Seasonal climatology of salinity fronts in the South Atlantic has been created from satellite SMOS sea surface salinity (SSS) measurements, 2011-2019, processed at the Barcelona Expert Center of Remote Sensing (BEC) and provided as high-resolution (1/20°) SSS data. The SSS fronts are identified with narrow zones of enhanced horizontal gradient magnitude (GM) of SSS computed with the Belkin-O’Reilly algorithm. Seasonal climatologies are generated for large-scale open-ocean SSS fronts and for low-salinity regions maintained by the Rio de la Plata discharge, Magellan Strait outflow, Congo River discharge, and Benguela upwelling. To facilitate feature recognition in satellite imagery, the salinity gradient has been log-transformed, which improved visual contrast of gradient maps, revealing new features. A 2000-km-long triangular area in the eastern tropical-subtropical Atlantic is filled with regular quasi-meridional mesoscale striations that form a giant ripple field with a 100-km wave length extending from Africa toward the NE Brazil. South of the Tropical Front, within the subtropical high-salinity pool, a trans-ocean quasi-zonal narrow linear belt of SSS maximum is documented. The Smax belt shifts north-south seasonally while retaining its well-defined linear morphology, which is suggestive of a mechanism that maintains this feature. In the SW Atlantic, the Rio de la Plata plume expands in winter (June-July), reaching across the South Brazilian Bight, up to Cabo Frio (23°S) and beyond. The Plata estuarine front moves in and out seasonally. Farther south, the Magellan Strait outflow expands northward in winter up to 39-40°S to nearly join the Plata outflow. In the SE Atlantic, the Congo River plume spreads radially from the river mouth, with the spreading direction varying seasonally and interannually. The plume is often bordered from the south by a quasi-zonal front along 6°S. The diluted Congo River water spreads southward seasonally down to the thermal Angola-Benguela Front at 16°S. The Benguela upwelling is delineated by a meridional front, which extends north alongshore up to 20°S, where the low-salinity Benguela upwelling water meets the high-salinity tropical water (“Angola water”) to form a salinity front, which is separate from the thermal Angola-Benguela Front at 16°S. The Angola water thus forms a wedge between the low-salinity waters of the Congo River outflow in the north and Benguela upwelling in the south. This high-salinity wedge is bordered by salinity fronts that migrate north-south seasonally.

The remote monitoring of clinical parameters plays a fundamental role in different situations, like pandemic health emergencies and post-surgery conditions. In these situations, the patients might be impeded in their movements and it could be difficult to have a specific health monitoring. In recent years, technological advancements in smartphones have opened up new possibilities in this landscape. The present work aims to propose a new method for respiratory kinematics monitoring via smartphone sensors. In particular, a specific application was developed to register Inertial Movement Unit (IMU) sensors data from the smartphone for respiratory kinematics measurement and to guide the user through a specific acquisition session. The session was defined to allow for the monitoring of the respiratory movement in five prescribed positions. The application and the sequence were successfully tested on a given population of 77 healthy volunteers. The resulting accelerometers and gyroscope signals were processed to evaluate the significance of differences according to participants sex, vector components and smartphone positioning and, finally, to estimate the respiratory rate. The emerged statistical differences revealed the significance of information in the different acquisition positions.

This paper presents the design and implementation of a remote supervision and control system for a gunpowder drum filling plant. The use of technologies such as SCADA (Ignition), IoT (Node-RED), ERP (Odoo) and digital twins (Factory I/O) to achieve remote monitoring and operation of the plant is described. The solution allows full control of the production process from any location through customized graphical interfaces. The challenges encountered in integrating multiple systems and platforms are discussed, along with the security measures implemented within the Azure cloud environment. The results demonstrate the effectiveness of 4.0 technologies in automating and digitizing critical industrial processes. This research will lay the groundwork for the adoption of these approaches in companies seeking greater efficiency, traceability and safety in their operations. The experience gained allows us to successfully face the digital transformation in modern manufacturing.

One of the future challenges of increasing the harnessed solar power is efficiency using of CubeSat sides. One of the approaches is using integrated antenna systems with solar panels or payloads. This study proposes a new approach to the use of integrated antenna systems in remote sensing missions. In this approach, due to the integration of antenna system with payload (in our case, with camera), it is possible to achieve a significant increase in the power generation. Calculations are carried out for cases when the antenna system are integrated with the optical system (α = 0°), which implies that they use one plane, in the second case they are spaced at an angle of α = 90° and in the third case when they are directed to opposite sides (α =180°). In the case of α = 0°, where the camera and antenna module are aligned co-axially, there is no energy expenditure for CubeSat orientation. However, in the other two cases, energy is required for rotation and maintenance of the specified orientation throughout the entire duration of the satellite's flight over the ground station, amounting to 111.99 mW when α = 90° and 44.33 mW when α = 180°.

Nowadays, the demand of agricultural production increase, Moreover, the rapid increase of the population in Egypt led to an increase in the demand for food, prompting the government to reclaim more desert lands. Such a study contributes to providing information that contributes to the promising optimization of land resources. This study carried out in an area south Egypt covering 386,171.04 hectares (ha). Remote sensed data were manipulated for defining the land cover/use features. Setting up DEM configures the network of natural drainage flows via the descending slopes. It was a guide for allocating micro dams as water harvesting sites as well as minimizing the runoff hazards and managing the priority of land use suitability versus flood hazards. Land cover/use units were defined as terrestrial irrigated areas of sequentially herbacous crops and permanent trees covering 1387.83 ha in levees; 1608.82 ha in point bar; 718.89 ha in bow bar and 23938.29 ha in alluvial plain. Terrestrial natural vegetation is dominated by xerophytes herbaceous in wadis (123533.56 ha). Terrestrial non-vegetated areas includes bare areas of rock land (98102.59 ha) and 67201.57 in bajada. Artificial non linear surfaces include buildings (5403.59 ha) and linear features include roads and railways (875.78 ha). Aquatic areas include artificial irrigation and drainage canals (1062.15 ha) and natural water body of River Nile course (2785.92 ha). Current land suitability for specific Land Utilization Types (LUTs) was assessed by matching soil attributes with the growth requirement of each LUT. Current land suitability can be improved by decreasing of salinity and framing practices. Accordingly, the land units can be potentially more profitable for increasing the ability of extra crops to be more productive. Old cultivated areas are the most profitable land cover/use units highly suitable (S1) for all LUTs comparing with other land cover categories.

The dairy industry relies heavily on milk pasteurization to ensure the safety and quality of milk products. Pasteurization is a process that involves heating milk to a specific temperature for a set period of time to kill harmful bacteria and other pathogens while preserving its nutritional value. recent years, newer techniques such as High Temperature Short Time (HTST) pasteurization have gained popularity due to their efficiency and effectiveness. HTST pasteurization involves rapidly heating milk to a high temperature for a short duration of time, followed by rapid cooling. This process helps to minimize the loss of flavor and nutrients in milk while ensuring its safety. However, there is still a need in the market for a compact and remotely controlled process plant for milk pasteurization. The proposed work aims to address this need by designing a process plant that is not only compact but also remotely controlled, allowing for efficient and convenient operation. can control the process plant remotely, allowing for flexibility and convenience in monitoring and managing the pasteurization process from anywhere with an internet connection

This study presents a cost-effective IoT-based remote monitoring system for solar PV energy systems, along with a machine learning-based PV power estimator. Remote access is crucial for tracking PV systems installed in remote areas. In this system, an open-source and IoT-compatible data logger is employed. The data logger collects important performance data of the PV system and transfers it to a server. Real-time visualization of this data is displayed in the designed web and mobile monitoring interfaces. The measured data includes the current, voltage, and temperature information of the PV generator and battery, as well as environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, this data is used for PV power estimation using machine learning methods. This enables the identification of maintenance requirements and the prediction of potential issues in the PV system. When a problem occurs in the PV system, the user is alerted through the mobile application. Early detection and intervention prevent power loss and damage to system components. When comparing the results of linear regression, SVM, decision trees, random forests, and KNN machine learning methods for power estimation based on performance evaluation criteria, it was observed that the random forests algorithm provided the best results. In conclusion, the developed monitoring and estimation system, along with web and mobile interfaces, is suitable for large-scale PV energy systems.

The construction of sustainable road and highway networks in the world, despite numerous feasibility, pre-feasibility and execution studies, are always confronted with the demands and vagaries of foreseeable and unforeseeable natural disasters. Studying cyclones, earthquakes, fracturing and landslide zones along roads is therefore a challenge for the sustainability of these infrastructures. In many countries around the world, the methods generally used for these studies are not only expensive and time-consuming, but the results obtained are not always efficient. This work examines whether Landsat 8 (with a high cloud level) and SRTM data can be used in both equatorial and coastal Central Africa zones to produce relevant mapping, locating fracture and landslide zones, in order to contribute not only to better road layout at lower cost and in a relatively short time, but also to better prevention of geological disasters that may occur on its network. To this end, a map of the main road network was produced and validated with field data, as well as the maps of the main unstable slopes, faults and fractures zones intersecting the road or highway network. These approaches are useful for sustainable planning, management, monitoring and extension of roads worldwide especially, in Central Africa.

Red color conversion materials have often been used in conventional white LEDs (light-emitting diodes) to enhance the insufficient deep red component and thus improve the color rendering property. Quantum dots (QDs) are one of the candidates due to their flexibility in controlling the emission wavelength, which is attributed to the quantum confinement effect. Two types of remote QD components, i.e., QD films and QD caps, were prepared and applied to conventional white LED illumination to improve the color rendering properties. Thanks to the red component near 630 nm caused by the QD components, the color rendering indices(CRIs) of both Ra and R9 could be increased to over 95. It was found that both the diffusing nature of the reflector and the light recycling process in the vertical cavity between the bottom reflector and the top optical films play important roles in improving the color conversion efficiency of remote QD components. The present study showed that the proper application of remote QDs combined with a suitable optical cavity can control the correlated color temperature of the illumination over a wide range, thus realizing different color appearances of white LED illumination. In addition, a high CRI of over 95 could be achieved due to sufficient excitation from fewer QDs due to the strong optical cavity effect.

In recent years, the concept of Fiducial Reference Measurements (FRM) has been developed to highlight the need for precise and well-characterised measurements tailored explicitly to the post-launch calibration and validation (Cal/Val) of Earth observation satellite missions. The confidence that stems from robust unambiguous uncertainty assessment of space observations is fundamental to assessing the changes in the Earth system and climate model prediction, and delivering the essential evidence-based input for policy makers and society striving to mitigate and adapt to climate change. The underlying concept of an FRM has long been a core element of a Cal/Val program, providing a ‘trustable’ reference against which performance can be anchored or assessed. The ‘FRM’ label was created to embody into such a reference a set of key criteria. These criteria included the establishment of documented evidence of uncertainty with respect to a community-agreed reference (ideally SI-traceable) and specific tailoring to the needs of a satellite mission. It, therefore, facilitates comparison and interoperability between products and missions in a cost-efficient manner. CEOS Working Group Cal/Val (WGCV) is now putting in place a framework to assess the maturity and compliance of a ‘Cal/Val reference measurement’ in terms of a set of community-agreed criteria which define it to be of CEOS-FRM quality. The assessment process is based on a maturity matrix that provides a visual assessment of the state of any FRM against each of a set of given criteria; making visible where it is mature and where evolution and effort needs to be done. This paper provides the overarching definition of what constitutes an FRM and introduces the new CEOS FRM assessment framework.

The calibration of length is related to the national economy and people's livelihood. The traditional calibration of length value was sent the uncalibrated instrument to the laboratory and the intrinsic error of the tested instrument was obtained in the laboratory. However, calibrating the instrument in the site produced additional error. In this article, a remote calibration method of length measurement based on optical fiber information transmission following with a proof of principle system was studied to reduce the additional error. The 1.0-level displacement guide rail was calibrated according to this method. The results showed that the relative error of guide rail displacement under different lengths was within 0.6%. The expanded uncertainties of 10 μm, 100 μm and 1 mm were analyzed respectively. The calibration results meet the actual standards of the guide rail. This optical method is a good candidate for remote calibration.

Multi-source image registration often suffered from great radiation and geometric differences. Specifically, gray scale and texture from similar landforms in different source images often show significantly different visual features. And these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness.

This review will serve as an entry point for international researchers who would like to tap into the vast scientific potential of Chinese-language literature on oceanic fronts. We focused on observational studies in descriptive physical oceanography of marine fronts. A thorough bibliographic search netted 93 papers published in 1982-2023, with a sharp increase of the total number of papers after 2010. This trend continued unabated through the early 2020s. Regionally, the overwhelming majority of papers is focused on the China Seas, particularly the East China Seas (Bohai, Huanghai, and Donghai, especially the Yangtze River Plume) and northern South China Sea (Guangdong Shelf). Elsewhere, a number of papers were dedicated to the Southern Ocean and North Atlantic. Thematically, papers on remote sensing of ocean fronts dominate, with special attention to the development of new advanced front-detection algorithms applied to satellite data on sea surface temperature and chlorophyll. Numerous papers on marine fronts in the China Seas present important results that have to be considered by international researchers. Overall, this review emphasizes the significant contributions made by Chinese oceanographers, particularly to the frontal oceanography of the China Seas.

For the first time VIS/NIR-SWIR (visible and near infrared – shortwave infrared) nighttime spectra of a satellite mission are analyzed, using the EnMAP (Environmental Mapping and Analysis Program) high-resolution imaging spectrometer. The focus of this article is set on the spectral characteristics. First, the spectral calibration of EnMAP is checked based on sodium emissions of lighting. Here, applying a realized novel general method, shifts of +0.3nm for VIS/NIR and −0.2nm for SWIR are identified with uncertainties analyzed to be in the range of [−0.4nm,+0.2nm] for VIS/NIR and [−1.2nm,+1.0nm] for SWIR. These results emphasize the high accuracy of the spectral calibration of EnMAP and illustrate the feasibility of methods based on nighttime Earth observations for the spectral calibration of future nighttime satellite missions. Second, applying a realized simple general method, the dominant lighting types of Las Vegas, Nevada, USA, and thermal emissions are identified per pixel and the consistency of the outcomes is considered. These results illustrate the feasibility of the precise identification of lighting types and thermal emissions based on nighttime high-resolution imaging spectroscopy satellite products and support the specification of, in particular, spectral characteristics of future nighttime missions.

As one of the common techniques for measuring coseismic deformations, optical image correlation techniques are capable of overcoming the drawbacks of inadequate coherence and phase blurring which can occur in radar interferometry, as well as the problem of low spatial resolution in radar pixel offset tracking. However, the scales of the correlation window in optical image correlation techniques typically influence the results, the conventional SAR POT method faces a fundamental trade-off between the accuracy of matching and the preservation of details in the matching window size. This study regards co-seismic deformation as a two-dimensional vector, and develops a new post-processing workflow called VACI-OIC to reduce the dependence of shift estimation on the size of the matching window. This paper takes the coseismic deformations in both the east-west and north-south directions into account at the same time, treating them as vectors, while also considering the similarity of displacement between adjacent points on the surface. Herein, the angular continuity index of the coseismic deformation vector was proposed as a more reasonable constraint condition to fuse the deformation field results obtained by optical image correlation across different matching window. Taking the earthquake of 2021 in Maduo, China, as the study area, the deformation with the highest spatial resolution in the violent surface rupture area was determined (which could not be provided by SAR data). Compared to the results of single-scale optical correlation, the presented results were more uniform (i.e., more consistent with published results). At the same time, the proposed index also detected the strip fracture zone of the earthquake with impressive clarity.

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a data set. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (Convolutional Block Attention Module, CBAM) is added for extracting spatial information in different remote sensing bands to improve the efficiency of the model. Long Short-Term Memory (LSTM) neural networks is also added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. The proposed model was tested on data from 2018 to 2020 showing an average R2 of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner.

This paper deals with the use of low earth orbit (LEO) satellite links in long-term monitoring of water, ice and snow levels across wide geographic areas, where no other communication links can be applied. Unlike geostationary satellites, LEO satellites do not maintain the same position relative to the ground station, and transmissions need to be scheduled for satellite overfly periods. For our application, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors. Our approach is based on online learning, and is applicable to any LEO satellite transmissions.

The quality of underground space information has become a major problem that endangers the safety of underground spaces. Currently, the main methods for the high-precision and long-distance transmission of detection information are radar and optical methods. However, in practical applications, we found that the radar method has the shortcomings of large energy loss and poor anti-jamming ability, which limit the accuracy of information data transmission and distance. The optical method has the shortcomings that the weather has a great impact on its accuracy and can only be applied to static objects above ground; therefore, it has the limitation of application objects and use environment. More importantly, the current high-precision information remote detection methods are limited to the detection of overground space objects and are not applicable to the detection of various information data in underground space. In this study, we analyze the spectral properties of the fractional differential operator and find that it is suitable for studying non-linear, non-causal, and non-stationary signals. The theory of fractional calculus is applied to the field of data processing, and a mathematical model of remote transmission and high-precision detection of information based on fractional difference is established, which realizes the functions of high-precision and remote detection of information. By fusing the information data to detect the mathematical model over a long distance and with high accuracy, a mathematical model for stratum data processing used to provide long-distance and high-accuracy data was established. Through application in engineering practice, the effectiveness of this method for underground space information data detection was verified.

This paper describes the use of an open source instrument called LANcube v2 to build a lighting device inventory. The LANcube v2 is and instrument having 5 color sensitive sensors, each on a face of a cube. The instrument can be mounted on a car roof in order to create a map of the artificial light at night while roaming the streets and roads. Based on the temporal variations of the detected signal on various cube’s faces, we developed a method of finding the position in 3D of each lighting device. The lamp spectral types can be determined thanks to the color balance of the raw Red (R) Green (G) Blue (B) and Clear (C) color bands. If one assumes a typical angular photometry of a lighting device with respect to its location, it is possible to estimate its luminous flux. Such information allows us to build a lighting devices inventory of a territory. One advantage of that new method is that it can provide information about the private lighting devices that are always excluded from public lighting inventories. We will compare the inventory extracted with that new methodology with an in situ lamp inventory made for two villages in Canada. This will allow us to emphasize the strengths and limitations of the method by comparing to the ground truth.

The practice of telework, remote work, and working from home has grown significantly across the pandemic era (2020+). These practices offer new ways of working but come with a lack of clarity as to the role it plays in supporting the wellbeing of staff. (1) Background: the purpose of this study is to examine the current literature on wellbeing outcomes and effects of telework; (2) Methods: this study adopts a systematic literature review from 2000-2022 using the PRISMA approach and thematic analysis guided by the United Nations Sustainable Development Goals (Wellbeing, Decent Work, Gender Equality, and Inclusive Production); (3) Results: it was evident that there is a lack of clarity on the actual effects of telework on employee wellbeing, but it appeared that it had a generally positive effect on short-term wellbeing of staff, and created more flexible and proactive work design opportunities; (4) Conclusions: there is a need for more targeted research into work designs that support wellbeing and productivity of staff, and consider the environmental sustainability changes from reduced office and onsite work and increased working from home.

The problem of reliable function computation is extended by imposing privacy, secrecy, and storage constraints on a remote source whose noisy measurements are observed by multiple parties. The main additions to the classic function computation problem include 1) privacy leakage to an eavesdropper is measured with respect to the remote source rather than the transmitting terminals’ observed sequences; 2) the information leakage to a fusion center with respect to the remote source is considered as a new privacy leakage metric; 3) the function computed is allowed to be a distorted version of the target function, which allows to reduce the storage rate as compared to a reliable function computation scenario in addition to reducing secrecy and privacy leakages; 4) two transmitting node observations are used to compute a function. Inner and outer bounds on the rate regions are derived for lossless and lossy single-function computation with two transmitting nodes, which recover previous results in the literature. For special cases that include invertible and partially-invertible functions, and degraded measurement channels, exact lossless and lossy rate regions are characterized, and one exact region is evaluated for an example scenario.

Abstract Epilepsy is a disorder of the brain and a seriously debilitating condition, which has been associated with injury, social stigmatisation and in some cases, sudden unexpected and premature death. A sense of profound isolation is felt by many individuals with epilepsy, and this community has expressed an urgent desire for an early warning system to allow them time to prepare for seizure onset. Surveys of dog owners with epilepsy have previously reported that some dogs can predict the onset of a seizure. The current study used an experimental design to investigate the alleged propensity of untrained pet dogs to react to human epileptic seizures. We hypothesised that seizures are associated with specific volatile organic compounds resulting in detectable odours which are the biomarker that triggers these reported behavioural changes in the dogs. Here we provide details of the first empirical test to demonstrate that pet dogs display several significant behavioural changes when they are exposed to seizure-related odours that apparently emanate from their owners. Using a repeated measures design experiment, recordings were made of the reactions of 19 untrained pet dogs to odours from sweat samples provided by three people with epilepsy and by two people without epilepsy (controls). The seizure-associated sweat samples were captured pre-seizure, during seizure and post-seizure. All samples were randomly delivered to individual dogs in a test area, using two bespoke pieces of apparatus called Remote Odour Delivery Mechanisms (RODM). One RODM delivered only experimental odours, the other delivered only control odours. Behavioural changes by the dogs on encountering the odour samples were recorded by video for later analysis. Consistent with our hypothesis, seizure-associated odours evoked significant behavioural changes in the dogs which were affiliative in nature and directed at their owners.

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of ecosystems productivity and related climate, topography, and edaphic impacts. The spatio-temporal changes of LAI were assessed throughout Ardabil Province, a host of relevant plant communities within the critical ecoregion of a semi-arid climate. In a comparative study, novel data from Google Earth Engine- GEE was tested against traditional ENVI measures to provide LAI estimations. Besides, it is of important practical significance for institutional networks to quantitatively and accurately estimate LAI at large areas in a short time and using appropriate baseline vegetation indices. Therefore, LAI was characterized for ecoregions of Ardabil Province using remote sensing indices extracted from Landsat 8 Operational Land Imager (OLI), including Enhanced Vegetation Index calculated in GEE (EVIG) and ENVI5.3 software (EVIE), as well as Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVIE). Besides, a new field measurement method, i.e., the LaiPen LP 100 portable device (LP 100), was used to evaluate the accuracy of the derived indices. Accordingly, the LAI was measured on June and July 2020 in 822 ground points distributed in 16 different ecoregions-sub ecoregions having various Plant Functional Types (PFTs) of the shrub, bush, and tree. The analyses revealed heterogeneous spatial and temporal variability in vegetation indices and LAIs within and between ecoregions. The mean (standard deviation) value of EVIG, EVIE, and NDVIE at Province scale yielded 1.1 (0.41), 2.20 (0.78), and 3.00 (1.01), respectively in June, and 0.67 (0.37), 0.80 (0.63), and 1.88 (1.23), in that respect in July. The highest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June are found in Meshginshahr (1.40), Meshginshahr (2.80), and Hir (4.33) ecoregions and in July are found in Andabil ecoregion respectively with values of 1.23, 1.5, and 3.64. The lowest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June were observed for Kowsar (0.67), Meshginshahr (1.8), and Neur (2.70), ecoregions and in July were for Bilesavar ecoregion respectively with values of 0.31, 0.31, and 0.81. High correlation and determination coefficients (r>0.83 and R2>0.68) between LP 100 and remote sensing derived LAI were observed in all three PFTs (except for NDVIE-LAI in June with r=0.56 and R2=0.31). On average, all three examined LAI measures tended to underestimation compared to LP 100-LAI (r>0.42). The findings of the present study can be promising for effective monitoring and proper management of vegetation and land use in Ardabil Province and other similar areas.

Land Use Land Cover (LULC) changes analysis is one of the most useful methodologies to understand how the land was used in the past years, what types of detections are to be expected in the future, as well as the driving forces and processes behind these changes. In Ethiopia, the rapidly changing of LULC is mainly due to population pressure, resettlement programs, climate change, and other human and nature-induced driving forces. Anthropogenic activities are the most significant factors adversely changing the natural status of the landscape and resources, which exerts unfavourable and adverse impacts on the environment and livelihood. The main goal of the present work is to review previous studies, discussing the spatio-temporal LULC changes in Ethiopian basins, to find out common points and gaps that exist in the current literature, to be eventually addressed in the future. Seventeen articles, published from 2011 to 2020, were selected and reviewed, focusing on LULC classification using ArcGIS and ERDAS imagine software by unsupervised and maximum likelihood supervised classification methods. Key informant interview (KII), focal group discussions (FGDs) and collection of ground truth data using ground positioning systems (GPS) for data validation were the major approaches discussed in most of the studies. All the analysed research showed that, during the last decades, Ethiopian lands changed to agricultural land use, waterbody, commercial farmland and built-up/settlement. Some parts of forest land, grazing land, swamp/wetland, shrubland, rangeland and bare/ rock out cropland cover class were changed to other LULC class types, mainly as a consequence of increasing anthropogenic pressure. In summary, these articles confirmed that LULC changes are a direct result of both natural and human influences. However, most of the study provided details of LULC for the past decades within a specific spatial location, while they did not address the challenge of forecasting future LULC changes at the basin scale.

This work presents the dynamics of forest clearing in the Brazilian Amazon during the period 2006–2019 in which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State and in the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess greenhouse gas (GHG) emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was also observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, a significant intensification comparatively higher than the previous ten years which was on an average of 6,760 km2. The steady increase of deforestation in the Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon (295 million tons of net CO2 equivalent in the year 2019). Better correlation of deforestation and fires occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, which degrades primary forest surrounding the deforested areas.

The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for reducing atmospheric contributions in UAS imagery and for producing scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions was used to develop and test a simplified ELM radiometric calibration framework designed specifically for the reduction of atmospheric contributions to UAS imagery in studies with limited site accessibility or data acquisition time constraints. The framework was effective in reducing atmospheric and other external contributions to UAS imagery. Unique to the proposed radiometric calibration framework is the radiance to reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equations development. This paper presents the simplified ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in the UAS imagery.

Anthropogenic change is associated with population growth, land use change, and changing economies. However, internal migration patterns and armed conflicts are also key drivers behind anthropogenic and demographic processes. To better understand this sort of change, we explore the spatial relationship between forced migration due to armed conflict and changing demographic factors in Colombia, a country which has a recent history of 7 million internal migrants. In addition, we use remote sensing, Google Earth Engine, as well as spatial statistical analyses of demographic data in order to measure anthropogenic change between 1984 and 2008; and we look into spatiotemporal relationships between both demographic and anthropogenic changes, which are caused by forced migration. We find, thus, that the latter is significantly and positively related to an increasing rural-urban kind of migration which originates in armed conflict, and results show that it is also negatively associated with interregional expulsion. Indeed, anthropogenic prints (term hereafter used to denote changes in nighttime satellite imagery) pertaining to different regions have had different sensitivities towards forced migration, and across different time periods. Finally we discuss how social and political phenomena such as Colombia’s armed conflict can have significant effects on the dynamics and motions of humans and territories in countries of the Global South.

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.

On January 8, 2005, a surface melt event began on the interior portion of Ross Ice Shelf. While many surface melt events on Ross Ice Shelf are caused by the advection of warm air onto the shelf from the Ross Sea, surface winds during this event were directed offshore and the spatial pattern of surface melt was inconsistent with the Southern Ocean serving as a heat source. Rather, due to the interior location of the surface melt coupled with prevailing wind direction and surface temperature data it is thought that adiabatic warming of Föhn winds is the driving cause of this melt event. Passive Microwave (SSM/I) imagery was used to determine surface melt occurrence and the event’s extent. Spatial patterns of surface melt were then compared to NCEP/NCAR reanalysis output for several synoptic weather variables including surface temperatures, sea level pressure and surface vector winds. Synoptic-scale weather conditions were consistent with those that would produce downsloping wind (föhn) conditions in the interior of the Ross Ice Shelf where the anomalous surface melt was located.

Over 55% of stoma patients suffer complications such as dehydration. Outcomes may be improved through communicating stoma output data to the patient and their clinical teams. Past artificial neural networks to improve accuracy in fluid level sensing were designed to account for ‘slosh’ caused by variable acceleration in one or two axes of movement. This paper describes the development of a novel sensor platform for non-invasive monitoring of stoma output in real time through incorporating a volumetric array consisting of thermistors and capacitive sensors into an ostomy appliance. Stoma output which exits the body at core temperature passes into a stoma appliance in a pattern which is dictated by water content, existing effluent within the bag and distortion of the usual bag shape. By using thermistors, a thermal boundary demarcates the accumulated level of fecal material as the effluent settles. A capacitive array allows the measurement of volume of output. The sensing components communicates via near field communication (NFC) and transmits data to a smartphone application by Bluetooth low energy (BLE). Testing of the device on 11 existing ileostomy patients with 51.6 bag hours of data found a correlation between measured volume and predictive value, supporting its use in this population.

Droughts occurring more and more frequently in recent years are usually associated with progressive climate changes. Modern monitoring methods is mainly associated with agricultural use due to the high market demand for services protecting against agricultural losses. In natural wetland ecosystem this problem is neglected which is probably due to fact that this are ecosystems rich in water by definition. Nevertheless this droughts might become a problem of wetlands, whose good condition depends on adequate hydration. Therefore this study focused on determination Crop Water Stress Index (CWSI) for natural heterogeneous wetland habitats. CWSI based on remotely sensed land surface temperature and associated basic meteorological (air temperature and humidity) measurements. CWSI was calculated based on high resolution LST measurements from UAV platform and compare with on ground measurements of soil moisture, CCI, fAPAR and meteorological conditions. Results shows that CWSI properly shows water condition of plants in measured habitats and revels high importance of metrological conditions during data acquisition.

A heat transfer model, has determined millimeter crude oil thicknesses on saltwater at night. Model inputs are calibrated thermographic imagery, weather station data, metrological observations, and crude oil thermal conductivity. Outdoor field-testing was performed at the National Oil Spill Response & Renewable Energy Test Facility (Ohmsett) to determine model accuracy. Alaskan North Slope (ANS), Hoover Offshore Oil Pipeline System Blend (HOOPS), and ROCK crude oils were placed at varying mm depths. A roof-top mounted thermal camera measured the average oil surface temperature for each target and converted to oil spill thickness. The fidelity of the thickness measurements is dependent on the accurate measurement of the atmospheric and weather parameters, sea state, heat transfer constants, crude oil evaporation rates, and calibration and stability of the thermal camera.

The ubiquitous diffusion of Power Electronic Converters (PECs) in many fields of application including traction and energy conversion is suggesting the possibility of new and better integration of advanced power conversion and ICT services. This work investigates the possible advancements in the use of optical fibers for control of PECs, using Plastic Optical Fiber. The optical communication link connects the switching control to the converter control, following the line of separation between the expertises of the power electronic engineer and the control engineer. Control wise, the PEC becomes a black box compatible with any off-board controller, now immune from the Electromagnetic Interference (EMI) produced by the power switches. The redundant optical link is ready for the high switching (and sampling) frequencies possible with the use of SiC power semiconductor devices (100 kHz+). Distributed control of multiple PEC units and advanced telemetry for diagnostics and prognostics are targeted. A proof-of-concept demonstrator is presented and tested. Moreover, the possible evolution towards a power electronic cloud with remote management and orchestration is described.

The Geostationary Ocean Color Imager (GOCI) of the Communication, Ocean, and Meteorological Satellite (COMS) increases the chance of acquiring images with greater clarity at eight times a day and is equipped with spectral bands suitable for monitoring crop yield with a moderate spatial resolution. The objectives of this study were to classify nationwide paddy fields and to project rice (Oryza sativa) yield and production using the grid-based GRAMI-rice model and GOCI satellite products over South Korea from 2011 to 2014. Solar insolation and temperatures were obtained from COMS and the Korea local analysis and prediction system for model inputs, respectively. The paddy fields and transplanting dates were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and land cover products. The crop model was calibrated using observed yield data in 11 counties and was applied to 62 counties in South Korea. The overall accuracies of the detected paddy fields ranged from 89.5 to 90.2%. The simulated rice yields statistically agreed with observed yields, with root-mean-square errors of 0.219 to 0.451 ton ha-1 and Nash–Sutcliffe efficiencies of 0.241 to 0.733 in four years, respectively. According to paired t-tests (α = 0.05), the simulated and observed rice yields were not significantly different. These results demonstrate the possible development of a crop information delivery system that can classify land cover, simulate crop yield, and monitor regional crop production on a national scale.

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.

To address the issue of low prediction accuracy for sub-grid scale turbulence-radiation interaction (SGS-TRI) characteristics, which impacts the precision of remote infrared signal calculations for gas jet, a term for correlation of radiation-participating component fluctuations with temperature fluctuation was introduced into the current sub-grid Snegirev equation; Simultaneously, we established a new method that is more convenient in complex projects for solving the dynamic sub-grid model coefficients, e.g., dynamic Smagorinsky model and temperature variance model. The validity of these algorithms was confirmed through direct numerical simulations (DNS) for isotropic turbulence and test data of high-temperature air jet flow. The Multi-Scale Multi-Group Wide-Band (MSMGWB) k-distribution model was used to calculate radiative transfer in strongly non-isothermal and inhomogeneous media such as the remote infrared sensing of jet plume scenario considering influences of TRI and SGS-TRI. The study for axisymmetric jet plume revealed that the TRI accounts for more than 1/3 of total remote infrared signal in the 3-5micron band, with the SGS-TRI effect contributing more than 10%. Additionally, this influence was found to further increase with increasing detection distances.

Land cover refers to the combination of various material types and their natural characteristics on the Earth. Accurately mapping the spatial distribution and temporal changes of the earth's land cover is of great significance for studying the energy balance and carbon cycle of the earth system. However, there is still a high degree of human participation in the production of multi-temporal land cover products. Developing an automated method for multi-epoch land cover mapping has become a key research focus. To this end, an automatic training sample extraction method was first employed using multi-source prior land cover products. Then, based on the generated training dataset and the Random Forest classifier, local adaptive land cover classification models of the reference year were developed. Finally, by migrating the classification model to the target epoch, the multi-epoch land cover products were generated. Yuli County in Xinjiang and Linxi County in Inner Mongolia were used as test cases. The classification models were first generated in 2020 and then transferred to 2010 to enable automatic classification of multi-temporal land cover. The mapping results showed high accuracy in both regions, with Yuli County achieving 92.52% in 2020 and 88.33% in 2010, and Linxi County achieving 90.28% in 2020 and 85.28% in 2010. Additionally, uncertainty analysis of the model migration method revealed that land types such as water bodies, wetlands, and impervious surfaces, which exhibit significant spectral changes over time, are less suitable for the model migration. Our research can offer valuable insights for fine-resolution land cover mapping. Furthermore, the approach provides a scalable solution for multi-period land cover monitoring, which could facilitate more efficient and accurate environmental assessments.

Biostimulants are organic agents employed for crop yield enhancement, quality improvement, and environmental stress mitigation, reducing, at the same time, reliance on inorganic inputs. With advancements in sustainable agriculture, data acquisition technologies have become crucial for monitoring the effects of such inputs. This study evaluates the impact of four biostimulant application rates on grapevines, using vegetation indices derived from Unmanned Aerial Systems (UAS), proximal and manual sensing tools, and qualitative and quantitative production assessments. The research was conducted over two seasons in a Malvasia Bianca vineyard in Sardinia, Italy. Results indicated that UAS-derived vegetation indices, consistent with traditional ground-based measurements, effectively monitored vegetative growth over time but revealed no significant differences between treatments, suggesting either a lack of vegetative indices sensitivity or that the applied biostimulant rates were insufficient to elicit a measurable response in the cultivar. Among the tools employed, only the SPAD 502 meter demonstrated the sensitivity required to detect treatment differences, primarily reflected in grape production outcomes. Future research will focus on validating these technologies for precision viticulture, particularly on long-term benefits.

Delving into the transformative role of digital health in Nigeria's remote communities, aiming to bridge healthcare gaps and enhance overall well-being. As digital interventions gain prominence, their impact on healthcare accessibility, quality, and awareness becomes pivotal in the unique context of remote regions. We explore the historical overview of digital health initiatives in Nigeria, contextualize healthcare challenges, and analyse the global and regional influences shaping digital health adoption. Challenges such as digital literacy, infrastructural constraints, and cultural barriers are addressed alongside solutions, emphasizing the need for inclusive policies and community engagement. Through examining the quantitative and qualitative impacts of digital health interventions, including telehealth, mobile applications, and electronic health records, we unveil the strides made in healthcare delivery. Voices from communities, healthcare providers, and leaders provide first-hand perspectives, offering insights into the effectiveness and challenges faced. Additionally, policy implications, grassroots advocacy, and collaborative efforts between public and private sectors are discussed as integral components for sustainable change. As we measure the data-driven insights and envision future directions, this article advocates for a comprehensive and inclusive approach to digital health, envisioning a future where remote communities in Nigeria experience improved healthcare outcomes and resilience.

Mangroves are critical ecosystems providing essential environmental services, making their conservation crucial. High-resolution remote sensing, particularly through unmanned aerial vehicles (UAVs), offers unprecedented resolution in ecosystem mapping. This study utilizes a multispectral sensor mounted on a UAV to map mangroves at Cardoso Island on Brazil’s southeastern coast and Suape on the northeastern coast. Using Geographic Object-Based Image Analysis (GEOBIA) for segmentation and classification, we employed machine learning algorithms to classify the primary mangrove species: Laguncularia racemosa, Rhizophora mangle, and Avicennia schaueriana. Two flight altitudes were used: a higher altitude for broad-scale mapping and a lower altitude for classification training, supplemented by field measurements for calibration. This method enabled the precise delineation of tree crowns and provided insights into species distribution and zonation, influenced by the distinct estuarine conditions of tides and sediment input. The high-resolution mapping achieved in this study enhances detailed analyses of mangroves, improving ecological models and biophysical estimations such as biomass and blue carbon stocks. This work highlights the potential of UAV-based multispectral imaging in advancing mangrove research and conservation efforts.

Global climate change primarily affects the spatiotemporal variation of physical quantities such as relative humidity, atmospheric pressure, ambient temperature, and notably, precipitation levels. Accurate precipitation predictions remain elusive, necessitating tools for detailed spatiotemporal analysis to better understand climate impacts on the environment, agriculture, and society. This study compares three learning models: ARIMA (Autoregressive Integrated Moving Average), RF-R (Random Forest Regression), and LSTM-NN (Long Short-Term Memory Neural Network), utilizing monthly precipitation data (in millimeters) from 757 locations in Boyacá, Colombia. The LSTM-NN model outperformed others, precisely replicating precipitation observations in both training and testing datasets, significantly reducing the root mean square error (RMSE) with average monthly deviations of approximately 19 mm per location. Evaluation metrics (RMSE, MAE, R², MSE) underscore the LSTM model’s robustness and accuracy in capturing precipitation patterns. Consequently, the LSTM model was chosen to predict precipitation over a 16-month period starting from August 2023, offering a reliable tool for future meteorological forecasting and planning in the region.

This study investigates the spatial urban growth patterns of cities along the Democratic Republic of Congo (DRC) and Zambia border, a region of significant economic importance characterized by cross-border trade. This activity has led to rapid, but unplanned urban growth. The objective is to quantify the spatial expansion of Congolese cities (Kipushi, Kasumbalesa, Mokambo, and Sakania) bordering Zambia and to evaluate associated landscape changes. The methodology integrates Landsat images from 1990, 2000, 2010, and 2023, coupled with Geographic Information Systems (GIS) and landscape ecology analysis tools. The results reveal substantial landscape transformations in the border cities between 1990 and 2023. These changes are primarily driven by rapid urban expansion, particularly pronounced in Kasumbalesa. Between 1990 and 2023, forest cover declined from 70% to less than 15% in Kipushi, from 80% to 10% in Kasumbalesa, from 90% to 30% in Mokambo, and from 80% to 15% in Sakania. This forest cover loss is accompanied by an increase in landscape element diversity, as indicated by the Shannon diversity index, except in Kipushi, suggesting a transition towards more heterogeneous landscapes. In these border cities, landscape dynamics are also characterized by the expansion of agriculture and savannas, highlighted by an increase in the disturbance index. Spatial pattern changes analysis shows that built-up areas, agriculture, and savannas exhibit trends of patch creation or aggregation, while forests are undergoing processes of dissection and patch attrition. Congolese cities bordering Zambia are undergoing substantial spatial changes propelled by intricate interactions between economic, demographic, and infrastructural factors. Our results highlight the necessity for sustainable development strategies to address challenges such as urban sprawl, deforestation, and fostering cross-border cooperation in border regions.

Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically-active water quality parameters via band ratio algorithms and machine learning methods. However, fitting and validating these models requires access to sufficient quantities of in situ reference data which are time-consuming and expensive to obtain. In this study, we demonstrate how the Generative Topographic Mapping (GTM), a Bayesian realization of the Self-organizing Map, can be used to visualize high-dimensional hyperspectral imagery and extract spectral signatures corresponding to unique endmembers present in the water. Using data collected across a North Texas pond, we first apply the GTM to visualize the distribution of captured reflectance spectra revealing small-scale spatial variability of water composition. Next, we demonstrate how the nodes of the fitted GTM can be interpreted as unique spectral endmembers. Using extracted endmembers together with the normalized spectral similarity score, we are able to efficiently map the abundance of near shore algae as well as the evolution of a rhodamine tracer dye used to simulate water contamination by a localized source.

Unmanned aerial vehicles (UAVs), commonly known as drones, present a cost-effective solution for the swift collection of data from vast and remote areas that are otherwise difficult to access. The Mediterranean Basin, known for being a hot spot for plant biodiversity, hosts several habitats and taxa of significant naturalistic value. However, many of these areas are often inaccessible to botanists, making exploration and research challenging. The aim of this paper is to involve the utilization of drone surveys and open-source software for botanical research. Our primary goal is to show the effectiveness of these tools in the field and demonstrate their practical application in Natura 2000 sites. The protected area chosen for this research is Rocca di Novara, situated in northeastern Sicily. Thanks to our drone investigations, we were able to capture images of the mountain’s side that is inaccessible to humans. This allowed us to observe the habitat of some species in detail. One of the most fascinating discoveries was the reappearance of Saxifraga callosa subsp. australis, which had not been confirmed in this area for over 140 years. Using drones for botanical research can boost field research, making monitoring easier and more cost-effective over time, especially in Natura 2000 sites.

Previous studies separately addressed the impact of urban expansion on vegetation cover and thermal conditions, as well as its effect on moisture and drought indicators, using geospatial techniques. Some studies, such as those by Malah et al. (2021) and Niu and Li (2020), focused on assessing urban environmental quality using an environmental quality index without considering urban expansion. Meanwhile, other studies, like those conducted by Indrawati et al. (2020), primarily focused on evaluating urban environ-ments using an integrated environmental index consisting of four indicators (greenness, moisture, drought, and cumulative clustering). The study by Zhu et al. (2019) applied the same integrated environmental index (IEI) and, by comparing it with temperature, re-vealed a negative correlation illustrating the effects of the environmental index on land surface temperature (LST). These two studies could be utilized to construct an index assessing the environmental condition in Riyadh. This is the case in the present study, where the addition of a temperature indicator due to the hot desert nature of the study area created the Urban Environmental Thermal Index to evaluate the urban en-vironment and provide results suitable to this region. It is expected that the present study will align with the findings of previous relevant studies, showing a negative im-pact of urban expansion on urban environmental components.

Wetland ecosystems in the Qinghai-Tibet Plateau are pivotal for global ecology and regional sustainability, contributing significantly to terrestrial ecosystems by regulating runoff, mitigating floods, and enhancing water quality. This study investigates the dynamic changes in wetland ecosystems within the Chaidamu Basin and their response to drought, aiming to foster sustainable wetland utilization in the Qinghai-Tibet Plateau. Using Landsat TM/ETM/OLI data on the Google Earth Engine platform, we employed a random forest method for annual long-term land cover classification. Meteorological drought conditions were assessed using SPEI3, SPEI6, SPEI9, and SPEI12, derived from monthly precipitation and evapotranspiration data. Pearson correlation analysis examined the relationship between wetland changes and various SPEI scales. The BFASAT method evaluated the impact of SPEI12 trends on wetlands, while cross-wavelet analysis explored teleconnections between SPEI12 and atmospheric circulation factors. Our findings revealed that the land cover dataset of the Chaidamu Basin (1990-2020) exhibited diverse categories with high classification accuracy (OA: 90.27%, Kappa: 88.34%). Wetlands, including lake, glacier, and marsh types, exhibited a noticeable increasing trend. Wetland expansion occurred during specific periods (1990-1997, 1998-2007, 2008-2020), featuring extensive conversions between wetland and other types, notably from other types to wetlands. Spatially, lake and marsh wetlands predominated in the low-latitude basin, while glacier wetlands were situated at higher altitudes. The study identified significant negative correlations between SPEI at various scales and total wetland area and types, with SPEI12 exhibiting the most substantial effect between September and December (r <-0.75) on wetlands. SPEI12 displayed a decreasing trend with non-stationarity and distinct breakpoints in 1996, 2002, and 2011, indicating heightened drought severity. Atmospheric circulation indices (ENSO, NAO, PDO, AO, WP) exhibited varying resonance with SPEI12, with NAO, PDO, AO, and WP demonstrating longer resonance times and pronounced responses.The continuous growth of wetlands amidst increasing aridification emphasizes the need for thoughtful wetland development to establish a sustainable "forest-lake-grass-field-river" ecological community. These findings underscore the significance of comprehending wetland changes and drought dynamics for effective ecological management in the Chaidamu Basin of the Qinghai-Tibet Plateau.

This study examines the capacity of a Site-Specific Fodder Management Decision Support System (SSFM-DSS) model to assist in successful cultivation of sericea lespedeza [SL; Lespedeza cuneata (Dum.-Cours.) G. Don.], with a specific focus on small-scale agricultural systems in the southeastern United States (U.S.). The study emphasizes incorporating advanced geospatial technologies, such as Geographic Information Systems (GIS), remote sensing, and Global Navigation Satellite Systems (GNSS), to improve fodder production. The research showcases the versatility of SL under varying environmental conditions, underscoring its significance in promoting sustainable livestock production. The methodology integrates empirical field data, geospatial analysis, and predictive modeling to formulate an SSFM strategy customized to address the distinct difficulties climate change presents, such as sudden and severe drought conditions. The study introduces an automated geospatial model for assessing the appropriateness of SL production throughout Alabama, Georgia, and South Carolina. The model uses many environmental criteria, including soil properties, climate fluctuations, and terrain. Furthermore, the research was instrumental in development of a WebGIS Dashboard, which offers farmers a decision assistance system to enhance the sustainable production of SL. The results highlight the significant impact that a SSFM-DSS can have on promoting sustainable agricultural practices, providing a solution to address the issues of food security and environmental concerns in agriculture.

In this paper, we studied the problem of early detection of natural phenomena associated with the formation of severe floods and the possibility of constant monitoring of their occurrence. The test object for the study was severe floods on the Crimean Peninsula, which occurred due to heavy rainfall in the summer of 2021. Based on the selected satellite and other data related to this period, an analysis carried out and key atmospheric objects identified, according to the proposed algorithm for their identification and grouping. Based on the results of the work, a positive conclusion made on the issue of their use to identify the likelihood of natural disasters that could lead to floods. In addition, the authors proposed to develop a portal for collecting and processing a large amount of satellite data related to flood prevention and monitoring the development of such processes, and stressed the need to create it.

Remote Sensing and Geographic Information System are recognized as crucial instruments for conducting geographical and geospatial research. RS and GIS technology was employed to assess the morphological Characteristics of the Kasura River Watershed situated within the Parbhani and Jalna Districts of Maharashtra, India. The entire drainage area of the Kasura River basin measures 490.13 square kilometers (km²). Various morphometric characteristic were calculated and examined, encompassing linear features such as stream number, stream order, mean stream length, stream length, and stream length ratio, areal attributes including elongation ratio, circularity ratio, form factor, constant of channel maintenance, drainage density and drainage texture and relief characteristics such as relief, relief ratio, maximum relief, ruggedness number, relative relief, and length of overland flow. These findings underscore the significance of employing morphometric analysis in watershed assessment, offering a quantitative method for characterizing the surface attributes of a drainage pattern and yielding valuable insights into the topography and runoff dynamics of the region.

Global climate change has induced a rise in sea surface temperature (SST), although this increase is not uniform across the world. Significant variations exist between coastal and offshore waters, particularly in regions affected by upwelling processes. This study focuses on the Canary Current Upwelling System (CCUS), stretching from Northwest Iberia to Northwest Africa. High-resolution remotely sensed SST data (0.05°) from the ODYSSEA Level 4 Sea Surface Temperature Reprocessed dataset were validated with in-situ measurements and employed to establish a regional climatological baseline for 1982 – 2012. Subsequent years were compared to this baseline to construct SST anomaly maps, revealing SST changes since 2012. The study area was further divided into sub-regions for comparative analysis. Results indicate that SST consistently increased at a higher rate offshore compared to the adjacent nearshore regions. A reference dataset spanning 1951 – 1981 was used to gauge SST variability between the two baselines. SST exhibited a 0.59°C increase from 1951 – 1981 to 1982 – 2012, with a slowing of SST trends beyond the 1982 – 2012 baseline. This research offers valuable insights into the climatological dynamics of the CCUS. These findings enhance our understanding of this critical coastal system's climatology, laying the groundwork for future investigations into evolving climate patterns in coastal regions.

The soil-structure interaction represents an important parameter when analyzing the dynamic behavior of bridges, taking into account the integration between soil, foundation, and structure to ensure an adequate structural representation. In this paper, a computational modeling strategy using soil-structure interaction (SSI) methodology was applied to represent the dynamic behavior of a pedestrian bridge (LOP). The computational models were validated using monitoring results from an extensive structural health monitoring campaign. The bridge monitoring program was characterized using embedded sensors and cutting-edge technology for remote monitoring: ground-based radar interferometry and video motion amplification. The instrumentation results were compared to establish the capabilities and advantages of this novel remote monitoring technique. The experimental results obtained from free and forced vibrations in the asset served as a basis for validating the developed computational model and allowed evaluating the asset be-havior in service for different loading conditions. This paper emphasizes the SSI approach to represent the pedestrian bridge dynamic behavior more accurately, allowing computational models to represent the LOP structural responses over the course of its life-cycle.

For the territory of the Republic of Tatarstan (Russia), a spatial model of forest communities was built as a basis for assessing the sequestration potential of ecosystems. Combination of multi-temporal and multi-spectral satellite imagery from the Landsat 8 and Landsat 9 platform as input data and Google Earth Engine cloud platform were used. The set of 292 vegetation indices and metrics computed from the pre-processed imagery, were combined into dataset. The Weka X-Means clustering algorithm was trained and applied to study area. The unsupervised classification was carried out by vegetation classes in the Braun-Blanquet system. The results of unsupervised classification were verified using data from more than 17,000 relevés with geographic references from the Flora database. For automatic classification, the EuroVeg Checklist expert system in the JUICE 7.1 package was used. The proposed methodology for obtaining initial data and unsupervised classification, supported by an automated expert system, made it possible to obtain a picture of the vegetation distribution in the study area with sufficient accuracy, and in the future, it will be used to assess the sequestration potential of the ecosystems of the region under study.

Floods are some of the most devastating crop disasters in Southeast Asia. The Pampanga River Basin in the Philippines is a representative flood-prone area, where cultivation patterns are varied according to the flood risk. However, quantitative analyses on the effects of flooding on cultivation patterns remain quite limited. Accordingly, this study analyzes MODIS LAI data (MCD15A2H) from 2007 to 2022 to evaluate annual and geographical differences in cultivation patterns in the Candaba municipality of the basin. The analysis consists of two stages of hierarchical clustering: a first stage for area classification and a second stage for the classification of annual LAI dynamics. As a result, Candaba is divided into four areas, which are found to be partly consistent with the observed flood risk. Subsequently, the annual LAI dynamics in each area are divided into two or three clusters. The obvious differences among the clusters are caused by flooding in the late rainy season, delaying the start of planting in the dry season. The clusters also indicate that the cultivation patterns slightly changed over the 16 years of the study period. The results of this study suggest that the two-stage clustering approach provides an effective tool for the analysis of MODIS LAI data when considering cultivation patterns characterized by annual and geographical differences.

Today, satellite imagery has made a major contribution to the reconstruction and spatial prediction of sensitive or complex areas such as Pendjari and W Transboundary Reserves which constitute biodiversity reservoirs and habitats for wildlife conservation. Despite the protection to which they are subject, they remain dependent on climatic hazards that can influence their stability. This study has applied remote sensing techniques, combined with climate records from the last thirty years, to analyze the past dynamics of land use and climate changes, to predict the future states of the vegetation cover of the two national parks in Benin, as well as their periphery. The methodology used remote sensing and GIS techniques that allowed the supervised classification of Landsat images of 1985, 2000 and 2015. Climatic data were combined in R software to identify the break periods for climatic parameters. Finally, the predictive vegetation cover for the year 2030 was made by combining vegetation and climate in the "Land Change Modeler" extension. Ten land use and land cover classes were obtained. These are the agglomerations, mosaics of fields and fallows, water bodies, dense forests, gallery forests, clear forests and wooded savannahs, swamp forests and shrubby wooded savannahs, saxicolous savannahs and bare ground. The natural vegetation decreased from 90.85% in 1985 to 83.54% in 2000 then to 79.56% in 2015, a decline of 11.39% over a 30-year period. The analysis of the climatic curves revealed the presence of break, meaning drought frequency. The predictive modeling revealed that land use units projected to 2030 are consistent with past trends, but with the continued expansion of fields and fallows (2%) at the expense of vegetation cover. This study will motivate many others in the field.

This study is to explore the feasibility using neural network (NN) and deep learning to diagnose three common respiratory diseases with only few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all these three diseases. They reach 90% in accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients understanding their conditions via a remote diagnosis. Unlike previous studies that focus on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-process.

In the context of freshwater ecosystems, turbidity and suspended solids play crucial roles, with their levels significantly influenced by anthropogenic activities. This study focuses on assessing and monitoring these parameters in Albufera de Valencia using Sentinel-2 imagery. The primary aim is to establish reliable estimation algorithms that can contribute to effective ecosystem management. The study calibrated and validated algorithms for estimating turbidity and suspended solids. The R783×R705/R490 model proved to have the best performance for estimating turbidity and total solids in the Albufera. However, R783/R490 obtained a higher coefficient of de-termination for the organic part, while the R705 band was selected for the inorganic part. However, to achieve better estimates of turbidity and inorganic matter, more research is needed in the future. The implications of excessive suspended solids are underscored, including the depletion of dissolved oxygen, and reduced primary productivity due to limited light penetration and habitat availability. Collaboration between disciplines such as limnology, optics and water chemistry are crucial to advance water quality estimation models in lakes and lagoons such as Albufera. By integrating expertise and approaches from these diverse fields, new knowledge can be gained and the basis for more effective management and conservation strategies can be laid.

This study investigates the intricate relationship between Venezuelan migration and urban growth in Colombia from 2018 to 2021. The study employs remote sensing data and social network metrics to uncover migration patterns and their impact on urban expansion. The methodology consists of three stages. Firstly, nighttime satellite imagery is used to analyze year-over-year urban growth in Colombia. Secondly, social network data estimates Venezuelan migration, overcoming challenges of underreporting and informal border crossings. Lastly, an econometric analysis explores the quantitative link between Venezuelan migration and urban growth, integrating socioeconomic variables to address endogeneity. The findings reveal the complex interplay of Venezuelan migration, socioeconomic factors, and urban growth. The study outlines remote sensing analysis, introducing the Anthropogenic Footprint Expansion Index (AFEI) to quantify urban growth. Facebook API data estimates migration trends and explores socioeconomic impacts on urban expansion. The analysis uncovers migration, poverty, aging, and urban population proportion as key factors affecting Colombia's urban landscape. Furthermore, the research underscores how Venezuelan migration affected short-term urban expansion pre- and post-COVID-19. Migration had a notable effect before the pandemic, but this influence waned afterward. The study highlights migration's short-term nature and emphasizes age demographics' role in medium-term dynamics.

This study is describing airborne geophysical survey, carried out in Northeastern Russia (Archangelsk region) in order to aimed at geological mapping of Grib kimberlite pipe, which is known to have strong presence of remanent magnetization. Airborne data were inverted using two techniques, the magnetization vector inversion (MVI) and a QDIK inversion, a proprietary algorithm developed at the Irkutsk State University (ISTU), which allows extracting four parameters from airborne magnetic data (magnetic susceptibility, inclination, declination and Koeniksberger ratio). The inversion results were further analyzed and verified against the known geology of the Grib kimberlite. The study allowed to better map geology of Grib kimberlite using only uninvasive (remote sensing) technology

Evaluating the performance of water indices and mapping the spatial distribution of water-related ecosystems are important for monitoring surface water resources. This is particularly the case for Ethiopia since there is limited information available on water resources development over time despite its relevance for the people and ecosystems. To address this problem, this paper evaluates the performance of seven water indices for country-scale surface water detection based on high spatial and multi-temporal resolution Sentinel-2 data, processed using the Google Earth Engine cloud computing system. Results show that the water index (WI) and automatic water extraction index with shadow (AWEIsh) are the most accurate ones to extract surface water. Comparisons are based on qualitative visual inspections and quantitative accuracy indicators. For the latter, WI and AWEIsh obtained kappa coefficients of 0.96 and 0.95, respectively, and an overall accuracy of 0.98 each. Both indices accounted for similar spatial coverages of surface waters with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia.

This paper is a review of the current state of IoT-based technologies in the healthcare field for the elderly population. The increasing population of elderly people has led to a greater demand for healthcare services to meet their unique needs. IoT-based technologies have emerged as a promising solution to address this demand. The paper analyses the current trends, challenges, and future opportunities for research in this area. It highlights how IoT-based technologies can be utilized to improve healthcare for older adults, from remote monitoring and telemedicine to fall detection and medication management. The paper also acknowledges that there are still challenges to be addressed, such as data privacy and security, but the potential benefits for older adults outweigh the challenges. The paper also examines the impact of the industrial revolution on the aging population and the corresponding increase in demand for geriatric healthcare. In summary, this paper delves into the current status of IoT-based technologies in geriatric healthcare and its future prospects, with a focus on addressing the needs of the aging population and improving their quality of life.

The increase in the production and entry of greenhouse gases into the earth's atmosphere has disturbed the balance of the environment. The signs of climate change (caused by global warming) are clearly visible and its effects are tangible. According to the United Nations, if the current trend continues, the global average temperature will increase by 3.2°C by the end of the century, which will have terrible consequences if it happens. This research aimed to investigate the effects of greenhouse gases and climate changes resulting from them on Hyrcanian forests. The Hyrcanian forests, as one of the oldest forest areas (remaining from the Paleogene era), were studied by telemetry from 2013 to 2021. The analysis of the images taken from the Landsat 8 satellite showed that during 9 years, the NDVI index decreased by 0.6 units and the average air temperature increased by 0.5°C. Although the average relative humidity has only increased by 2%, the average annual rainfall has recorded an increase of 25mm. The analysis of the statistics showed that the rains occur irregularly and are often torrential. Therefore, it is predicted that as the average temperature continues to increase, the NDVI index will further decrease, and as a result, the forest cover will become weaker and the soil will lose more water absorption power, and due to the increase in average rainfall. successive floods will occur. Therefore, soil erosion increases and the extinction and migration of plant and animal species increase significantly.

The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping, and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments.

A neglected dimension of the fire regime concept is fire patchiness. Habitat mosaics that emerge from the grain of burned and unburned patches (pyrodiversity) are critical for the persistence of a diverse range of plant and animal species. This issue is of particular importance in frequently burned tropical Eucalyptus savannas, where coarse fire mosaics have been hypothesized to have caused the recent drastic population declines of small mammals. Satellites routinely used for fire mapping in these systems are unable to accurately map fine-grained fire mosaics, frustrating our ability to determine whether declines in biodiversity are associated with local pyrodiversity. To advance this problem, we have developed a novel method (we call ‘double-differenced dNBR’) that combines the infrequent (c. bi-monthly) detailed spatial resolution Landsat with daily coarse scale coverage of MODIS and VIIRS to map pyrodiversity in the savannas of Kakadu National Park. We used seasonal Landsat mosaics and differenced Normalized Burn Ratio (dNBR) to define burned areas, with a modification to dNBR that subtracts long-term average dNBR to increase contrast. Our results show this approach is effective in mapping fine-scale fire mosaics in the homogenous lowland savannas, although inappropriate for nearby heterogenous landscapes. Comparison of this methods to other fire metrics (e.g., area burned, seasonality) based on Landsat and MODIS imagery suggest this method is likely accurate and better at quantifying fine-scale patchiness of fire, albeit it demands detailed field validation.

Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. Inpainting, dataset augmentation using artificial samples or increasing resolution of aerial imagery are only a few notable examples of utilizing GANs in remote sensing. This is due to a unique construction and training process expressed as a duel between GAN components. In the following research, GAN has been applied to enhance panchromatic images with Normalized Difference Vegetation Index (NDVI). Panchromatic orthoimagery dataset with NDVI ground-truth labels was prepared by combining RGB and NIR orthophoto. The dataset was then utilized as input for a conditional generative adversarial network (cGAN) to perform an image-to-image translation. The main goal of the neural network was to generate an artificial NDVI channel for each processed 256px × 256px patch using only information available in the panchromatic imagery. The network achieved 0.9869 ± 0.0099 SSIM, 47.1635643 ± 4.0527963 PNSR and 0.0048023 ± 0.0018756 RSME on the test set. Perceptual evaluation was performed to verify the usability of the method when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial remote sensing image contains sufficient information for NDVI estimation for various objects of urban space. Even though these results can be used to highlight areas rich in vegetation and distinguish them from urban background, there is still room for improvement in terms of accuracy of estimated values.

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement was supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often characterized by an underlying climate gradient, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated variability. To solve this limitation, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial patern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern. Both calibrations reached comparable performance of Q. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

Urban areas have very complex spatial structures. These spatial structures are primarily composed of a complex network of built environments, which evolve rapidly as the cities expand to meet the growing population’s demand and economic development. Therefore, studying the impact of spatial structures on urban heat patterns is extremely important for sustainable urban planning and growth. We investigated the relationship between surface temperature obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, at 90 m spatial resolution) on the current EOS-Terra platform and different urban components based on the classification of high-resolution QuickBird imagery. We further investigated the relationships between surface temperature and building footprint and land use information acquired from the New York City (NYC) Department of City Planning. The ASTER image reveals fine-scale urban heat patterns in the NYC metropolitan region. The dark and medium-dark impervious surfaces, along with bright surfaces, generate higher surface temperatures. Even with highly reflective urban materials, the presence of impervious materials leads to an increased surface temperature. At the same time, trees and shadows are effective in reducing urban heat. The data aggregated to the census tract reveals high-temperature clusters in Queens, Brooklyn, and the Bronx region of NYC. These clusters are associated with industrial and manufacturing areas and multi-family walk-up buildings as dominant land use. The census tracts with more trees and higher building height variability generate lower surface temperatures, consistent with shadow cast by high-rise buildings and trees. The results of this study can be valuable for urban heat island modeling on the effects of building heights variability and tree shadows on small-scale surface temperature patterns. It can also help identify the risk areas during extreme heat events to protect public health.

The scarcity of surface water resources in the dry season in the Kilinochchi district increases the demand for freshwater. Therefore, the existing groundwater resources should be managed to overcome the situation. Several authors worldwide have published studies on the delineation of potential groundwater zone. However, only a few studies addressed the delineation of potential groundwater zones in the Kilinochchi district. This study aims to delineate potential groundwater zones in Kilinochchi, Sri Lanka using integrated Remote Sensing, Geographic Information Systems, and Analytic Hierarchy Process techniques. Groundwater potential zones are demarcated for the Kilinochchi district by overlaying thematic layers: geology, geomorphology, land use/land cover, soil types, drainage density, slope, lineament, and rainfall. Saaty's scale was applied to the assigned weights of the chosen thematic layers and their features. The thematic layers were integrated into a Geographic Information System, and a weighted overlay analysis is carried out to delineate groundwater zones. Thus the resultant map is categorized into five different potential zones: very low, low, moderate, high, and very high. It was found that the very high groundwater potential zone is mainly found in the north-eastern part of the study area covering 111.26 km2. The upper north-western, middle, and eastern parts of the study area fall within the high groundwater potential zone covering about 507.74 km2. The moderate groundwater potential zones (309.89 km2) mainly occurred in the western part, and the extreme west part of the study area falls under low (207.78 km2) and very low (59.12 km2) zones. The groundwater potential map was validated with the existing seventy-nine wells, which indicated a good prediction accuracy of 81.8%. This research will help policymakers better manage the Kilinochchi district's groundwater resources and gives scope for further research into groundwater exploration in the area.

We have an unprecedented ability to map the Earth’s surface as deep learning technologies are applied to an abundance of high-frequency Earth observation data. Simple, free, and effective methods are needed to enable a variety of stakeholders to use these tools to improve scientific knowledge and decision making. Here we present a trained U-Net model that can map and delineate ground mounted solar arrays using publicly available Sentinel-2 imagery, and that requires minimal data pre-processing and no feature engineering. By using label overloading and image augmentation during training, the model is robust to temporal and spatial variation in imagery. The trained model achieved a precision and recall of 91.5% each and an intersection over union of 84.3% on independent validation data from two distinct geographies. This generalizability in space and time makes the model useful for repeatedly mapping solar arrays. We use this model to delineate all ground mounted solar arrays in North Carolina and the Chesapeake Bay watershed to illustrate how these methods can be used to quickly and easily produce accurate maps of solar infrastructure.

Extensive, but remote oil and gas fields of the United States, Canada, and Russia require the construction and operation of extremely long pipelines. Global warming and local heating effects lead to rising soil temperatures and thus a reduction in the sub-grade capacity of the soils; this causes changes in the spatial positions and forms of the pipelines, consequently increasing the number of accidents. Oil operators are compelled to monitor the soil temperature along the routes of the remoted pipelines in order to be able to perform remedial measures in time. They are therefore seeking methods for the analysis of volumetric diagnostic information. To forecast soil temperatures at the different depths we propose compiling a multidimensional dataset, defining descriptive statistics; selecting uncorrelated time series; generating synthetic features; robust scaling temperature series, tuning the additive regression model to forecast soil temperatures.

Background: In the midst of a worldwide COVID-19 pandemic music therapists previously not involved in telehealth had to develop effective remote forms of music therapy. The objective of this review was to systematically explore how music therapists previously working in-person adapted to the transfer to remote forms of therapy in the context of the coronavirus outbreak. Methods: We searched Scopus, Web of Science Core Collection, CINAHL, Medline, ProQuest Central, PubMed, EMBASE, PsycINFO and PsyARTICLES, grey literature (October 2020), and websites of professional organizations. We followed the JBI methodology for scoping reviews. Results: Out of the 194 screened texts, we included ten very heterogenous articles with an overall very low quality. Most texts described remote therapy in the form of synchronous video calls using the Internet, one paper described a concert in a patio of a residential home. We report the authors´ experience with the adaptation and activities, challenges and benefits of remote forms of therapy, recommendations of organizations, and examples and tips for online therapies. Conclusions: Music therapists have adapted the musical instruments, the hours, the technology used, the therapeutic goals, the way they prepared their clients for sessions, and other aspects. They needed to be more flexible, consult with colleagues more, and mind the client-therapist relationship's boundaries. It seems, when taken as a necessary short-term measure, online music therapy works sufficiently well. The majority of papers stated that benefits outweighed the challenges, although many benefits were directly linked with the pandemic context.

’Lockdown’ periods in response to COVID-19 have provided a unique opportunity to study the impacts of economic activity on environmental pollution (e.g. NO2, aerosols, noise, light). The effects on NO2 and aerosols have been very noticeable and readily demonstrated, but that on light pollution has proven challenging to determine. The main reason for this difficulty is that the primary source of nighttime satellite imagery of the earth is the SNPP-VIIRS/DNB instrument, which acquires data late at night after most human nocturnal activity has already occurred and much associated lighting has been turned off. Here, to analyze the effect of lockdown on urban light emissions, we use ground and satellite data for Granada, Spain, during the COVID-19 induced confinement of the city’s population from March 14 until May 31, 2020. We find a clear decrease in light pollution due both to a decrease in light emissions from the city and to a decrease in anthropogenic aerosol content in the atmosphere which resulted in less light being scattered. A clear correlation between the abundance of PM10 particles and sky brightness is observed, such that the more polluted the atmosphere the brighter the urban night sky. An empirical expression is determined that relates PM10 particle abundance and sky brightness at three different wavelength bands.

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aircraft systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of this research were to (1) evaluate three supervised classification algorithms (support vector machine, maximum likelihood, and random forest) for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAV imagery as an alternative to traditional methods of visual herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = -0.83 to -0.94; p < 0.0001) were observed between the normalized difference vegetative index (NDVI) and ground-measured herbicide injury. Therefore we conclude that VIs collected with UAS coupled with machine learning image classification, has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.

Phenology is one of the ubiquitous fingerprints of climate change on our ecosystems. Monitoring the spatiotemporal patterns of vegetation phenology is thus critical. A wide range of sensors have been used to monitor vegetation phenology. Sensor point of view and resolution can potentially impact estimates of phenology. We compared three different sensors from three different remote sensing platforms—a UAV mounted RGB camera, an under canopy, upward facing hemispherical camera with R, G and NIR capabilities, and a tower mounted RGB PhenoCam—to estimate spring phenological transition in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: 1) to compare the above- and below- canopy inference of canopy greenness (green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching under-canopy hemispherical photos with high spatial resolution (0.03 m) drone imagery to find the appropriate spatial coverage and resolution for comparison; 2) to compare how each sensor performed in estimating the temporality of the spring phenological transition. We find that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to under-canopy imagery in this system. Sensors and platforms agree within +/- 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. This work has implications for paring UAV imagery with both tower-based observation platforms, as well as plot-based studies (e.g. long-term monitoring, existing research networks, permanent plots).

Metabolic syndrome, diabetes and ischemic heart disease are among the leading causes of death and disability in Western countries. Diabetic cardiomyopathy is responsible for the most severe signs and symptoms. An important strategy for reducing the incidence of cardiovascular disease is regular exercise. Remote ischemic conditioning has some similarity with exercise, and can be induced by short periods of ischemia and reperfusion of a limb, and it can be performed in people who cannot exercise. There is abundant evidence that exercise is beneficial in diabetes and ischemic heart disease, but there is a need to elucidate the specific cardiovascular effects of emerging and unconventional forms of exercise in people with diabetes. Also, remote ischemic conditioning may be considered among the options to induce beneficial effects in these patients. The characteristics and interactions of diabetes and ischemic heart disease, and the known effects of exercise and remote ischemic conditioning in the presence of metabolic syndrome and diabetes, are analyzed in this brief review.

There are frequently interactions between active folds and major rivers (mean annual water discharges > 70 m³s^-1). The major river may incise across the fold, to produce a water gap across the fold, or a bevelling (or lateral planation) of the top of the fold. Alternatively, the major river may be defeated to produce a diversion of the river around the fold, with wind gaps forming across the fold in some cases, or ponding of the river behind the fold. Why a river incises or diverts is often unclear, though influential characteristics and processes have been identified. A new scheme for investigating fold-river interactions has been devised, involving a short description of the major river, climate, and structural geology, and 13 characteristics of river and fold geomorphology: 1) Channel width at location of fold axis, w, 2) Channel-belt width at location of fold axis, cbw, 3) Floodplain width at location of fold axis, fpw, 4) Channel sinuosity, Sc, 5) Braiding index, BI, 6) General river course direction, RCD, 7) Distance from fold core to location of river crossing, C-RC, 8) Distance from fold core to river basin margin, C-BM, 9) Width of geological structure at location of river crossing, Wgs, 10) Estimate of erosion resistance of surface sediments/rocks and deeper sediments/rocks in fold, ERs, ERd, 11) Channel water surface slope at location of fold axis, s, 12) Average channel migration rate, Rm, 13) Estimate of fold total uplift rate, TUR. The first 10 geomorphological characteristics should be readily determinable for nearly all major rivers using widely available satellite imagery and fine scale geological maps. The last 3 characteristics should be determinable for most major rivers where other data sources are available. This study demonstrates the methodology of this scheme, using the example of the major rivers Karun and Dez interacting with active folds in the foreland basin tectonic setting of lowland south-west Iran. For the rivers Karun and Dez (mean annual water discharges 575 m³s^-1 and 230 m³s^-1, respectively), it was found that geomorphological characteristics Nos. 2, 3 and 7 had statistically significant differences (p-value ≤ 0.05) between the categories of river incision across a fold and river diversion around a fold. For river incision, at the fold axis, channel-belt width was always < 2.7 km, and floodplain width was generally (80 % of cases) < 5.7 km; whereas for river diversion, at the projection of the fold axis, these two characteristics had a wide range of values. For river incision, the distance from the fold core to the location where the river channel crossed the fold axis, was generally (80 % of cases) ≤ 8.5 km; whereas for river diversion, this distance was always > 22 km. Since it is highly likely that different characteristics will be important for other major rivers interacting with other folds, it is recommended that this scheme is now used to investigate a variety of major rivers from across the globe. By comparing the same parameters for different major rivers, a better understanding of fold-river interactions should be achieved.

Land degradation monitoring and assessment in the Sahel zone has relied substantially on temporal trends of remote sensing-based vegetation indices, which are proxies for the bioproductivity of the land. However, prior studies have shown that negative or positive trends in bioproductivity are not necessarily associated with degradation or improvement of land condition. In this short communication, while acknowledging the contributions of remote sensing-based indices and global-scale datasets to dismantling an outdated desertification narrative, we argue that local land users have much to contribute to our understanding of land degradation, and particularly to ensuring that scientific assessments of degradation capture variables relevant to them. We used the participatory photo elicitation method in three sites in the Senegalese Ferlo in order to elicit local pastoralists’ perspectives on land degradation and identify the indicators that they use to characterize pasture quality, while empowering them to lead the discussion. The discussion revealed indicators far beyond bioproductivity, including livestock performance as well as composition and quality of the herbaceous and woody vegetative cover, invasive species, soil quality and water availability. We found that the pastoralists’ knowledge and interest in the issue could potentially be harnessed more systematically, and at larger scales, in order to build a spatially explicit field-based knowledge base of land degradation complementary to remote sensing-based maps of trends in bioproductivity. Such a dataset could serve as a standalone product or as a reference dataset for development and validation of remote sensing-based indicators.

Several long-term monitoring of aerosol datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra/Aqua, Multi-angle Imaging SpectroRadiometer (MISR), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) are used to derive the dust aerosol optical depth (DOD) in Central Asia based on the Angstrom exponent parameter and/or the particle shape. All sensors agree very well on the interannual variability of DOD. The seasonal analysis of DOD and dust occurrences identified the largest dust loading and the most frequent dust occurrence in the spring and summer, respectively. No significant trend is found during the research period in terms of both DOD and the dust occurrence. Further analysis of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) aerosol products on a case-by-case basis in most dust months of 2007 suggested that the vertical structure is varying in terms of the extension and the dust loading from one event to another, although dust particles of most episodes have similar physical characteristics (the particle shape and size). Our analysis on the vertical structure of dust plumes, the layer-integrated color ratio and depolarization ratio indicates a varied climate effect (e.g., the direct radiative impact) by mineral dust, dependent on the event being observed in Central Asia.

The Kabul River basin (KRB) of Afghanistan, a lifeline of around 10 million people, has multiplicity of governance, management and development related challenges leading to inequity, inadequacy and unreliability of irrigation water distribution. Prior to any uplifting intervention, there is a need to evaluate the performance of irrigation system on long term basis to identify the existing bottlenecks. Although there are several indicators used for the performance evaluation of the irrigation schemes, but we used the coefficient of variation (CV) of actual evapotranspiration (ETa) in space (basin, sub-basin, and provincial level), relative evapotranspiration (RET) and temporal CV of RET to assess the equity, adequacy and reliability of water distribution respectively, from 2003 to 2013. The ETa was estimated through surface energy balance system (SEBS) algorithm and the ETa estimates were validated using advection aridity (AA) method with R2 value of 0.81 and 0.77 at Nawabad and Sultanpur stations respectively. The global land data assimilation system (GLDAS) and moderate-resolution imaging spectroradiometer (MODIS) products were used as main inputs to the SEBS. Results show that mean seasonal sub-based RET values during summer (May – September) (0.37 ± 0.06) and winter (October – April) (0.40 ± 0.08) are below the target values (RET ≥0.75) during 2003-2013. The CV of mean ETa within sub-basins and provinces for the entire study period has equitable distribution of water from October-January (0.09±0.04) whereas the highest inequity (0.24±0.08) in water distribution is during early summer. The range of the CV of mean ETa (0.04-0.06) on monthly and seasonal basis shows the unreliability of water supplies in several provinces or sub-basins. The analysis of temporal CV of mean RET highlights unreliable water supplies across the entire basin. The maximum ETa during the study period was estimated for Shamal sub-basin (552±43mm) while among provinces Kunar experienced the highest ETa (544±39mm). This study highlights the dire need for interventions to improve the irrigation performance in time and space. The proposed methodology can be used as a framework for monitoring and implementing the water distribution plans in future.

Wetlands are among the most bio-diverse and highest productivity ecosystems on earth, making their monitoring a high priority to conservation, protection and management interests. Although visual interpretation of satellite images is generally precise for monitoring wetlands, recent works have emphasized computerized classification methods because of the reduction in analyst time. However, it is difficult to automatically identify wetland solely based on spectral characteristics due to the complexity of wetland ecosystems. The ability to extract wetland information rapidly and accurately is the basis and the key to wetland mapping at a large scale. Here we propose an operational method to map China wetlands based on Landsat TM data and ancillary data. On the basis of theoretical analysis of wetland automatic classification, we developed a revised multi-layer wetland classification scheme and a rule-based classification model. In the latter, supervised classification (SVM and decision tree) and unsupervised classification (ISODATA) methods were tested. Four Landsat TM images, representing various wetland eco-regions in China (i.e. the Sanjiang Plain in the northeast China, the North China Plain, the Zoige Plateau in the southwest China and the Pearl River Estuary in southeast China), were automatically classified. The overall classification accuracies were 86.57%, 96.00%, 84.51% and 88.30%, respectively, which we considered to be satisfactory accuracy. Our results indicate that issues such as the resolution of geographic data and the understanding of wetland samples should be carefully addressed in the future.

The use of haptic devices in rehabilitation of impaired limbs has become rather popular, given the proven effectiveness in promoting recovery. In a standard framework, such devices are used in rehabilitation centers, where patients interact with virtual tasks, presented on a screen. To track their sessions, kinematic/dynamic parameters or performance scores are recorded. However, as Internet access is now available at almost every home, and in order to reduce the hospitalization time of the patient, the idea of doing rehabilitation at home is gaining wide consent. Medical care programs can be synchronized with the home rehabilitation device; patient data can be sent to the central server that could redirect to the therapist laptop (tele-healthcare). The controversial issue is that the recorded data do not actually represent the clinical conditions of the patients according to the medical assessment scales, forcing them to frequently undergo clinical tests at the hospital. To respond to this demand, we propose the use of a bilateral master/slave haptic system that could allow the clinician, who interacts with the master, to assess remotely and in real time the clinical conditions of the patient that uses the home rehabilitation device as the slave. In this paper, we describe a proof of concept to highlight the main issues of such an application, limited to one degree of freedom, and to the measure of the stiffness and range of motion of the hand.

Landslides in the hilly regions of Al‐Madinah Al‐Munawarah are a natural hazard that endangers both infrastructure and human lives. The objective of this work is to create maps that show the likelihood of landslides occurring on the Al Figrah Road, which is a vital road connects the cities of Al‐Madinah and Yanbu. This will be done using the analytical hierarchy process (AHP) approach. The landslide susceptibility maps have been created by taking into account various parameters such as elevation, slope, aspect, drainage density, lithology, soil, and precipitation. Assess the significance of these thematic layers in relation to the frequency of landslides in the research area based on historical landslide data and field verification, in order to allocate suitable weights. The sensitivity maps of the study region delineated areas with low risk (93,552,333 m2), moderate risk (271,180,722 m2), and high risk (33,053,839 m2). 28.63% of the Al Figrah total Road is classified as low risk, 48.09% as moderate risk, and 23.28% as high risk. A total of 10 landslides were identified in areas with steep slopes and denudational hills, confirming the findings of the AHP analysis conducted in high‐risk zones. The integrity of these slide zones was greatly impacted by the presence of joints, faults, foliation, and shear zones within igneous and metamorphic rocks. Examining the susceptibility of landslides in this region enhances our understanding of landslides on other steep routes in Saudi Arabia.

Climate variability influences plant growth. With the variation in atmospheric CO2 concentration leading to global warming, it is urgent to monitor the performance of plant ecosystems for optimal carbon sequestration. Net primary productivity (NPP) is the perfect measurement tool as it measures the net carbon flux between the atmosphere and green plants and the factors that affect it. This study applied remote sensing techniques, specifically one of the radiation use efficiency models; the CASA model (Carnegie-Ames-Stanford approach) to assess the spatio-temporal dy-namics of NPP in Togo from 1987 to 2022 and the climatic parameters that influence it. The annual average NPP over the 36 years of the study is 4565.31 Kg C ha⁻¹. Variability is observed over the years with extremes in 2017 (6312.26 Kg C ha⁻¹) and 1996 (3394.29 Kg C ha⁻¹). Natural formations identified as high-production areas saw their productivity increase between 2000 and 2022. The interaction between climate change and land use changes negatively influences the variation of Total Production (PT) between 2000 and 2022, while individually, these parameters positively influence the variation of NPP (58.28% and 188.63%). The correlation result is positive and higher between NPP and light use efficiency (LUE) (r² = 0.75). Actual evapotranspiration also shows a positive correlation with NPP (r² = 0.43). A positive but weak correlation is observed between NPP and precipitation, potential evapotranspiration (r² = 0.20; 0.10 respectively). Temperatures have almost no correlation with NPP (r² = 0.5). Climatic parameters as a whole under the LUE banner influence NPP more. This study helps understand ecosystem performance in the context of To-go's commitments to reduce greenhouse gas emissions and combat climate change.

Accurate fire severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where vegetation experiences above-ground combustion, but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush fire severity and post-burn coppicing/regeneration events of woody vegetation in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.5% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a &gt;50% probability of woody vegetation regrowth in high severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas.

Digital solutions in the field of restorative neurology offer significant assistance, enabling patients to engage in rehabilitation activities remotely. This research introduces ReMoVES, an Internet of Medical Things (IoMT) system delivering telemedicine services specifically tailored for Multiple Sclerosis rehabilitation, within the overarching framework of the STORMS project. The ReMoVES platform facilitates the provision of a rehabilitative exercise protocol, seamlessly integrated into the Individual Rehabilitation Project, curated by a multidimensional medical team operating remotely. This manuscript delves into the second phase of the study, elucidating the technology employed, the outcomes achieved, and the practical, professional, and academic implications. The STORMS initiative, as the genesis of digital telerehabilitation solutions, aims to enhance the quality of life for Multiple Sclerosis patients.

Introduction: Rural Americans face significant health disparities due to limited access to healthcare services. Telehealth has emerged as a promising solution to mitigate these disparities by facilitating remote access to medical care. This paper comprehensively reviews the advantages, challenges, and implementation strategies of telehealth in rural areas.Method: A narrative review methodology was employed to synthesize existing literature and case studies on telehealth in rural settings. Databases such as PubMed, Google Scholar, and JSTOR were searched for relevant studies published in the last three decades. Keywords such as "telehealth," "rural health," and "healthcare disparities" guided the search process. The review focused on studies assessing the impact of telehealth on healthcare access, cost-effectiveness, and quality of care in rural areas.Results: The review findings indicate that telehealth significantly enhances healthcare access by reducing travel distances, increasing availability of specialist consultations, and promoting patient engagement. It improves chronic disease management and patient satisfaction by enabling convenient access to healthcare services at home. Despite initial setup costs and challenges such as broadband access and regulatory issues, telehealth demonstrates long-term cost savings through reduced travel expenses and improved healthcare efficiency. Quality of care is enhanced through improved communication between healthcare providers and patients, leading to better treatment adherence and health outcomes.Conclusion: Telehealth represents a transformative approach to address healthcare disparities in rural areas. It offers a cost-effective solution to improve access to healthcare services, enhance patient outcomes, and reduce healthcare costs in the long run. However, successful implementation requires addressing infrastructure challenges, refining reimbursement policies, and ensuring regulatory support. Future efforts should focus on expanding broadband infrastructure, refining policy frameworks, and conducting further research to optimize telehealth's impact on rural healthcare delivery.

Researchers need to delineate the shoreline for different applications with no access to costly resources such as topographic maps and high-resolution satellite images. With the increase of open source data, this study aims to present a methodology to use open source data in the best possible way to map the shoreline. Several methods have been tested using open source remote sensing data (Landsat and Aster), such as supervised classification, unsupervised classification, manual mapping, and by applying some spectral indices, among others. The accuracy of the extracted shoreline data was verified using high-resolution open database images (such as Google Earth basemap). The results showed that the manually mapped shoreline through applying spectral index (green- near infrared/green+ near infrared) is the most accurate, although it remains important to modify it using high-resolution images of open databases. Open-source data showed acceptable accuracy in mapping the shoreline.

Artisanal and Small-scale Gold Mining (ASGM) has caused several environmental impacts, resulting in significant siltation of water bodies due to the deposition of sediments on river banks. Based on this perspective, this study aims to investigate the water bodies and regions most impacted by mining activities, especially in relation to the increase in the Total Suspended Solids (TSS) caused by ASGM, focusing on the territories of Suriname and French Guiana, over the period from 2017 to 2023, through the creation of an algorithm in Google Earth Engine. The research also aims to map and describe active mining in this region using the Classification and Regression Tree (CART) method, which achieved an overall accuracy of 82% and a kappa index of 0.77. The results reveal that from 2017 to 2024 there was an increase of 148.09 km² of mining, with an average increase in TSS of up to 167 mg/l in water bodies most affected by mining activities. Finally, the continued importance of using remote sensing technologies, such as GEE, together with innovative methodological approaches, to monitor and manage natural resources in a sustainable manner is highlighted.

Thoracic aortopathy encompasses a spectrum of conditions affecting the aorta, ranging from aneurysms to dissections, posing significant challenges in management and care. In recent years, remote care, facilitated by telemedicine and digital health technologies, has emerged as a promising approach to overcome barriers in delivering timely and efficient healthcare services. This paper explores the practical considerations and challenges associated with implementing remote care strategies in the management of thoracic aortopathy in India with the largest population in the world. By evaluating existing literature and drawing upon clinical experiences, we discuss the potential benefits, limitations, and best practices for incorporating remote care into the management of patients with thoracic aortopathy. Additionally, we address important factors such as patient engagement, technology integration, privacy concerns, and regulatory requirements to optimize the delivery of remote care in this specialized field.

We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from Unmanned Aerial Vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood utilizing calculated masks over each component of the LAB colorspace, as well an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying the hysteresis thresholding segmentation. The proposed method is tested and compared with variations, and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-Score, respectively. The results and computational efficiency of the proposed method show that it is suitable for on board data execution and decision-making during UAVs flight.

Environmental monitoring programs rely on periodic spot sampling at specific locations, followed by laboratory analysis, aiming at the evaluation of water quality at a catchment scale. For this purpose, automatic telemetric stations for specific parameters have been installed by the Institute of Marine Biological Resources and Inland Waters of Hellenic Centre for Marine Research (IMBRIW-HCMR) within several Greek rivers and lakes, providing continuous and temporal monitoring possibilities. Nevertheless, the employment of unmanned surface vehicles (USV) equipped with integrated sensors represents a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The USVs have been deployed by Athens Water and Sewerage Company (EYDAP) as a cost-effective tool for the environmental monitoring of surface water bodies of interest, with emphasis on the spatial fluctuations of chlorophyllα, electrical conductivity, dissolved oxygen and pH, observed in Koumoundourou Lake and rivers Acheloos, Asopos and Kifissos. The effectiveness of an innovative heavy metal (HM) system installed in the USV for thein situ determination of copper and lead was also evaluated herewith. The results obtained demonstrate that both the timely detection of potential pollution from both anthropogenic activities and natural processes as well as the monitoring of inland waters are made feasible through the employment of USV.

This paper shows the firsts results of a methodological approach in which Remote Sensing technologies (Satellite imagery and LiDAR) combined with Least Cost Path algorithm on GIS are being tested like tools to overcome locally-based interpretations about populational patterns and communication routes during Flavian municipality in the Comarca de El Condado region. The identification of fourteen hypothetical communication routes between already known archaeological sites thanks to a recent archaeological prospection, and the confirmation of three sections of them and a new archaeological site demonstrate that the combined use of the aforementioned technics has a very useful to delve more deeply, more widely and at a variety of different scales into the relation between the twelve known/selected Early Roman Empire sites and the surrounding landscape in the right bank of Guadalén River. It is expected that, in next phases, this methodological approach will allow us to go deeper in analysing and understanding spatial distribution, exploitation, and interrelation between different Early Roman Empire sites, helping us to fill the aforementioned gaps in our understanding of the archaeological continuum in the region reducing time and costs.

Indigenous Peoples in Canada have shown great strength and resilience in maintaining their cultures and ways of life to date in the face of settler colonialism. Centering the water crises within Indigenous sovereignty and self-determination, we explore the impacts these crises are having on community members. Particularly, the continuous failure of the Canadian government to end the water crises in remote Indigenous communities, Star Blanket Cree Nation, is investigated in this paper. What implications have these water governance gaps had on Indigenous Peoples’ sovereignty and self-determination? We adopted an Indigenist theoretical framework to guide the study. Additionally, a community-based participatory research approach was adopted. To achieve our research goals of investigating the implications of the current water crises for Indigenous sovereignty and self-determination, specific methods of sharing circles were used to gather knowledge from community members, Elders, and knowledge keepers. The research findings strongly highlight the strength and resilience shown by remote Indigenous communities in the face of the current water crises and continuous government failure. Solving the current water crises will involve remote Indigenous communities taking charge of their own water governance through Indigenous-led water governance systems. Additionally, taking steps to rebuild trust through genuine reconciliation will be key. Therefore, listening to remote Indigenous communities and taking collaborative action is fundamental.

The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. LiDAR was initially developed to integrate laser-focused imaging with the capability to determine distances through the measurement of signal return times, utilizing suitable sensors and data acquisition electronics. Nowadays, these sensors have transformed into robust instruments, offering novel opportunities for mapping terrain, canopy heights, and estimating above-ground biomass (AGB) across local to regional scales. This work aims to analyze the scientific impact of these sensors on large-scale for-est mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. In this way, a worldwide bibliometric analysis of this topic was carried out based on up to 412 publications in-dexed in the Scopus database during the period 2004-2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commis-sioning of two new LiDAR space missions: Ice, Cloud and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions are providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was "Remote Sensing," and among the leading contributors, the top five countries in terms of publica-tions were the USA, China, the UK, France, and Germany. In the realm of prominent research in-stitutions, France boasted six, the USA had four, China had three, while the UK and Canada each had one. The upward trajectory in the number of publications recorded from 2004 to 2022 catego-rizes the subject under investigation as a highly trending research topic, particularly within the context of enhancing the administration of forest resources and engaging in global climate treaty frameworks mandating the surveillance and reporting of carbon stocks in forests. The recent launch in August 2022 of the Terrestrial Ecosystem Carbon Monitoring Satellite (TECMS; China State Administration of Forestry and Grassland), along with the planned launch in the coming years of up to three new space sensors, such as the Multi-footprint Observation LiDAR and Im-ager (Japan Aerospace Exploration Agency), the BIOMASS P-band Synthetic Aperture Radar (SAR) (European Space Agency), and the LiDAR Surface Topography (LIST; NASA), will greatly contribute to expanding the ability to map and monitor forest systems at very large scales. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years.

Anthropogenic disturbances interacting with natural disturbances, ecological and socio-economic factors can provoke forest degradation and modulate ecosystem resilience. In Haiti, protected areas recently created to reduce forest loss, require studies about forest dynamics and land use to assure the development of sustainable management policies. We combine field interview with Landsat satellite images of the Macaya National Park collected between 1985 and 2021 to investigate: 1) how much of the original forest area has been converted into agricultural or logged area since 1985; and 2) how do elevation and slope influence the expansion of human-induced land-use changes. Our results indicate an increase of 11.36% in forest cover and a reduction of 75.34% in agriculture class cover, despite the passage of seven hurricanes in the region. Forest recovery was apparently unrelated to elevation and slope, and likely reflected the implementation of new environmental laws and policies with the creation of the Macaya National Park in 1983. Restoration and protection programs contributed to ensure access to financial resources, technical assistance and new technology, thus promoting a shift into a more resilient forest system. This study brings hope for forest conservation in Haiti by showing that positive results can be achieved through inclusive collaboration.

The advent of remote work has ushered in a new era of economic transformation, redefining the dynamics of local economies, and unleashing the multiplier effect on a national scale. This abstract explores the profound implications of remote work on local economies, dissecting how the multiplier effect operates within a nation's economic landscape. The multiplier effect is a fundamental economic concept that highlights how changes in spending and investment can create a ripple effect, leading to a magnified impact on an economy. This concept gains new significance in the context of remote work, as it triggers a chain reaction of economic activities that radiate from remote workers to their local communities. The increased flexibility and reduced geographical constraints of remote work have reshaped the allocation of resources. The continued reliance on remote work may lead to a reduction in demand for commercial real estate in urban centers, necessitating adaptive urban planning strategies. In conclusion, the rise of remote work has initiated a transformative era for local economies, driven by the multiplier effect. By amplifying the impacts of spending and investment, remote work fosters job diversification, innovation, and infrastructure development within communities. As remote work continues to reshape the landscape of work and life, understanding and harnessing its multiplier effect remains paramount for policymakers, businesses, and local communities alike. The multiplier effect is a concept in economics that illustrates how changes in spending can lead to larger impacts on a nation's economy through a chain reaction of economic activities. It demonstrates that an initial injection of spending, whether from government expenditure, investment, or consumption, doesn't just have a one-time impact. Instead, it sets off a series of interconnected spending and income-generation processes that result in a larger overall increase in economic activity. t's important to note that the multiplier effect can work in reverse as well. If there's a decrease in spending, it can lead to a decrease in economic activity and income through the same chain reaction process. This concept highlights the interconnected nature of economic activities and emphasizes the significance of changes in spending patterns on a nation's overall economic health. The magnitude of the multiplier effect is influenced by factors such as the marginal propensity to consume (the proportion of additional income that households spend), leakages (savings and taxes that reduce the multiplier effect), and the extent to which additional spending creates additional production capacity within the economy. Understanding the multiplier effect is essential for policymakers, as it helps them gauge the potential impacts of changes in fiscal and monetary policies on the economy.

The rapid growth of urban areas is a major challenge facing cities around the world. This growth can have a significant impact on the local climate, leading to higher temperatures and other changes. In desert climates, the effects of urban expansion can be particularly pronounced. This study investigated the impact of urban expansion on land surface temperature (LST) in Baghdad, Iraq. Notably, this study employs a sophisticated artificial intelligence method known as Random Forest for Land Use Land Cover (LULC) classification, utilizing three Landsat images spanning the temporal spectrum from 1985 to 2021 to meticulously monitor land use transformations and associated LST variations. The results showed that vegetated areas declined by 46.8% during the study period, while built-up areas increased by 124.7%. This decline in vegetation was accompanied by an increase in LST, with bare soil recording the highest temperatures. The study also found that LST has a strong inverse relationship with vegetation and moisture. This means that areas with more vegetation and moisture tend to have lower LSTs. These findings suggest that urban expansion can lead to higher LSTs in desert climates, which can have implications for the health and wellbeing of residents. The study has important implications for urban planners and policymakers in Baghdad and other cities in desert climates. By identifying the main factors that control LST, the study provides insights into strategies for mitigating the effects of urban expansion on temperature.

In this study, we investigated water transparency estimation models in the hypertrophic lagoon of the Albufera of Valencia using Sentinel-2 images. Water transparency, a crucial environmental indicator, was assessed via Secchi disk depth (ZSD) measurements. Three optical models (R490/R560, R490/R705, R560/R705) were explored to establish a robust algorithm for ZSD estimation. Through extensive field sampling and laboratory analyses, weekly data spanning 2018 to 2023 were collected, including water transparency, temperature, conductivity, and chlorophyll-a concentration. Remote sensing imagery from the Sentinel-2 mission was employed, and images were processed using SNAP software. The R560/R705 model, calibrated for turbid lakes, emerged as the most suitable. The algorithm's calibration was validated with high correlation coefficients (R2) in both calibration (0.6149) and validation (0.916) phases, demonstrating the model's accuracy in estimating ZSD. This new algorithm significantly outperformed a previous approach, highlighting the importance of tailoring algorithms to specific water body characteristics. The study contributes to improved water quality assessment and resource management, underscoring the value of remote sensing in environmental research.

Oriented object detection is a challenging task in scene text detection and remote sensing image analysis, which has attracted extensive attention in recent years with the development of deep learning. Currently, mainstream oriented object detectors are based on preset anchor boxes. This method increases the computational load of the network and cause a large amount of anchor box redundancy. To solve this problem, we propose anchor-free oriented object detection based on the Gaussian centerness(AOGC), a single-stage anchor-free detection method. Our method uses contextual attention FPN(CAFPN) to obtain the contextual information of the target. Then we design a label assignment method for oriented objects. Finally, we develop a Gaussian kernel-based centerness branch, which can effectively determine the significance of different anchors. AOGC achieves mAP of 74.30% on the DOTA-1.0 datasets and 89.80% on the HRSC2016 datasets, respectively. AOGC exhibits superior performance to other methods in oriented anchor-free object detection methods.

Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions. The article begins by introducing the sources and processing methods of RS data, which serve as the foundation for subsequent analysis. Moreover, it highlights the techniques and methodologies employed for lithological classification in vegetated areas. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact accurate lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to enhance classification accuracy and the exploration of novel RS technologies and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.