Utilizing Multi-Temporal Thermal Data To Assess Environmental Land Degradation Impacts: Example From Suez Canal Region, Egypt.

Land surface temperature (LST) analysis of Satellite data is critical for studying the impacts of geo-environmental, hydrometeorological, and land degradation. However, challenges arise to resolve the LST and ground field data resulting from the constant development of land use and land cover (LULC). This study aims to monitor, analyze, assess, and map the environmental land degradation impacts utilizing image processing and GIS tools of space-borne thermal data and fieldwork. Two thermal and optical sets of multi-temporal Landsat TM+5 and TIRS+8 satellite data dated 1984 and 2018 were used to test, detect, and map the thermal and LULC change and their land degradation in the Suez Canal region (SCR). The LULC classification was categorized into seven classes: water bodies, urban, agricultural land, barren land, wetland, clay, and salt crust. LULC and LST change detection and mapping results revealed that the impervious surface, industrial area, saline soil, and urban area have high LST, while wetlands, vegetation cover, and water bodies suffered low LST. The spectral, LST profiles and statistical analyses examined the association between LST and LULC deriving factors. The cluster analyses defined the relationship between LST and LC patterns at the LU level, where the fast transformation of LULC had significant changes in LST. According to these analyses and the fieldwork observations, the SCR was divided into six main areas. These areas vary in LST in association with land degradation and hydro-environmental impacts such as rising groundwater levels, salt accumulation, active seismic fault zones, water pollution, and urban and agricultural activities.

Screening of coformers for quercetin cocrystals through mechanochemical methods

Quercetin (QUE) is a nutraceutical compound that exhibits pharmacological properties such as antioxidant, cardioprotective, anti-ulcer, and anti-inflammatory effects. Although QUE is well-known for its benefits, its efficacy is limited due to low solubility. Thus, cocrystallization acts as an interesting approach to improve the solubility�among other properties�of this compound. In this work, cocrystallization screening was applied through neat grinding (NG) and liquid-assisted grinding (LAG), in which QUE and four cocrystal formers (benzamide,�picolinamide, isonicotinamide, and pyrazinoic acid) were tested. The precursors and QUE-coformer systems were characterized using thermoanalytical techniques (TG-DTA), X-ray powder diffraction (XRPD), and Fourier transform infrared (FTIR) spectroscopy. The results showed the formation of QUE cocrystals with picolinamide and isonicotinamide coformers in a 1:1 stoichiometric ratio. Furthermore, although coformers are isomers, spectroscopic and thermal data suggest that the supramolecular synthons involved in cocrystallization are different.

Consciousness Detection on Injured Simulated Patients Using Manual and Automatic Classification via Visible and Infrared Imaging

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient’s consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims’ mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies’ longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.

OIL PATTERN IDENTIFICATION ANALYSIS USING SEMANTIC DEEP LEARNING METHOD FROM PLEIADES-1B SATELIITE IMAGERY WITH ARCGIS PRO SOFTWARE (Case Study: Village “A”)

There are many petroleum mining activities scattered in developing countries, such as Indonesia. Indonesia is one of the largest oil-producing countries in Southeast Asia with the 23rd ranking. Since the Dutch era, Indonesia has produced a very large amount of petroleum. One of the oil producing areas is “A” Village. There is an old well that produces petroleum oil which is still active with an age of more than 100 years, for now the oil well is still used by the local community as the main source of livelihood. With this activity, resulting in an oil pattern around the old oil refinery, which over time will absorb into the ground. This study aims to analyze and identify the oil pattern around the old oil refinery in the “A” area. The data used is in the form of High-Resolution Satellite Imagery (CSRT), namely Pleiades-1B with a spatial resolution of 1.5 meters. Data were identified using the Deep Learning Semantic method. For the limitation of this research is the administrative limit of XX Regency with a scale of 1: 25,000 as supporting data when cutting the image. The method used is the Deep Learning Convolutional Neural Network series. This research is based on how to wait for the method of the former oil spill which is the consideration of the consideration used. This study produced a land cover map that was classified into 3 categories, namely oil patterns area, area not affected by oil and vegetation. As a supporting value to show the accuracy of the classification results, an accuracy test method is used with the confusion matrix method. To show the accuracy of this study using thermal data taken from the field. Thermal data used in the form of numbers that show the temperature of each land cover. Based on the above reference, a research related to the analysis of very high-resolution image data (Pleiades-1B) will be conducted to examine the oil pattern. This research uses the deep learning series convolutional neural network (CNN) method. With this research, it is hoped that it can help agencies in knowing the right method to identify oil in mainland areas.

Combined Use of Space Borne Optical and SAR Data to Improve Knowledge about Sea Ice for Shipping

As a first step towards a new combined product for sea ice classification based on optical/thermal data collected by Sentinel-3 satellites and SAR data from Sentinel-1 satellites, which can be used as an appropriate support for navigation in Arctic and sub-Arctic waters, two existing classification algorithms are adapted to these data. The classification based on optical data has improved, so it is expected that the results will be ideally suited to be processed together with SAR data into significantly improved sea ice information products to support marine navigation. The usefulness of the combined processing is demonstrated by means of two simple algorithms and a more sophisticated approach is outlined, which will be realized in the future in order to form the basis for an integration into an operational service with the involvement of further partners and users.

Identification of the Thermal Conductance of a Hidden Barrier from Outer Thermal Data

Hidden defects affecting the interface in a composite slab are evaluated from thermal data collected on the upper side of the specimen. First we restrict the problem to the upper component of the object. Then we investigate heat transfer through, the inaccessible interface by means of Thin Plate Approximation. Finally, a Fast Fourier Transform is used to filter data. In this way, we obtain a reliable reconstruction of simulated flaws in thermal contact conductance corresponding to appreciable defects of the interface.

A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data

Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

Utilizing a novel mobile diagnostics lab to validate the impact of vegetative wall coverings in building cooling load reduction

Building cooling loads are driven by heat gains through enclosures. This research identifies possible ways of reducing the building cooling loads through vegetative shading. Vegetative shading reduces heat gains by blocking radiation and by evaporative air cooling. Few measured data exist, so we gathered thermal data from a vegetative wall grown in front of a Mobile Diagnostics Lab (MDL), a trailer with one conditioned room with instrumentation that collects thermal data from heat flux sensors and thermistors within its walls. In spring 2020 a variety of plants were cultivated in a greenhouse and planted in front of the south façade of the MDL, which was placed in direct sunlight to collect heat flux data. The plants acted as a barrier for solar radiation and reduced the amount of thermal energy affecting the trailer surface. Data were collected through the use of 16 heat flux sensors and development of continuous infrared (IR) images indicating surface temperature with and without plant cover. The façade surface beneath the plants was 10-30 °C cooler than exposed façade areas. In further analyses, the heat-flux data were compared to IR temperature data.

Empower parameterized generative adversarial networks using a novel particle swarm optimizer: algorithms and applications

In this paper, a novel parameterized generative adversarial network (GAN) is proposed where the parameters are introduced to enhance the performance of image segmentation. The developed algorithm is applied to the image-based crack detection problem on the thermal data obtained through the non-destructive testing process. A new regularization term, which contains three tunable hyperparameters, embedded into the objective function of the GAN in order to improve the contrast ratio of certain areas of the image so as to benefit the crack detection process. To automate the selection of the optimal hyperparameters of the GAN, a new particle swarm optimization (PSO) algorithm is put forward where a neighborhood-based velocity updating strategy is developed for the purpose of thoroughly exploring the problem space. The proposed PSO-based GAN algorithm is shown to 1) work well in detecting cracks on the thermal data generated by the eddy current pulsed thermography technique; and 2) outperforms other conventional GAN algorithms.

Unsupervised Man Overboard Detection Using Thermal Imagery and Spatiotemporal Autoencoders

Man overboard incidents in a maritime vessel are serious accidents where the rapid detection of the even is crucial for the safe retrieval of the person. To this end, the use of deep learning models as automatic detectors of these scenarios has been tested and proven efficient, however, the use of correct capturing methods is imperative in order for the learning framework to operate well. Thermal data can be a suitable method of monitoring, as they are not affected by illumination changes and are able to operate in rough conditions, such as open sea travel. We investigate the use of a convolutional autoencoder trained over thermal data, as a mechanism for the automatic detection of man overboard scenarios. Morever, we present a dataset that was created to emulate such events and was used for training and testing the algorithm.