High Spatial Resolution Images Research Articles

Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments

Abstract. The hydrological cycle is strongly influenced by the accumulation and melting of seasonal snow. For this reason, mountains are often claimed to be the “water towers” of the world. In this context, a key variable is the snow water equivalent (SWE). However, the complex processes of snow accumulation, redistribution, and ablation make its quantification and prediction very challenging. In this work, we explore the use of multi-source data to reconstruct SWE at a high spatial resolution (HR) of 25 m. To this purpose, we propose a novel approach based on (i) in situ snow depth or SWE observations, temperature data and synthetic aperture radar (SAR) images to determine the pixel state, i.e., whether it is undergoing an SWE increase (accumulation) or decrease (ablation), (ii) a daily HR time series of snow cover area (SCA) maps derived by high- and low-resolution multispectral optical satellite images to define the days of snow presence, and (iii) a degree-day model driven by in situ temperature to determine the potential melting. Given the typical high spatial heterogeneity of snow in mountainous areas, the use of HR images represents an important novelty that allows us to sample its distribution more adequately, thus resulting in highly detailed spatialized information. The proposed SWE reconstruction approach also foresees a novel SCA time series regularization technique that models impossible transitions based on the pixel state, i.e., the erroneous change in the pixel class from snow to snow-free when it is expected to be in accumulation or equilibrium and, vice versa, from snow-free to snow when it is expected to be in ablation or equilibrium. Furthermore, it reconstructs the SWE for the entire hydrological season, including late snowfall. The approach does not require spatialized precipitation information as input, which is usually affected by uncertainty. The method provided good results in two different test catchments: the South Fork of the San Joaquin River, California, and the Schnals catchment, Italy. It obtained good agreement when evaluated against HR spatialized reference maps (showing an average bias of −22 mm, a root mean square error – RMSE – of 212 mm, and a correlation of 0.74), against a daily dataset at coarser resolution (showing an average bias of −44 mm, an RMSE of 127 mm, and a correlation of 0.66), and against manual measurements (showing an average bias of −5 mm, an RMSE of 191 mm, and a correlation of 0.35). The main sources of error are discussed to provide insights into the main advantages and disadvantages of the method that may be of interest for several hydrological and ecological applications.

Dynamic DNA nanomachines for amplification imaging of diseased cells based on stimuli-responsive mechanism

As a basic functional unit, living cell with sophisticated structures play an indispensable role in life activities. Since the abnormality of important molecules inside cells is closely related to diseases, the dynamic analysis and spatio-temporal monitoring of specific molecules in living cells can provide precious information for the diagnosis and treatment of diseases. More recently, DNA has not only been recognized as the carrier of genetic information, but has also used as a robust building block for the assembly of multitudinous nanoscale structures due to the intrinsic advantages of high programmability of classic Watson–Crick base-pairing rule. Intensive study promotes the rapid progress of nanotechnology in various fields, such as bioimaging, diagnosis, and therapeutics. Among numerous well-defined DNA nanomaterials, DNA nanomachines have been widely exploited in cell imaging owing to their desirable ability to achieve high-resolution temporal and spatial images in response to endogenous or exogenous stimuli. In brief, elaborate DNA nanomachines can undergo structural changes upon the stimuli of target analytes or environmental factors, resulting in rapid increase or reduction of output signals and thereby indirectly reflecting the expression level of targets. DNA nanomachines with high sensitivity and specificity contribute to the recognition of diseased tissues. In this review, we introduce the basic assembly modules of DNA nanomachines and summarize the recent advances in dynamic DNA nanomachines for diseased-cell imaging. Finally, the current challenges and future directions of DNA nanomachines for bioimaging are discussed.

Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli.

The glomerulus is a multicellular functional tissue unit (FTU) of the nephron that is responsible for blood filtration. Each glomerulus contains multiple substructures and cell types that are crucial for their function. To understand normal aging and disease in kidneys, methods for high spatial resolution molecular imaging within these FTUs across whole slide images is required. Here we demonstrate a workflow using microscopy-driven selected sampling to enable 5 μm pixel size matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of all glomeruli within whole slide human kidney tissues. Such high spatial resolution imaging entails large numbers of pixels, increasing the data acquisition times. Automating FTU-specific tissue sampling enables high-resolution analysis of critical tissue structures, while concurrently maintaining throughput. Glomeruli were automatically segmented using coregistered autofluorescence microscopy data, and these segmentations were translated into MALDI IMS measurement regions. This allowed high-throughput acquisition of 268 glomeruli from a single whole slide human kidney tissue section. Unsupervised machine learning methods were used to discover molecular profiles of glomerular subregions and differentiate between healthy and diseased glomeruli. Average spectra for each glomerulus were analyzed using Uniform Manifold Approximation and Projection (UMAP) and k-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise k-means clustering was applied to all glomeruli, showing unique molecular profiles localized to subregions within each glomerulus. Automated microscopy-driven, FTU-targeted acquisition for high spatial resolution molecular imaging maintains high-throughput and enables rapid assessment of whole slide images at cellular resolution and identification of tissue features associated with normal aging and disease.

How exotic Sonneratia species affect the spatiotemporal dynamics of mangroves in Shenzhen Bay, China: A remote sensing perspective

Mangroves are essential forest communities in tropical and subtropical coastal zones, providing unique ecological functions and significant social and economic value. Accurate and efficient monitoring of mangroves is crucial for their protection and management. The impact of exotic species on native species is still debated, especially for mangroves, the spatiotemporal dynamics of introduced species and native mangrove species need to be closely monitored. This study aimed to investigate how exotic mangrove species affects the spatial dynamics of mangroves in Shenzhen Bay, China. Yearly Landsat images from 2000 to 2011 and Chinese high spatial resolution images during 2012–2022 were obtained and the pixel-based and multiscale object-based methods were used to obtain mangrove distribution. The results showed that: (1) Overall, mangroves in the study area increased from 281.51 ha to 526.43 ha from 2000 to 2022, with different spatiotemporal patterns in Shenzhen (+5.14%) and Hong Kong (+2.38%); (2) rapid spread of Sonneratia species was one of the main contributions to the growth of mangrove hotspots, and growth hotspots were mainly concentrated in the estuary delta; and (3) Sonneratia mangrove species was dominant in interspecific competition, not only invading the habitat of local mangroves, but also preventing the expansion of native mangrove growth. The results provide a reference for fine-scale and accurate mangrove mapping with high spatial and temporal resolutions, and highlight the need to pay more attentions to the spread and invasive potential of exotic mangrove species in the study area, as well as cooperation in adjacent reserves.

High spatial resolution imaging of light localization in hyperuniform disordered patterns of circular air pores in a dielectric slab

Hyperuniform disordered photonic structures are a peculiar category of disordered photonic heterostructures located between random structures and ordered photonic crystals. These materials, thanks to the presence of a photonic bandgap, exhibit the advantages of random and ordered structures since they have been shown to support in a small spatial footprint a high density of Anderson-localized modes, which naturally occur at the bandgap edges with peculiar features like relatively high Q/V ratios. Different localization behaviors have been recently reported in hyperuniform disordered luminescent materials, with a well-established and widely studied design, based on disordered networks. Here, we explore an alternative design, based on circular holes of different sizes hyperuniformely distributed, that we investigate theoretically and experimentally by means of scanning near-field optical microscopy. We report that the spectral features of hyperuniform disordered networks can also be extended to a different design, which, in turn, displays pseudo-photonic bandgaps and light localization. The ability of generating different kinds of hyperuniform disordered photonic systems that share the same theoretical and experimental optical features can largely extend practical potentialities and integration in many optoelectronic applications.

Thin Luminous Tracks of Particles Released from Electrodes with A Small Radius of Curvature in Pulsed Nanosecond Discharges in Air and Argon

Features of the nanosecond discharge development in a non-uniform electric field are studied experimentally. High spatial resolution imaging showed that thin luminous tracks of great length with a cross-section of a few microns are observed against the background of discharge glow in air and argon. It has been established that the detected tracks are adjacent to brightly luminous white spots on the electrodes or in the vicinity of these spots, and are associated with the flight of small particles. It is shown that the tracks have various shapes and change from pulse to pulse. The particle tracks may look like curvy or straight lines. In some photos, they can change their direction of movement to the opposite. It was found that the particle’s track abruptly breaks and a bright flash is visible at the break point. The color of the tracks differs from that of the spark leaders, while the bands of the second positive nitrogen system dominate in the plasma emission spectra during the existence of a diffuse discharge. Areas of blue light are visible near the electrodes as well. The development of glow and thin luminous tracks in the gap during its breakdown is revealed using an ICCD camera. Physical reasons for the observed phenomena are discussed.

Spatially Resolved Properties of the GW170817 Host Galaxy

GW170817 is the unique gravitational-wave (GW) event associated with the electromagnetic (EM) counterpart GRB 170 817A. NGC 4993 is identified as the host galaxy of GW170817/GRB 170 817A. In this paper, we focus on the spatially resolved properties of NGC 4993. We present the photometric results from the comprehensive data analysis of the high spatial-resolution images in the different optical bands. The morphological analysis reveals that NGC 4993 is a typical early-type galaxy without significant remnants of a major galaxy merger. The spatially resolved stellar population properties of NGC 4993 suggest that the galaxy center has passive evolution with the outskirt formed by gas accretion. We derive the merging rate of the compact object per galaxy by a co-evolution scenario of a supermassive black hole and its host galaxy. If the galaxy formation is at redshift 1.0, the merging rate per galaxy is from 3.2 × 10−4 to 7.7 × 10−5 within the merging decay time from 1.0 to 5.0 Gyr. The results provide vital information for ongoing GW EM counterpart detections. The Hubble space telescope data analysis presented in this paper can be also applied to Chinese Space Station Telescope research in the future.

Combination Strategies of Variables with Various Spatial Resolutions Derived from GF-2 Images for Mapping Forest Stock Volume

Spectral features (SFs) and texture features (TFs) extracted from optical remote sensing images can capture the structural composition and growth information of forests, and combining remote sensing variables with a few ground measurement samples is a common method for mapping forest stock volume (FSV). However, the accuracy of mapping FSV using optical images with a high spatial resolution (one meter or sub-meters) is often lower than medium resolutions (larger than 10 m) using the same types of features and approaches. To overcome the limitations of high spatial resolution images in mapping FSV, down-scaled images with spatial resolution ranging from 1 to 30 m were obtained by GF-2 image to interpret the relationships between spatial resolutions of features and the accuracy of mapping FSV, and combination strategies of variables with various spatial resolutions were proposed to improve the accuracy of mapping FSV. The results show that the spatial resolution of features significantly affects the performance of employed models in estimating FSV, the sensitivity between SFs and FSV gradually increases with the decreasing of spatial resolution, and the optimal spatial resolutions of two types of features (SFs and TFs) are not synchronized in mapping forest FSV. After using combination strategies of variables with various spatial resolutions, the accuracy of mapping FSV is significantly higher than those derived from variable sets with the same spatial resolutions. It is proved that TFs derived from GF-2 images have great potential to improve the accuracy of mapping FSV, and the contribution of features depends on the approaches of extracting and combination strategies.

Phenotyping heart failure by cardiac magnetic resonance imaging of cardiac macro- and microscopic structure: state of the art review.

Heart failure demographics have evolved in past decades with the development of improved diagnostics, therapies, and prevention. Cardiac magnetic resonance (CMR) has developed in a similar timeframe to become the gold-standard non-invasive imaging modality for characterizing diseases causing heart failure. CMR techniques to assess cardiac morphology and function have progressed since their first use in the 1980s. Increasingly efficient acquisition protocols generate high spatial and temporal resolution images in less time. This has enabled new methods of characterizing cardiac systolic and diastolic function such as strain analysis, exercise real-time cine imaging and four-dimensional flow. A key strength of CMR is its ability to non-invasively interrogate the myocardial tissue composition. Gadolinium contrast agents revolutionized non-invasive cardiac imaging with the late gadolinium enhancement technique. Further advances enabled quantitative parametric mapping to increase sensitivity at detecting diffuse pathology. Novel methods such as diffusion tensor imaging and artificial intelligence-enhanced image generation are on the horizon. Magnetic resonance spectroscopy (MRS) provides a window into the molecular environment of the myocardium. Phosphorus (31P) spectroscopy can inform the status of cardiac energetics in health and disease. Proton (1H) spectroscopy complements this by measuring creatine and intramyocardial lipids. Hyperpolarized carbon (13C) spectroscopy is a novel method that could further our understanding of dynamic cardiac metabolism. CMR of other organs such as the lungs may add further depth into phenotypes of heart failure. The vast capabilities of CMR should be deployed and interpreted in context of current heart failure challenges.

A Semi-Automated Two-Step Building Stock Monitoring Methodology for Supporting Immediate Solutions in Urban Issues

The Sustainable Development Goals (SDGs) have addressed environmental and social issues in cities, such as insecure land tenure, climate change, and vulnerability to natural disasters. SDGs have motivated authorities to adopt urban land policies that support the quality and safety of urban life. Reliable, accurate, and up-to-date building information should be provided to develop effective land policies to solve the challenges of urbanization. Creating comprehensive and effective systems for land management in urban areas requires a significant long-term effort. However, some procedures should be undertaken immediately to mitigate the potential negative impacts of urban problems on human life. In developing countries, public records may not reflect the current status of buildings. Thus, implementing an automated and rapid building monitoring system using the potential of high-spatial-resolution satellite images and street views may be ideal for urban areas. This study proposed a two-step automated building stock monitoring mechanism. Our proposed method can identify critical building features, such as the building footprint and the number of floors. In the first step, buildings were automatically detected by using the object-based image analysis (OBIA) method on high-resolution spatial satellite images. In the second step, vertical images of the buildings were collected. Then, the number of the building floors was determined automatically using Google Street View Images (GSVI) via the YOLOv5 algorithm and the kernel density estimation method. The first step of the experiment was applied to the high-resolution images of the Pleiades satellite, which covers three different urban areas in Istanbul. The average accuracy metrics of the OBIA experiment for Area 1, Area 2, and Area 3 were 92.74%, 92.23%, and 92.92%, respectively. The second step of the experiment was applied to the image dataset containing the GSVIs of several buildings in different Istanbul streets. The perspective effect, the presence of more than one building in the photograph, some obstacles around the buildings, and different window sizes caused errors in the floor estimations. For this reason, the operator’s manual interpretation when obtaining SVIs increases the floor estimation accuracy. The proposed algorithm estimates the number of floors at a rate of 79.2% accuracy for the SVIs collected by operator interpretation. Consequently, our methodology can easily be used to monitor and document the critical features of the existing buildings. This approach can support an immediate emergency action plan to reduce the possible losses caused by urban problems. In addition, this method can be utilized to analyze the previous conditions after damage or losses occur.

Slum image detection and localization using transfer learning: a case study in Northern Morocco

<span lang="EN-US">Developing countries are faced with social and economic challenges, including the emergence and proliferation of slums. Slum detection and localization methods typically rely on regular topographic surveys or on visual identification of high-resolution spatial satellite images, as well as socio-environmental surveys from land surveys and general population censuses. Yet, they consume so much time and effort. To overcome these problems, this paper exploits well-known seven pretrained models using transfer learning approaches such as MobileNets, InceptionV3, NASNetMobile, Xception, VGG16, EfficientNet, and ResNet50, consecutively, on a smaller dataset of medium-resolution satellite imagery. The accuracies obtained from these experiments, respectively, demonstrate that the top three pretrained models achieve 98.78%, 97.9%, and 97.56%. Besides, MobileNets have the smallest memory sizes of 9.1 Mo and the shortest latency of 17.01 s, which can be implemented as needed. The results show the good performance of the top three pretrained models to be used for detecting and localizing slum housing in northern Morocco.</span>

Improving the urban features classification accuracy by fusion of optical and radar high spatial resolution images

Improving the urban features classification accuracy by fusion of optical and radar high spatial resolution images

Visible Radiophotoluminescence of Color Centers in Lithium Fluoride Thin Films for High Spatial Resolution Imaging Detectors for Hard X-rays

Passive solid-state detectors based on the visible radiophotoluminescence (RPL) of stable aggregate F2 and F3 + color centers in lithium fluoride (LiF) are successfully used for X-ray imaging and advanced diagnostics of intense X-rays sources. Among their advantages, these detectors offer a wide dynamic range and simplicity of use. They can be read non-destructively using a fluorescence microscope, enabling high spatial resolution over a large field of view. Optically transparent LiF films, of three different increasing thicknesses, were grown by thermal evaporation on glass and silicon substrates and subsequently irradiated with monochromatic 7 keV X-rays at several doses from 1.3 × 101 to 4.5 × 103 Gy at the SOLEIL synchrotron facility. For all the LiF films, the RPL response was found to depend linearly on the irradiation dose, with films grown on Si(100) substrates exhibiting up to a 50% higher response compared to those grown on glass. A minimum dose of 13 Gy was detected, despite the low thickness of the irradiated films. The limited thickness of the homogeneously colored LiF film allowed to obtain a spatial resolution of (0.44 ± 0.04) μm in edge-enhancement imaging experiments conducted by placing an Au mesh in front of the samples.

Advanced Nanomaterials and Characterization Techniques for Photovoltaic and Photocatalysis Applications

ConspectusSolar energy is one of the most promising energy sources to replace traditional fossil fuels due to its renewable and green features, which can be converted to electrical and chemical energy through photon-enabled applications. To improve the utilization efficiency of solar energy, solar energy “converters”, such as photovoltaic and photocatalytic systems, have been extensively studied. It is noteworthy that the common issues of narrow optical absorption and rapid charge carrier recombination limit solar energy utilization. The development of advanced functional nanomaterials plays a decisive role in addressing these issues. For instance, plasmonic nanomaterials with a localized surface plasmon resonance (LSPR) effect can effectively extend and enhance light absorption; heterojunction- and homojunction-based semiconductors can facilitate the spatial separation of electron–hole pairs. Therefore, rational design of functional nanomaterials through integrating plasmonic nanomaterials and creating heterojunctions and homojunctions can amplify their structural advantages, leading to the achievement of the state-of-the-art photon-conversion performance. Besides, the in-depth understanding of the relationship between materials and performance via advanced characterization techniques, such as high spatial-resolution imaging and in situ spectroscopy, provides a fundamental and solid basis for optimizing advanced functional materials in photon-enabled applications. Along with theoretical calculation and algorithm-driven data analysis during advanced characterizations, more quantified information can be obtained for deeper insights into physics. In this Account, we first summarize recent works in our research group on the rational design of advanced functional materials, including plasmonic metallic materials, plasmonic semiconductors, two-dimensional-material-based heterojunctions, and metal–organic-framework-based homojunctions, and their working mechanisms for the enhancement of photovoltaic and photocatalytic performance. We then show how we employed developed X-ray-based, electron-based, and spectroscopic techniques for characterizing elemental composition, materials structure, and physicochemical properties, which provides effective ways to resolve complex structures and processes and understand their underlying physics. Furthermore, we discuss the photogenerated charge carrier dynamics in solar cells and photocatalysis using in situ and time-resolved techniques, by underlining the use of these advanced techniques for specific materials. Then, we briefly introduce the algorithm-driven data analysis compiled in analytical techniques in our works to quantify materials information. Finally, we briefly present perspectives for addressing the challenges and fundamental issues as well as guidance for the future development of photon-enabled applications, e.g., the development of high-performance functional materials and advanced characterization techniques. This Account shows some ideas and directions for the rational design and optimization of advanced functional materials for various photon-enabled applications and for the proper utilization of advanced characterization techniques, which may provide guidance and prospects for future research.

Fully automated land surface temperature downscaling based on RGB very high spatial resolution images

Downscaling is a particularly needed process in many environmental, social and governance applications at the fine scale. The need for an automated and reliable very high spatial resolution downscaling approach is then required. In this paper, a fully-automated open-access downscaling approach was proposed, named HSR-LST. It is based on the High Spatial Resolution (HSR) Red, Green and Blue (RGB) bands collected from commercial and free-to-access satellite images, generating LST values lower than 2-m spatial resolutions. This is based on the Landsat-8 thermal datasets and while implementing a fully-automated Ordinary Least Squares (OLS) approach. HSR-LST was implemented over Beirut, Boston and Dubai between 2016 and 2018. In comparison to an airborne LST image captured over ElKhorn River in Nebraska, USA, HSR-LST showed an AME of 0.88 °C and a R-squared value of 86.33%. Main results showed the variability of LST based on the sensed land features’ type. Different LST distribution footprints (i.e., irregular in Beirut, intermitted in Boston, systematic in Dubai) were highlighted depicting a characteristic urban configuration in each city. This latter along buildings’ material, density and height appear also to show a different effect on the local and surrounding LST values. By implementing the automated HSR-LST model in cities around the Globe, urban planners, policy makers and inhabitants can acquire improved information to assess urban heat islands, to propose more adequate planning policies, but more importantly to tackle urban heat and thermal comfort at the finest scales. HST-LST will effectively address the low spatial resolution of thermal bands. As HSR-LST is both automated and dynamic, it can be portable to other urban areas with diverse climatic regions.

CREATION OF A GEODATABASE ON GEOMORPHOMETRIC PARAMETERS OF THE RELIEF BY THE EXAMPLE OF THE NOVOSIBIRSK REGION USING SATELLITE IMAGES

Traditional methods of qualitative description of relief used in the process of landscape analysis, zoning, forecasting of exogenous processes, assessment of agricultural land do not allow to distinguish objectively forms of relief, to reveal statistically reliable relations between relief indicators and components of geosystems. For agro-ecological evaluation and land grouping with the help of modern methods of data classification, a geodatabase with quantitative indicators of relief is needed. Space images of high and medium spatial resolution are necessary for formation of geodata base of geomorphometric indicators during regional researches.
 Background. Quantitative (geomorphometric) indicators of relief are of great practical importance when identifying and describing landforms and relief elements. One or two indicators are often used for agricultural land assessment. In foreign literature various combined topographic indices are widely used for land classification. The scientific significance of the research is associated with modern methods of geomorphometric analysis of the relief based on remote sensing data and their application for automated mapping of relief types.
 Purpose. Creation of a geodatabase (GDB) of geomorphometric parameters of the Novosibirsk region on the basis of satellite data.
 Materials and methods. Digital modeling and geomorphometry methods were used in the work.
 Results. The database of geomorphometric parameters of the relief with the use of middle and high spatial resolution space images ALOP PALSAR (12.5 m/pixel) and ALOS DSM (30 m/pixel), topographic maps of M 1:25000 were developed. A geodatabase of geomorphometric parameters of the relief by the example of the Novosibirsk Region was developed including GIS information layers with geomorphometric parameters of the relief and attribute tables. The LGB has been used to map the types of relief in the Novosibirsk Region agrolandscapes. Automatic classification of relief types using multispectral images of K-Nearest Neighbor (KNN or k-NN, nearest neighbor) teacherless algorithms and the iterative self-organizing algorithm ISODATA (Self-Organizing Data Analysis) were performed. The input raster data for the terrain type classification were terrain parameters and vegetation indices NDVI, EVI, OSAVI, TSAVI, determined on the basis of multispectral satellite images Sentinel-2 A. By the example of Priobskiy central forest-steppe agrolandscape mapping of relief types was carried out: watershed drained undrained flat-leveled, watershed drained weakly drained, interfluvial undrained flatwaterlogged. The main relief-forming processes in the Priobskoye central forest-steppe agro-landscape are planar washout and waterlogging.
 Conclusion. Modern methods of geomorphometry and geoinformatics allow you to create a spatial database of geomorphometric parameters for a comprehensive assessment of the land on the basis of remote information. The use of a geomorphometric database of geomorphometric parameters of relief and vegetation indices made it possible to carry out mapping of relief types for the Novosibirsk region agrolandscapes.

Application of UAV Push-Broom Hyperspectral Images in Water Quality Assessments for Inland Water Protection: A Case Study of Zhang Wei Xin River in Dezhou Distinct, China

A water quality parameter retrieval scheme based on the UAV push-broom hyperspectral images was designed and validated for assessing the ecological health of Zhang Wei Xin River in Dezhou distinct, China. First, a UAV carrying a push-broom hyperspectral imager that is lightweight and has a small size was used to acquire high spatial and hyperspectral resolution images. Then, the mosaicked reflectance data of the whole river were produced by a seamless image mosaicking method with high geometrical accuracy and spectral fidelity. Next, the in-field measurements of different parameters and the corresponding spectral reflectance from the mosaicked images at the sampling points were used to build the water quality parameter retrieval models for total phosphorus (TP), chlorophyll a (Chla), and total suspended solids (TSS). To validate the model, the retrieval results of the testing sampling points were compared with the measured parameters. The coefficients of determination R2 of TP, Chla, and TSS were 0.886, 0.918, and 0.968, respectively. The retrieved TP, Chla, and TSS maps showed that the water pollution of Zhang Wei Xin River is serious, the total phosphorus exceeds the standard, and the water body is in a state of eutrophication. The UAV-based hyperspectral remote sensing technique provides a cost-effective method for inland water monitoring at a local scale with high accuracy.

Analysis of Anthropic Pressure on Urban Mangroves: Subsidies for Environmental Protection and Territorial Planning

The mangrove is a transitional ecosystem between terrestrial and marine environments and represents an important ecological indicator for the ecosystem services provided. However, despite its ecological relevance, it still suffers anthropic pressures and has been losing in extent and environmental quality. In Brazil, and specifically in the municipality of Paranaguá, a mix of anthropogenic use has caused significant pressures on mangroves. In this sense, the aim of this study is to assess the degree of anthropization of the urban mangroves in the city of Paranaguá (PR), through the use of very high spatial resolution images, obtained from Remotely Piloted Aircraft in order to support municipal policies for land-use planning. From the methodological point of view, from field campaigns, maps were produced with the aid of QGIS 3.10 software, from the photointerpretation of orthomosaics. The legend of the anthropization classes covered: deposition of household waste (i), rubble (ii), discharge of untreated domestic effluents (iii) and manholes (iv). The results indicated a total of 475 points of anthropic pressure on the 22 mangrove patches analysed. The most prominent class was household waste, followed respectively by untreated domestic effluent discharge, debris (construction material) and, finally, shackle. In conclusion, the use of high-resolution orthomosaics proved to be an important and effective tool in the analysis of anthropization of urban mangroves, thus highlighting the areas with greater pressure and contributing to a continuous monitoring. The products of this research can help in the elaboration of land-use planning instruments for the municipality, highlighting the need for the construction of a holistic view in favor of mangrove conservation.

Multi-image hybrid super-resolution reconstruction via interpolation and multi-scale residual networks

High spatial resolution is necessary for several applications such as visual inspection, and can be achieved using high-resolution (HR) image sensors or through image super-resolution (SR) algorithms. Currently, SR algorithms are applied to either single low-resolution (LR) images or multiple LR image sequences. In this paper, we propose a hybrid super-resolution (HYSR) framework to generate HR images by combining multi-image super-resolution (MISR) and single-image super-resolution (SISR) to obtain high spatial resolution images. This method comprehensively utilizes sub-pixel-level high-frequency detail information between multiple images and co-occurrence prior of a single image to reconstruct SR images with a larger scale factor than the existing methods. Generally, the HYSR reconstruction results have more satisfactory details and visual quality than the SISR or MISR reconstruction results. A large number of qualitative and quantitative evaluation results demonstrate the effectiveness and superiority of the HYSR method over traditional MISR and SISR methods.

Quantifying lumen diameter in coronary artery stents with high-resolution photon counting detector CT and convolutional neural network denoising.

Small coronary arteries containing stents pose a challenge in CT imaging due to metal-induced blooming artifact. High spatial resolution imaging capability is as the presence of highly attenuating materials limits noninvasive assessment of luminal patency. The purpose of this study was to quantify the effective lumen diameter within coronary stents using a clinical photon-counting-detector (PCD) CT in concert with a convolutional neural network (CNN) denoising algorithm, compared to an energy-integrating-detector (EID) CT system. Seven coronary stents of different materials and inner diameters between 3.43 and 4.72mm were placed in plastic tubes of diameters 3.96-4.87mm containing 20mg/mL of iodine solution, mimicking stented contrast-enhanced coronary arteries. Tubes were placed parallel with or perpendicular to the scanner's z-axis in an anthropomorphic phantom emulating an average-sized patient and scanned with a clinical EID-CT and PCD-CT. EID scans were performed using our standard coronary computed tomography angiography (cCTA) protocol (120kV, 180 quality reference mAs). PCD scans were performed using the ultra-high-resolution (UHR) mode (120×0.2mm collimation) at 120kV with tube current adjusted so that CTDIvol was matched to that of EID scans. EID images were reconstructed per our routine clinical protocol (Br40, 0.6mm thickness), and with the sharpest available kernel (Br69). PCD images were reconstructed at a thickness of 0.6mm and a dedicated sharp kernel (Br89) which is only possible with the PCD UHR mode. To address increased image noise introduced by the Br89 kernel, an image-based CNN denoising algorithm was applied to the PCD images of stents scanned parallel to the scanner's z-axis. Stents were segmented based on full-width half maximum thresholding and morphological operations, from which effective lumen diameter was calculated and compared to reference sizes measured with a caliper. Substantial blooming artifacts were observed on EID Br40 images, resulting in larger stent struts and reduced lumen diameter (effective diameter underestimated by 41% and 47% for parallel and perpendicular orientations, respectively). Blooming artifacts were observed on EID Br69 images with 19% and 31% underestimation of lumen diameter compared to the caliper for parallel and perpendicular scans, respectively. Overall image quality was substantially improved on PCD, with higher spatial resolution and reduced blooming artifacts, resulting in the clearer delineation of stent struts. Effective lumen diameters were underestimated by 9% and 19% relative to the reference for parallel and perpendicular scans, respectively. CNN reduced image noise by about 50% on PCD images without impacting lumen quantification(<0.3% difference). The PCD UHR mode improved in-stent lumen quantification for all seven stents as compared to EID images due to decreased blooming artifacts. Implementation of CNN denoising algorithms to PCD data substantially improved image quality.