Leveraging very-high spatial resolution hyperspectral and thermal UAV imageries for characterizing diurnal indicators of grapevine physiology

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs

Utilization of a thermal camera in aerial photography taken with a drone

Dronejen käyttö on lisääntynyt voimakkaasti viimeisten vuosien aikana. Ensimmäiset dronet on kehitetty jo 1900-luvun alussa. Myös lämpökameroita on käytetty jo useamman vuosikymmenen ajan. Näiden yhteiskäyttö on yleistynyt 2010-luvun aikana. Dronen ja lämpökameran avulla on tutkittu erityisesti kasvien lämpöstressiä. Tavoitteena oli selvittää, millaisia käyttömahdollisuuksia droneen kiinnitetyllä lämpökameralla on ja ovatko kameran antamat tulokset riittävän tarkkoja esimerkiksi täsmäviljelyssä hyödyntämiseen. Lisäksi tutkittiin kuvausprosessin käyttökelpoisuutta ja lämpökameran kalibrointia. Tutkimuksessa käytettiin lämpökameran rinnalla kolmea muuta eri lämpötilan mittausmenetelmää. Tutkimus suoritettiin Helsingin yliopiston Viikin tutkimustilalla. Kuvattavana kohteina olivat nurmi, jonka kasvuaste oli Zadoksin (BBCH) asteikolla 12–13, edellisenä syksynä kultivoitu maa sekä kynnetty maa. Tutkimuksessa oli käytössä itserakennettu drone (Tarot Ironman:n runko) sekä Flir Duo Pro R -lämpökamera. Tutkimus suoritettiin touko-kesäkuussa 2022. Lämpökuvien käsittely tehtiin Pix4D ja Matlab-ohjelmilla. Lämpökameralla saatiin kuvattua kaikki peltolohkot. Jokaisesta koelohkosta mitattiin vertailulämpötilat, jotta voitiin tutkia ilmasta otetun kuvan paikkaansa pitävyyttä. Kontrollipisteet mitattiin GCP-pisteiden (Ground Control Point) läheisyydestä kolmen metrin etäisyydeltä merkkitolpasta. Dronella otettujen lämpökuvien ja Ahlbornin mittarin tulosten välinen korrelaatiokerroin oli 0,67; joka on kohtalaisen korkea. Flir-käsilämpökameran ja dronella otettujen lämpökuvien välinen korrelaatio ei osoittautunut tilastollisesti merkitseväksi. Tähän vaikutti luultavasti Flir-käsilämpökameralla otettujen mittauspisteiden epätarkkuus kunnollisen kuvaustelineen puuttuessa. Maaperäskannerin tuottaman lämpökartan ja dronella otettujen lämpökuvien välinen korrelaatio oli -0,11. Tutkimuksessa havaittiin myös kameran kulma-anturissa olevan jotain häiriötä, koska kaikki sen ottamat kuvat olivat virtuaalisella karttatasolla 90 astetta väärässä kulmassa. Tämä saatiin korjattua kuvankäsittelyohjelmalla. Dronen lämpökameran kalibrointi todettiin riittäväksi tutkimuksen olosuhteissa. Droneen kiinnitetty lämpökamera on riittävän tarkka mitattaessa lämpötiloja ilmasta, jos olosuhteet ovat kameralle oikeat. Tulevia kuvauksia varten kasvustoon tulisi saada lisää kiintopisteitä, jotta analysointiohjelma saisi muodostettua kohdealueelta luotettavan lämpökuvan. Myös säähän olisi kiinnitettävä huomiota, sillä vähäinenkin pilvisyys vaikuttaa lopputulokseen kameran ominaisuuksista johtuen. Myös maasta mitattujen kontrollipisteiden tarkkuuteen tulisi kiinnittää enemmän huomiota, sillä niillä on suuri vaikutus tuloksiin, koska maan lämpötila voi vaihdella hyvin pienenkin alueen sisällä. Tässäkin tutkimuksessa vierekkäisten mittauspisteiden välillä oli jopa useiden asteiden lämpötilaeroja

Remote Thermal Signature Point Target Acquisition System Using Continuous PID Algorithm and Thermal Image Filtration for UAV Systems

The aspects relating to unmanned aircraft and their target acquisition systems are continuously being developed. The subject of the study is a target acquisition system with a thermal camera. A control system is presented consisting of three subsystems. Open- and closed-loop control systems are used. Experimental results unambiguously show that this is a promising line of research and form the basis for further efforts on the topic

Thermography to assess grapevine status and traits opportunities and limitations in crop monitoring and phenotyping – a review

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoClimate change and the increasing water shortage pose increasing challenges to agriculture and viticulture, especially in typically dry and hot areas such as the Mediterranean and demand for solutions to use water resources more effectively. For this reason, new tools are needed to precisely monitor water stress in crops such as grapevine in order to save irrigation water, while guaranteeing yield. Imaging technologies and remote sensing tools are becoming more common in agriculture and plant/crop science research namely to perform phenotyping/selection or for crop stress monitoring purposes. Thermography emerged as important tool for the industry and agriculture. It allows detection of the emitted infrared thermal radiation and conversion of infrared radiation into temperature distribution maps. Considering that leaf temperature is a feasible indicator of stress and/or stomatal behavior, thermography showed to be capable to support characterization of novel genotypes and/or monitor crop’s stress. However, there are still limitations in the use of the technique that need to be minimized such as the accuracy of thermal data due to variable weather conditions, limitations due to the high costs of the equipment/platforms and limitations related to image analysis and processing to extract meaningful thermal data. This work revises the role of remote sensing and imaging in modern viticulture as well as the advantages and disadvantages of thermography and future developments, focusing on viticultureN/

Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum

Screening for drought tolerance is critical to ensure high biomass production of bioenergy sorghum in arid or semi-arid environments. The bottleneck in drought tolerance selection is the challenge of accurately predicting biomass for a large number of genotypes. Although biomass prediction by low-altitude remote sensing has been widely investigated on various crops, the performance of the predictions are not consistent, especially when applied in a breeding context with hundreds of genotypes. In some cases, biomass prediction of a large group of genotypes benefited from multimodal remote sensing data; while in other cases, the benefits were not obvious. In this study, we evaluated the performance of single and multimodal data (thermal, RGB, and multispectral) derived from an unmanned aerial vehicle (UAV) for biomass prediction for drought tolerance assessments within a context of bioenergy sorghum breeding. The biomass of 360 sorghum genotypes grown under well-watered and water-stressed regimes was predicted with a series of UAV-derived canopy features, including canopy structure, spectral reflectance, and thermal radiation features. Biomass predictions using canopy features derived from the multimodal data showed comparable performance with the best results obtained with the single modal data with coefficients of determination (R2) ranging from 0.40 to 0.53 under water-stressed environment and 0.11 to 0.35 under well-watered environment. The significance in biomass prediction was highest with multispectral followed by RGB and lowest with the thermal sensor. Finally, two well-recognized yield-based drought tolerance indices were calculated from ground truth biomass data and UAV predicted biomass, respectively. Results showed that the geometric mean productivity index outperformed the yield stability index in terms of the potential for reliable predictions by the remotely sensed data. Collectively, this study demonstrated a promising strategy for the use of different UAV-based imaging sensors to quantify yield-based drought tolerance

IR Thermography from UAVs to Monitor Thermal Anomalies in the Envelopes of Traditional Wine Cellars: Field Test

Infrared thermography (IRT) techniques for building inspection are currently becoming increasingly popular as non-destructive methods that provide valuable information about surface temperature (ST) and ST contrast (delta-T). With the advent of unmanned aerial vehicle (UAV)-mounted thermal cameras, IRT technology is now endowed with improved flexibility from an aerial perspective for the study of building envelopes. A case study cellar in Northwest (NW) Spain is used to assess the capability and reliability of low-altitude passive IRT in evaluating a typical semi-buried building. The study comparatively assesses the use of a pole-mounted FLIR B335 camera and a drone-mounted FLIR Vue Pro R camera for this purpose. Both tested IRT systems demonstrate good e ectiveness in detecting thermal anomalies (e.g., thermal bridges, air leakages, constructive singularities, and moisture in the walls of the cellar) but pose some di culties in performing accurate ST measurements under real operating conditions. Working with UAVs gives great flexibility for the inspection, but the angle of view strongly influences the radiometric data captured and must be taken into account to avoid disturbances due to specular reflections.This work was supported by the Spanish Ministry of Science, Innovation and Universities under the National Programme for Research Aimed at the Challenges of Society grant for the project “Bioclimatic Design Strategies in Wine Cellars as Nearly Zero-Energy Building Models” [BIA2014-54291-R]S

Thermal Imaging of Beach-Nesting Bird Habitat with Unmanned Aerial Vehicles: Considerations for Reducing Disturbance and Enhanced Image Accuracy

Knowledge of temperature variation within and across beach-nesting bird habitat, and how such variation may affect the nesting success and survival of these species, is currently lacking. This type of data is furthermore needed to refine predictions of population changes due to climate change, identify important breeding habitat, and guide habitat restoration efforts. Thermal imagery collected with unmanned aerial vehicles (UAVs) provides a potential approach to fill current knowledge gaps and accomplish these goals. Our research outlines a novel methodology for collecting and implementing active thermal ground control points (GCPs) and assess the accuracy of the resulting imagery using an off-the-shelf commercial fixed-wing UAV that allows for the reconstruction of thermal landscapes at high spatial, temporal, and radiometric resolutions. Additionally, we observed and documented the behavioral responses of beach-nesting birds to UAV flights and modifications made to flight plans or the physical appearance of the UAV to minimize disturbance. We found strong evidence that flying on cloudless days and using sky-blue camouflage greatly reduced disturbance to nesting birds. The incorporation of the novel active thermal GCPs into the processing workflow increased image spatial accuracy an average of 12 m horizontally (mean root mean square error of checkpoints in imagery with and without GCPs was 0.59 m and 23.75 m, respectively). The final thermal indices generated had a ground sampling distance of 25.10 cm and a thermal accuracy of less than 1 °C. This practical approach to collecting highly accurate thermal data for beach-nesting bird habitat while avoiding disturbance is a crucial step towards the continued monitoring and modeling of beach-nesting birds and their habitat

Performance of the Two-Source Energy Balance (TSEB) Model as a Tool for Monitoring the Response of Durum Wheat to Drought by High-Throughput Field Phenotyping

The current lack of efficient methods for high throughput field phenotyping is a constraint on the goal of increasing durum wheat yields. This study illustrates a comprehensive methodology for phenotyping this crop's water use through the use of the two-source energy balance (TSEB) model employing very high resolution imagery. An unmanned aerial vehicle (UAV) equipped with multispectral and thermal cameras was used to phenotype 19 durum wheat cultivars grown under three contrasting irrigation treatments matching crop evapotranspiration levels (ETc): 100%ETc treatment meeting all crop water requirements (450 mm), 50%ETc treatment meeting half of them (285 mm), and a rainfed treatment (122 mm). Yield reductions of 18.3 and 48.0% were recorded in the 50%ETc and rainfed treatments, respectively, in comparison with the 100%ETc treatment. UAV flights were carried out during jointing (April 4th), anthesis (April 30th), and grain-filling (May 22nd). Remotely-sensed data were used to estimate: (1) plant height from a digital surface model (H, R2 = 0.95, RMSE = 0.18m), (2) leaf area index from multispectral vegetation indices (LAI, R2 = 0.78, RMSE = 0.63), and (3) actual evapotranspiration (ETa) and transpiration (T) through the TSEB model (R2 = 0.50, RMSE = 0.24 mm/h). Compared with ground measurements, the four traits estimated at grain-filling provided a good prediction of days from sowing to heading (DH, r = 0.58–0.86), to anthesis (DA, r = 0.59–0.85) and to maturity (r = 0.67–0.95), grain-filling duration (GFD, r = 0.54–0.74), plant height (r = 0.62–0.69), number of grains per spike (NGS, r = 0.41–0.64), and thousand kernel weight (TKW, r = 0.37–0.42). The best trait to estimate yield, DH, DA, and GFD was ETa at anthesis or during grain filling. Better forecasts for yield-related traits were recorded in the irrigated treatments than in the rainfed one. These results show a promising perspective in the use of energy balance models for the phenotyping of large numbers of durum wheat genotypes under Mediterranean conditions.info:eu-repo/semantics/publishedVersio

Remote Detection of water and nutritional status of soybeans using UAV-based images.

Digital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remotely detect water and nutritional status in two soybean cultivars for nitrogen. The soybean cultivars BONUS and BRS-8980 were evaluated at the phenological stages R5 and R3 (beginning of seed enlargement), respectively. To do so, plants were subjected to two water regimes (100% ETc and 50% ETc) and two nitrogen (N) supplementation levels (with and without). Thirty-five VIs from multispectral aerial images were evaluated and correlated with stomatal conductance (gs) and leaf N content (NF) measurements. Near-infrared (NIR) spectral band, enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and renormalized difference vegetation index (RDVI) showed linear correlation (p<0.001) with gs, standing out as promising indexes for detection of soybean water status. In turn, simplified canopy chlorophyll content index (SCCCI), red-edge chlorophyll index (RECI), green ratio vegetation index (GRVI), and chlorophyll vegetation index (CVI) were correlated with NF (p<0.001), thus being considered promising for the detection of leaf N content in soybeans

Mapping evapotranspiration to disaggregate eddy-covariance footprints

Land-atmosphere interactions are commonly quantified using eddy-covariance (EC) equipment. This technique provides fluxes which are attributed to an area-averaged two-dimensional flux footprint. Although source flux heterogeneity is present within these footprints, current EC footprint models are unable to distinguish the variable flux contributions (although they are incorporated into the bulk EC flux). A disaggregation method would increase the value of EC data by allowing users to isolate individual fluxes from features within the flux footprint; furthermore, this may extend the useability of the EC method to more complex terrain of mixed land classifications. It remains unexplored how high-resolution surface energy balance (SEB) models can be used to disaggregate these EC footprints. This thesis presents a SEB workflow using Unoccupied Aerial Vehicles (UAVs) to generate high-resolution patterns of evapotranspiration (ET) to disaggregate EC footprints in a novel disaggregated flux footprint prediction (disFFP) method. This workflow begins by using novel UAV Light Ranging And Detection (LiDAR) techniques to derive detailed maps of canopy height (h), effective leaf area index (LAI_e), and canopy viewing fraction (f_c), consistent with known vegetation patterns and field-average observations (LAI_e RMSE=0.08-0.81 m^2 m^(-2)). It then follows an atmospheric correction (path transmissivity and upward-welling radiance) and an ensemble emissivity adjustment (brightness to radiometric temperature), testing these with UAV thermal data to determine their absolute (magnitude) and relative (spatial pattern) effects on temperature. A modified t-test - considering autocorrelation - showed that a raw brightness temperature has similar spatial patterns (r>.99, p_val≫0.001) to brightness and radiometric temperatures. Combining these thermal and LiDAR UAV inputs with a high-resolution SEB model (HRMET), the model performance was then compared against EC fluxes. It followed that HRMET tended to overestimate latent heat over full canopies when surface-air temperature differences exceed 4-5°C. Overall, HRMET succeeded at replicating EC latent heat flux within RMSE of 79-136 W m^(-2) using raw brightness and ensemble emissivity corrected (observed radiometric) temperatures. The resultant relative ET maps (ET_R) provided a coherent chronology of the changing flux landscape. Furthermore, the ET_R trials using corrected temperatures (brightness, radiometric, and observed radiometric) had similar spatial patterns to those found using just raw brightness temperature (r>.93, p_val≫0.001). This implies that a raw brightness temperature is sufficient for determining relative patterns of evapotranspiration. The next part of the workflow uses ET_R to disaggregate a well-known parameterization of a backwards-Lagrangian flux footprint model. The proposed disaggregation method (disFFP) uses the concept of ET period to describe an interval of time (day scale) where the EC flux environment remains relatively constant (constant-rate cumulative ET rate). ET periods were determined using a piece-wise regression of the cumulative day-over-day EC latent flux rate. Each season was divided into five ET periods and compared with the two other seasons to discover potential metrics for further classifying ET periods. It was found that ET rates were consistent across comparable ET periods, 2-38 % (standard deviation as a percentage of sample mean), and that additional metrics (plant phenology, growing degree day, standard precipitation index) played an important role in characterizing ET periods for inter-seasonal work. Eddy-covariance and UAV data were coupled using climatology footprints of ET periods and the coinciding ET patterns (ET_R and coefficient of variation). The disFFP combination of these two products (footprint-weighted factoring) provided disaggregated footprints (EC bulk ET of 144 mm) that reflected increased contributions (180-200 mm) over high ET areas and diminished values (90-100 mm) over lower ET areas. These preliminary findings present an exciting new opportunity to connect discrete UAV data with continuous EC flux monitoring