Thermal Infrared Imagery Research Articles

Validation of Landsat-9 and Landsat-8 Surface Temperature and Reflectance during the Underfly Event

With the launch of Landsat-9 on 27 September 2021, Landsat continues its fifty-year continuity mission of providing users with calibrated Earth observations. It has become a requirement that an underflight experiment be performed during commissioning to support sensor cross-calibration. In this most recent experiment, Landsat-9 flew under Landsat-8 for nearly three days with over 50% ground overlap, from 13 to 15 November 2021. To address the scarcity of reference data that are available to support calibration and validation early-on in the mission, a ground campaign was planned and executed by the Rochester Institute of Technology (RIT) on 14 November 2021 to provide full spectrum measurements for early mission comparisons. The primary experiment was conducted in the Outer Banks, North Carolina at Jockey’s Ridge Sand Dunes. Full-spectrum ground-based measurements were acquired with calibrated reference equipment, while a novel Unmanned Aircraft System (UAS)-based platforms acquired hyperspectral visible and near-infrared (VNIR)/Short-wave infrared (SWIR) imagery data and coincident broadband cooled thermal infrared (TIR) imagery. Results of satellite/UAS/ground comparisons were an indicator, during the commissioning phase, that Landsat-9 is behaving consistently with Landsat-8, ground reference, and UAS measurements. In the thermal infrared, all measurements agree to be within 1 K over water and to within 2 K over sand, which represents the most challenging material for estimating surface temperature. For the surface reflectance product(s), Landsat-8 and -9 are in good agreement and only deviate slightly from ground reference in the SWIR bands; a deviation of 2% in the VNIR and 5–8% in the SWIR regime. Subsequent longer-term studies indicate that Landsat 9 continues to perform as expected. The behavior of Thermal Infrared Sensor-2 (TIRS-2) against reference is also shown for the first year of the mission to illustrate its consistent performance.

Geovisualization and Analysis of Landscape-Level Wildfire Behavior Using Repeat Pass Airborne Thermal Infrared Imagery

Geovisualization tools can supplement the statistical analyses of landscape-level wildfire behavior by enabling the discovery of nuanced information regarding the relationships between fire spread, topography, fuels, and weather. The objectives of this study were to develop and evaluate the effectiveness of geovisualization tools for analyzing wildfire behavior and specifically to apply those tools to study portions of the Thomas and Detwiler wildfire events that occurred in California in 2017. Fire features such as active fire fronts and rate of spread (ROS) vectors derived from repetitive airborne thermal infrared (ATIR) imagery sequences were incorporated into geovisualization tools hosted in a web geographic information systems application. This geovisualization application included ATIR imagery, fire features derived from ATIR imagery (rate of spread vectors and fire front delineations), growth form maps derived from NAIP imagery, and enhanced topographic rasters for visualizing changes in local topography. These tools aided in visualizing and analyzing landscape-level wildfire behavior for study portions of the Thomas and Detwiler fires. The primary components or processes of fire behavior analyzed in this study were ROS, spotting, fire spread impedance, and fire spread over multidirectional slopes. Professionals and researchers specializing in wildfire-related topics provided feedback on the effectiveness and utility of the geovisualization tools. The geovisualization tools were generally effective for visualizing and analyzing (1) fire spread over multidirectional slopes; (2) differences in spread magnitudes within and between sequences over time; and (3) the relative contributions of fuels, slope, and weather at any given point within the sequences. Survey respondents found the tools to be moderately effective, with an average effectiveness score of 6.6 (n = 5) for the visualization tools on a scale of 1 (ineffective) to 10 (effective) for postfire spread analysis and visualizing fire spread over multidirectional slopes. The results of the descriptive analysis indicate that medium- and fine-scale topographic features, roads, and riparian fuels coincided with cases of fire spread impedance and exerted control over fire behavior. Major topographic features such as ridges and valleys slowed, or halted, fire spread consistently between study areas. The relationships between spotting, fuels, and topography were inconclusive.

Areca Yellow Leaf Disease Severity Monitoring Using UAV-Based Multispectral and Thermal Infrared Imagery

The areca nut is the primary economic source for some farmers in southeast Asia. However, the emergence of areca yellow leaf disease (YLD) has seriously reduced the annual production of areca nuts. There is an urgent need for an effective method to monitor the severity of areca yellow leaf disease (SAYD). This study selected an areca orchard with a high incidence of areca YLD as the study area. An unmanned aerial vehicle (UAV) was used to acquire multispectral and thermal infrared data from the experimental area. The ReliefF algorithm was selected as the feature selection algorithm and ten selected vegetation indices were used as the feature variables to build six machine-learning classification models. The experimental results showed that the combination of ReliefF and the Random Forest algorithm achieved the highest accuracy in the prediction of SAYD. Compared to manually annotated true values, the R2 value, root mean square error, and mean absolute percentage error reached 0.955, 0.049, and 1.958%, respectively. The Pearson correlation coefficient between SAYD and areca canopy temperature (CT) was 0.753 (p value < 0.001). The experimental region was partitioned, and a nonlinear fit was performed using CT versus SAYD. Cross-validation was performed on different regions, and the results showed that the R2 value between the predicted result of SAYD by the CT and actual value reached 0.723. This study proposes a high-precision SAYD prediction method and demonstrates the correlation between the CT and SAYD. The results and methods can also provide new research insights and technical tools for botanical researchers and areca practitioners, and have the potential to be extended to more plants.

A New and Automated Method for Improving Georeferencing in Nighttime Thermal ECOSTRESS Imagery.

Georeferencing accuracy plays a crucial role in providing high-quality ready-to-use remote sensing data. The georeferencing of nighttime thermal satellite imagery conducted by matching to a basemap is challenging due to the complexity of thermal radiation patterns in the diurnal cycle and the coarse resolution of thermal sensors in comparison to sensors used for imaging in the visual spectral range (which is typically used for creating basemaps). The presented paper introduces a novel approach for the improvement of the georeferencing of nighttime thermal ECOSTRESS imagery: an up-to-date reference is created for each to-be-georeferenced image, derived from land cover classification products. In the proposed method, edges of water bodies are used as matching objects, since water bodies exhibit a relatively high contrast with adjacent areas in nighttime thermal infrared imagery. The method was tested on imagery of the East African Rift and validated using manually set ground control check points. The results show that the proposed method improves the existing georeferencing of the tested ECOSTRESS images by 12.0 pixels on average. The strongest source of uncertainty for the proposed method is the accuracy of cloud masks because cloud edges can be mistaken for water body edges and included in fitting transformation parameters. The georeferencing improvement method is based on the physical properties of radiation for land masses and water bodies, which makes it potentially globally applicable, and is feasible to use with nighttime thermal infrared data from different sensors.

On the correlation between thermal imagery and fugitive CH4 emissions from MSW landfills

Landfill gas (LFG) is related to the biochemical processes generating heat and releasing CH4, CO2, and other gases in lower concentrations, which result in environmental impacts and risk of local explosion. Thermal infrared imagery (TIR) is employed to detect CH4 leakage as a risk control approach. However, the challenge for LFG leakage detection using TIR is establishing a relation between the gas flux and the ground temperature. This study evaluates the problem of a heated gas flowing through a porous medium column where the upward surface exchanges heat by radiation and convection to the environment. A heat transfer model that considers the upward LFG flow is proposed, and a sensibility analysis is developed to relate the flux to the ground temperature level in the condition of non-income solar radiation. An explicit equation to predict CH4 fugitive flow as a function of temperature anomalies of the ground was presented for the first time. The results show that the predicted ground surface temperatures are consistent with the literature’s experimental observations. Moreover, the model was complementarily applied to a Brazilian landfill, with in situ TIR measurements in an area with a slightly fractured cover. In this field observation, the predicted CH4 flux was around 9025 g m−2 d−1. Model limitations concerning the soil homogeneity, the transient variation of atmospheric conditions or local pressure, and soil temperature difference in low-flux conditions (related to TIR-cameras accuracy) require further validation. Results could help landfill monitoring in conditions of a high-temperature ground anomaly in dry seasons.

Unmanned aerial vehicles (UAVs) with thermal infrared (TIR) sensors are effective for monitoring and counting threatened Vietnamese primates.

Monitoring the population size of threatened primate species with minimal disturbance is becoming an outstanding requirement for conservation and wildlife management. Drones with thermal infrared (TIR) and visible spectrum (RGB) imaging are increasingly used to survey arboreal primates, but ground-truthing is still required to assess the effectiveness of drone-based count estimates. Our pilot study aims to assess the ability of a drone with both TIR and RGB sensors to detect, count, and identify semi-wild population of four endangered species of langurs and gibbon in the Endangered Primate Rescue Center (EPRC) in northern Vietnam. We found that TIR imagery enabled higher detection rates compared to RGB imagery and obtained an accurate count with the TIR only after four drone flights. We could identify langurs species based on thermal signature at a flight height of 50m from the ground level (max tree height = 15m), via size and shape of the body. With TIR imagery, we were able to record inconspicuous behaviors such as foraging and play. While some individuals initially showed flight or avoidance behaviors when the drone was sighted, these behaviors decreased or were absent on following drone surveys. Our study suggests that monitoring and precisely counting langur and gibbon species populations could be successful with the use of thermal drones only.

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.

Weekly high-resolution multi-spectral and thermal uncrewed-aerial-system mapping of an alpine catchment during summer snowmelt, Niwot Ridge, Colorado

Abstract. Alpine ecosystems are experiencing rapid change as a result of warming temperatures and changes in the quantity, timing and phase of precipitation. This in turn impacts patterns and processes of ecohydrologic connectivity, vegetation productivity and water provision to downstream regions. The fine-scale heterogeneous nature of these environments makes them challenging areas to measure with traditional instrumentation and spatiotemporally coarse satellite imagery. This paper describes the data collection, processing, accuracy assessment and availability of a series of approximately weekly-interval uncrewed-aerial-system (UAS) surveys flown over the Niwot Ridge Long Term Ecological Research site during the 2017 summer-snowmelt season. Visible, near-infrared and thermal-infrared imagery was collected. This unique series of 5–25 cm resolution multi-spectral and thermal orthomosaics provides a unique snapshot of seasonal transitions in a high alpine catchment. Weekly radiometrically calibrated normalised difference vegetation index maps can be used to track vegetation health at the pixel scale through time. Thermal imagery can be used to map the movement of snowmelt across and within the near sub-surface as well as identify locations where groundwater is discharging to the surface. A 10 cm resolution digital surface model and dense point cloud (146 points m−2) are also provided for topographic analysis of the snow-free surface. These datasets augment ongoing data collection within this heavily studied and important alpine site; they are made publicly available to facilitate wider use by the research community. Datasets and related metadata can be accessed through the Environmental Data Initiative Data Portal, https://doi.org/10.6073/pasta/dadd5c2e4a65c781c2371643f7ff9dc4 (Wigmore, 2022a), https://doi.org/10.6073/pasta/073a5a67ddba08ba3a24fe85c5154da7 (Wigmore, 2022c), https://doi.org/10.6073/pasta/a4f57c82ad274aa2640e0a79649290ca (Wigmore and Niwot Ridge LTER, 2021a), https://doi.org/10.6073/pasta/444a7923deebc4b660436e76ffa3130c (Wigmore and Niwot Ridge LTER, 2021b), https://doi.org/10.6073/pasta/1289b3b41a46284d2a1c42f1b08b3807 (Wigmore and Niwot Ridge LTER, 2022a), https://doi.org/10.6073/pasta/70518d55a8d6ec95f04f2d8a0920b7b8 (Wigmore and Niwot Ridge LTER, 2022b). A summary of the available datasets can be found in the data availability section below.

Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

Different methods exist to measure or estimate actual crop evapotranspiration (ETa). However, some methods require a large number of data input or strict field conditions. Remote sensing based ETa algorithms based on extreme thermal pixels (hot and cold) have limitations when required extreme pixels are not present in the acquired thermal infra-red imagery. In addition, satellite overpass frequency and spatial pixel resolution may be a limitation for some agricultural fields and micro-climates. Surface energy balance methods that use surface radiometric temperatures often fail to perform well under drought, limited irrigation, salt affected soils, or under sparse vegetation conditions. One option is to measure or estimate the crop/surface sensible heat flux through the aerodynamic temperature approach, then calculate the available energy and solve the energy balance for latent heat flux. Thus, this study presents different published algorithms that characterize the crop or field surface aerodynamic temperature and then applies them to different conditions for evaluation. Determining spatial ETa continuously has the potential to improve the irrigation water management decision making. The aerodynamic temperature approach was initially developed with good results as a function of surface radiometric temperature, air temperature, crop leaf area index, and wind speed or surface aerodynamic resistance. However, the inclusion of the crop fractional percent cover and of a new resistance term (turbulent-mixing row resistance) greatly improved the estimation of the sensible heat and latent heat fluxes, when evaluated with heat flux data derived from eddy covariance energy balance towers. Results also indicate that the aerodynamic method has transferability potential to different regions, crops, and irrigation methods than the conditions encountered in the method development.

Analysis of Large-Scale Groundwater-Driven Cooling Zones in Rivers Using Thermal Infrared Imagery and Radon Measurements

The role of groundwater (GW) discharge on surface water (SW) quantity, quality and temperature is known to be important. Moreover, the effect of GW contributions to river thermal budgets is critical in natural rivers considering that water temperature plays a vital role in fish survival during extreme heat events. The identification of zones with GW input in rivers can, thus, help river management plans. However, detecting these zones at the watershed scale can be a challenge. This work combines thermal infrared (TIR) imagery of rivers and water sampling for radon measurements for better documentation of GW in rivers. The Sainte-Marguerite and Berard Rivers, both located in Quebec, Canada, are known for their abundance of salmonids. Their water temperature profiles were plotted using TIR imagery, and five cooling zones in the Berard River and two for the Sainte-Marguerite River were identified in which notable GW–SW exchange was the suspected cause. Radon concentrations measured within the cooling zones showed clear GW contribution to SW. TIR imagery is an effective and fast way to identify GW seepage at the watershed scale. Radon can be used as a complementary natural tracer of GW in rivers at finer scales. The combination of both methods was shown to be reliable for the identification of GW in rivers. This can help for a better anticipation of GW effects in management plans to deal with extreme heat waves that are predicted to occur more frequently under future climate change scenarios.

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

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

Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery

Abstract. Areas of thin sea ice in the polar regions not only are experiencing the highest rate of sea-ice production but also are, therefore, important hot spots for ocean ventilation as well as heat and moisture exchange between the ocean and the atmosphere. Through co-location of (1) an unsupervised waveform classification (UWC) approach applied to CryoSat-2 radar waveforms with (2) Moderate Resolution Imaging Spectroradiometer-derived (MODIS) thin-ice-thickness estimates and (3) Sentinel-1A/B synthetic-aperture radar (SAR) reference data, thin-ice-based waveform shapes are identified, referenced, and discussed with regard to a manifold of waveform shape parameters. Here, strong linear dependencies are found between binned thin-ice thickness up to 25 cm from MODIS and the CryoSat-2 waveform shape parameters that show the possibility of either developing simple correction terms for altimeter ranges over thin ice or directing adjustments to current retracker algorithms specifically for very thin sea ice. This highlights the potential of CryoSat-2-based SAR altimetry to reliably discriminate between occurrences of thick sea ice, open-water leads, and thin ice within recently refrozen leads or areas of thin sea ice. Furthermore, a comparison to the ESA Climate Change Initiative's (CCI) CryoSat-2 surface type classification with classes sea ice, lead, and unknown reveals that the newly found thin-ice-related waveforms are divided up almost equally between unknown (46.3 %) and lead type (53.4 %) classifications. Overall, the UWC results in far fewer unknown classifications (1.4 % to 38.7 %). Thus, UWC provides more usable information for sea-ice freeboard and thickness retrieval and at the same time reduces range biases from thin-ice waveforms processed as regular sea ice in the CCI classification.

Dust Detection Over East Asia From Multispectral and Multi‐Temporal Himawari‐8/AHI Thermal Infrared Observations

AbstractThe frequent outbreak of dust storms every spring is one of the extreme weathers in East Asia. Geostationary meteorological satellite thermal infrared (TIR) imagery provides observations for high‐frequency, all‐day, large‐scale monitoring of dust sources and transport. This study proposes an integrated method to detect dust over East Asia by using the multispectral and multi‐temporal Himawari‐8/Advanced Himawari Imager (AHI) TIR observations, based on a Feedforward Neural Network (FNN) model and the Robust Principal Component Analysis based Anomalous Dust Detection (ADD) method. The FNN model is trained to map the multispectral Brightness Temperatures (BTs) to the three category probabilities (i.e., cloud, dust, and clear sky), and the ADD method calculates the dust score by normalizing multi‐temporal BT difference anomalous variation. The Integrated Dust Index (IDI) reflects the quantified dust confidence of the AHI pixel, which is defined as the sum of the dust probability and the dust score. The IDI was evaluated using 2 months of the Sentinel‐5P TROPOMI Ultraviolet Aerosol Index and achieved a total dust detection accuracy above 90%. A comparison with Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation nighttime observations shows that the IDI can also detect dust at night. The times series IDI of the massive 14–16 March 2021 dust storm show its capabilities to trace dust sources and monitor dust transport.

Monitoring of Heat Flux Energy in the Northernmost Part of Sumatra Volcano Using Landsat 8 and Meteorological Data

Geothermal energy, as a part of green and renewable energy, has been widely developed in the world to replace the current conventional fossil energy. Peut Sagoe is an active volcano in the northern part of Sumatra. The volcanic mountain has not been completely explored for geothermal and energy reserves study. This is due to the volcano locates in a high topography and surrounded by dense tropical forest, which makes it challenging to deploy geophysical instruments in the area. The Landsat 8 thermal infrared and meteorological data from 2013 – 2020 were used to estimate the energy resources by calculating the radiative heat flux (RHF) and measuring the energy lost annually through the heat discharge rate (HDR). We also used the normalized differential vegetation index (NDVI) for vegetation analysis, and estimation of its emissivity data. The mono-window algorithm was used to calculate the land surface temperature (LST). The Stefan–Boltzmann equation was utilized to analyze thermal infrared data for RHF, and ambient temperature and relative humidity data were acquired from the Indonesian Meteorological Agency (BMKG) database. The results showed that low vegetation values and high LST of 25°C–35°C were found in crater areas, which indicate the underground thermal activities of the mountain. It demonstrates that the maximum RHF values were 55 W/m2 in 2013 and 37 W/m2 in 2020. The HDR data were calculated by applying 15% of the RHF data, and the amounts of energy lost were 132.5 MWe and 64.5 MWe in 2013 and 2015 respectively. It increased to 186.4 MWe in 2017 and 89 MWe in 2020. Based on these predicted results, we conclude that the combination of thermal infrared imagery of Landsat 8 and meteorological data is an effective approach in estimating geothermal energy potential and energy loss of volcanoes situated in remote areas

Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods

In the upcoming irrigation management in agricultural production, accurate mapping of crop water consumption with a high spatial and temporal resolution at a farm scale is needed. In this study, we developed models for crop coefficients (Kc) estimation using unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) techniques for irrigated maize in a semi-arid region in Northwest China. Kc values were calculated using a procedure given in FAO56 manual using field measurements. Multispectral vegetation indices (VIs), vegetation fraction (VF), thermal-based VIs, and texture information (TI) were derived from UAV-based multispectral, RGB, and thermal infrared imagery, respectively. These remotely sensed variables and their combinations were used to develop prediction models using six ML algorithms (linear regression-LR, polynomial regression-PR, exponential regression-ER, random forest regression-RFR, support vector regression-SVR, and deep neural network-DNN). Among these models, the RFR with the highest accuracy (R2 = 0.69, RMSE = 0.1019) was recommended to estimate maize Kc. The multispectral and thermal-based VIs and texture of the near-infrared band had greater contributions than RGB-based VF and TI in the Kc-RFR model under different irrigation treatments. Furthermore, the maize Kc-RFR prediction model had high accuracy in estimating cumulative evapotranspiration (R2 = 0.89, RMSE = 15.0 mm/stage) during different growth stages and daily soil water content (R2 = 0.85, RMSE = 0.0089 m3/m3) in the root zone. These results show that the integration of UAV remote sensing and ML provides a promising tool to help farmers make decisions using timely mapped crop water consumption, especially under water shortages or drought conditions.

Cooler, bigger; warmer, smaller: Fine‐scale thermal heterogeneity maps age class and species distribution in behaviourally thermoregulating salmonids

AbstractBehavioural thermoregulation is a survival strategy that occurs in response to an exceedance of thermal stress‐inducing thresholds. When salmonids experience exceedance of these thresholds, they seek regions of colder water, known as thermal refuges. During an extreme temperature event of summer 2021 (main stem ~31.5°C) a large aggregation of Atlantic salmon (Salmo salar—all age classes) and brook trout (Salvelinus fontinalis—&gt;20 cm) was observed on the Little Southwest Miramichi River in New Brunswick, Canada. Using a drone‐mounted thermal infrared (TIR) sensor, fine‐scale TIR imagery of the occupied refuge was acquired. Polarized glasses were worn by an onshore observer to make visual observations. Constructing maps from these data we examined the spatial distribution of fish, and the corresponding temperature of the areas they occupied. Salmonids were found to be distributed by age class and species, with the distribution driven by the mosaic of temperatures in the refuge. Young of the year (YOY), 1+, 2+ and adult Atlantic salmon occupied areas with average temperatures ~30.1, 28.8, 25.7 and 21.9°C, respectively; whilst mature brook trout occupied areas ~21.8°C. Noteworthy is the observation of thermally aggregating young of the year Atlantic salmon, and the range in temperatures they occupied (~8°C). One isolated, shallow, cold water patch (~22°C) exclusively held YOY Atlantic salmon. Our findings highlight the importance of several different thermal characteristics of thermal refuges and their link to salmonid occupancy and can aid the design of ecologically meaningful thermal refuge augmentation/restoration projects.

Characterizing forest disturbance and recovery with thermal trajectories derived from Landsat time series data

The increasing frequency of forest disturbances caused by climate change has highlighted the importance of understanding the entire process of disturbance, from its onset to forest recovery. Many previous studies have used multispectral Landsat time-series data describing forest dynamics. However, there is a lack of studies using thermal imagery, which may provide information about bio-climatic changes and energy balance during the forest disturbance and recovery. Our objective in this research was to detect the main features of insect forest disturbance and subsequent forest recovery (disturbance duration, disturbance severity, recovery duration) using thermal Landsat imagery. We also aimed to determine the relationship between the identified thermal features and topography. The study area was a Norway spruce (Picea abies [L.] Karst.) dominated forest located in a Central European border region between the Czech Republic (Šumava Mountains) and Germany (Bavarian Forest). For more than three decades, forests in this area have experienced widespread defoliation due to the spruce bark beetle (Ips typographus L.). We determined the forest's surface temperature (ST) between 1985 and 2015 from thermal Landsat time-series data and then normalized it (STn) to be comparable across the years in question. We plotted thermal trajectories for this period and then clustered them based on their variances. The resulting thermal trajectories well described both disturbance and recovery, showing a rise in the STn during the onset of the disturbance, the severity, and a decrease during recovery. The mean disturbance duration was 8.44 years (SD = ±3.775), with a maximum severity as indicated by the STn of 4.92 (SD = ±1.5) and a duration of 12.84 years (SD = ±3.38) until 50% recovery and 17.85 years (SD = ±1.74) until 80% recovery. Clustering of the thermal trajectories revealed clusters with similar year-groups of bark beetle attack and a spatially aggregated pattern closely related to topography. Severity showed a positive correlation with altitude, whereas the spatially aggregated patterns of disturbance and recovery duration can be attributed to more complex topographical characteristics. Our study demonstrates the ability of thermal infrared imagery to provide highly relevant data for assessing not only the main features of forest disturbance and recovery, but also bio-climatic canopy functions in terms of topography and other environmental variables.

Identification of Thermal Refuges and Water Temperature Patterns in Salmonid-Bearing Subarctic Rivers of Northern Quebec

In summer, salmonids can experience thermal stress during extreme weather conditions. This may affect their growth and even threaten their survival. Cool water zones in rivers constitute thermal refuges, allowing fish to be more comfortable to grow and survive in extreme events. Therefore, identifying and understanding the spatiotemporal variability of discrete thermal refuges and larger scale cooling zones in rivers is of fundamental interest. This study analyzes thermal refuges as well as cooling zones in two salmonid rivers in a subarctic climate by use of thermal infrared (TIR) imagery. The two studied rivers are the Koroc and Berard Rivers, in Nunavik, Quebec, Canada. On the 17 km studied section of the Berard River, four thermal refuges and five cooling zones were detected, covering 46% of the surveyed section of the river. On the 41 km section studied for the Koroc River, 67 thermal refuges and five cooling zones were identified which represent 32% of the studied section of the river. 89% of identified thermal refuges and about 60% of cooling zones are groundwater-controlled. Continuity of permafrost and shape of the river valley were found to be the main parameters controlling the distribution of refuges and cooling zones. These data provide important insights into planning and conservation measures for the salmonid population of subarctic Nunavik rivers.

Evaluating GOES-16 ABI surface brightness temperature observation biases over the central Sierra Nevada of California

Thermal infrared imagery from NOAA's Geostationary Operational Environmental Satellites (GOES) R-series presents an opportunity to observe mountain surface temperatures at high temporal resolution. These observations are needed to better understand the transient surface energy balance, influenced by near-surface air temperatures and water fluxes ranging from evapotranspiration to snowmelt. From geostationary orbit, the GOES Advanced Baseline Imager (ABI) instrument views the Earth from a fixed longitude above the equator. ABI images therefore have increasingly off-nadir view angles and increasingly larger pixel dimensions the further a point on the Earth's surface is from the sub-satellite point. In thermal infrared imagery from ABI, these 2+ km pixels blur together the different surface temperatures of heterogeneous mountain landscapes. We compared GOES-16 ABI band 14 (11.2 μm) brightness temperatures to 90 m spatial resolution ASTER band 14 (11.3 μm) and 1 km spatial resolution MODIS band 31 (11.03 μm) brightness temperatures and investigated how differences change over space and time for a study region in the central Sierra Nevada of California for the 2017–2020 snow seasons. We demonstrated the necessity of orthorectifying ABI imagery of mountain terrain to correct for the parallax effect in off-nadir imagery. This reduced the mean difference between ABI and ASTER ~11 μm brightness temperatures from 1.6 °C to 1.0 °C and increased the r-squared value between ABI and ASTER thermal infrared brightness temperatures from 0.43 to 0.93. The ABI brightness temperatures were found to more closely match those of forest canopy temperatures than snow surface (with RMS differences of 1.2 °C and 3.0 °C respectively), and those of sunlit slopes as opposed to slopes facing away from the sun (with RMS differences of 1.6 °C and 3.4 °C respectively), in coincident ASTER imagery. This work demonstrates the use of GOES ABI, and associated challenges, for observing surface temperatures of forested mountain environments with seasonal snow.

The Efficiency of Foliar Kaolin Spray Assessed through UAV-Based Thermal Infrared Imagery

The water content in an agricultural crop is of crucial importance and can either be estimated through proximal or remote sensing techniques, allowing better irrigation scheduling and avoiding extreme water stress periods. However, the current climate change context is increasing the use of eco-friendly practices to reconcile water management and thermal protection from sunburn. These approaches aim to mitigate summer stress factors (high temperature, high radiation, and water shortage) and improve the plants’ thermal efficiency. In this study, data from unmanned aerial vehicles (UAVs) were used to monitor the efficiency of foliar kaolin application (5%) in a commercial vineyard. Thermal infrared imagery (TIR) was used to compare the canopy temperature of grapevines with and without kaolin and to compute crop water stress and stomatal conductance indices. The gas exchange parameters of single leaves were also analysed to ascertain the physiological performance of vines and validate the UAV-based TIR data. Generally, plants sprayed with kaolin presented a lower temperature compared to untreated plants. Moreover, UAV-based data also showed a lower water stress index and higher stomatal conductance, which relate to eco-physiological measurements carried out in the field. Thus, the suitability of UAV-based TIR data proved to be a good approach to monitor entire vineyards in regions affected by periods of heatwaves, as is the case of the analysed study area.