Search Results (38)

Multispectral imaging technology holds significant promise in the field of thermal imaging applications, primarily due to its unique ability to provide comprehensive two-dimensional spectral data distributions without the need for any form of scanning. This paper focuses on the development of an accessible basic design concept and a method for estimating temperature maps using a four-channel spectral imaging system. The research examines key design considerations and establishes a workflow for data correction and processing. It involves preliminary camera calibration procedures, which are essential for accurately assessing and compensating for the characteristic properties of optical elements and image sensors. The developed method is validated through testing using a blackbody source, demonstrating a mean relative temperature error of 1%. Practical application of the method is demonstrated through temperature mapping of a tungsten lamp filament. Experiments demonstrated the capability of the developed multispectral camera to detect and visualize non-uniform temperature distributions and localized temperature deviations with sufficient spatial resolution. Full article

Laser-induced breakdown spectroscopy (LIBS) was applied to the analysis of aerosolized Cu- and Pb-bearing particles generated from aqueous solutions. A nitrogen-driven nebulizer was utilized to aerosolize Cu- and Pb-spiked solutions. The liquid matrix of the aqueous droplets was evaporated before the LIBS analysis, and the remaining gas-phase analyte-rich aerosols were analyzed in a LIBS system that featured a 1064 nm Nd:YAG laser, a Czerny–Turner spectrometer, and an ICCD camera. The Cu and Pb concentrations in the aerosol streams were 0.26–1.29 ppm and 0.40–1.19 ppm, respectively. Laser diffraction and the particle size distributions of the aqueous aerosols were obtained to indirectly demonstrate the evaporation of the liquid matrix. Highly linear calibration curves (R² = 0.995 for Cu and R² = 0.987 for Pb) and acceptable limits of detection (2 ppb for Cu and 9 ppb for Pb) and quantification (5 ppb and 28 ppb) were obtained. The applications of the presented methodology include the near-real-time and in situ analysis of wastewater and gas-phase aerosols contaminated with heavy metals. Full article

Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm–840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor’s viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal/val requirements in the peatland site. Full article

The study presents a bioindication complex and a technology of the experiment based on a submersible digital holographic camera with advanced monitoring capabilities for the study of plankton and its behavioral characteristics in situ. Additional mechanical and software options expand the capabilities of the digital holographic camera, thus making it possible to adapt the depth of the holographing scene to the parameters of the plankton habitat, perform automatic registration of the “zero” frame and automatic calibration, and carry out natural experiments with plankton photostimulation. The paper considers the results of a long-term digital holographic experiment on the biotesting of the water area in Arctic latitudes. It shows additional possibilities arising during the spectral processing of long time series of plankton parameters obtained during monitoring measurements by a submersible digital holographic camera. In particular, information on the rhythmic components of the ecosystem and behavioral characteristics of plankton, which can be used as a marker of the ecosystem well-being disturbance, is thus obtained. Full article

Complex coastal environments pose unique logistical challenges when deploying unmanned aerial vehicles (UAVs) for real-time image acquisition during monitoring operations of marine water quality. One of the key challenges is the difficulty in synchronizing the images acquired by UAV spectral sensors and ground-truth in situ water quality measurements for calibration, due to a typical time delay between these two modes of data acquisition. This study investigates the logistics for the concurrent deployment of the UAV-borne spectral sensors and a sampling vessel for water quality measurements and the effects on the turbidity predictions due to the time delay between these two operations. The results show that minimizing the time delay can significantly enhance the efficiency of data acquisition and consequently improve the calibration process. In particular, the outcomes highlight notable improvements in the model’s predictive accuracy for turbidity distribution derived from UAV-borne spectral images. Furthermore, a comparative analysis based on a pilot study is conducted between two multirotor UAV configurations: the DJI M600 Pro with a hyperspectral camera and the DJI M300 RTK with a multispectral camera. The performance evaluation includes the deployment complexity, image processing productivity, and sensitivity to environmental noises. The DJI M300 RTK, equipped with a multispectral camera, is found to offer higher cost-effectiveness, faster setup times, and better endurance while yielding good image quality at the same time. It is therefore a more compelling choice for widespread industry adoption. Overall, the results from this study contribute to advancement in the deployment of UAVs for marine water quality monitoring. Full article

The Gaofen-1 satellite is equipped with four wide-field-of-view (WFV) instruments, enabling an impressive spatial resolution of 16 m and a combined swath exceeding 800 km. These WFV images have shown their valuable applications across diverse fields. However, achieving accurate radiometric calibration is an essential prerequisite for establishing reliable connections between satellite signals and biophysical, as well as biochemical, parameters. However, observations with large viewing angles (>20°) pose new challenges due to the bidirectional reflectance distribution function (BRDF) effect having a pronounced impact on the accuracy of cross-radiation calibrations, especially for the off-nadir WFV1 and WFV4 cameras. To overcome this challenge, a novel approach was introduced utilizing the combined observations from the Gaofen-1 and Gaofen-6 satellites, with Landsat-8 OLI serving as a reference sensor. The key advantage of this synergistic observation strategy is the ability to obtain a greater number of image pairs that closely resemble Landsat-8 OLI reference images in terms of geometry and observation dates. This increased availability of matching images ensures a more representative dataset of the observation geometry, enabling the derived calibration coefficients to be applicable across various sun–target–sensor geometries. Then, the geometry angles and bidirectional reflectance information were put into a Particle Swarm Optimization (PSO) algorithm incorporating radiative transfer modeling. This PSO-based approach formulates cross-calibration as an optimization problem, eliminating the reliance on complex BRDF models and satellite-based BRDF products that can be affected by cloud contamination. Extensive validation experiments involving satellite data and in situ measurements demonstrated an average uncertainty of less than eight percent for the proposed cross-radiation calibration scheme. Comparisons of top-of-atmosphere (TOA) results calibrated using our proposed scheme, the previous traditional radiative transfer modeling using MODIS BRDF products for BRDF correction (RTM-BRDF) method, and official coefficients reveal the superior accuracy of our method. The proposed scheme achieves a 36.99% decrease in root mean square error (RMSE) and a 38.13% increase in mean absolute error (MAE) compared to official coefficients. Moreover, it achieves comparable accuracy to the RTM-BRDF method while eliminating the need for MODIS BRDF products, with a decrease in RMSE exceeding 14% for the off-nadir WFV1 and WFV4 cameras. The results substantiate the efficacy of the proposed scheme in enhancing cross-calibration accuracy by improving image match-up selection, efficiently removing BRDF effects, and expanding applicability to diverse observation geometries. Full article

Unmanned Aerial Systems (UAS) Photogrammetry has become widely used for spatial data acquisition. Nowadays, RTK (Real Time Kinematic) and PPK (Post Processed Kinematic) are the main correction methods for accurate positioning used for direct measurements of camera station coordinates in UAS imagery. Thus, 3D camera coordinates are commonly used as additional observations in Bundle Block Adjustment to perform Global Navigation Satellite System-Assisted Aerial Triangulation (GNSS-AAT). This process requires accurate Interior Orientation Parameters to ensure the quality of photogrammetric intersection. Therefore, this study investigates the influence of on-site camera calibration with a sub-block of images on the accuracy of spatial data obtained by PPK-based UAS Photogrammetry. For this purpose, experiments of on-the-job camera self-calibration in the Metashape software with the SfM approach were performed. Afterward, experiments of GNSS-Assisted Aerial Triangulation with on-site calibration in the Erdas Imagine software were performed. The outcomes show that only the experiment of GNSS-AAT with three Ground Control Points yielded horizontal and vertical accuracies close to nominal precisions of the camera station positions by GNSS-PPK measurements adopted in this study, showing horizontal RMSE (Root-Mean Square Error) of 0.222 m and vertical RMSE of 0.154 m. Furthermore, the on-site camera calibration with a sub-block of images significantly improved the vertical accuracy of the spatial information extraction. Full article

The present study reports the development and application of a rapid, low-cost in-situ method for the quantification of tartrazine in carbonated beverages using a smartphone-based colorimetric device with molecularly imprinted polymer (MIP). The MIP was synthesized using the free radical precipitation method with acrylamide (AC) as the functional monomer, N,N′-methylenebisacrylamide (NMBA) as the cross linker, and potassium persulfate (KPS) as radical initiator. The smartphone (RadesPhone)-operated rapid analysis device proposed in this study has dimensions of 10 × 10 × 15 cm and is illuminated internally by light emitting diode (LED) lights with intensity of 170 lux. The analytical methodology involved the use of a smartphone camera to capture images of MIP at various tartrazine concentrations, and the subsequent application of the Image-J software to calculate the red, green, blue (RGB) color values and hue, saturation, value (HSV) values from these images. A multivariate calibration analysis of tartrazine in the range of 0 to 30 mg/L was performed, and the optimum working range was determined to be 0 to 20 mg/L using five principal components and a limit of detection (LOD) of 1.2 mg/L was obtained. Repeatability analysis of tartrazine solutions with concentrations of 4, 8, and 15 mg/L (n = 10) showed a coefficient of variation (% RSD) of less than 6%. The proposed technique was applied to the analysis of five Peruvian soda drinks and the results were compared with the UHPLC reference method. The proposed technique showed a relative error between 6% and 16% and % RSD lower than 6.3%. The results of this study demonstrate that the smartphone-based device is a suitable analytical tool that offers an on-site, cost-effective, and rapid alternative for the quantification of tartrazine in soda drinks. This color analysis device can be used in other molecularly imprinted polymer systems and offers a wide range of possibilities for the detection and quantification of compounds in various industrial and environmental matrices that generate a color change in the MIP matrix. Full article

The utilization of stationary underwater cameras is a modern and well-adapted approach to provide a continuous and cost-effective long-term solution to monitor underwater habitats of particular interest. A common goal of such monitoring systems is to gain better insight into the dynamics and condition of populations of various marine organisms, such as migratory or commercially relevant fish taxa. This paper describes a complete processing pipeline to automatically determine the abundance, type and estimate the size of biological taxa from stereoscopic video data captured by the stereo camera of a stationary Underwater Fish Observatory (UFO). A calibration of the recording system was carried out in situ and, afterward, validated using the synchronously recorded sonar data. The video data were recorded continuously for nearly one year in the Kiel Fjord, an inlet of the Baltic Sea in northern Germany. It shows underwater organisms in their natural behavior, as passive low-light cameras were used instead of active lighting to dampen attraction effects and allow for the least invasive recording possible. The recorded raw data are pre-filtered by an adaptive background estimation to extract sequences with activity, which are then processed by a deep detection network, i.e., Yolov5. This provides the location and type of organisms detected in each video frame of both cameras, which are used to calculate stereo correspondences following a basic matching scheme. In a subsequent step, the size and distance of the depicted organisms are approximated using the corner coordinates of the matched bounding boxes. The Yolov5 model employed in this study was trained on a novel dataset comprising 73,144 images and 92,899 bounding box annotations for 10 categories of marine animals. The model achieved a mean detection accuracy of 92.4%, a mean average precision (mAP) of 94.8% and an F1 score of 93%. Full article

Particle image velocimetry (PIV) is a quantitative flow visualization technique, which greatly improves the ability to characterize various complex flows in laboratory and field environments. However, the deployment of reference objects or ground control points (GCPs) for velocity calibration is still a challenge for in situ free-surface velocity measurements. By combining space-time image velocimetry (STIV) with direct sensor orientation (DSO) photogrammetry, a laser distance meter (LDM)-supported photogrammetric device is designed, to realize the GCPs-free surface velocity measurement under an oblique shooting angle. The velocity calibration with DSO is based on the collinear equation, while the lens distortion, oblique shooting angle, water level variation, and water surface slope are introduced to build an imaging measurement model with explicit physical meaning for parameters. To accurately obtain the in situ position and orientations of the camera utilizing the LDM and its embedded tilt sensor, the camera’s intrinsic parameters and relative position within the LDM are previously calibrated with a planar chessboard. A flume experiment is designed to evaluate the uncertainty of optical flow estimation and velocity calibration. Results show that the proposed DSO-STIV has good transferability and operability for in situ measurements. It is superior to propeller current meters and surface velocity radars in characterizing shallow free-surface flows; this is attributed to its non-intrusive, whole-field, and high-resolution features. In addition, the combined uncertainty of free-surface velocity measurement is analyzed, which provides an alternative solution for error assessment when comparing measurement failures. Full article

In March 2019, the PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral satellite was launched by the Italian Space Agency (ASI), and it is currently operational on a global basis. The mission includes the hyperspectral imager PRISMA working in the 400–2500 nm spectral range with 237 bands and a panchromatic (PAN) camera (400–750 nm). This paper presents an evaluation of the PRISMA top-of-atmosphere (TOA) L1 products using different in situ measurements acquired over a fragmented rural area in Southern Italy (Pignola) between October 2019 and July 2021. L1 radiance values were compared with the TOA radiances simulated with a radiative transfer code configured using measurements of the atmospheric profile and the surface spectral characteristics. The L2 reflectance products were also compared with the data obtained by using the ImACor code atmospheric correction tool. A preliminary assessment to identify PRISMA noise characteristics was also conducted. The results showed that: (i) the PRISMA performance, as measured at the Pignola site over different seasons, is characterized by relative mean absolute differences (RMAD) of about 5–7% up to 1800 nm, while a decrease in accuracy was observed in the SWIR; (ii) a coherent noise could be observed in all the analyzed images below the 630th scan line, with a frequency of about 0.3–0.4 cycles/pixel; (iii) the most recent version of the standard reflectance L2 product (i.e., Version 2.05) matched well the reflectance values obtained by using the ImACor atmospheric correction tool. All these preliminary results confirm that PRISMA imagery is suitable for an accurate retrieval of the bio-geochemical variables pertaining to a complex fragmented ecosystem such as that of the Southern Apennines. Further studies are needed to confirm and monitor PRISMA data performance on different land-cover areas and on the Radiometric Calibration Network (RadCalNet) targets. Full article

High-resolution data are increasingly used for various applications, yet the revisit time is still low for some applications, particularly in frequently cloud-covered areas. Therefore, sensors are often combined, which raises issues on data consistency. In this study, we start from L1 to L3 data, and investigate the impact of harmonization measures, correcting for difference in radiometric gain and spectral response function (SRF), and the use of a common processing chain with the same atmospheric correction for Sentinel-2A/B, Landsat-8, DEIMOS-1, and Proba-V center cameras. These harmonization measures are evaluated step-wise in two applications: (1) agricultural monitoring, and (2) hydrological modelling in an urban context, using biophysical parameters and NDVI. The evaluation includes validation with in situ data, relative consistency analysis between different sensors, and the evaluation of the time series noise. A higher accuracy was not obtained when validating against in situ data. Yet, the relative analysis and the time series noise analysis clearly demonstrated that the largest improvement in consistency between sensors was obtained when applying the same atmospheric correction to all sensors. The gain correction obtained and its impact on the results was small, indicating that the sensors were already well calibrated. We could not demonstrate an improved consistency after SRF correction. It is likely that other factors, such as anisotropy effects, play a larger role, requiring further research. Full article

Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations) is a custom, mini-UAV (unmanned aerial vehicle) platform (<20 kg), equipped with a light push broom hyperspectral sensor combined with a navigation module measuring position and orientation. Because of the particularities of UAV surveys (low flight altitude, small spatial scale, and high resolution), dedicated pre-processing methods have to be developed when reconstructing hyperspectral imagery. This article presents light, easy-implementation, in situ methods, using only two Spectralon^® and a field spectrometer, allowing performance of an initial calibration of the sensor in order to correct “vignetting effects” and a field standardization to convert digital numbers (DN) collected by the hyperspectral camera to reflectance, taking into account the time-varying illumination conditions. Radiometric corrections are applied to a subset of a dataset collected above mudflats colonized by pioneer mangroves in French Guiana. The efficiency of the radiometric corrections is assessed by comparing spectra from Hyper-DRELIO imagery to in situ spectrometer measurements above the intertidal benthic biofilm and mangroves. The shapes of the spectra were consistent, and the spectral angle mapper (SAM) distance was 0.039 above the benthic biofilm and 0.159 above the mangroves. These preliminary results provide new perspectives for quantifying and mapping the benthic biofilm and mangroves at the scale of the Guianese intertidal mudbanks system, given their importance in the coastal food webs, biogeochemical cycles, and the sediment stabilization. Full article

The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array; (2) inconsistencies between images relative to in-flight effects (wind-speed and direction); (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 °C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 °C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics. Full article

This study was conducted using a drone with advanced mobility to develop a unified sensor and communication system as a new platform for in situ atmospheric measurements. As a major cause of air pollution, particulate matter (PM) has been attracting attention globally. We developed a small, lightweight, simple, and cost-effective multi-sensor system for multiple measurements of atmospheric phenomena and related environmental information. For in situ local area measurements, we used a long-range wireless communication module with real-time monitoring and visualizing software applications. Moreover, we developed four prototype brackets with optimal assignment of sensors, devices, and a camera for mounting on a drone as a unified system platform. Results of calibration experiments, when compared to data from two upper-grade PM${}_{2.5}$ sensors, demonstrated that our sensor system followed the overall tendencies and changes. We obtained original datasets after conducting flight measurement experiments at three sites with differing surrounding environments. The experimentally obtained prediction results matched regional PM${}_{2.5}$ trends obtained using long short-term memory (LSTM) networks trained using the respective datasets. Full article