Spatial Depth Research Articles

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions

Abstract. Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high-spatial-resolution data, we found a systematic increase (2–20 cm) and then a decline in snow depth near the canopy with increasing distance (from 1 to 2.5 m) of the peak value through the snow season. This study highlights the applicability of the UAS–SfM in high-resolution monitoring of snow depth in multiple land cover types and snow–vegetation interactions in subarctic and remote areas where field data are not available or where the available data are collected using classic point measurements or snow courses.

Real-Time Acoustic Simulation Framework for tFUS: A Feasibility Study Using Navigation System

Transcranial focused ultrasound (tFUS), in which acoustic energy is focused on a small region in the brain through the skull, is a non-invasive therapeutic method with high spatial resolution and depth penetration. Image-guided navigation has been widely utilized to visualize the location of acoustic focus in the cranial cavity. However, this system is often inaccurate because of the significant aberrations caused by the skull. Therefore, acoustic simulations using a numerical solver have been widely adopted to compensate for this inaccuracy. Although the simulation can predict the intracranial acoustic pressure field, real-time application during tFUS treatment is almost impossible due to the high computational cost. In this study, we propose a neural network-based real-time acoustic simulation framework and test its feasibility by implementing a simulation-guided navigation (SGN) system. Real-time acoustic simulation is performed using a 3D conditional generative adversarial network (3D-cGAN) model featuring residual blocks and multiple loss functions. This network was trained by the conventional numerical acoustic simulation program (i.e., k-Wave). The SGN system is then implemented by integrating real-time acoustic simulation with a conventional image-guided navigation system. The proposed system can provide simulation results with a frame rate of 5 Hz (i.e., about 0.2 s), including all processing times. In numerical validation (3D-cGAN vs. k-Wave), the average peak intracranial pressure error was 6.8 ± 5.5%, and the average acoustic focus position error was 5.3 ± 7.7 mm. In experimental validation using a skull phantom (3D-cGAN vs. actual measurement), the average peak intracranial pressure error was 4.5%, and the average acoustic focus position error was 6.6 mm. These results demonstrate that the SGN system can predict the intracranial acoustic field according to transducer placement in real-time.

A transducer positioning method for transcranial focused ultrasound treatment of brain tumors.

As a non-invasive method for brain diseases, transcranial focused ultrasound (tFUS) offers higher spatial precision and regulation depth. Due to the altered path and intensity of sonication penetrating the skull, the focus and intensity in the skull are difficult to determine, making the use of ultrasound therapy for cancer treatment experimental and not widely available. The deficiency can be effectively addressed by numerical simulation methods, which enable the optimization of sonication modulation parameters and the determination of precise transducer positioning. A 3D skull model was established using binarized brain CT images. The selection of the transducer matrix was performed using the radius positioning (RP) method after identifying the intracranial target region. Simulations were performed, encompassing acoustic pressure (AP), acoustic field, and temperature field, in order to provide compelling evidence of the safety of tFUS in sonication-induced thermal effects. It was found that the angle of sonication path to the coronal plane obtained at all precision and frequency models did not exceed 10° and 15° to the transverse plane. The results of thermal effects illustrated that the peak temperatures of tFUS were 43.73°C, which did not reach the point of tissue degeneration. Once positioned, tFUS effectively delivers a Full Width at Half Maximum (FWHM) stimulation that targets tumors with diameters of up to 3.72 mm in a one-off. The original precision model showed an attenuation of 24.47 ± 6.13 mm in length and 2.40 ± 1.42 mm in width for the FWHM of sonication after penetrating the skull. The vector angles of the sonication path in each direction were determined based on the transducer positioning results. It has been suggested that when time is limited for precise transducer positioning, fixing the transducer on the horizontal surface of the target region can also yield positive results for stimulation. This framework used a new transducer localization method to offer a reliable basis for further research and offered new methods for the use of tFUS in brain tumor-related research.

The JWST view of the barred galaxy population in the SMACS0723 galaxy cluster

Context. The cosmic evolution of the barred galaxy population provides key information about the secular evolution of galaxies and the settling of rotationally dominated discs. Aims. We study the bar fraction in the SMACSJ0723.37323 (SMACS0723) cluster of galaxies at z = 0.39 using the Early Release Observations obtained with the NIRCam instrument mounted on the JWST telescope. Methods. We visually inspected all cluster member galaxies using the images from the NIRCam F200W filter. We classified the galaxies into ellipticals and discs and determine the presence of a bar. The cluster member selection was based on a combined method using both the available spectroscopy and the color-magnitude relation. Results. As has previously been found in nearby galaxy samples, we find that the bar fraction distribution of SMACS0723 is a strong function of the galaxy stellar luminosity (or stellar mass). The analogy with local clusters, such as Virgo and Coma, reveals a similar distribution among the three clusters for low-mass galaxies (log(M⋆/M⊙)≤9.5). The comparison with a sample of local galaxies in a field environment shows a remarkable lack of bars in this low-mass regime for the SMACS0723 cluster (and, therefore, in Virgo and Coma) with respect to the field. At high masses (log(M⋆/M⊙)≥10.25), galaxies in SMACS0723 show a slightly lower bar fraction than those in Coma. At these high masses, we find a much larger bar fraction in SMACS0723 than previous works on field galaxies at z ∼ 0.4. Nevertheless, the difference is only marginal when we compare with a sample of well-resolved local field galaxies. Thus, we suggest that the improved capabilities of JWST with respect to HST in terms of both spatial resolution and image depth are responsible for the higher bar fraction we obtained. Conclusions. Our results support a scenario where cluster environment affects the formation of bars in a mass-dependent way. At high masses, the mild increase in the bar fraction of local clusters (Coma) with respect to both SMACS0723 and local field galaxies suggests a weak effect coming from the cluster environment possibly triggering bar formation. On the other hand, low-mass galaxies show the same bar fraction in the three clusters (different redshifts) and a significant drop with respect to field galaxies at z = 0, thus suggesting that: (i) the bar fraction of low-mass galaxies in clusters is not evolving during the last 4 Gyr; and (ii) bar formation is severely inhibited in low-mass galaxies residing in clusters.

Ecolo-zip: A global, rich and granular characterization of biogeophysical ecology for 1.5 million postal codes

The inescapable fact that human life is perpetually embedded in a tangible biogeophysical environment – and the consequences that this has for individuals and societies – have long fascinated scholars of all backgrounds. Technological progress and the advent of big data have spurred ever-more precise attempts to quantify our biogeophysical environments. However, many such datasets lack spatial granularity, global coverage, content depth, or accessibility. Here, we introduce ecolo-zip, a novel geospatial dataset that provides a granular-yet-global, parsimonious-yet-rich ecological characterization of over 1.5 million postal codes across 94 countries and regions. Combining two large-scale satellite image resources (ASTER; SRTM, ICC = 0.999) and a customized geospatial sampling model, we provide high-resolution indicators of physical topography (elevation, mountainousness, distance to sea), vegetation (normalized difference vegetation index), and climate (surface temperature). With this resource – featuring methodological details, visualizations, and application suggestions – we hope to contribute towards understanding the multi-faceted interactions between humans and their environments.

An integrated network based on 2D/3D feature correlations for benign-malignant tumor classification and uncertainty estimation in digital breast tomosynthesis

Objective. Classification of benign and malignant tumors is important for the early diagnosis of breast cancer. Over the last decade, digital breast tomosynthesis (DBT) has gradually become an effective imaging modality for breast cancer diagnosis due to its ability to generate three-dimensional (3D) visualizations. However, computer-aided diagnosis (CAD) systems based on 3D images require high computational costs and time. Furthermore, there is considerable redundant information in 3D images. Most CAD systems are designed based on 2D images, which may lose the spatial depth information of tumors. In this study, we propose a 2D/3D integrated network for the diagnosis of benign and malignant breast tumors. Approach. We introduce a correlation strategy to describe feature correlations between slices in 3D volumes, corresponding to the tissue relationship and spatial depth features of tumors. The correlation strategy can be used to extract spatial features with little computational cost. In the prediction stage, 3D spatial correlation features and 2D features are both used for classification. Main results. Experimental results demonstrate that our proposed framework achieves higher accuracy and reliability than pure 2D or 3D models. Our framework has a high area under the curve of 0.88 and accuracy of 0.82. The parameter size of the feature extractor in our framework is only 35% of that of the 3D models. In reliability evaluations, our proposed model is more reliable than pure 2D or 3D models because of its effective and nonredundant features. Significance. This study successfully combines 3D spatial correlation features and 2D features for the diagnosis of benign and malignant breast tumors in DBT. In addition to high accuracy and low computational cost, our model is more reliable and can output uncertainty value. From this point of view, the proposed method has the potential to be applied in clinic.

The Influence of Bottom Sediment on the Propagation of Caustic Beams in Oceanic Waveguides

Numerical modeling with the use of mode theory was used to investigate the regularities of the spatial (depth and horizontal distance) distribution of the acoustic field intensity formed by multiple interaction of a caustic beam with a layered bottom in a shallow oceanic waveguide with an underwater sound channel open to the bottom. It has been established that at the values of the sound velocity at the upper boundary in the sedimentary layer, less than the sound velocity at the bottom in the water layer, the formation of a multi-beam structure of the acoustic field is possible. It was found that, starting from certain distances, newly formed beams can play the main role in the spatial distribution of the acoustic field intensity.

Periodic patterns for resolution limit characterization of correlation plenoptic imaging

The measurement of the spatio-temporal correlations of light provides an interesting tool to overcome the traditional limitations of standard imaging, such as the strong trade-off between spatial resolution and depth of field. In particular, using correlation plenoptic imaging, one can detect both the spatial distribution and the direction of light in a scene, pushing both resolution and depth of field to the fundamental limit imposed by wave optics. This allows one to perform refocusing of different axial planes and three-dimensional reconstruction without any spatial scanning. In the present work, we investigate the resolution limit in a particular correlation plenoptic imaging scheme, by considering periodic test patterns, which provide, through analytical results, a deeper insight in the resolution properties of this second-order imaging technique, also in comparison with standard imaging.

Caught on the surface: Tustin on autistic experience.

According to Frances Tustin, the core of autism is found in sensory modifications-and tactile modifications in particular. Tustin argues that sensory experiences may become self-absorbed to such an extent that the sensory environment experientially flattens into a two-dimensional "feel," which complicates the individual's relations with the external environment and other people. Focusing on these fundamental modifications and their experiential consequences, the article introduces Tustin's main insight in terms of collapse of intentional depth, suggesting that this collapse concerns not only concrete spatial depth, but symbolic and intersubjective depth as well. By so doing, the article illustrates how Tustin's ideas render intelligible certain commonly recognized features of autism, such as "deficits in the ability to initiate and to sustain reciprocal social interaction and social communication" and "restricted, repetitive, and inflexible patterns of behavior, interests or activities" (ICD-11).

Measurement setup to simultaneously explore the location and energy of trapped charges in thin polymer films

This research proposes a unique system that combines charge density measurements by the laser intensity modulation method with optically excited current acquisitions using the photo-stimulated discharge technique (PSD). The purpose of this setup is to investigate the relationship between space charge properties (such as density, spatial depth, and time evolution) and the photocurrent-associated energies in order to gain new insights into the trap population and detrapping mechanisms in thin polymer films. This paper presents a description of the technical principles of both methods as well as the whole combined system. The results on a 12 μm-thick polyethylene naphthalate film show pyroelectric currents, which after processing indicate the formation of homocharges whose magnitude and depth decrease after light irradiation. The PSD currents allow the identification of two major energy bands at 3.4 and 5.9 eV (360 and 207 nm, respectively), possibly related to charge detrapping. In addition, current transients during constant wavelength irradiation show that incident photons can interact differently with trapped charges depending on the applied field.

Short-wave infrared fluorescence imaging of near-infrared dyes with robust end-tail emission using a small-animal imaging device.

Commercially available near-infrared (NIR) dyes, including indocyanine green (ICG), display an end-tail of the fluorescence emission spectrum detectable in the short-wave infrared (SWIR) window. Imaging methods based on the second NIR spectral region (1,000-1,700 nm) are gaining interest within the biomedical imaging community due to minimal autofluorescence and scattering, allowing higher spatial resolution and depth sensitivity. Using a SWIR fluorescence imaging device, the properties of ICG vs. heptamethine cyanine dyes with emission >800 nm were evaluated using tissue-simulating phantoms and animal experiments. In this study, we tested the hypothesis that an increased rigidity of the heptamethine chain may increase the SWIR imaging performance due to the bathochromic shift of the emission spectrum. Fluorescence SWIR imaging of capillary plastic tubes filled with dyes was followed by experiments on healthy animals in which a time series of fluorescence hindlimb images were analyzed. Our findings suggest that higher spatial resolution can be achieved even at greater depths (>5 mm) or longer wavelengths (>1,100 nm), in both tissue phantoms and animals, opening the possibility to translate the SWIR prototype toward clinical application.

SSTNet: Saliency sparse transformers network with tokenized dilation for salient object detection

AbstractThe vision Transformer structure performs better in salient object detection than the convolutional neural network (CNN)‐based approach. Vision Transformer predicts saliency by modelling long‐range dependencies from sequence to sequence with convolution‐free. It is challenging to distinguish the salient objects' location and obtain structural details for the influence of extracting irrelevant contextual information. A novel saliency sparse Transformer network is proposed to exploit sparse attention to guide saliency prediction. The convolution‐like with dilation in the token to token (T2T) module is replaced to achieve relationships in larger regions and to improve contextual information fusion. An adaptive position bias module is designed for the Vision Transformer to make position bias suitable for variable‐sized RGB images. A saliency sparse Transformer module is designed to improve the concentration of attention on the global context by selecting the Top‐k of the most relevant segments to improve the detection results further. Besides, cross‐modality to exploit the complementary RGB and depth modality fusion module (CMF) is used to take advantage of the complementary RGB image features and spatial depth information to enhance the feature fusion performance. Extensive experiments on multiple benchmark datasets demonstrate this method's effectiveness and superiority that it is suitable for saliency prediction comparable to state‐of‐the‐art RGB and RGB‐D saliency methods.

Improved stereo perception in coronary angiography using the X-ray tube as the viewpoint and validation with 3D printed models

Coronary angiography (CAG) provides two-dimensional images, but a clinician who is experienced in percutaneous coronary interventions can use information from these images to interpret spatial depth and infer the three-dimensional (3D) locations of vessels. We hypothesized that CAG results were equivalent to the mirror image of a coronary artery perspective projection, and a stereo perception could be easily established when the viewpoint of the angiogram was the X-ray tube instead of the detector. To eliminate the influence of heartbeat and respiration, a 3D-printed a coronary artery model was constructed for analysis. The effects of gantry movements during digital subtraction angiography (DSA) on the image were used to identify factors that affected DSA image transformation. Then, based on these factors, DSA imaging was simulated using UG NX software with three methods: (i) a perspective projection with the detector as the viewpoint; (ii) a parallel projection; and (iii) a mirror image of the perspective projection with the X-ray tube as the viewpoint. Finally, the resulting 3D images were compared with the DSA image. Our mirror image of the coronary artery perspective projection that used the X-ray tube as the viewpoint fused precisely with the CAG results and provided exact simulations of all the effects of DSA gantry movements on the DSA image. CAG results were equivalent to the mirror image of coronary artery perspective projection, and the stereo perception was easily established using the X-ray tube as the viewpoint.

A novel intrarenal multimodal 2D/3D registration algorithm and preliminary phantom study.

Retrograde intrarenal surgery (RIRS) is a widely utilized diagnostic and therapeutic tool for multiple upper urinary tract pathologies. The image-guided navigation system can assist the surgeon to perform precise surgery by providing the relative position between the lesion and the instrument after the intraoperative image is registered with the preoperative model. However, due to the structural complexity and diversity of multi-branched organs such as kidneys, bronchi, etc., the consistency of the intensity distribution of virtual and real images will be challenged, which makes the classical pure intensity registration method prone to bias and random results in a wide search domain. In this paper, we propose a structural feature similarity-based method combined with a semantic style transfer network, which significantly improves the registration accuracy when the initial state deviation is obvious. Furthermore, multi-view constraints are introduced to compensate for the collapse of spatial depth information and improve the robustness of the algorithm. Experimental studies were conducted on two models generated from patient data to evaluate the performance of the method and competing algorithms. The proposed method obtains mean target error (mTRE) of 0.971±0.585mm and 1.266±0.416mm respectively, with better accuracy and robustness overall. Experimental results demonstrate that the proposed method has the potential to be applied to RIRS and extended to other organs with similar structures.

Machine Learning–Based Cloud Forecast Corrections for Fusions of Numerical Weather Prediction Model and Satellite Data

Abstract Given the diversity of cloud-forcing mechanisms, it is difficult to classify and characterize all cloud types through the depth of a specific troposphere. Importantly, different cloud families often coexist even at the same atmospheric level. The Naval Research Laboratory (NRL) is developing machine learning–based cloud forecast models to fuse numerical weather prediction model and satellite data. These models were built for the dual purpose of understanding numerical weather prediction model error trends as well as improving the accuracy and sensitivity of the forecasts. The framework implements a UNet convolutional neural network (UNet-CNN) with features extracted from clouds observed by the Geostationary Operational Environmental Satellite-16 (GOES-16) as well as clouds predicted by the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). The fundamental idea behind this novel framework is the application of UNet-CNN for separate variable sets extracted from GOES-16 and COAMPS to characterize and predict broad families of clouds that share similar physical characteristics. A quantitative assessment and evaluation based on an independent dataset for upper-tropospheric (high) clouds suggests that UNet-CNN models capture the complexity and error trends of combined data from GOES-16 and COAMPS, and also improve forecast accuracy and sensitivity for different lead time forecasts (3–12 h). This paper includes an overview of the machine learning frameworks as well as an illustrative example of their application and a comparative assessment of results for upper-tropospheric clouds. Significance Statement Clouds are difficult to forecast because they require, in addition to spatial location, accurate height, depth, and cloud type. Satellite imagery is useful for verifying geographical location but is limited by 2D technology. Multiple cloud types can coexist at various heights within the same pixel. In this situation, cloud/no cloud verification does not convey much information about why the forecast went wrong. Sorting clouds by physical attributes such as cloud-top height, atmospheric stability, and cloud thickness contributes to a better understanding since very different physical mechanisms produce various types of clouds. Using a fusion of numerical model outputs and GOES-16 observations, we derive variables related to atmospheric conditions that form and move the clouds for our machine learning–based cloud forecast. The resulting verification over the U.S. mid-Atlantic region revealed our machine learning–based cloud forecast corrects systematic errors associated with high atmospheric clouds and provides accurate and consistent cloud forecasts from 3 to 12 h lead times.

Upwelling impact and lateral transport of dissolved PAHs in the Taiwan Strait and adjacent South China Sea

The spatial distribution and depth profile of dissolved polycyclic aromatic hydrocarbons (PAHs) were investigated in the western Taiwan Strait (TWS) and northeastern South China Sea (SCS) during the southwest monsoon for a comprehensive study of spatial distribution, potential sources, upwelling, and lateral PAHs transport flux to assess the impacts of oceanic processes. The concentrations of ∑14PAHs were 33 ± 14 ng L−1 and 23 ± 11 ng L−1 in western TWS and northeastern SCS, respectively. A minor difference in potential sources in different areas was shown in principle component analysis results, which illustrated mixed sources (petrogenic and pyrogenic) in western TWS and petrogenic sources in northeastern SCS. An “enrichment in surface or deep but depletion in medium water” distribution pattern of PAHs depth profile during summertime was observed in Taiwan Bank, which was potentially influenced by the upwelling. The greatest lateral ∑14PAHs transport flux was found along the Taiwan Strait Current area (43.51 g s−1), followed by those along South China Sea Warm Current and Guangdong Coastal Current areas. Though the oceanic response to PAHs varied relatively slowly, the ocean current was a less-dominant pathway for PAHs exchange between the SCS and the East China Sea (ECS).

Should an already Virtual Model be modelled?

Three-dimensional models are made by creating digital objects that contain spatial depth and detail. These models are used in a wide range of fields, including computer graphics, game development, architecture, product design, animation and many others. There are many types of modelling as a process. We can talk about manual modelling, script-based modelling or even modelling with three-dimensional modelling programs. However, as a result of digitization, in most cases we usually use the term model to refer to a three-dimensional virtual copy of a real object. This paper, however, deals with an extreme case where a virtual model is already given, which we try to recreate using a traditional photo-based technique with the Pix4D modelling software. The method used is to maximize the overlap between the images that make up the object to be modelled, so we needed to find a virtual model that could be accessed from all perspectives during the image creation process. For this purpose, we used the PokémonGO game database developed by Niantic, which makes virtual models of characters of different shapes available. Our results suggest that traditional methods of image collection are not the most useful for virtual models that are already virtual by default. For comparison, we have modelled a real object in our paper. The reports obtained by the modelling software show that although we used the same device to capture a series ofimages of both the real monument and the virtual model, the images of the virtual model used the camera system of our device in a different way. The results clearly show that the general photography method is adequate for real object modelling, while it produces incomplete results for the virtual model. Based on the method studied, however, it cannot be generalized that re-modelling virtual models is impractical, and we therefore propose to investigate other types of methods in the future.

Surrounded by Kindred: Spermophilus major Hybridization with Other Spermophilus Species in Space and Time.

Among the numerous described cases of hybridization in mammals, the most intriguing are (a) cases of introgressive hybridization deeply affecting the evolutionary history of species, and (b) models involving not a pair of species but a multi-species complex. Therefore, the hybridization history of the russet ground squirrel Spermophilus major, whose range has repeatedly changed due to climatic fluctuations and now borders the ranges of four related species, is of great interest. The main aims of this study were to determine the direction and intensity of gene introgression, the spatial depth of the infiltration of extraneous genes into the S. major range, and to refine the hypothesis of the hybridogenic replacement of mitochondrial genomes in the studied group. Using phylogenetic analysis of the variability of mitochondrial (CR, cytb) and nuclear (SmcY, BGN, PRKCI, c-myc, i6p53) markers, we determined the contribution of neighboring species to the S. major genome. We showed that 36% of S. major individuals had extraneous alleles. All peripheral species that were in contact with S. major contributed towards its genetic variability. We also proposed a hypothesis for the sequence and localization of serial hybridization events. Our assessment of the S. major genome implications of introgression highlights the importance of implementing conservation measures to protect this species.

Mapping snow depth on Canadian sub-arctic lakes using ground-penetrating radar

Abstract. Ice thickness across lake ice is mainly influenced by the presence of snow and its distribution, which affects the rate of lake ice growth. The distribution of snow depth over lake ice varies due to wind redistribution and snowpack metamorphism, affecting the variability of lake ice thickness. Accurate and consistent snow depth data on lake ice are sparse and challenging to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models to improve the understanding of how the varying distribution of snow depth influences lake ice formation and growth. This study was conducted using ground-penetrating radar (GPR) acquisitions with ∼9 cm sampling resolution along transects totalling ∼44 km to map snow depth over four Canadian sub-arctic freshwater lakes. The lake snow depth derived from GPR two-way travel time (TWT) resulted in an average relative error of under 10 % when compared to 2430 in situ snow depth observations for the early and late winter season. The snow depth derived from GPR TWTs for the early winter season was estimated with a root mean square error (RMSE) of 1.6 cm and a mean bias error of 0.01 cm, while the accuracy for the late winter season on a deeper snowpack was estimated with a RMSE of 2.9 cm and a mean bias error of 0.4 cm. The GPR-derived snow depths were interpolated to create 1 m spatial resolution snow depth maps. The findings showed improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information. The results suggest that GPR acquisitions can be used to derive lake snow depth, providing a viable alternative to manual snow depth monitoring methods. The findings can lead to an improved understanding of snow and lake ice interactions, which is essential for northern communities' safety and wellbeing and the scientific modelling community.

Lanthanide‐doped rare earth nanoparticles for near‐infrared‐II imaging and cancer therapy

AbstractThe optical nanoprobes with emissions in the second near‐infrared window (NIR‐II, 1000–1700 nm) show low tissue autofluorescence and photon scattering; therefore, they provide high spatial resolution and acceptable tissue penetration depth. These advantages make them appropriate for in vivo applications, including bioimaging, NIR‐II triggered disease therapy, and even on‐site efficacy monitoring. Among the various developed NIR‐II fluorescence probes, lanthanide‐doped nanoparticles (LDNPs) exhibit high photo stability and narrow emission bandwidths with long photoluminescence lifetimes and low cytotoxicity; therefore, they have been widely studied in the biomedical field. This review summarizes the typical compositions and optical properties of recently developed NIR‐II emitting LDNPs. Their applications in in vivo NIR‐II bioimaging and cancer therapy are reviewed. The perspectives and challenges of NIR‐II LDNPs are also discussed.