Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics.

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment's findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4-5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

Marine litter weight estimation from UAV imagery: Three potential methodologies to advance macrolitter reports.

In the context of marine litter monitoring, reporting the weight of beached litter can contribute to a better understanding of pollution sources and support clean-up activities. However, the litter scaling task requires considerable effort and specific equipment. This experimental study proposes and evaluates three methods to estimate beached litter weight from aerial images, employing different levels of litter categorization. The most promising approach (accuracy of 80 %) combined the outcomes of manual image screening with a generalized litter mean weight (14 g) derived from studies in the literature. Although the other two methods returned values of the same magnitude as the ground-truth, they were found less feasible for the aim. This study represents the first attempt to assess marine litter weight using remote sensing technology. Considering the exploratory nature of this study, further research is needed to enhance the reliability and robustness of the methods.

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing.

Semantic segmentation of Remote Sensing (RS) images involves the classification of each pixel in a satellite image into distinct and non-overlapping regions or segments. This task is crucial in various domains, including land cover classification, autonomous driving, and scene understanding. While deep learning has shown promising results, there is limited research that specifically addresses the challenge of processing fine details in RS images while also considering the high computational demands. To tackle this issue, we propose a novel approach that combines convolutional and transformer architectures. Our design incorporates convolutional layers with a low receptive field to generate fine-grained feature maps for small objects in very high-resolution images. On the other hand, transformer blocks are utilized to capture contextual information from the input. By leveraging convolution and self-attention in this manner, we reduce the need for extensive downsampling and enable the network to work with full-resolution features, which is particularly beneficial for handling small objects. Additionally, our approach eliminates the requirement for vast datasets, which is often necessary for purely transformer-based networks. In our experimental results, we demonstrate the effectiveness of our method in generating local and contextual features using convolutional and transformer layers, respectively. Our approach achieves a mean dice score of 80.41%, outperforming other well-known techniques such as UNet, Fully-Connected Network (FCN), Pyramid Scene Parsing Network (PSP Net), and the recent Convolutional vision Transformer (CvT) model, which achieved mean dice scores of 78.57%, 74.57%, 73.45%, and 62.97% respectively, under the same training conditions and using the same training dataset.

Morphometric analysis of Halda River basin, Bangladesh, using GIS and remote sensing techniques.

GIS and remote sensing techniques were effectively used to analyse the morphometric parameters including linear, geometric, basin texture (aerial) and relief aspects of the Halda River Basin, Bangladesh. Along with measuring the morphometric parameters using predetermined formulas, advanced geo-computing tools of spatial analysis, cartography, math, geoprocessing and geometric analysis were employed to carry out the spatial distribution of selected parameters, especially aerial parameters. The linear aspect indicates the basin is six-order and oval-shaped. The bifurcation ratio (4.03) and relevant parameters indicate the moderate effect of geology and structural control is evident. The mean stream length (1.27) and Rho value (ranges between 0.11 and 0.20) indicate high runoff in steep areas and hydrologic storage capacity in flat areas. The stream frequency (0.83), drainage density (1.22), drainage intensity (0.68), infiltration ratio (1.02), length of the overland flow (0.41), and constant of channel maintenance (0.82) indicate the presence of moderate hard rock, less structural disturbances and moderate to high surface runoff in the basin. Basin relief (489 m), relative relief (2.02), ruggedness number (400), Melton's ruggedness number (12.43), and mean slope (9.33%) indicate the potential of high erosion and material transfer. The spatial distribution of selected aerial aspects significantly correlated to elevation and slope. The hierarchical pattern and spatial distribution of the morphometric parameters indicate areas with high slopes and lower-order streams have a high potential to be affected by soil erosion, landslides and flash floods, elsewhere, the areas with low slopes are prone to short-duration riverine floods. The research findings will help policymakers for integrated river basin management, agricultural development, and water management. In addition, researchers of morphohydrological, geological and climatological research will be beneficiary.

Factors That Influence Successful Adoption of Real-Time Location Systems for Use in a Dementia Care Setting: Mixed Methods Study.

BACKGROUND: Technology has been identified as a potential solution to alleviate resource gaps and augment care delivery in dementia care settings such as hospitals, long-term care, and retirement homes. There has been an increasing interest in using real-time location systems (RTLS) across health care settings for older adults with dementia, specifically related to the ability to track a person's movement and location. OBJECTIVE: In this study, we aimed to explore the factors that influence the adoption or nonadoption of an RTLS during its implementation in a specialized inpatient dementia unit in a tertiary care rehabilitation hospital. METHODS: The study included data from a brief quantitative survey and interviews from a convenience sample of frontline participants. Our deductive analysis of the interview used the 3 categories of the Fit Between Individuals, Task, and Technology framework as follows: individual and task, individual and technology, and task and technology. The purpose of using this framework was to assess the quality of the fit between technology attributes and an individual's self-reported intentions to adopt RTLS technology. RESULTS: A total of 20 health care providers (HCPs) completed the survey, of which 16 (80%) participated in interviews. Coding and subsequent analysis identified 2 conceptual subthemes in the individual-task fit category, including the identification of the task and the perception that participants were missing at-risk patient events. The task-technology fit category consisted of 3 subthemes, including reorganization of the task, personal control in relation to the task, and efficiency or resource allocation. A total of 4 subthemes were identified in the individual-technology fit category, including privacy and personal agency, trust in the technology, user interfaces, and perceptions of increased safety. CONCLUSIONS: By the end of the study, most of the unit's HCPs were using the tablet app based on their perception of its usefulness, its alignment with their comfort level with technology, and its ability to help them perform job responsibilities. HCPs perceived that they were able to reduce patient search time dramatically, yet any improvements in care were noted to be implied, as this was not measured. There was limited anecdotal evidence of reduced patient risk or adverse events, but greater reported peace of mind for HCPs overseeing patients' activity levels.

Estimating deep soil water depletion and availability under planted forest on the Loess Plateau, China.

Deep soil water (DSW) plays a pivotal role in tree growth, susceptibility to drought-induced mortality, and belowground carbon and nutrient cycling. Assessing DSW depletion is essential for evaluating the resilience and sustainability of planted forests. But, due to the poor accessibility of deep soil layers, little is known about large scale DSW depletion. In this study, we leverage the concept that "plants are reliable indicators of deep soil water" to estimate DSW depletion in planted forests within the arid and semi-arid regions of the Chinese Loess Plateau (CLP). Our approach involves establishing a model that correlates forest age with DSW depletion. We then employ this model to estimate DSW depletion across the region, utilizing readily available data on the distribution of forest age and utilize the boundary models to consider the variability of DSW depletion estimated with forest age. Our results indicate that the model effectively estimates DSW depletion in planted forests, demonstrating a strong fit with an R2 of 0.71 and a low root mean square error (RMSE) of 332 mm. Notably, a substantial portion of the planted forest areas on the CLP has experienced DSW depletion from 800 mm to 1600 mm, and totaling 2.41 × 1010 m3 DSW depletion from 1995 to 2020 based on the general model. However, the available DSW in the existing planted forests on the CLP is estimated at only 1.73 × 1010 m3 by 2038. This suggests that there is potential risks and unsustainability for further afforestation efforts and carbon sequestration on the CLP under the current continuous afforestation measures. Our study holds significant implications for sustainable regional ecological management and quantifying water resources for carbon trading through afforestation.

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia.

Evaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer's accuracy, and user's accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country's reservoirs, which are important for the country's energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images.

Rapid and continuous advancements in remote sensing technology have resulted in finer resolutions and higher acquisition rates of hyperspectral images (HSIs). These developments have triggered a need for new processing techniques brought about by the confined power and constrained hardware resources aboard satellites. This article proposes two novel lossless and near-lossless compression methods, employing our recent seed generation and quadrature-based square rooting algorithms, respectively. The main advantage of the former method lies in its acceptable complexity utilizing simple arithmetic operations, making it suitable for real-time onboard compression. In addition, this near-lossless compressor could be incorporated for hard-to-compress images offering a stabilized reduction at nearly 40% with a maximum relative error of 0.33 and a maximum absolute error of 30. Our results also show that a lossless compression performance, in terms of compression ratio, of up to 2.6 is achieved when testing with hyperspectral images from the Corpus dataset. Further, an improvement in the compression rate over the state-of-the-art k2-raster technique is realized for most of these HSIs by all four variations of our proposed lossless compression method. In particular, a data reduction enhancement of up to 29.89% is realized when comparing their respective geometric mean values.

Integrated Management of the Cotton Charcoal Rot Disease Using Biological Agents and Chemical Pesticides.

Charcoal rot disease (CRD), caused by the phytopathogenic fungus Macrophomina phaseolina, is a significant threat to cotton production in Israel and worldwide. The pathogen secretes toxins and degrading enzymes that disrupt the water and nutrient uptake, leading to death at the late stages of growth. While many control strategies were tested over the years to reduce CRD impact, reaching that goal remains a significant challenge. The current study aimed to establish, improve, and deepen our understanding of a new approach combining biological agents and chemical pesticides. Such intervention relies on reducing fungicides while providing stability and a head start to eco-friendly bio-protective Trichoderma species. The research design included sprouts in a growth room and commercial field plants receiving the same treatments. Under a controlled environment, comparing the bio-based coating treatments with their corresponding chemical coating partners resulted in similar outcomes in most measures. At 52 days, these practices gained up to 38% and 45% higher root and shoot weight and up to 78% decreased pathogen root infection (tracked by Real-Time PCR), compared to non-infected control plants. Yet, in the shoot weight assessment (day 29 post-sowing), the treatment with only biological seed coating outperformed (p < 0.05) all other biological-based treatments and all Azoxystrobin-based irrigation treatments. In contrast, adverse effects are observed in the chemical seed coating group, particularly in above ground plant parts, which are attributable to the addition of Azoxystrobin irrigation. In the field, the biological treatments had the same impact as the chemical intervention, increasing the cotton plants' yield (up to 17%), improving the health (up to 27%) and reducing M. phaseolina DNA in the roots (up to 37%). When considering all treatments within each approach, a significant benefit to plant health was observed with the bio-chemo integrated management compared to using only chemical interventions. Specific integrated treatments have shown potential in reducing CRD symptoms, such as applying bio-coating and sprinkling Azoxystrobin during sowing. Aerial remote sensing based on high-resolution visible-channel (RGB), green-red vegetation index (GRVI), and thermal imaging supported the above findings and proved its value for studying CRD control management. This research validates the combined biological and chemical intervention potential to shield cotton crops from CRD.

Satellite-enabled Enviromics to Enhance Crop Improvement.

Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., classifying environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management (G × E × M). To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, radar, and LiDAR sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for sustainable breeding, a few obstacles remain, such as the need for 1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; 2) state-of-the-art artificial intelligence (AI) models for data integration, simulation, and prediction; 3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and 4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.

Research on the extraction method of coastal wetlands based on sentinel-2 data.

Wetlands play an important role in ecological health and sustainable development, and dynamic monitoring of their spatial distribution is crucial for developing management and conservation measures. The types of coastal wetlands are complex and diverse, natural and artificial wetlands are easily confused, making precise classification more difficult. The coastal wetland of Chongming Island in China, which has diverse types and unique and complex ecological and hydrological characteristics, was deliberately chosen as a challenging case study. The objective of this study was to research effective method of fine classification of coastal wetlands, by constructing feature variables and proposing strategies for multi-level selection and fusion of feature variables. Sentinel-2 data with rich spectral information and high spatial resolution was be used. In this study, firstly, the classification effect of characteristic variables such as vegetation index, water body index, red edge index, and texture index were evaluated. Focusing on the "different objects with same spectra" of the humid planning land and farm growing ponds, the spectral characteristics of them were analyzed and a "water-rich soil index (WRSI)" was established. Subsequently, correlation analysis and J-M distance method were used to multi-level selection for the feature variables and four sets of features combination schemes were established. Finally, random forest (RF) was applied to classify coastal wetlands using different feature combination schemes, and the accuracy of different schemes was compared and verified. The results show the following: 1)Texture features have a promoting effect on improving classification accuracy. The constructed "water rich soil index"(WRSI) has the effectively contribution to identification and classification of farm growing ponds and humid planned land, improving the overall classification accuracy by 6.52%. 2)By multi-level selecting and fusion of feature variable sets, both accuracy and efficiency for classification are improved. For different features combination schemes, the classification accuracy is up to 90.03% by integrating spectral features, spectral index, texture index, and WRSI. This study evaluates the potential of Sentinel-2 data in coastal wetland classification, constructs effective feature parameters, and provides a new idea for wetland information extraction. The resulting classification map can be used for sustainable management, ecological assessment and conservation of the coastal wetland.

A new method for accurate inversion of Forel-Ule index using MODIS images - revealing the water color evolution in China's large lakes and reservoirs over the past two decades.

The Forel Ule water color index (FUI) based on satellite inversion can characterize the comprehensive characteristics of water quality on a large spatiotemporal scale. The high-frequency observations and rich historical data of the MODIS surface reflectance product (MODIS-500 m) provide important data support for monitoring the FUI of inland lakes. However, MODIS-500 m has only three bands in the visible light range, resulting in significant uncertainty in FUI inversion. To address this problem, this study developed an improved FUI inversion model using 500 synthetic spectra covering natural waters. The model, with a performance threshold set at 170° (FUI = 8), used a segmented algorithm across the entire color space. Validated with on-site measurement datasets (3500 samples), the model exhibited excellent performance, with mean relative error (MRE) and root mean square error (RMSE) of 1.71 % and 3.63°, respectively. Compared to existing models, it was more suitable for long-term FUI inversion in various types of lakes, particularly in eutrophic regions. Subsequently, the model was applied to MODIS-500 m observations from 2000 to 2022, revealing the spatiotemporal dynamics of FUI in 180 large lakes and reservoirs (hereinafter referred to as lakes) in China. The results indicated that the long-term mean FUI in the study area was 9, with 7 and 12 in the western and eastern regions, respectively, showing a distinct spatial distribution of "blue in the west and green in the east." The mean change rate of hue angle for all lakes was -0.085°/yr, showing an overall decreasing trend. Environmental factors' relative contributions to long-term water color changes in each lake region were quantified using the multiple general linear model (GLM). Although each lake region exhibited different driving forces, they were primarily influenced by vegetation, lake surface area, and anthropogenic factors. Additionally, the seasonal types of lake water color were analyzed, with the west and east showing opposite patterns, reflecting the significant influence of topographic features and seasonal changes in climate on water color. The research results provide techniques for accurate inversion of FUI using MODIS-500 m data, while deepening the understanding of long-term water color changes in inland lakes in China.

[Ecological Environment Dynamical Evaluation of Hutuo River Basin Using Remote Sensing].

Hutuo River Basin straddles Shanxi and Hebei provinces, and Hutuo River was once cut off due to economic development and urban expansion after 2000; however, with the national emphasis on ecological civilization and the implementation of the South-North Water Diversion Project, the ecological protection of Hutuo River Basin has been significantly improved. MODIS data, Landsat data, and night light remote sensing data were selected based on the google earth engine (GEE) platform, and a new evaluation index system was generated by combining the biological richness index, vegetation cover index, land stress index, and pollution load index in the ecological environment index (EI) and the humidity index in the remote sensing ecological index (RSEI), using the variation coefficient method and entropy weighting method to assign weights to these indices. An ecological environment evaluation model was constructed to evaluate and classify the ecological environment quality of Hutuo River Basin from 2000 to 2020, and the driving factors were interpreted by using geographic probes. The results showed that:① on a time scale, the ecological environment of Hutuo River Basin was in a decline period from 2000 to 2015 and a recovery period from 2015 to 2020. From a grid scale, the ecological environment quality in the central part of the basin showed a state of improvement year by year, and in the western and eastern parts of the basin, the ecological environment quality in the decline period decreased year by year, whereas the ecological environment quality in the recovery period improved. ② Hot spot analysis showed that the spatial distribution of the ecological environment quality in Hutuo River Basin was high in the middle and low on both sides. Cold spot regions were mainly located in major cities and towns in the eastern and southern parts and scattered in the river valley area on the west side. ③ Geodetection analysis showed that the single factor detection drivers were mainly population density, vegetation net primary productivity (NPP), fractional vegetation cover (FVC), and geomorphological type. The dominant factor of cross-detection was "geomorphological type + FVC." With the deepening of ecological civilization construction and the implementation of Hutuo River Protection Regulations, in combination with different factors such as the natural environment and social characteristics in this basin, the research on ecological environment evaluation in Hutuo River Basin can provide data support for proposing localized policies to improve the ecological environment.

Monitoring dynamics of Kyagar Glacier surge and repeated draining of Ice-dammed lake using multi-source remote sensing.

Glacier surges, a primary factor contributing to various glacial hazards, has long captivated the attention of the global glaciological community. This study delves into the dynamics of Kyagar Glacier surging and the associated drainage features of its Ice-dammed lake, employing high temporal resolution optical imagery. Our findings indicate that the surge on Kyagar Glacier began in late spring and early summer of 2014 and concluded during the summer of 2016. This surge resulted in the transfer of 0.321 ± 0.012 km3 of glacier mass from the reservoir zone to the receiving zone, leading to the formation of an ice-dammed lake at the glacier's terminus. The lake experienced five outbursts between 2015 and 2019, with the largest discharge occurring in 2017. And the maximum water depth during this period was 112 ± 11 m, resulting in a water storage volume of (158.37 ± 28.32) × 106 m3. On the other hand, our analysis of the relationship between glacier surface velocity and albedo, coupled with an examination of subglacial dynamics, revealed that increased precipitation during the active phase of the Kyagar Glacier results in accumulation of mass in the upper glacier. This accumulation induces changes in basal shear stress, triggering the glacier's transition into an unstable state. Consequently, glacier deformation rates escalate, surface crevasses proliferate, potentially providing conduits for surface meltwater to infiltrate the glacier bed. This, in turn, leaded to elevated basal water pressure, initiating glacier sliding. Furthermore, we postulated that the repetitive drainage of Kyagar Ice-dammed lake was primarily influenced by the opening and closing of subglacial drainage pathways and variations in inflow volumes. Future endeavors necessitate rigorous field observations to enhance glacier surge simulations, deepening our comprehension of glacier surge mechanisms and mitigating the impact of associated glacial hazards.

Modelling Europe-wide fine resolution daily ambient temperature for 2003-2020 using machine learning.

To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003-2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.

Mapping coastal groundwater potential zones using remote sensing based AHP model in Al Qunfudhah region along Red Sea, Saudi Arabia.

Due to the increases in agriculture and industry sector as well as high population, lack of water is becoming a major problem in the Middle East especially in arid regions. Saudi Arabia needs more groundwater research and explorations because of its higher water use and no source of freshwater. Assessing groundwater zonation in semi-arid locations is essential due to the significant degree of variation in groundwater depth, aquifer features, topographical characteristics, and insufficient precipitation. Mapping prospective groundwater zones in Al Qunfudhah region of southwestern Saudi Arabia has utilized the capability of the multi-criteria decision approaches (MCDA), and the Geographic information system (GIS). We have used the analytical hierarchy process (AHP) as one of the MCDA that is applied to achieve the objective of the current study by integrating twelve controlling factors. These factors are represented by the thematic layers; slope, precipitation, soil type, land use/cover (LULC), drainage density (DD), normalized difference vegetation index (NDVI), curvature, topographic position index (TPI), Terrain Ruggedness Index (TRI), drainage density (DD), and Lineament Density (LD). These thematic layers are combined with GIS to delineate the zones of groundwater potentialities. All factors were classified and weighted according to their importance and its effect on groundwater zones. Their normalized weights were evaluated using a pairwise comparison matrix. The present study shows that the groundwater potential zones (GWPZs) map is represented by five groups ranging between a very high zone with an area of 23781.06 Km2 that represents 4.04 % of the studied area, and a very poor GWPZ with an area of 182944.4 Km2 that represents 31.09 % of the studied area. The AHP model suggests that lineament density, slope, and drainage density are more important for determining the groundwater potentiality than other physiographic factors. The study's findings will be helpful in developing practical strategies for the region's groundwater supply. This analysis shows how the methodology may be used to study a broad coastal groundwater basin. The current study will give the decision makers to select suitable sites with a high groundwater potential.

Remote and low-cost intraocular pressure monitoring by deep learning of speckle patterns.

Significance: Glaucoma, a leading cause of global blindness, disproportionately affects low-income regions due to expensive diagnostic methods. Affordable intraocular pressure (IOP) measurement is crucial for early detection, especially in low- and middle-income countries. Aim: We developed a remote photonic IOP biomonitoring method by deep learning of the speckle patterns reflected from an eye sclera stimulated by a sound source. We aimed to achieve precise IOP measurements. Approach: IOP was artificially raised in 24 pig eyeballs, considered similar to human eyes, to apply our biomonitoring method. By deep learning of the speckle pattern videos, we analyzed the data for accurate IOP determination. Results: Our method demonstrated the possibility of high-precision IOP measurements. Deep learning effectively analyzed the speckle patterns, enabling accurate IOP determination, with the potential for global use. Conclusions: The novel, affordable, and accurate remote photonic IOP biomonitoring method for glaucoma diagnosis, tested on pig eyes, shows promising results. Leveraging deep learning and speckle pattern analysis, together with the development of a prototype for human eyes testing, could enhance diagnosis and management, particularly in resource-constrained settings worldwide.

Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach.

Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonstrates an integrated approach utilizing machine learning and Google Earth Engine to enable real-time flood assessment. Synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform were employed to generate near real-time flood maps of the 2020 flood in Kurigram and Lalmonirhat. An automatic thresholding technique quantified flooded areas. For land use/land cover (LULC) analysis, Sentinel-2's high resolution and machine learning models like artificial neural networks (ANN), random forests (RF) and support vector machines (SVM) were leveraged. ANN delivered the best LULC mapping with 0.94 accuracy based on metrics like accuracy, kappa, mean F1 score, mean sensitivity, mean specificity, mean positive predictive value, mean negative value, mean precision, mean recall, mean detection rate and mean balanced accuracy. Results showed over 600,000 people exposed at peak inundation in July-about 17% of the population. The machine learning-enabled LULC maps reliably identified vulnerable areas to prioritize flood management. Over half of croplands flooded in July. This research demonstrates the potential of integrating SAR, machine learning and cloud computing to empower authorities through real-time monitoring and accurate LULC mapping essential for effective flood response. The proposed comprehensive methodology can assist stakeholders in developing data-driven flood management strategies to reduce impacts.

Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images.

Introduction: In the context of climate change, monitoring the spatial and temporal variability of plant physiological parameters has become increasingly important. Remote spectral imaging and GIS software have shown effectiveness in mapping field variability. Additionally, the application of machine learning techniques, essential for processing large data volumes, has seen a significant rise in agricultural applications. This research was focused on carob tree, a drought-resistant tree crop spread through the Mediterranean basin. The study aimed to develop robust models to predict the net assimilation and stomatal conductance of carob trees and to use these models to analyze seasonal variability and the impact of different irrigation systems. Methods: Planet satellite images were acquired on the day of field data measurement. The reflectance values of Planet spectral bands were used as predictors to develop the models. The study employed the Random Forest modeling approach, and its performances were compared with that of traditional multiple linear regression. Results and discussion: The findings reveal that Random Forest, utilizing Planet spectral bands as predictors, achieved high accuracy in predicting net assimilation (R² = 0.81) and stomatal conductance (R² = 0.70), with the yellow and red spectral regions being particularly influential. Furthermore, the research indicates no significant difference in intrinsic water use efficiency between the various irrigation systems and rainfed conditions. This work highlighted the potential of combining satellite remote sensing and machine learning in precision agriculture, with the goal of the efficient monitoring of physiological parameters.

Effect of the Photoexcitation Wavelength and Polarization on the Generated Heat by a Nd-Doped Microspinner at the Microscale.

Thermal control at small scales is critical for studying temperature-dependent biological systems and microfluidic processes. Concerning this, optical trapping provides a contactless method to remotely study microsized heating sources. This work introduces a birefringent luminescent microparticle of NaLuF4:Nd3+ as a local heater in a liquid system. When optically trapped with a circularly polarized laser beam, the microparticle rotates and heating is induced through multiphonon relaxation of the Nd3+ ions. The temperature increment in the surrounding medium is investigated, reaching a maximum heating of ≈5 °C within a 30 µm radius around the static particle under 51 mW laser excitation at 790 nm. Surprisingly, this study reveals that the particle's rotation minimally affects the temperature distribution, contrary to the intuitive expectation of liquid stirring. The influence of the microparticle rotation on the reduction of heating transfer is analyzed. Numerical simulations confirm that the thermal distribution remains consistent regardless of spinning. Instead, the orientation-dependence of the luminescence process emerges as a key factor responsible for the reduction in heating. The anisotropy in particle absorption and the lag between the orientation of the particle and the laser polarization angle contribute to this effect. Therefore, caution must be exercised when employing spinning polarization-dependent luminescent particles for microscale thermal analysis using rotation dynamics.