-1

With the rapid development of the aviation industry, the impact of contrails on global warming is becoming increasingly significant. However, due to limitations in sensor technology and data sources, precise, all-weather observations of contrails remain a significant challenge. To address this, this study utilizes thermal imagery data from the SDGSAT-1 to classify newly formed contrails based on their length, creating SDGCONTR, the first three-band thermal infrared contrail dataset with a 30-meter spatial resolution. Based on this dataset, we propose an effective contrail image classification method: the SDGContrail Images Classification Method. This method builds on the existing Multi-axis Vision Transformer deep learning model. In the preprocessing stage, it employs an innovative contrail enhancement method by creating masks based on contrail brightness temperature and brightness temperature ratio percentiles in thermal infrared images. Our method achieved optimal classification performance compared to other methods, with the F1 Score of 95.2%. Using this model, we further analyzed the geographic and diurnal distribution patterns of long and short contrails, validating the mechanism of contrail formation. Our method not only enhances the ability to conduct large-scale, all-weather, and detailed contrail observations, but also sets a new standard for environmental monitoring and aviation safety.

Surface Heat Monitoring with High-Resolution UAV Thermal Imaging: Assessing Accuracy and Applications in Urban Environments

The urban heat island (UHI) effect, where urban areas experience higher temperatures than surrounding rural regions, necessitates effective monitoring to estimate and address its diverse impacts. Many existing studies on urban heat dynamics rely on satellite data with coarse resolutions, posing challenges in analyzing heterogeneous urban surfaces. Unmanned aerial vehicles (UAVs) offer a solution by providing thermal imagery at a resolution finer than 1 m. Despite UAV thermal imaging being extensively explored in agriculture, its application in urban environments, specifically for surface temperatures, remains underexplored. A pilot project conducted in Athens, Georgia, utilized a UAV with a FLIR Vue Pro R 640 thermal camera to collect thermal data from two neighborhoods. Ground data, obtained using a handheld FLIR E6-XT infrared imaging camera, were compared with UAV thermal imagery. The study aimed to assess the accuracy of the UAV camera and the handheld camera for urban monitoring. Initial testing revealed the handheld’s accuracy but tendency to underpredict, while UAV camera testing highlighted considerations for altitude in both the rjpg and tiff image pixel conversion models. Despite challenges, the study demonstrates the potential of UAV-derived thermal data for monitoring urban surface temperatures, emphasizing the need for careful model considerations in data interpretation.

A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography

Abstract. Debris-covered glaciers exist in many mountain ranges and play an important role in the regional water cycle. However, modelling the surface mass balance, runoff contribution and future evolution of debris-covered glaciers is fraught with uncertainty as accurate observations on small-scale variations in debris thickness and sub-debris ice melt rates are only available for a few locations worldwide. Here we describe a customised low-cost unoccupied aerial vehicle (UAV) for high-resolution thermal imaging of mountain glaciers and present a complete open-source pipeline that facilitates the generation of accurate surface temperature and debris thickness maps from radiometric images. First, a radiometric orthophoto is computed from individual radiometric UAV images using structure-from-motion and multi-view-stereo techniques. User-specific calibration and correction procedures can then be applied to the radiometric orthophoto to account for atmospheric and environmental influences that affect the radiometric measurement. The thermal orthophoto reveals distinct spatial variations in surface temperature across the surveyed debris-covered area. Finally, a high-resolution debris thickness map is derived from the corrected thermal orthophoto using an empirical or inverse surface energy balance model that relates surface temperature to debris thickness and is calibrated against in situ measurements. Our results from a small-scale experiment on the Kanderfirn (also known as Kander Neve) in the Swiss Alps show that the surface temperature and thickness of a relatively thin debris layer (ca. 0–15 cm) can be mapped with high accuracy using an empirical or physical model. On snow and ice surfaces, the mean deviation of the mapped surface temperature from the melting point (∼ 0 ∘C) was 0.6 ± 2.0 ∘C. The root-mean-square error of the modelled debris thickness was 1.3 cm. Through the detailed mapping, typical small-scale debris features and debris thickness patterns become visible, which are not spatially resolved by the thermal infrared sensors of current-generation satellites. The presented approach paves the way for comprehensive high-resolution supraglacial debris thickness mapping and opens up new opportunities for more accurate monitoring and modelling of debris-covered glaciers.

Precise Drought Threshold Monitoring in Winter Wheat Using the Unmanned Aerial Vehicle Thermal Method

Accurate monitoring of crop drought thresholds at different growth periods is crucial for drought monitoring. In this study, the canopy temperature (Tc) of winter wheat (‘Weilong 169’ variety) during the three main growth periods was extracted from high-resolution thermal and multispectral images taken by a complete unmanned aerial vehicle (UAV) system. Canopy-air temperature difference (ΔT) and statistic Crop Water Stress Index (CWSIsi) indicators were constructed based on Tc. Combined experiment data from the field and drought thresholds for the ΔT and CWSIsi indicators for different drought levels at three main growth periods were monitored. The results showed a strong correlation between the Tc extracted using the NDVI-OTSU method and ground-truth temperature, with an R2 value of 0.94. The CWSIsi was more stable than the ΔT index in monitoring the drought level affecting winter wheat. The threshold ranges of the CWSIsi for different drought levels of winter wheat at three main growth periods were as follows: the jointing–heading period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.30, 0.30–0.42, 0.42–0.48, and >0.48, respectively; the heading–filling period, where the threshold ranges for normal, and mild, moderate, and severe drought are <0.33, 0.33–0.47, 0.44–0.53, and >0.53, respectively; and the filling–maturation period, where the threshold ranges for normal, mild drought, moderate drought, and severe drought are <0.41, 0.41–0.54, 0.54–0.59, and >0.59, respectively. The UAV thermal threshold method system can improve the accuracy of crop drought monitoring and has considerable potential in crop drought disaster identification.

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.

Stress-strain analysis of steel S235JRH pipe ring tensile specimens using 3D optical methods

Various pipe production procedures, including the construction of seamless pipes using a shaft with the conical tip in the oil industry, require data on the circumferential strain and stress state of the pipe. A method for measuring the displacement, strain and stress behavior of a pipe ring tensile specimen (PRTS) in the hoop direction is what this study’s goal was to develop, as there was a lack of a standardized testing process. For the study, five single S235JRH steel PRTS were tested. In the paper, it is presented how to test S235JRH steel PRTS using a Digital Image Correlation method, Thermal Imaging camera, and 3D scanner. A specially designed steel tool with two D blocks was developed for testing steel PRTS. The strain progression was documented using the 3D Digital Image Correlation method. Using a thermographic camera, an attempt was made to analyze the change in the temperature field of the PRTS during the test. All the specimens were subjected to 3D scanning to verify the cross-sectional geometry of the PRTS after a fracture. Fractures of all Single PRTS occurred in the break zone and almost identical places. The deviation value for the Experimental Ultimate strength value (true stress value) higher than the theoretical Ultimate strength value. Deviation value for the Experimental 0.2% Offset Yield Strength value is higher than the theoretical value. The results of the analysis of cross-sectional dimensions revealed a more significant variation in the thickness of the PRTS compared to its width. A potential area of literature review is the application of a high-resolution thermal imaging camera and the analysis of the stress state of the material using it.

Eyes in the Sky: Drones Applications in the Built Environment under Climate Change Challenges

This paper reviews the diverse applications of drone technologies in the built environment and their role in climate change research. Drones, or unmanned aerial vehicles (UAVs), have emerged as valuable tools for environmental scientists, offering new possibilities for data collection, monitoring, and analysis in the urban environment. The paper begins by providing an overview of the different types of drones used in the built environment, including quadcopters, fixed-wing drones, and hybrid models. It explores their capabilities and features, such as high-resolution cameras, LiDAR sensors, and thermal imaging, which enable detailed data acquisition for studying climate change impacts in urban areas. The paper then examines the specific applications of drones in the built environment and their contribution to climate change research. These applications include mapping urban heat islands, assessing the energy efficiency of buildings, monitoring air quality, and identifying sources of greenhouse gas emissions. UAVs enable researchers to collect spatially and temporally rich data, allowing for a detailed analysis and identifying trends and patterns. Furthermore, the paper discusses integrating UAVs with artificial intelligence (AI) to derive insights and develop predictive models for climate change mitigation and adaptation in urban environments. Finally, the paper addresses drone technologies’ challenges and the future directions in the built environment. These challenges encompass regulatory frameworks, privacy concerns, data management, and the need for an interdisciplinary collaboration. By harnessing the potential of drones, environmental scientists can enhance their understanding of climate change impacts in urban areas and contribute to developing sustainable strategies for resilient cities.

UAV-based thermography reveals spatial and temporal variability of evapotranspiration from a tropical rainforest

Evapotranspiration (ET) from tropical forests plays a significant role in regulating the climate system. Forests are diverse ecosystems, encompass heterogeneous site conditions and experience seasonal fluctuations of rainfall. Our objectives were to quantify ET from a tropical rainforest using high-resolution thermal images and a simple modeling framework. In lowland Sumatra, thermal infrared (TIR) images were taken from an uncrewed aerial vehicle (UAV) of upland and riparian sites during both dry and wet seasons. We predicted ET from land surface temperature data retrieved from the TIR images by applying the DATTUTDUT energy balance model. We further compared the ET estimates to ground-based sap flux measurements for selected trees and assessed the plot-level spatial and temporal variability of ET across sites and seasons. Average ET across sites and seasons was 0.48 mm h–1, which is comparable to ET from a nearby commercial oil palm plantation where this method has been validated against eddy covariance measurements. For given trees, a positive correlation was found between UAV-based ET and tree transpiration derived from ground-based sap flux measurements, thereby corroborating the observed spatial patterns. Evapotranspiration at upland sites was 11% higher than at riparian sites across all seasons. The heterogeneity of ET was lower at upland sites than at riparian sites, and increased from the dry season to the wet season. This seasonally enhanced ET variability can be an effect of local site conditions including partial flooding and diverse responses of tree species to moisture conditions. These results improve our understanding of forest-water interactions in tropical forests and can aid the further development of vegetation-atmosphere models. Further, we found that UAV-based thermography using a simple, energy balance modeling scheme is a promising method for ET assessments of natural (forest) ecosystems, notably in data scarce regions of the world.

High-resolution melt pool thermal imaging for metals additive manufacturing using the two-color method with a color camera

High-resolution melt pool thermal imaging for metals additive manufacturing using the two-color method with a color camera

Detection of symptoms induced by vascular plant pathogens in tree crops using high-resolution satellite data: Modelling and assessment with airborne hyperspectral imagery

Infection by the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf) threatens the production of olives (Olea europaea L.) and almonds (Prunus dulcis Mill.) worldwide. Producing symptoms that resemble water stress or nutrient deficiency, infection by these vascular pathogens restricts water and nutrient flow through the xylem. Hyperspectral, narrow-band multispectral, and thermal imagery acquired at a high spatial resolution can detect disease symptoms, even before they are visible, potentially allowing growers to distinguish infected plants from those affected by confounding environmental stresses. Nevertheless, operational detection of vascular disease using high-resolution commercial satellite multispectral images remains to be evaluated. Here, we assessed the capacity of high-resolution Worldview-2 and -3 multispectral imagery to detect Xf and Vd infections in olive and almond orchards in Spain, Italy, and Australia between 2011 and 2021. We compared the accuracy of detecting both pathogens using the satellite imagery with results obtained using aerial high-resolution hyperspectral and thermal imaging, with model-inverted plant traits, solar-induced chlorophyll fluorescence (SIF), and thermal data as a reference. Our results using spectral plant traits to examine disease progression at all stages showed that traits and their importance varied as a function of disease severity. Worldview-2 and -3 detected the disease incidence with overall accuracies ranging from 0.63 to 0.83 and kappa coefficients (κ) ranging from 0.29 to 0.68. Nevertheless, detecting the early stages of disease with multispectral satellite data yielded poorer results, with κ values of 0.22–0.45, compared with κ values of 0.3–0.69 obtained from hyperspectral data. Typical multispectral bandsets available from satellite sensors cannot measure important plant traits such as the blue index NPQI, xanthophyll proxy PRIn, SIF, and anthocyanin levels, thus explaining the poorer results obtained from multispectral satellite data for the early detection of vascular diseases. Adding a thermal-based crop water stress indicator to the satellite data improved the overall accuracies by 10–15% and increased κ by >0.2 units. This work shows that commercial multispectral high-spatial resolution imagery can be used to detect intermediate and advanced Xf and Vd infection, but that the early detection of disease symptoms requires hyperspectral and thermal data.

High-resolution thermal imaging in the Antarctic marginal ice zone: Skin temperature heterogeneity and effects on heat fluxes

High-resolution thermal imaging in the Antarctic marginal ice zone: Skin temperature heterogeneity and effects on heat fluxes

Experimental investigation of uncoated, single layer, and multilayer-coated TiAlN/TiN tool inserts in dry orthogonal cutting of AISI/SAE 4140 alloy steel

Significant temperature rise is stimulated during high-speed machining of metals, specifically in dry cutting conditions. This experimental investigation undertakes the effects of uncoated, single layer coatings (TiN and TiAlN), and multilayer coatings [TiAlN/TiN (9 + 5 µm), (9 + 3 µm), (9 + 1 µm), and (5 + 1 µm)] on cutting tool material during dry orthogonal cutting of AISI/SAE 4140 alloy steel. The study compares crucial parameters such as cutting and feed forces, chip-tool contact area, thickness of deformed chip, chip reduction coefficient, heat partition ratio, flank wear, and shear angle. The layer TiAlN used as the first layer from the Tungsten Carbide (WC) substrate owing to its better adhesion property, whereas TiN used as the second layer (top coating) owing to its better sliding behavior. In this research, cutting operations have carried out using Dean Smith and Grace Lathe machine. A Scanning Electron Microscope (SEM) also used along with a Kistler 9263 dynamometer to measure cutting forces. The cutting temperatures are measured with the help of an infrared, high-resolution thermal imaging camera, ThermaCAM model SC3000 by FLIR. Results have shown that layers act as a thermal barrier that block excessive heat generation in the cutting tool. Thus, this increasing the surface finish, tool life, and overall efficiency, while reducing the abrasive wear, tear, and parallel ridges due to rubbing friction. Furthermore, it has found that multilayer coating of (9 + 5 = 14 µm) shown good consistent results with less variance, specifically in the high-speed machining regions. Hence, the study evidently justifies the usage of coated tool inserts in high-speed dry cutting operations.

Nanoscale Thermal Strain Engineering-Driven Ferroelastic Domain Evolution in CH3NH3PbI3 Perovskites.

A local thermal strain engineering approach via an ac-heated thermal probe was incorporated into methylammonium lead triiodide (MAPbI3) crystals and acts as a driving force for ferroic twin domain dynamics, local ion migration, and property tailoring. Periodically, striped ferroic twin domains and their dynamic evolutions were successfully induced by local thermal strain and high-resolution thermal imaging, giving decisive evidence of the ferroelastic nature in MAPbI3 perovskites at room temperature. Local thermal ionic imaging and chemical mappings demonstrate that domain contrasts are from local methylammonium (MA+) redistribution into the stripes of chemical segregation in response to the local thermal strain fields. The present results reveal an inherent coupling among local thermal strains, ferroelastic twin domains, local chemical-ion segregations, and physical properties and offer a potential path to improve the functionality of metal halide perovskite-based solar cells.

A New Perspective on the Diagnosis of Septic Arthritis: High-Resolution Thermal Imaging.

An increase in temperature in an area suspected of septic arthritis is a clinically important finding. The aim of this study is to evaluate temperature changes in septic arthritis with a high-resolution thermal camera. A total of 49 patients, who were evaluated with a prediagnosis of arthritis (septic or non-septic), were included in this study. A temperature increase in the knee with suspected septic arthritis was evaluated by using thermal imaging and compared with the opposite-side joint. Then, in order to confirm the diagnosis, a culture was taken using routine intra-articular aspiration. The thermal measurements were compared in 15 patients with septic arthritis and 34 patients with non-septic arthritis. The mean temperature was 37.93 °C in the septic group, while it was 36.79 °C in the non-septic group (p < 0.000 *). The mean temperature difference in both joints was 3.40 °C in the septic group, while it was 0.94 °C in the non-septic group (p < 0.000 *). While the mean temperature was 37.10 °C in the group with septic arthritis, it was measured to be 35.89 °C in the group non-septic arthritis (p < 0.020). A very strong positive correlation was found between the difference in the mean temperatures of both groups and the values of the hottest and coldest points (r = 0.960, r = 0.902). In the diagnosis of septic arthritis, thermal imagers can be used as a non-invasive diagnostic tool. A quantitative value can be obtained to indicate to a local temperature increase. In future studies, specially designed thermal devices can be developed for septic arthritis.

Thermal resistance of two and three-layer fabric assemblies for extreme cold weather protective clothing

Two (FA2) and three (FA3) layer fabric assemblies have been fabricated in different configurations using cloth shirting angola, pile acrylic fabric, non-woven polyester fabric (batting), coated nylon fabric and nappa leather for designing and development of multilayer clothing to provide protection in extreme cold weather. The configurations of FA2 are cloth shirting angola, PU coated nylon fabric; pile acrylic fabric, PU coated nylon fabric; cloth shirting angola, nappa leather; and acrylic pile fabric, nappa leather. On the other hand, the configurations of FA3 are cloth shirting angola, non-woven polyester fabric, PU coated nylon fabric; pile acrylic fabric, non-woven polyester fabric, PU coated nylon fabric; cloth shirting angola, non-woven polyester fabric, nappa leather; and pile acrylic fabric, non-woven polyester fabric, nappa leather. Thermal resistance of these multilayer clothing assemblies has been evaluated using high-resolution FLIR thermal imaging camera operating in 8-12µm waveband and PID controlled Acmas hot plate in natural convection mode. The maximum thermal resistance, offered by FA2 and FA3, are 5.59 and 6.24 tog respectively. Rational spatial configurations of FA2 and FA3 are as per the temperature of human body parts, and these may be used in designing and development of protective clothing for extreme cold weather.

Detecting Xylella fastidiosa in a machine learning framework using Vcmax and leaf biochemistry quantified with airborne hyperspectral imagery

The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Here, we assessed changes in biochemical, biophysical and photosynthetic traits, inferred using biophysical models, in Xf-affected almond orchards under rainfed and irrigated conditions on the Island of Majorca (Balearic Islands, Spain). Recent research has demonstrated the early detection of Xf-infections by monitoring spectral changes associated with pigments, canopy structural traits, fluorescence emission and transpiration. Nevertheless, there is still a need to make further progress in monitoring physiological processes (e.g., photosynthesis rate) to be able to efficiently detect when Xf-infection causes subtle spectral changes in photosynthesis. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (Vcmax), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (Tc) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images. We showed that the relative importance of Vcmax and leaf biochemical constituents (e.g., CBC) in the ML model for the detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were Vcmax, pigments, CBC, and Tc, and, particularly in rainfed-trees, anthocyanins, and Tc. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters.

Fluctuation-driven thermal transport in graphene double-layers at charge neutrality

We develop a theory of fluctuation-driven phenomena in thermal transport in graphene double-layers. We work in the regime of electron hydrodynamics and focus on the double charge neutrality point. Although at the neutrality point charge transport is decoupled from the hydrodynamic flow, thermal fluctuations of electron density cause both drag and heat transfer between the layers. The thermal transport in the bilayer system is governed by these two phenomena. We express the drag friction coefficient and the interlayer thermal conductivity in terms of the interlayer distance and the intrinsic conductivity of the electron liquid. We then obtain the thermal conductance matrix and determine the spatial dependence of the hydrodynamic velocity and temperature in the system. For shorter system the thermal drag resistance is determined by drag. In longer systems the situation of perfect thermal drag is realized, in which the hydrodynamic velocities in both layers become equal in the interior of the systems. Estimates are given for the monolayer and bilayer graphene devices. The predictions of our theory can be tested by the high-resolution thermal imaging and Johnson-Nyquist nonlocal noise thermometry.