Melt Spinning of Flexible and Conductive Immiscible Thermoplastic/Elastomer Monofilament for Water Detection

In many textile fields, such as industrial structures or clothes, one way to detect a specific liquid leak is the electrical conductivity variation of a yarn. This yarn can be developed using melt spun of Conductive Polymer Composites (CPCs), which blend insulating polymer and electrically conductive fillers. This study examines the influence of the proportions of an immiscible thermoplastic/elastomer blend for its implementation and its water detection. The thermoplastic polymer used for the detection property is the polyamide 6.6 (PA6.6) filled with enough carbon nanotubes (CNT) to exceed the percolation threshold. However, the addition of fillers decreases the polymer fluidity, resulting in the difficulty to implement the CPC. Using an immiscible polymers blend with an elastomer, which is a propylene-based elastomer (PBE) permits to increase this fluidity and to create a flexible conductive monofilament. After characterizations (morphology, rheological and mechanical) of this blend (PA6.6CNT/PBE) in different proportions, two principles of water detection are established and carried out with the monofilaments: the principle of absorption and the short circuit. It is found that the morphology of the immiscible polymer blend had a significant role in the water detection.

UAV Thermal Images for Water Presence Detection in a Mediterranean Headwater Catchment

As Mediterranean streams are highly dynamic, reconstructing space–time water presence in such systems is particularly important for understanding the expansion and contraction phases of the flowing network and the related hydro–ecological processes. Unmanned aerial vehicles (UAVs) can support such monitoring when wide or inaccessible areas are investigated. In this study, an innovative method for water presence detection in the river network based on UAV thermal infrared remote sensing (TIR) images supported by RGB images is evaluated using data gathered in a representative catchment located in Southern Italy. Fourteen flights were performed at different times of the day in three periods, namely, October 2019, February 2020, and July 2020, at two different heights leading to ground sample distances (GSD) of 2 cm and 5 cm. A simple methodology that relies on the analysis of raw data without any calibration is proposed. The method is based on the identification of the thermal signature of water and other land surface elements targeted by the TIR sensor using specific control matrices in the image. Regardless of the GSD, the proposed methodology allows active stream identification under weather conditions that favor sufficient drying and heating of the surrounding bare soil and vegetation. In the surveys performed, ideal conditions for unambiguous water detection in the river network were found with air–water thermal differences higher than 5 °C and accumulated reference evapotranspiration before the survey time of at least 2.4 mm. Such conditions were not found during cold season surveys, which provided many false water pixel detections, even though allowing the extraction of useful information. The results achieved led to the definition of tailored strategies for flight scheduling with different levels of complexity, the simplest of them based on choosing early afternoon as the survey time. Overall, the method proved to be effective, at the same time allowing simplified monitoring with only TIR and RGB images, avoiding any photogrammetric processes, and minimizing postprocessing efforts.

Arctic low-level clouds and their importance for radiative transfer simulations

<p>The observation of low-level stratocumulus cloud decks in the Arctic poses challenges to ground-based remote sensing. These clouds frequently occur during summer below the lowest range gate of common zenith-pointing cloud radar instruments, like the KAZR and the Mira-35. In addition, the optical thickness of these low-level clouds often do cause a complete attenuation of the lidar beam. For remote-sensing instrument synergy retrievals, as Cloudnet (Illingworth, 2007) or ARSCL (Active Remote Sensing of Clouds, Shupe, 2007), liquid-water detection in clouds is usually based on lidar backscatter. Thus, a complete attenuation can cause misclassification of mixed-phase clouds as pure-ice clouds. Moreover, the missing cloud radar information makes it difficult to derive the cloud microphysical properties, as most common retrievals are based on cloud radar reflectivity.</p> <p>A new low-level stratus detection mask (Griesche, 2020) was used to detect these clouds. The liquid-water cloud microphysical properties were derived by a simple but effective analysis of the liquid-water path. This approach was applied to remote-sensing data from a shipborne expedition performed in the Arctic summer 2017. The values calculated by applying the adjusted method improve the results of radiative transfer simulations yielding the determination of radiative closure.</p> <p> </p> <p> </p> <p>Illingworth et al. (2007). “Cloudnet”. BAMS.</p> <p>Shupe (2007). “A ground-based multisensor cloud phase classifier”. GRL.</p> <p>Griesche et al. (2020). “Application of the shipborne remote sensing supersite OCEANET for profiling of Arctic aerosols and clouds during Polarstern cruise PS106”. AMT.</p>

Application of Spectral Index-Based Logistic Regression to Detect Inland Water in the South Caucasus

The Caucasus is a diverse region with many climate zones that range from subtropical lowlands to mountainous alpine areas. The region is marked by irrigated croplands fed by irrigation canals, heavily vegetated wetlands, lakes, and reservoirs. In this study, we demonstrate the development of an improved surface water map based on a global water dataset to get a better understanding of the spatial distribution of small water bodies. First, we used the global water product from the European Commission Joint Research Center (JRC) to generate training data points by stratified random sampling. Next, we applied the optimal probability cut-off logistic regression model to develop surface water datasets for the entire Caucasus region, covering 19 Landsat tiles from May to October 2019. Finally, we used 6745 manually classified points (3261 non-water, 3484 water) to validate both the newly developed water dataset and the JRC global surface water dataset using an estimated proportion of area error matrix to evaluate accuracy. Our approach produced surface water extent maps with higher accuracy (89.2%) and detected 392 km2 more water than the global product (86.7% accuracy). We demonstrate that the newly developed method enables surface water detection of small ponds and lakes, flooded agricultural fields, and narrow irrigation channels, which are particularly important for mosquito-borne diseases.

The Global Water Body Layer from TanDEM-X Interferometric SAR Data

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.

Automatic Open Water Flood Detection from Sentinel-1 Multi-Temporal Imagery

Many technical infrastructure operators manage facilities distributed over large areas. They face the problem of finding out if a flood hit a specific facility located in the open countryside. Physical inspection after every heavy rain is time and personnel consuming, and equipping all facilities with flood detection is expensive. Therefore, methods are being sought to ensure that these facilities are monitored at a minimum cost. One of the possibilities is using remote sensing, especially radar data regularly scanned by satellites. A significant challenge in this area was the launch of Sentinel-1 providing free-of-charge data with adequate spatial resolution and relatively high revisit time. This paper presents a developed automatic processing chain for flood detection in the open landscape from Sentinel-1 data. Flood detection can be started on-demand; however, it mainly focuses on autonomous near real-time monitoring. It is based on a combination of algorithms for multi-temporal change detection and histogram thresholding open-water detection. The solution was validated on five flood events in four European countries by comparing its results with flood delineation derived from reference datasets. Long-term tests were also performed to evaluate the potential for a false positive occurrence. In the statistical classification assessments, the mean value of user accuracy (producer accuracy) for open-water class reached 83% (65%). The developed solution typically provided flooded polygons in the same areas as the reference dataset, but of a smaller size. This fact is mainly attributed to the use of universal sensitivity parameters, independent of the specific location, which ensure almost complete successful suppression of false alarms.