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Efficient and continuous monitoring of surface water is essential for water resource management. Much effort has been devoted to the task of water mapping based on remote sensing images. However, few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner. Moreover, water area statistics are sensitive to the satellite image observation quality. This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine (GEE) with a supervised random forest classifier. A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products. The samples for permanent and seasonal water were mapped and collected separately, so that the supplement of seasonal samples can increase the spectral diversity of the sample space. To reduce the uncertainty of the derived water occurrences, temporal correction was applied to repair the classification maps with invalid observations. Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results. Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods, as well as the GSWD and GLAD datasets.

The study introduces an automated approach for extracting water bodies from satellite images using the Faster R-CNN algorithm. The approach was tested on two datasets consisting of water body images collected from Sentinel-2 and Landsat-8 (OLI) satellite images, totaling over 3500 images. The results showed that the proposed approach achieved an accuracy of 98.7% and 96.1% for the two datasets, respectively. This is significantly higher than the accuracy achieved by the convolutional neural network (CNN) approach, which achieved 96% and 80% for the two datasets, respectively. These findings highlight the effectiveness of the proposed approach in accurately mapping water bodies from satellite imagery. Additionally, the Sentinel-2 dataset performed better than the Landsat dataset in both the Faster R-CNN and CNN approaches for water body extraction. [ABSTRACT FROM AUTHOR]

The detection and mapping of surface water resources for South America (DMSWSA) in Remote sensing context is a highly relevant issue from a scientific point of view due to the impact on the understanding of changes in the hydrological cycle, water availability, and climate in global terms since 45% of all water resources available on the globe are present on this continent. This study presents a new approach to evaluating the scientific literature of the last 21 years focused on DMSWSA in a Remote sensing scientific literature. Our study aims to carry out a bibliometric analysis on the application of the DMSWSA in a Remote sensing dominium to assess researchers, countries, and trends. We used the Scopus database for the literature search. Then we used bibliometric tools to access information and reveal quantification patterns of literature. Our results show that the most relevant contributions involved Brazil and Argentina. DMSWSA has only shown an expansion in recent years regarding the number of articles published and citations. It was possible to show that the DMSWSA in a Remote sensing scientific area needs further collaboration expansion between countries within South America and beyond this continental border. We reveal aspects of great importance and interest in the literature using bibliometric approaches to give a clear view of research trends for DMSWSA. [ABSTRACT FROM AUTHOR]

Real-time satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena such as floods, earthquakes, etc. One important constraint of satellite imaging is the trade-off between spatial/spectral resolution and their revisiting time, a consequence of design and physical constraints imposed by satellite orbit among other technical limitations. In this paper, we focus on fusing multi-temporal, multi-spectral images where data acquired from different instruments with different spatial resolutions is used. We leverage the spatial relationship between images at multiple modalities to generate high-resolution image sequences at higher revisiting rates. To achieve this goal, we formulate the fusion method as a recursive state estimation problem and study its performance in filtering and smoothing contexts. Furthermore, a calibration strategy is proposed to estimate the time-varying temporal dynamics of the image sequence using only a small amount of historical image data. Differently from the training process in traditional machine learning algorithms, which usually require large datasets and computation times, the parameters of the temporal dynamical model are calibrated based on an analytical expression that uses only two of the images in the historical dataset. A distributed version of the Bayesian filtering and smoothing strategies is also proposed to reduce its computational complexity. To evaluate the proposed methodology we consider a water mapping task where real data acquired by the Landsat and MODIS instruments are fused generating high spatial–temporal resolution image estimates. Our experiments show that the proposed methodology outperforms the competing methods in both estimation accuracy and water mapping tasks. [ABSTRACT FROM AUTHOR]

Efficient and continuous monitoring of surface water is essential for water resource management. Much effort has been devoted to the task of water mapping based on remote sensing images. However, few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner. Moreover, water area statistics are sensitive to the satellite image observation quality. This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine (GEE) with a supervised random forest classifier. A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products. The samples for permanent and seasonal water were mapped and collected separately, so that the supplement of seasonal samples can increase the spectral diversity of the sample space. To reduce the uncertainty of the derived water occurrences, temporal correction was applied to repair the classification maps with invalid observations. Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results. Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods, as well as the GSWD and GLAD datasets. [ABSTRACT FROM AUTHOR]

Climate change studies require increasingly detailed information on land cover and land use, to precisely model and predict climate based on their status and changes. A fundamental land cover type that needs to be constantly monitored by the climate change community is water, but currently there is a lack of high-resolution water body maps at the global scale. In this article, we present a fully automated procedure for the extraction of fine spatial resolution (10 m) inland water land cover maps for any region of the Earth by means of a relatively simple k-means clustering model applied to multitemporal features extracted from Sentinel-1 SAR sequences. Indeed, due to heavy cloud coverage conditions in many locations, multispectral sensors are not suitable for global water body mapping. For this reason, in this work, we deal only with SAR data, and specifically with multitemporal Sentinel-1 data sequences. The experimental results, obtained for three geographical areas selected because of their wide diversity in terms of geomorphology and climate, show an almost complete consistency with existing datasets, and improve them thanks to their finer spatial details.

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Small water bodies (< 0.01 km2) showing diverse limnological properties occur in great abundance across the boreal forest and tundra landscapes of the Arctic and Subarctic. However, their classification, geographical distribution and collective importance for water, heat, nutrient, contaminant and carbon cycles are still poorly constrained. One important step for better understanding the role and evolution of small water bodies in the fast-changing northern landscapes is to develop image analysis protocols that allow their automatic remote sensing detection, delineation and inventory. In this study, we set an image analysis protocol (High Latitude Water – HLWATER V1.0) based on a trained supervised Mask R-CNN deep learning model over PlanetScope imagery for the automatic detection and delineation of small lakes and ponds that were absent in existing datasets. Most of our training dataset comprised water bodies smaller than 0.01 km2 (97%) and spanned a wide range of environmental and hydrological settings, from the sporadic to the continuous permafrost zones of Canada. The model was tested as a fully autonomous approach for eastern Hudson Bay, Nunavik (Subarctic Canada), a region that poses challenges for water remote sensing given the abundance and variety of small water bodies. These are mainly permafrost thaw and glacial basin ponds in the boreal forest-tundra in challenging optical settings influenced by vegetation or topography shadowing, or revealing peat water logging, fen and bog pond conditions. A multi-scale validation approach was developed using water body delineations from PlanetScope imagery and ultra-high resolution orthomosaics from Unoccupied Aerial Systems. This procedure allowed a sub-pixel assessment and identified the limitations and strengths of the trained model for detecting small and large water bodies. The results varied according to different landscape units, with mean Intersection over Union (IoU) 0.5 F1 Scores of 0.53 to 0.71 and mean F1 Scores of 0.62 to 0.95. Considering 166 m2 as the minimum pond size detection threshold, the IoU 0.5 F1 Scores were 0.7 to 0.91 and F1 Scores were 0.76 to 0.83, evaluated by comparing the model results with ultra-high resolution manual delineations. The image analysis protocol and trained model show high potential for extension to other boreal forest-tundra regions of the Arctic and Subarctic, allowing for detailed inventories of optically and morphologically diverse small water bodies over large areas of the circumpolar North. • HLWATER is a new model for autonomous small water bodies delineation. • HLWATER was trained over PlanetScope using a dataset with 23,107 water bodies. • The trained Mask R-CNN model is robust in detecting optically diverse water bodies. • HLWATER can efficiently segment ponds in dense and heterogeneous sectors. • The HLWATER model is publicly available. [ABSTRACT FROM AUTHOR]

The interferometric synthetic aperture radar (InSAR) data set, acquired by the TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) mission (TDM), represents a unique data source to derive geo-information products at a global scale. The complete Earth’s landmasses have been surveyed at least twice during the mission bistatic operation, which started at the end of 2010. Examples of the delivered global products are the TanDEM-X digital elevation model (DEM) (at a final independent posting of 12 m × 12 m) or the TanDEM-X global Forest/Non-Forest (FNF) map. The need for a reliable water product from TanDEM-X data was dictated by the limited accuracy and difficulty of use of the TDX Water Indication Mask (WAM), delivered as by-product of the global DEM, which jeopardizes its use for scientific applications, as well. Similarly as it has been done for the generation of the FNF map; in this work, we utilize the global data set of TanDEM-X quicklook images at 50 m × 50 m resolution, acquired between 2011 and 2016, to derive a new global water body layer (WBL), covering a range from −60∘ to +90∘ latitudes. The bistatic interferometric coherence is used as the primary input feature for performing water detection. We classify water surfaces in single TanDEM-X images, by considering the system’s geometric configuration and exploiting a watershed-based segmentation algorithm. Subsequently, single overlapping acquisitions are mosaicked together in a two-step logically weighting process to derive the global TDM WBL product, which comprises a binary averaged water/non-water layer as well as a permanent/temporary water indication layer. The accuracy of the new TDM WBL has been assessed over Europe, through a comparison with the Copernicus water and wetness layer, provided by the European Space Agency (ESA), at a 20 m × 20 m resolution. The F-score ranges from 83%, when considering all geocells (of 1∘ latitudes × 1∘ longitudes) over Europe, up to 93%, when considering only the geocells with a water content higher than 1%. At global scale, the quality of the product has been evaluated, by intercomparison, with other existing global water maps, resulting in an overall agreement that often exceeds 85% (F-score) when the content in the geocell is higher than 1%. The global TDM WBL presented in this study will be made available to the scientific community for free download and usage.

Biodeterioration phenomena are of great relevance in rock settlements, due to favourable environmental conditions, such as the infiltration of rainwaters, condensation phenomena and abundance of salts and organic nutrients. Rinaldi’s rock shelter in Filiano, which is located in a natural forest of mixed oaks is of great value due to the important traces of prehistoric paintings. It is an emblematic case of the delicate balance, achieved throughout the centuries, between the environment and artwork. During the plurimillenarian history of the site, a portion of the ceiling that covered the shelter collapsed, leaving signs that are still visible today, together with traces of blackening left by the fires of ancient settlements. Several of the biodeteriogens typical of rocky habitats have already been detected and include algae, cyanobacteria, mosses, lichens, vascular plants and fungi, which form macroscopic communities.Each community has an ecological preference and the mapping of their distribution is a suitable tool for understanding variations in the environmental factors that most affect them. Relating ecological data to the taxonomical characterization of the species and to the spatial distribution of each community, a site map of the humidity and of the nutrients was obtained. Among the various communities, microcolonial fungi (MCF), which appear as little black spots, here, represent the most critical risk factor, due to their low water needs. An evaluation of the biological risk for the possible future attack of such a biological community was made, suggesting indirect mitigation measures, through modification of the microclimatic and local ventilation conditions.

Abstract Evaporation from open surface water is a critical and continuous process in the water cycle. Globally, evaporation losses from reservoirs are estimated to be greater than the combined consumption from industrial and domestic water uses. However, this large volume of water loss is only coarsely considered in modern water resources management practices due to the complexities involved with quantifying these losses. By fusing remote sensing and modeling approaches, this study developed a novel method to accurately estimate the evaporation losses from 721 reservoirs in the contiguous United States (CONUS). Reservoir surface areas were extracted and enhanced from the Landsat based Global Surface Water Dataset (GSWD) from March 1984 to October 2015. The evaporation rate was modeled using the Penman Equation in which the lake heat storage term was considered. Validation results using in situ observations suggest that this approach can significantly improve the accuracy of the simulated monthly reservoir evaporation rate. The evaporation losses were subsequently estimated as the product of the surface area and evaporation rate. This paper presents a first of its kind, comprehensively validated, locally practical, and continentally consistent reservoir evaporation dataset. The results suggest that the long term averaged annual evaporation volume from these 721 reservoirs is 33.73 × 109 m3, which is equivalent to 93% of the annual public water supply of the United States (in 2010). An increasing trend of the evaporation rate (0.0076 mm/d/year) and a slightly decreasing trend of the total surface area (−0.011 × 109 m2/year) were both detected during the study period. As a result, the total evaporation shows an insignificant trend, yet with significant spatial heterogeneity. This new reservoir evaporation dataset can help facilitate more efficient water management practices. Highlights • A long term, comprehensive evaporation dataset was developed for 721 US reservoirs. • The calculated evaporation rate was improved by including the heat storage effect. • Driven by global brightening, evaporation rate has increased over most reservoirs. • The long term annual evaporation amount from these reservoirs is 33.73 × 109 m3/year. [ABSTRACT FROM AUTHOR]

Timely information on the extent of open water surfaces and wetland dynamics can be useful for decision makers to rapidly respond to flood or drought events and improve our understanding of large scale eco-hydrological variability. Episodic inundation is an important factor for biological and ecosystem processes and sustains a wealth of ecosystem services. This study tests 14 spectral indices and their appropriate thresholds for water mapping, using 500 m resolution MODIS surface reflectance data for two coastal sites in Mexico. The fraction of water within each MODIS pixel was estimated using 30 m Landsat-derived water masks. The error introduced by upscaling to coarser resolutions as well as the error associated with each index threshold was analyzed through an omission-commission error space. The lowest cost according to a hyperbolic function, and the lowest area difference relative to the reference water area served as criteria for definition of optimal fractions and identification of the most appropriate indices and their optimal thresholds. Our study shows that the normalized difference between the MODIS green (band 4) and short wave infrared (band 6) “MNDWI6” consistently performed best at mapping of water. MNDWI6 showed little variations of the optimal index value (−0.14 ± 0.05) and a low sensitivity to changes in the threshold value. Other useful indices for water extraction identified in this study are the MNDWI5, MSAVI2 and Tasseled Cap Wetness Indices. [ABSTRACT FROM AUTHOR]

In this study, a new method is proposed for semi-automated surface water detection using synthetic aperture radar data via a combination of radiometric thresholding and image segmentation based on the simple linear iterative clustering superpixel algorithm. Consistent intensity thresholds are selected by assessing the statistical distribution of backscatter values applied to the mean of each superpixel. Higher-order texture measures, such as variance, are used to improve accuracy by removing false positives via an additional thresholding process used to identify the boundaries of water bodies. Results applied to quad-polarized RADARSAT-2 data show that the threshold value for the variance texture measure can be approximated using a constant value for different scenes, and thus it can be used in a fully automated cleanup procedure. Compared to similar approaches, errors of omission and commission are improved with the proposed method. For example, we observed that a threshold-only approach consistently tends to underestimate the extent of water bodies compared to combined thresholding and segmentation, mainly due to the poor performance of the former at the edges of water bodies. The proposed method can be used for monitoring changes in surface water extent within wetlands or other areas, and while presented for use with radar data, it can also be used to detect surface water in optical images. [ABSTRACT FROM AUTHOR]

Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.

The leukotriene A 4 hydrolase (LTA 4 H) is a bifunctional enzyme, containing a peptidase and a hydrolase activity both activities having opposing functions regulating inflammatory response. The hydrolase activity is responsible for the conversion of leukotriene A 4 to pro-inflammatory leukotriene B 4 , and hence, selective inhibitors of the hydrolase activity are of high pharmacological interest. Here we present the thermodynamic characterization of structurally distinct inhibitors of the LTA 4 H that occupy different regions of the binding site using different biophysical methods. An in silico method for the determination of stabilized water molecules in the binding site of the apo structure of LTA 4 H is used to interpret the measured thermodynamic data and provided insights for design of novel LTA 4 H inhibitors. [ABSTRACT FROM AUTHOR]

Water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. Remote sensing has increasingly been used for water mapping in rural areas; however, when applied to urban areas, this spatially- explicit approach is a challenging task due to the fact that the water bodies are often of a small size and spectral confusion is common between water and the complex features in the urban environment. Water indexes are the most common method of water extraction at the pixel level. More recently, spectral mixture analysis (SMA) has been widely employed in analyzing the urban environment at the subpixel level. The objective of this study is to develop an automatic subpixel water mapping method (ASWM) which can achieve a high accuracy in urban areas. Specifically, we first apply a water index for the automatic extraction of mixed land-water pixels, and the pure water pixels that are generated in this process are exported as the final result. Secondly, the SMA technique is applied to the mixed land-water pixels for water abundance estimation. As for obtaining the most representative endmembers, we propose an adaptive iterative endmember selection method based on the spatial similarity of adjacent ground surfaces. One classical water index method (the modified normalized difference water index (MNDWI)), a pixel-level target detection method (constrained energy minimization (CEM)), and two widely used SMA methods (fully constrained least squares (FCLS) and multiple endmember spectral mixture analysis (MESMA)) were chosen for the water mapping comparison in the experiments. The results indicate that the proposed ASWM was able to detect water pixels more efficiency than other unsupervised water extraction methods, and the water fractions estimated by the proposed ASWM method correspond closely to the reference fractions with the slopes of 0.97, 1.02, 1.04, and 0.98 and the R-squared values of 0.9454, 0.9486, 0.9665, and 0.9607 in regression analysis corresponding to different test regions. In the quantitative accuracy assessment, the ASWM method shows the best performance in water mapping with the mean kappa coefficient of 0.862, mean producer's accuracy of 82.8%, and mean user's accuracy of 91.8% for test regions. [ABSTRACT FROM AUTHOR]

Land surface water mapping is one of the most basic classification tasks to distinguish water bodies from dry land surfaces. In this paper, a water mapping method was proposed based on multi-scale level sets and a visual saliency model (MLSVS), to overcome the lack of an operational solution for automatically, rapidly and reliably extracting water from large-area and fine spatial resolution Synthetic Aperture Radar (SAR) images. This paper has two main contributions, as follows: (1) The method integrated the advantages of both level sets and the visual saliency model. First, the visual saliency map was applied to detect the suspected water regions (SWR), and then the level set method only needed to be applied to the SWR regions to accurately extract the water bodies, thereby yielding a simultaneous reduction in time cost and increase in accuracy; (2) In order to make the classical Itti model more suitable for extracting water in SAR imagery, an improved texture weighted with the Itti model (TW-Itti) is employed to detect those suspected water regions, which take into account texture features generated by the Gray Level Co-occurrence Matrix (GLCM) algorithm, Furthermore, a novel calculation method for center-surround differences was merged into this model. The proposed method was tested on both Radarsat-2 and TerraSAR-X images, and experiments demonstrated the effectiveness of the proposed method, the overall accuracy of water mapping is 98.48% and the Kappa coefficient is 0.856. [ABSTRACT FROM AUTHOR]

Traditional on-site methods for mapping and monitoring surface water extent are prohibitively expensive at a national scale within Canada. Despite successful cost-sharing programs between the provinces and the federal government, an extensive number of water features within the country remain unmonitored. Particularly difficult to monitor are the potholes in the Canadian Prairie region, most of which are ephemeral in nature and represent a discontinuous flow that influences water pathways, runoff response, flooding and local weather. Radarsat-2 and the Radarsat Constellation Mission (RCM) offer unique capabilities to map the extent of water bodies at a national scale, including unmonitored sites, and leverage the current infrastructure of the Meteorological Service of Canada to monitor water information in remote regions. An analysis of the technical requirements of the Radarsat-2 beam mode, polarization and resolution is presented. A threshold-based procedure to map locations of non-vegetated water bodies after the ice break-up is used and complemented with a texture-based indicator to capture the most homogeneous water areas and automatically delineate their extents. Some strategies to cope with the radiometric artifacts of noise inherent to Synthetic Aperture Radar (SAR) images are also discussed. Our results show that Radarsat-2 Fine mode can capture 88% of the total water area in a fully automated way. This will greatly improve current operational procedures for surface water monitoring information and impact a number of applications including weather forecasting, hydrological modeling, and drought/flood predictions. [ABSTRACT FROM AUTHOR]

Copyright of Conservation Science in Cultural Heritage / Quaderni di Scienza della Conservazione is the property of Salvatore Lorusso and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a new method is proposed for semi-automated surface water detection using synthetic aperture radar data via a combination of radiometric thresholding and image segmentation based on the simple linear iterative clustering superpixel algorithm. Consistent intensity thresholds are selected by assessing the statistical distribution of backscatter values applied to the mean of each superpixel. Higher-order texture measures, such as variance, are used to improve accuracy by removing false positives via an additional thresholding process used to identify the boundaries of water bodies. Results applied to quad-polarized RADARSAT-2 data show that the threshold value for the variance texture measure can be approximated using a constant value for different scenes, and thus it can be used in a fully automated cleanup procedure. Compared to similar approaches, errors of omission and commission are improved with the proposed method. For example, we observed that a threshold-only approach consistently tends to underestimate the extent of water bodies compared to combined thresholding and segmentation, mainly due to the poor performance of the former at the edges of water bodies. The proposed method can be used for monitoring changes in surface water extent within wetlands or other areas, and while presented for use with radar data, it can also be used to detect surface water in optical images.

In situ diagnostic techniques provide a means of understanding the internal workings of fuel cells so that improved designs and operating regimes can be identified. Here, a novel metrology approach is reported that combines current and temperature mapping with water visualisation using neutron radiography. The approach enables a hydro-electro-thermal performance map to be generated that is applied to an air-cooled, open-cathode polymer electrolyte fuel cell. This type of fuel cell exhibits a particularly interesting coupled relationship between water, current and heat, as the air supply has the due role of cooling the stack as well as providing the cathode reactant feed via a single source. It is found that water predominantly accumulates under the cooling channels (thickness of 70-100 μm under the cooling channels and 5-25 μm in the active channels at 0.5 A cm −2 ), in a similar fashion to the lands in a closed-cathode design, but contrary to passive open-cathode systems. The relationship between current, temperature and water accumulation is complex and highly dependent on location within the cell. However, there is a general trend that higher currents and cooling limitations, especially above 0.7 A cm −2 and below 3.9 × 10 −3 m 3 s −1 , leads to temperatures above 60 °C, which dehydrate the membrane (water thickness of 10-25 um) and the cell operates below 0.5 V. [ABSTRACT FROM AUTHOR]

Traditional on-site methods for mapping and monitoring surface water extent are prohibitively expensive at a national scale within Canada. Despite successful cost-sharing programs between the provinces and the federal government, an extensive number of water features within the country remain unmonitored. Particularly difficult to monitor are the potholes in the Canadian Prairie region, most of which are ephemeral in nature and represent a discontinuous flow that influences water pathways, runoff response, flooding and local weather. Radarsat-2 and the Radarsat Constellation Mission (RCM) offer unique capabilities to map the extent of water bodies at a national scale, including unmonitored sites, and leverage the current infrastructure of the Meteorological Service of Canada to monitor water information in remote regions. An analysis of the technical requirements of the Radarsat-2 beam mode, polarization and resolution is presented. A threshold-based procedure to map locations of non-vegetated water bodies after the ice break-up is used and complemented with a texture-based indicator to capture the most homogeneous water areas and automatically delineate their extents. Some strategies to cope with the radiometric artifacts of noise inherent to Synthetic Aperture Radar (SAR) images are also discussed. Our results show that Radarsat-2 Fine mode can capture 88% of the total water area in a fully automated way. This will greatly improve current operational procedures for surface water monitoring information and impact a number of applications including weather forecasting, hydrological modeling, and drought/flood predictions.

The evolution of hydrogen from many hydrated cryo-preserved soft materials under electron irradiation in the transmission electron microscope can be observed at doses of the order of 1000 e nm−2 and above. Such hydrogen causes artefacts in conventional transmission electron microscope or scanning transmission electron microscopy (STEM) imaging as well as in analyses by electron energy-loss spectroscopy. Here we show that the evolution of hydrogen depends on specimen thickness. Using wedge-shaped specimens of frozen-hydrated Nafion, a perfluorinated ionomer, saturated with the organic solvent DMMP together with both thin and thick sections of frozen-hydrated porcine skin, we show that there is a thickness below which hydrogen evolution is not detected either by bubble observation in transmission electron microscope image mode or by spectroscopic analysis in STEM electron energy-loss spectroscopy mode. We suggest that this effect is due to the diffusion of hydrogen, whose diffusivity remains significant even at liquid nitrogen temperature over the length scales and time scales relevant to transmission electron microscopy analysis of thin specimens. In short, we speculate that sufficient hydrogen can diffuse to the specimen surface in thin sections so that concentrations are too low for bubbling or for spectroscopic detection. Significantly, this finding indicates that higher electron doses can be used during the imaging of radiation-sensitive hydrated soft materials and, consequently, higher spatial resolution can be achieved, if sufficiently thin specimens are used in order to avoid the evolution of hydrogen-based artefacts. [ABSTRACT FROM AUTHOR]

The interferometric synthetic aperture radar (InSAR) data set, acquired by the TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) mission (TDM), represents a unique data source to derive geo-information products at a global scale. The complete Earth's landmasses have been surveyed at least twice during the mission bistatic operation, which started at the end of 2010. Examples of the delivered global products are the TanDEM-X digital elevation model (DEM) (at a final independent posting of 12 m × 12 m) or the TanDEM-X global Forest/Non-Forest (FNF) map. The need for a reliable water product from TanDEM-X data was dictated by the limited accuracy and difficulty of use of the TDX Water Indication Mask (WAM), delivered as by-product of the global DEM, which jeopardizes its use for scientific applications, as well. Similarly as it has been done for the generation of the FNF map; in this work, we utilize the global data set of TanDEM-X quicklook images at 50 m × 50 m resolution, acquired between 2011 and 2016, to derive a new global water body layer (WBL), covering a range from −60 ∘ to +90 ∘ latitudes. The bistatic interferometric coherence is used as the primary input feature for performing water detection. We classify water surfaces in single TanDEM-X images, by considering the system's geometric configuration and exploiting a watershed-based segmentation algorithm. Subsequently, single overlapping acquisitions are mosaicked together in a two-step logically weighting process to derive the global TDM WBL product, which comprises a binary averaged water/non-water layer as well as a permanent/temporary water indication layer. The accuracy of the new TDM WBL has been assessed over Europe, through a comparison with the Copernicus water and wetness layer, provided by the European Space Agency (ESA), at a 20 m × 20 m resolution. The F-score ranges from 83%, when considering all geocells (of 1 ∘ latitudes × 1 ∘ longitudes) over Europe, up to 93%, when considering only the geocells with a water content higher than 1%. At global scale, the quality of the product has been evaluated, by intercomparison, with other existing global water maps, resulting in an overall agreement that often exceeds 85% (F-score) when the content in the geocell is higher than 1%. The global TDM WBL presented in this study will be made available to the scientific community for free download and usage. [ABSTRACT FROM AUTHOR]

We address the practical issue of using thermal image data without adjustment or calibration for projects which do not require actual temperatures per se. Large scale airborne scanning in the thermal band at 8.5–13 μm was obtained for a mangrove and salt marsh in subtropical eastern Australia. For open sites, the raw image values were strongly positively correlated with ground level temperatures. For sites under mangrove canopy cover, image values indicated temperatures 2–4°C lower than those measured on the ground. The raw image was useful in identifying water bodies under canopy and has the potential for locating channel lines of deeper water. This could facilitate modification to increase flushing in the system, thereby reducing mosquito larval survival. [ABSTRACT FROM AUTHOR]

Purpose. To assess the potential of Magnetic Resonance Imaging (MRI) as a method of detecting water movement during the extrusion of pastes. Methods. Plug samples were made from mixtures of model materials and microcrystalline cellulose with two water contents at two different ram speeds to simulate ram extrusion. The extrusion process was stopped at different stages and analyzed for water distribution using MRI to assess the influence of water content and the speed of ram on water movement as the extrusion process progresses. Results. Two types of water movement were detected: vertical and radial. When extruding at the faster ram speed, water moved predominantly in the vertical direction, whereas when extruding at a slower ram speed it moved predominantly in the radial direction. At the beginning of the extrusion process a greater water movement in the wetter formulations was observed. Conclusions. MRI appears to be a useful approach to non-invasive water mapping, and is expected to contribute towards a greater understanding of the role of water in the extrusion of pastes. [ABSTRACT FROM AUTHOR]

Effective water management in polymer electrolyte fuel cells (PEFCs) is essential to achieving optimal performance. Mapping the water distribution is therefore a useful tool for understanding operation and designing new components. One of the most powerful ways of visualising water distribution is through neutron imaging. However, this has limitations in terms of expense, accessibility and the ability to conduct representative operando experiments in a neutron beam line. Here, a simple, cost-effective, lab-based, non-invasive water visualisation tool is presented that allows the effect of operating conditions to be assessed in operando. The approach applies lock-in thermography, whereby a controlled sinusoidal heat pulse on one side of a fuel cell (here using a printed circuit board arrangement), is applied using a Peltier device, and the temperature perturbation on the other side of the fuel cell is monitored using a thermal imaging camera. By 'locking in' to the frequency of the imposed heat stimulus, it is possible to observe water build-up within the cell by monitoring the phase shift between heat pulse and measured temperature. This work validates the use of the lock-in thermography technique by mapping the water distribution under different current densities and under dry and humidified conditions. Image 1 • Peltier device used to heat and cool fuel cell in sinusoidal fashion. • Lock-in thermography allows internal structure of fuel cell to be examined. • Frequency variation allows crude depth profiling within cell. • Internal water identified by phase shift in lock-in response. • Lock-in thermography water imaging consistent with neutron imaging. [ABSTRACT FROM AUTHOR]