Ultra-high Spatial Resolution Research Articles

Comparison of visibility of in-stent restenosis between conventional- and ultra-high spatial resolution computed tomography: coronary arterial phantom study.

The purposes of this experimental study were to compare the quantitative and qualitative visibility of in-stent restenosis between conventional-resolution CT (CRCT) and ultra-high-resolution CT (U-HRCT) and to investigate the effects of the image reconstruction techniques on the visualization of in-stent restenosis. A vessel tube with non-calcified plaque in a 3.0-mm stent was scanned by using CRCT and U-HRCT at 4 stent directions (0, 30, 60, and 90 degrees) to the through-plane direction. Hybrid iterative reconstruction (HIR); model-based iterative reconstruction (MBIR); deep-learning-based reconstruction (DLR) were used as reconstruction methods. The lumen size was assessed using the full width at half maximum method, and image quality was visually evaluated using 4-point scale. U-HRCT had the significantly wider lumen sizes and narrower stent strut thickness than CRCT in three types of the reconstruction methods (P < 0.01). The lumen sizes for U-HRCT with 90 degrees were narrower than those with the other angle directions regardless of the reconstruction methods. Visual score was significantly higher for U-HRCT than CRCT (3.2 ± 0.7 vs 2.0 ± 0.4, P < 0.001). U-HRCT quantitatively and qualitatively provided better visualization of in-stent restenosis compared to CRCT. Image quality of U-HRCT may be affected by stent angle.

Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial resolution UAV data

Karst wetlands have the characteristics of small scale and poor stability. At present, the wetland is being severely damaged and its area is seriously degraded, and the accurate identification of vegetation communities is very important for the rapid assessment and management of karst wetland. In this paper, Huixian Karst National Wetland Park, located in Guilin city, China, was taken as the study area, the digital orthophoto map (DOM) and digital surface model (DSM) of UAV images were selected as the data sources, and the vegetation communities of karst wetland were classified by using the object-based Random Forest (RF)-Decision Tree (DT) algorithm and SegNet algorithm. When the object-based RF algorithm and SegNet algorithm were used for coarse classification of karst vegetation, the parameters (mtry, ntree) of the object-based RF algorithm were optimized, and the data dimensionality reduction and RFE variable selection algorithm were used for selecting feature, and the single-class SegNet model was integrated based on the soft voting method to improve the applicability of vegetation classification in karst wetlands. In the classification of vegetation communities in karst wetlands, the optimized object-based RF-DT algorithm were used to extract the vegetation communities in the Areas A, B, and C. The statistical analysis of the importance of the feature variables (spectral features, texture features, geometric features, and position features) of various types of land cover in the three areas was carried out to explore the optimal classification variables of various types of vegetation. The results showed that: (1) the optimized object-based RF algorithm performed better than the SegNet algorithm in classifying karst vegetation at 95% confidence level during the coarse classification. The average accuracy of wetland vegetation was improved by 1.06–13.58%; (2) the object-based RF-DT algorithm had high classification ability for the karst wetland vegetation community, with overall accuracy and kappa coefficient above 0.85; and that (3) although geometric features accounted for the largest proportion (52.2%) in the classification of bermudagrass, water hyacinth, lotus, linden and other vegetation, texture features accounted for the highest proportion of 56.3% in the classification of vegetation whose importance was more than 90.

Retinal Vasculopathy in Alzheimer's Disease.

The retina has been increasingly investigated as a site of Alzheimer’s disease (AD) manifestation for over a decade. Early reports documented degeneration of retinal ganglion cells and their axonal projections. Our group provided the first evidence of the key pathological hallmarks of AD, amyloid β-protein (Aβ) plaques including vascular Aβ deposits, in the retina of AD and mild cognitively impaired (MCI) patients. Subsequent studies validated these findings and further identified electroretinography and vision deficits, retinal (p)tau and inflammation, intracellular Aβ accumulation, and retinal ganglion cell-subtype degeneration surrounding Aβ plaques in these patients. Our data suggest that the brain and retina follow a similar trajectory during AD progression, probably due to their common embryonic origin and anatomical proximity. However, the retina is the only CNS organ feasible for direct, repeated, and non-invasive ophthalmic examination with ultra-high spatial resolution and sensitivity. Neurovascular unit integrity is key to maintaining normal CNS function and cerebral vascular abnormalities are increasingly recognized as early and pivotal factors driving cognitive impairment in AD. Likewise, retinal vascular abnormalities such as changes in vessel density and fractal dimensions, blood flow, foveal avascular zone, curvature tortuosity, and arteriole-to-venule ratio were described in AD patients including early-stage cases. A rapidly growing number of reports have suggested that cerebral and retinal vasculopathy are tightly associated with cognitive deficits in AD patients and animal models. Importantly, we recently identified early and progressive deficiency in retinal vascular platelet-derived growth factor receptor-β (PDGFRβ) expression and pericyte loss that were associated with retinal vascular amyloidosis and cerebral amyloid angiopathy in MCI and AD patients. Other studies utilizing optical coherence tomography (OCT), retinal amyloid-fluorescence imaging and retinal hyperspectral imaging have made significant progress in visualizing and quantifying AD pathology through the retina. With new advances in OCT angiography, OCT leakage, scanning laser microscopy, fluorescein angiography and adaptive optics imaging, future studies focusing on retinal vascular AD pathologies could transform non-invasive pre-clinical AD diagnosis and monitoring.

Unmanned Aerial Vehicle (UAV)-Based Mapping of Acacia saligna Invasion in the Mediterranean Coast

Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and mapping. This work develops an operational workflow for detecting and mapping Acacia saligna invasion along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green, Blue) and multispectral (Green, Red, Red Edge, Near Infra—Red) UAV images collected in pre-flowering and flowering phenological stages for detecting and mapping A. saligna. After ortho—mosaics generation, we derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables, and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two phenological stages we built a set of raster stacks which include different combination of variables. For image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on pre-flowering and flowering stages and using different combinations of variables) produced A. saligna maps with acceptable accuracy values, with higher performances on classification derived from flowering period images, especially using DSM + HIS combination. The adopted approach resulted an efficient method for mapping and early detection of IAPs, also in complex environments offering a sound support to the prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014.

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health statuses at the landscape scale in Alaska, USA, this study conducted spectral and structural fusion of data from: (1) Unoccupied aerial vehicle (UAV) multispectral (6 cm) + structure from motion point clouds (~700 pts m−2); and (2) Goddard Lidar Hyperspectral Thermal (G-LiHT) hyperspectral (400 to 1000 nm, 0.5 m) + SWIR-band lidar (~32 pts m−2). We achieved 78% accuracy for all three health statuses using spectral + structural fusion from either UAV or G-LiHT and 97% accuracy for non-infested/dead using G-LiHT. We confirm that UAV 3D spectral (e.g., greenness above versus below median height in crown) and lidar apparent reflectance metrics (e.g., mean reflectance at 99th percentile height in crown), are of high value, perhaps capturing the vertical gradient of needle degradation. In most classification exercises, UAV accuracy was lower than G-LiHT indicating that collecting ultra-high spatial resolution data might be less important than high spectral resolution information. While the value of passive optical spectral information was largely confined to the discrimination of non-infested versus dead crowns, G-LiHT hyperspectral band selection (~400, 675, 755, and 940 nm) could inform future FHM mission planning regarding optimal wavelengths for this task. Interestingly, the selected regions mostly did not align with the band designations for our UAV multispectral data but do correspond to, e.g., Sentinel-2 red edge bands, suggesting a path forward for moderate scale bark beetle detection when paired with suitable structural data.

Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects

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.

Probing Ultralow Energy Excitations at Ultrahigh Spatial Resolution with Monochromated Electron Energy Loss Spectroscopy

Probing Ultralow Energy Excitations at Ultrahigh Spatial Resolution with Monochromated Electron Energy Loss Spectroscopy

Nanoscale Chemical Imaging of Supported Lipid Monolayers using Tip-Enhanced Raman Spectroscopy.

Visualizing the molecular organization of lipid membranes is essential to comprehend their biological functions. However, current analytical techniques fail to provide a non‐destructive and label‐free characterization of lipid films under ambient conditions at nanometer length scales. In this work, we demonstrate the capability of tip‐enhanced Raman spectroscopy (TERS) to probe the molecular organization of supported DPPC monolayers on Au (111), prepared using the Langmuir–Blodgett (LB) technique. High‐quality TERS spectra were obtained, that permitted a direct correlation of the topography of the lipid monolayer with its TERS image for the first time. Furthermore, hyperspectral TERS imaging revealed the presence of nanometer‐sized holes within a continuous DPPC monolayer structure. This shows that a homogeneously transferred LB monolayer is heterogeneous at the nanoscale. Finally, the high sensitivity and spatial resolution down to 20 nm of TERS imaging enabled reproducible, hyperspectral visualization of molecular disorder in the DPPC monolayers, demonstrating that TERS is a promising nanoanalytical tool to investigate the molecular organization of lipid membranes.

Dynamic signal of live biological cells under interferometric scattering (iSCAT) microscopy and its impacts on single-particle tracking

Interferometric scattering (iSCAT) microscopy has been demonstrated as a powerful tool for tracking single nanoparticles at ultrahigh spatial and temporal resolutions. The unmatched performance of iSCAT single-particle tracking (SPT) relies on the fact that the scattering signal is steady and can be linearly increased under strong illumination, circumventing the problems of photobleaching and saturation of fluorescence-based approaches. However, the scattering-based imaging is complicated by the presence of nonspecific scattering background, e.g. in the complex samples of biological cells. To distinguish the signal of interest from the nonspecific scattering background, metallic nanoparticles are often used as the efficient scattering probes. In many applications, reduction of the particle size is favorable because the loading of particle may introduce labeling artifacts. To work with smaller particles of weaker signal, suppression of the heterogeneous background is necessary. In this work, we start with the characterization of dynamic iSCAT signal of living cells by calculating its spatial and temporal Fourier spectra. To reduce the influence of cell background to SPT, a common strategy of background estimation and correction by temporal average filtering is considered. The effect of residual cell background to SPT is evaluated systematically with simulated image data of various signal-to-background ratios. This work benchmarks the localization errors caused by the cell background in SPT, providing a guideline for the interpretation of single-particle diffusion data of iSCAT microscopy.

Topological polar structures in ferroelectric oxide films

The continuous rotation of electric dipoles, which is inspired by unusual spin textures in magnetic materials, has been envisioned by theoretical modelings in last two decades. Although in electrically polar systems it was thought to be difficult to introduce continuous rotation of electric dipoles since similar Dzyaloshinskii–Moriya interaction like that of ferromagnets is still under study, external strains and interface depolarization fields have been then identified to be critical for rotating electric dipoles in nano-scale oxide films/superlattices. In this Perspective, we will briefly summarize the experimental finding of the newly identified topological polar structures and corresponding properties, such as polar flux-closure, vortex, skyrmion lattice, and meron. The critical importance of microscopy technologies, especially the advanced aberration-corrected transmission electron microscopy with ultra-high spatial resolutions, will be emphasized. Moreover, physical aspects to be addressed in the future, such as the strain maintenance and relaxation mechanisms of polar systems/superlattices, atomic maps of three-dimensional topological polar structures, and flexoelectricity-related properties, will be highlighted and envisioned.

Plant drought impact detection using ultra-high spatial resolution hyperspectral images and machine learning

Early drought stress detection is crucial for restoring productivity, ensuring recovery, and providing vital information for mortality prevention. Hyperspectral remote sensing which is sensitive to subtle changes in leaf constituents and canopy structure is widely used to study how drought stress manifests and evolves over time. Drought impacts are complicated, affecting multiple leaf properties at different stages of severity. Spectral indices are widely used for stress monitoring, given their sensitivity to one or more plant biochemical, biophysical, and structural properties. However, appropriate spectral bands are not always apparent for early drought detection and stress progression monitoring since drought response varies by species, from tolerators to competitors. A tolerator can produce secondary compounds to enhance leaf survival while a competitor allows stressed leaves to perish so that new growth can occur. Also, spectral indices typically utilize a few bands and therefore do not exploit information in other spectral regions that are also influenced by drought. With the development of machine learning and increased computational power, the use of full spectra and derivative spectra becomes possible, holding great potential to offer new insights into the drought impacts. In this study, full spectra and derivative spectra were integrated with advanced machine learning algorithms for early drought detection and classification. The results from these models were compared to those from traditional spectral index-based methods. The models were tested using drought-stressed smooth brome (Bromus inermis) grass grown in a chamber with 0% and 25% field precipitation, using close-range hyperspectral images. The results showed that the use of derivative spectra and the deep learning model allowed accurate drought detection with an overall accuracy of up to 97.5% and 100% while spectral indices and spectral index models either failed to detect early stress or could detect early stress but with much lower accuracy. This study demonstrated the usefulness of integrating deep learning and hyperspectral data in plant stress research for stress impact assessment, recovery, and mitigation.

Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.

The high energy Universe at ultra-high resolution: the power and promise of X-ray interferometry

We propose the development of X-ray interferometry (XRI), to reveal the Universe at high energies with ultra-high spatial resolution. With baselines which can be accommodated on a single spacecraft, XRI can reach 100 μ as resolution at 10 Å (1.2 keV) and 20 μ as at 2 Å (6 keV), enabling imaging and imaging-spectroscopy of (for example) X-ray coronae of nearby accreting supermassive black holes (SMBH) and the SMBH ‘shadow’; SMBH accretion flows and outflows; X-ray binary winds and orbits; stellar coronae within sim 100 pc and many exoplanets which transit across them. For sufficiently luminous sources XRI will resolve sub-pc scales across the entire observable Universe, revealing accreting binary SMBHs and enabling trigonometric measurements of the Hubble constant with X-ray light echoes from quasars or explosive transients. A multi-spacecraft ‘constellation’ interferometer would resolve well below 1 μ as, enabling SMBH event horizons to be resolved in many active galaxies and the detailed study of the effects of strong field gravity on the dynamics and emission from accreting gas close to the black hole.

Simulation experiments on high-precision VGOS time transfer for future geopotential difference determination

Known for unique ultrahigh spatial resolution and timing accuracy, the VLBI global observing system (VGOS) time transfer of ultralong baselines can achieve better long-term stability compared to other techniques. Because of the 1 mm positioning accuracy of the VGOS, comparing high-performance atomic clocks with VGOS time transfer can be used to accurately determine the geopotential difference between two remote stations and unify the world height system in the framework of the theory of general relativity. To investigate its performance in determining geopotential differences, we schedule continuous VGOS sessions with a 7-station network, simulate and estimate the corresponding VGOS observables and determine the geopotential differences of six baselines. Furthermore, the coefficients of the tropospheric turbulence model and clock stability are varied to analyze their influences. The results show that when using atomic clocks with 1.0 × 10 - 16 @1 d stability, the accuracy and precision of the weighted averaged geopotential difference remain at the level of 8 m 2 s −2 and are sensitive to variations in the troposphere. The improvement by upgrading clock performance is limited by other error sources. In addition, connecting twin telescopes with identical atomic clocks and combining clock parameters in estimation can improve the precision of the estimated geopotential difference by approximately 4%.

Imaging Dual-Moiré Lattices in Twisted Bilayer Graphene Aligned on Hexagonal Boron Nitride Using Microwave Impedance Microscopy.

Moiré superlattices (MSLs) formed in van der Waals materials have become a promising platform to realize novel two-dimensional electronic states. Angle-aligned trilayer structures can form two sets of MSLs which could potentially interfere. In this work, we directly image the moiré patterns in both monolayer and twisted bilayer graphene aligned on hexagonal boron nitride (hBN), using combined scanning microwave impedance microscopy and conductive atomic force microscopy. Correlation of the two techniques reveals the contrast mechanism for the achieved ultrahigh spatial resolution (<2 nm). We observe two sets of MSLs with different periodicities in the trilayer stack. The smaller MSL breaks the 6-fold rotational symmetry and exhibits abrupt discontinuities at the boundaries of the larger MSL. Using a rigid atomic-stacking model, we demonstrate that the hBN layer considerably modifies the MSL of twisted bilayer graphene. We further analyze its effect on the reciprocal space spectrum of the dual-moiré system.

Development of novel projection-type imaging mass spectrometer.

We developed a novel imaging mass spectrometer based on our accumulating technology for projection-type imaging mass spectrometry, the simulation of an accurate ion trajectory, and the theory for ion optics. The newly developed apparatus yields high spatial resolution with a substantially shorter image-acquisition time compared with conventional scanning-type imaging mass spectrometers. In order to maintain a high mass resolution, a multi-turn time-of-flight mass spectrometer is combined with post-extraction differential acceleration methods. Consequently, a mass resolution of m/Δm ∼ 10 000 and a spatial resolution of 1μm were achieved simultaneously in this study. Application of our newly established apparatus to biological samples accomplished successful imaging mass spectrometry by exhibiting an organ-specific distribution of endogenous ions as well as a localized distribution of exogenously applied ions with an ultra-high spatial resolution image in the size of 18.5 megapixels.

Virtual probe stimulated tip-enhanced Raman spectroscopy: The extreme field enhancement in virtual-real probe dimer

Tip-enhanced Raman spectroscopy (TERS) can be used for scanning imaging, molecular detection, and chemical analysis. The improvement of detection sensitivity, which is related to the electric field enhancement in the TERS substrate, has attracted much attention from researchers. In this work, we numerically studied the local electric field enhancement in the virtual-real probe dimer structure with a vertical gap. We mainly analyzed the influence of the structure parameters on the field enhancement using the finite-difference time-domain method. The Raman enhancement factor could reach up to 1.6×1015. The local field enhancement benefits from plasmon hybridization between the longitudinal component of the virtual probe and the local surface plasmon of the real probe. We also found that the full width at half maximum of the electric field was as narrow as 7.8 nm, and the volume of the hotspot for single-molecule detection can reach a maximum value of 155 nm3. The virtual-real probe dimer structure has ultrahigh field enhancement and spatial resolution, which is promising for high-sensitivity detection and high-resolution imaging.

RECENT ADVANCES IN BOND-RESOLVED SCANNING TUNNELING MICROSCOPY

Recent advances in bond-resolved scanning tunneling microscopy (BRSTM) have demonstrated the tremendous potential of this characterization technique to attain an ultra-high spatial resolution at the level of a single chemical bond. Due to such a unique ability to visualize chemical bonds, BRSTM has been recognized as a valuable characterization tool in the rapidly developing field of on-surface chemistry. In this paper, we discuss the recent experimental advances in BRSTM imaging techniques and their applications in the characterization of a wide scope of functional nanostructures, including individual molecules, elusive nanographenes fabricated by means of surface-assisted synthetic strategies and extended supramolecular self-assemblies.

Fluorescence live cell imaging revealed wogonin targets mitochondria

Scutellaria baicalensis is one of the widely used Chinese traditional medicines, and wogonin is one of major active components in it. However, the mechanism of action of wogonin has largely remained unclear. In this work, we designed a fluorescent probe, namely ATTO565-WGN, by conjugating wogonin with the fluorophore ATTO565 based on Mannich reaction via a flexible chain linker. In vitro assays verified that the ATTO565-WGN conjugate has a similar anti-proliferative activity to wogonin against human A549 and HeLa cancer cell lines. Combining co-localization and competition studies, confocal fluorescence imaging clearly demonstrated that the fluorescent wogonin probe predominantly located in mitochondrial area of living cells, indicating that wogonin acts at mitochondrion to exert its pharmacological functions. Significantly, the conjugated ATTO565 fluorophore conferred the wogonin probe STED (Stimulated Emission Depletion) feature, enabling STED fluorescence living cell imaging with a 55 nm of ultrahigh spatial resolution. This will greatly beneficial for the in situ investigation of interactions between wogonin and biological targets at the finely organized and dynamic mitochondria of living cells. Moreover, this work also provides novel insights into rational design of mitochondrion targeting fluorescence probes for ultrahigh resolution living cell imaging.

Assessing the impact of illumination on UAV pushbroom hyperspectral imagery collected under various cloud cover conditions

The recent development of small form-factor (<6 kg), full range (400–2500 nm) pushbroom hyperspectral imaging systems (HSI) for unmanned aerial vehicles (UAV) poses a new range of opportunities for passive remote sensing applications. The flexible deployment of these UAV-HSI systems have the potential to expand the data acquisition window to acceptable (though non-ideal) atmospheric conditions. This is an important consideration for time-sensitive applications (e.g. phenology) in areas with persistent cloud cover. Since the majority of UAV studies have focused on applications with ideal illumination conditions (e.g. minimal or non-cloud cover), little is known to what extent UAV-HSI data are affected by changes in illumination conditions due to variable cloud cover. In this study, we acquired UAV pushbroom HSI (400–2500 nm) over three consecutive days with various illumination conditions (i.e. cloud cover), which were complemented with downwelling irradiance data to characterize illumination conditions and in-situ and laboratory reference panel measurements across a range of reflectivity (i.e. 2%, 10%, 18% and 50%) used to evaluate reflectance products. Using these data we address four fundamental aspects for UAV-HSI acquired under various conditions ranging from high (624.6 ± 16.63 W·m2) to low (2.5 ± 0.9 W·m2) direct irradiance: atmospheric compensation, signal-to-noise ratio (SNR), spectral vegetation indices and endmembers extraction. For instance, two atmospheric compensation methods were applied, a radiative transfer model suitable for high direct irradiance, and an Empirical Line Model (ELM) for diffuse irradiance conditions. SNR results for two distinctive vegetation classes (i.e. tree canopy vs herbaceous vegetation) reveal wavelength dependent attenuation by cloud cover, with higher SNR under high direct irradiance for canopy vegetation. Spectral vegetation index (SVIs) results revealed high variability and index dependent effects. For example, NDVI had significant differences (p < 0.05) across illumination conditions, while NDWI appeared insensitive at the canopy level. Finally, often neglected diffuse illumination conditions may be beneficial for revealing spectral features in vegetation that are obscured by the predominantly non-Lambertian reflectance encountered under high direct illumination. To our knowledge, our study is the first to use a full range pushbroom UAV sensor (400–2500 nm) for assessing illumination effects on the aforementioned variables. Our findings pave the way for understanding the advantages and limitations of ultra-high spatial resolution full range high fidelity UAV-HSI for ecological and other applications.