Area Estimation Research Articles

Deep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites.

The link between the macroscopic properties of polymer nanocomposites and the underlying microstructural features necessitates an understanding of nanoparticle dispersion. The dispersion of nanoparticles introduces variability, potentially leading to clustering and localized accumulation of nanoparticles. This non-uniform dispersion impacts the accuracy of predictive models. In response to this challenge, this study developed an automated and precise technique for particle recognition and detailed mapping of particle positions in scanning electron microscopy (SEM) micrographs. This was achieved by integrating deep convolutional neural networks with advanced image processing techniques. Following particle detection, two dispersion factors were introduced, namely size uniformity and supercritical clustering, to quantify the impact of particle dispersion on properties. These factors, estimated using the computer vision technique, were subsequently used to calculate the effective load-bearing area influenced by the particles. An adapted micromechanical model was then employed to quantify the interfacial strength and thickness of the nanocomposites. This approach enabled the establishment of a correlation between dispersion characteristics and interfacial properties by integrating experimental data, relevant micromechanical models, and quantified dispersion factors. The proposed systematic procedure demonstrates considerable promise in utilizing deep learning to capture and quantify particle dispersion characteristics for structure-property analyses in polymer nanocomposites.

Remote Sensing of Residential Landscape Irrigation in Weber County, Utah: Implications for Water Conservation, Image Analysis, and Drone Applications

Analyzing irrigation patterns to promote efficient water use in urban areas is challenging. Analysis of irrigation by remote sensing (AIRS) combines multispectral aerial imagery, evapotranspiration data, and ground-truth measurements to overcome these challenges. We demonstrate AIRS on eight neighborhoods in Weber County, Utah, using 0.6 m National Agriculture Imagery Program (NAIP) and 0.07 m drone imagery, reference evapotranspiration (ET), and water use records. We calculate the difference between the actual and hypothetical water required for each parcel and compare water use over three time periods (2018, 2021, and 2023). We find that the quantity of overwatering, as well as the number of customers overwatering, is decreasing over time. AIRS provides repeatable estimates of irrigated area and irrigation demand that allow water utilities to track water user habits and landscape changes over time and, when controlling for other variables, see if water conservation efforts are effective. In terms of image analysis, we find that (1) both NAIP and drone imagery are sufficient to measure irrigated area in urban settings, (2) the selection of a threshold value for the normalized difference vegetation index (NDVI) becomes less critical for higher-resolution imagery, and (3) irrigated area measurement can be enhanced by combining NDVI with other tools such as building footprint extraction, object classification, and deep learning.

Artificial intelligence to predict estimates of physical activity in small geographic areas

Abstract Background Artificial intelligence (AI) predictive models have been used to estimate the risk of a certain outcome occurring, using all available variables. Methods Using AI methods we have developed a small area estimation (SAE) technique to estimate the prevalence of leisure-time physical activity (LTPA), or who practices 150 minutes of LTPA per week, in small areas, in Belo Horizonte, Brazil, between 2009 and 2018, by the usage of k-means clustering. The VIGITEL, national telephone survey of Risk Factors for non-communicable diseases, was the main source. We applied available data to estimate prevalence of LTPA in 3300 census tracts, aggregated in 150 Primary Health Care (PHC) areas. The prevalence of PA was analyzed between two time periods, due to the low number of census tract interviews. With the available data, we estimate vulnerability clusters, derived from a Health Vulnerability Index (IVS), for the city of Belo Horizonte, according to socioeconomic, sanitation and health indicators, which measure the percentage of the population living in areas of vulnerability in the city. The index main purpose is to support public policy implementation. Results Nine clusters were identified according to the Silhouette coefficient analysis, i.e.: low (LO-0, LO-1), medium (ME-0, ME-1), high (HI-0, HI-1, HI-2) and very high) vulnerability (VH-0, VH-1. The prevalence of physical activity ranged from 23.7% (worst prevalence), in area of high vulnerability cluster (VH-1) to 45.5% in a low-risk cluster (LO-1) in period from 2009 to 2014. The prevalence of physical activity increased to 31.4% (high vulnerability cluster) to 52.81% (low-risk cluster) from 2014 to 2018. Conclusions The use of AI to estimate the prevalence of physical activity in small areas proved to be effective and highlighted important differences in the city. Thus, national surveys, with a small number of interviews per census sector, can be used to predict risks and health outcomes for small areas. Key messages • The study highlights the importance of using artificial intelligence to predict estimates of risk factors for chronic non-communicable diseases in small geographic areas. • The study showed less physical activity (PA) in areas of greater vulnerability. This can support health promotion policies and programs that encourage PA.

Large disagreements in estimates of urban land across scales and their implications

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts.

Estimation of Rice Crop Acreage in Kuttanad Region, Kerala Using Landsat 8 OLI IMAGES and GIS Techniques

Aims: The study aimed to delineate rice accurately (Oryza sativa L.) cultivation areas in the Kuttanad region, Kerala, during the Puncha season of 2023-24 using medium- to high-resolution optical satellite data, particularly Landsat 8 Operational Land Imager (OLI), to aid in preharvest prediction of agricultural production and policy making. Study Design: This study used a remote sensing-based approach for rice area estimation, focusing on supervised classification methods. Place and Duration of Study: The study was conducted in Kuttanad, Kerala, a low-lying agroecosystem, during the Puncha season of 2023-24. Methodology: The study utilised two cloud-free Landsat 8 OLI images to delineate rice-growing areas. The images were pre-processed, mosaicked, and analysed using ArcGIS software. A supervised classification approach was employed using the Maximum Likelihood Classification algorithm. The study area was classified into five categories: rice fields, other crops, low vegetation, built-up areas, and water bodies. Ground-truth data was used to validate the classification accuracy. Results: The total rice area delineated during the Puncha season was 43,550.28 hectares. The classification achieved an accuracy of 93.33%, with a kappa coefficient of 0.87, indicating high reliability. Conclusion: The accurate delineation of rice-growing areas using satellite imagery provides valuable information for assessing production levels and planning for food security. This methodology can aid in agricultural planning and contingency strategies, particularly in regions like Kuttanad, which face challenges such as flooding and soil toxicity.

Deep Learning Approaches for Wildfire Severity Prediction: A Comparative Study of Image Segmentation Networks and Visual Transformers on the EO4WildFires Dataset

This paper investigates the applicability of deep learning models for predicting the severity of forest wildfires, utilizing an innovative benchmark dataset called EO4WildFires. EO4WildFires integrates multispectral imagery from Sentinel-2, SAR data from Sentinel-1, and meteorological data from NASA Power annotated with EFFIS data for forest fire detection and size estimation. These data cover 45 countries with a total of 31,730 wildfire events from 2018 to 2022. All of these various sources of data are archived into data cubes, with the intention of assessing wildfire severity by considering both current and historical forest conditions, utilizing a broad range of data including temperature, precipitation, and soil moisture. The experimental setup has been arranged to test the effectiveness of different deep learning architectures in predicting the size and shape of wildfire-burned areas. This study incorporates both image segmentation networks and visual transformers, employing a consistent experimental design across various models to ensure the comparability of the results. Adjustments were made to the training data, such as the exclusion of empty labels and very small events, to refine the focus on more significant wildfire events and potentially improve prediction accuracy. The models’ performance was evaluated using metrics like F1 score, IoU score, and Average Percentage Difference (aPD). These metrics offer a multi-faceted view of model performance, assessing aspects such as precision, sensitivity, and the accuracy of the burned area estimation. Through extensive testing the final model utilizing LinkNet and ResNet-34 as backbones, we obtained the following metric results on the test set: 0.86 F1 score, 0.75 IoU, and 70% aPD. These results were obtained when all of the available samples were used. When the empty labels were absent during the training and testing, the model increased its performance significantly: 0.87 F1 score, 0.77 IoU, and 44.8% aPD. This indicates that the number of samples, as well as their respectively size (area), tend to have an impact on the model’s robustness. This restriction is well known in the remote sensing domain, as accessible, accurately labeled data may be limited. Visual transformers like TeleViT showed potential but underperformed compared to segmentation networks in terms of F1 and IoU scores.

Model for estimating red mulberry leaf area using a genetic algorithm

The success of sericulture depends directly on the quality and quantity of mulberry leaves, as it is essential for the feeding and development of silkworm caterpillars and, consequently, influences the quality of the silk thread manufactured. The estimation of mulberry leaf area is important to have plant development and growth indicators, such as transpiration intensity, net assimilation rate, leaf area ratio, specific leaf area and leaf area index, which allow predicting crop productivity. Thus, the objective of this study was to develop and test a model capable of estimating the red mulberry leaf area using a genetic algorithm. The model was adjusted with the proposed stochastic optimization method. The mean error found for the tested dataset was approximately 228.17 mm2 in sample space with mean leaf area of 6515.55 mm2. The information generated allows applying the model to estimate red mulberry leaf area in future studies.

Uncertainty in model estimates of global groundwater depth

Abstract Knowing the depth at which groundwater can be found below the land surface is critical for understanding its potential accessibility by ecosystems and society. Uncertainty in global scale water table depth (WTD) limits our ability to assess groundwater’s role in a water cycle altered by changing climate, land cover, and human water use. Global groundwater models offer a top–down pathway to gain this knowledge, but their uncertainty is currently poorly quantified. Here, we investigate four global groundwater models and reveal steady-state WTD disagreements of more than 100 m for one-third of the global land area. We find that model estimates of land areas with shallow groundwater at <10 m depth vary from 10% to 71% (mean of 23%). This uncertainty directly translates into subsequent assessments, as land areas with potential groundwater accessibility for forests, population, and areas equipped for irrigation, differ substantially depending on the chosen model. We explore reasons for these differences and find that contrary to observations, 3 out of 4 models show deeper water tables in humid than in arid climates and greatly overestimate how strongly topographic slope controls WTD. These results highlight substantial uncertainty associated with any global-scale groundwater analysis, which should be considered and ultimately reduced.

Photosynthetic temperature responses in leaves and canopies: why temperature optima may disagree at different scales.

Understanding how canopy-scale photosynthesis responds to temperature is of paramount importance for realistic prediction of the likely impact of climate change on forest growth. The effects of temperature on leaf-scale photosynthesis have been extensively documented but data demonstrating the temperature response of canopy-scale photosynthesis are relatively rare, and the mechanisms that determine the response are not well quantified. Here, we compared leaf- and canopy-scale photosynthesis responses to temperature measured in a whole-tree chamber experiment and tested mechanisms that could explain the difference between leaf and crown scale temperature optima for photosynthesis. We hypothesised that 1) there is a large contribution of non-light saturated leaves to total crown photosynthesis; 2) photosynthetic component processes vary vertically through the canopy following the gradient in incident light; and 3) seasonal temperature acclimation of photosynthetic biochemistry has a significant role in determining the overall temperature response of canopy photosynthesis. We tested these hypotheses using three models of canopy radiation interception and photosynthesis parameterized with leaf-level physiological data and estimates of canopy leaf area. Our results identified the influence of non-light saturated leaves as a key determinant of the lower temperature optimum of canopy photosynthesis, which reduced the temperature optimum of canopy photosynthesis by 6-8°C compared to the leaf scale. Further, we demonstrate the importance of accounting for within-canopy variation and seasonal temperature acclimation of photosynthetic biochemistry in determining the magnitude of canopy photosynthesis. Overall, our study identifies key processes that need to be incorporated in terrestrial biosphere models to accurately predict temperature responses of whole-tree photosynthesis.

Brain morphometry and chronic inflammation in Bangladeshi children growing up in extreme poverty

Abstract Over three hundred million children live in environments of extreme poverty, and the biological and psychosocial hazards endemic to these environments often expose these children to infection, disease, and inflammatory responses. Chronic inflammation in early childhood has been associated with diminished cognitive outcomes, and despite this established relationship, the mechanisms explaining how inflammation affects brain development are not well known. Importantly, the prevalence of chronic inflammation in areas of extreme poverty raises the possibility that it may also serve as a mechanism explaining the known relationship between low socioeconomic status (SES) and altered brain development. To examine these potential pathways, seventy-nine children growing up in an extremely poor, urban area of Bangladesh underwent MRI scanning at 6 years of age. Structural brain images were submitted to Mindboggle software, a Docker-compliant and high-reproducibility tool for regional estimations of volume, surface area, cortical thickness, sulcal depth, and mean curvature. C-reactive protein was assayed at eight time points between infancy and 5 years of age, and the frequency with which children had elevated concentrations of inflammatory marker represented the measure of chronic inflammation. Childhood SES was measured with maternal education and income-to-needs (i.e., monthly household income divided by the number of household members). Chronic inflammation predicted volume in bilateral basal ganglia structures and mediated the link between maternal education and bilateral putamen volumes. These findings suggest that chronic inflammation is associated with brain morphometry in the basal ganglia, predominantly the putamen, and further offers inflammation as a potential mechanism linking SES to brain development.

Small Area Estimation of Household Economic Indicators under Unit-Level Generalized Additive Models for Location, Scale and Shape

Abstract We propose a small area estimation model based on Generalized Additive Models for Location, Scale and Shape (SAE-GAMLSS) for the estimation of household economic indicators. SAE-GAMLSS relax the exponential family distributional assumption and allow each distributional parameter to depend on covariates. A bootstrap approach to estimate the MSE is proposed. The SAE-GAMLSS estimator shows a largely better performance than the well-known Empirical Best Linear Unbiased Predictor (EBLUP) under various simulated scenarios. Per-capita consumption of Italian and foreign households in Italian regions, in urban and rural areas, is estimated using SAE-GAMLSS. Results show that the well-known Italian North–South divide does not hold for foreigners.

Enhancing the electrochemical performance of graphite sheet electrodes for ketamine detection

Flexible graphite sheets (GS) have been a promising material for developing electrochemical sensors. In this work, we showed that the electrochemical activation protocol (+1.4 V followed by -1.0 V vs. Ag|AgCl|KCl(sat.) both at 200 s) in alkaline medium (0.5 mol l-1 NaOH) enhances the electrochemical activity of GS. Before the treatment, cyclic voltammetric profile of inner-sphere [Fe(CN)6]3-/4- redox couple at 50 mV s-1 exhibited an ill-defined voltammetric profile (ΔEp = 800 mV) while an increase in the reversibility was achieved (ΔEp = 225 mV) after the treatment. Additionally, the lower charge transfer resistance (Rct) measured by Electrochemical Impedance Spectroscopy (EIS) and a higher heterogeneous electron transfer constant (k0) corroborate with the enhancement in the electrochemical performance of treated GS electrodes. The SEM images revealed an irregular pattern with number of grooves, indicating a partially surface exfoliation and AFM analysis displayed an increase in the roughness. These findings agreed with the estimation of electroactive area which increased 3-times after the treatment. As proof of concept, ketamine (KET), an anesthetic often used as drug of abuse, was determined in synthetic saliva and veterinary pharmaceutical samples using treated GS electrodes and square wave voltammetric measurements. Linear range of 5.0–100.0 µmol l-1 with a limit of detection value of 2.1 µmol l-1 was obtained for KET. Appropriate recovery values (∼105 %) for the analysis of samples were achieved.

Does Local Area Social Mobility Affect Political Alienation?

While existing research has considered how individual-level social mobility experiences affect a person’s political outlook, less attention has been paid to how historic levels of social mobility in local areas influence political attitudes and political behaviour. We link individual-level data from the UK Household Longitudinal Study to small area estimates of social class mobility derived from the decennial census. We find that living in a low absolute social mobility area was associated with a higher probability of voting ‘Leave’ in the 2016 UK European Union membership referendum. However, we find no evidence that historical social mobility rates in the local area predict abstention in general elections or attitudinal indicators of political alienation. Given declining rates of upwards mobility, and increasing levels of downwards mobility, our results have important implications for understanding geographies of political discontent.

Special issue SMA: big data and alternative data sources for small area estimation

Special issue SMA: big data and alternative data sources for small area estimation

Machine Learning Based Hybrid Approach for Estimation of Jute Area Using Optical and Microwave Satellite Imagery

Machine Learning Based Hybrid Approach for Estimation of Jute Area Using Optical and Microwave Satellite Imagery

Children had smaller brain volumes and cortical surface areas after prenatal opioid maintenance therapy exposure.

The studies have shown that infants with prenatal OMT exposure had smaller brain volumes than non-exposed controls, but long-term outcome data are lacking. We examined 5-13-year-old OMT-exposed children with brain MRI and tested motor and visual-motor functions and possible associations between brain morphology and outcome. To this retrospective cohort study, we recruited 55 children with prenatal OMT exposure and 59 age- and gender-matched controls. They were examined with brain MRI, Movement-ABC and Beery-VMI. MRI images were processed with the Free Surfer® software to obtain volumetrics and estimates of cortical surface area and thickness. We used a general linear regression model (GLM) to calculate group differences. The children in the OMT group had smaller mean total intracranial volume (ICV), 1407 cm3 (CI 95% 1379-1434) versus 1450 cm3 (CI 95% 1423-1476) in the control group (p = 0.026). After adjusting for ICV, significant group differences persisted for volumes of amygdala, basal ganglia and mid-posterior part of corpus callosum. Cortical surface area was smaller in the left caudal middle frontal gyrus and the right inferior parietal lobule in the OMT-group. Visual-motor function was significantly correlated with ICV. Prenatal OMT exposure may alter early brain development with possible negative long-term functional consequences.

Radiographic Estimation of Superior Canal Dehiscence Area in a Prediction Model of Surgical Outcome.

Computed tomography (CT) imaging of the temporal bone constitutes an integral element in the diagnostic workup of superior canal dehiscence (SCD). This study explores the utility of a clinically efficient method of manually estimating SCD size on CT imaging in predicting surgical outcomes. Cohort study. Tertiary center. Consecutive middle fossa repairs of SCD between 2011 and 2022 were included. Measurements of approximate dehiscence area (ADA), a previously established estimation method, on temporal bone CT imaging were performed by trained raters blind to clinical information. Surgical outcomes, evaluated with symptom resolution and audiometric improvement following repair, were assessed in multivariable regression models with ADA as the primary predictor and patient demographics and other history factors as covariates. Among 311 cases included, mean ADA was 2.68 mm2, and 71% of cases achieved overall symptom improvement (OSI). Every 1 mm2 rise in ADA predicted on average 3 dB greater narrowing of low-frequency air-bone gap (β 95% confidence interval, CI [-5.4, -0.7]) but 50% lower odds of OSI (adjusted odds ratio: 95% CI [0.32, 0.78]) among unilateral SCD cases. A model incorporating ADA, patient demographics, and history factors predicted OSI with a sensitivity, specificity combination as high as 85%, 73% and a positive predictive value, negative predictive value combination as high as 85%, 82% (area under the curve: 95% CI: 0.76-0.93). Increased ADA predicts more pronounced audiometric improvement but poorer symptomatic response among unilateral SCD cases. ADA is an efficient and practical method of estimating SCD size and demonstrated clinical utility in accurately predicting surgical outcomes.

Exploring Veteran Mental Health Disparities: A Comparative Analysis of Rural and Urban Communities in the Midwest of the United States.

Veterans face unique mental health challenges influenced by their service experiences and post-military transitions into civilian life. Geographic location also plays an integral role in impacting veterans' outcomes and access to proper care. The purpose of this case study is to examine disparities between rural and urban veterans in the Midwest using data collected from the 2022 Behavioral Risk Factor Surveillance System (BRFSS). Self-reported bad mental health days among veterans in rural and urban regions across twelve Midwestern states were analyzed through direct rate estimation and small area estimation techniques utilizing RStudio software. Higher rates of poor mental health days were ultimately observed among urban veterans in most states through both analyses. The results of the direct rate analysis revealed 13.5% of veterans reporting 14 + poor mental health days per month versus 9.5% in rural areas. Likewise, the results of the small area analysis demonstrated 12.2% of veterans reporting 14 + days of poor mental health days per month in urban areas versus 9.8% in rural areas. This highlights the significance of environmental stressors and social determinants of health in differentially impacting mental health outcomes. Thus, tailored interventions utilizing interdisciplinary teams are needed to meet the unique barriers for veterans in different geographic contexts. Despite the cross-sectional nature of the study and reliance on self-reported data, this case study provides valuable insights for mental health disparities among Midwest veterans. Creating a more equitable and accessible mental health landscape for veterans will require targeted and collaborative approaches.

Satellite-based estimation of monthly mean hourly 1-km urban air temperature using a diurnal temperature cycle model

Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (Ta) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate Ta, high-resolution hourly Ta estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h Ta observations from weather stations. To address these limitations, we introduce a method that combines a diurnal temperature cycle (DTC) model with a random forest model to estimate monthly mean hourly urban Ta at 1-km resolution. This approach leverages a limited number of diurnal Ta observations from weather stations, MODIS LST data, and ancillary information. The core idea of the proposed method is to transform the estimation of monthly mean hourly 1-km Ta into estimating 1-km DTC model parameters, primarily daily maximum and minimum Ta values. This method capitalizes on MODIS LST's ability to estimate daily Ta extremes and requires only four diurnal Ta observations within a daily cycle to estimate monthly mean hourly 1-km Ta. Station-based five-fold cross-validation yields overall RMSE values consistently below 1.0 °C across nine cities with diverse geographic and climatic contexts. The accuracy achieved with only four diurnal Ta observations rivals that obtained using continuous 24-h Ta observations. Even with a limited training set of ten stations, the overall RMSE remains below 1.0 °C for most cities. The proposed method proves effective for both single-city and multi-city modeling and can estimate daily hourly 1-km Ta under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km Ta, which can be readily applied to other cities and holds potential for various applications.

Estimation of Periodontal Inflamed Surface Area by Salivary Lactate Dehydrogenase Level Using a Test Kit.

Background: Salivary lactate dehydrogenase (LD) levels are a feasible and useful parameter for screening periodontal diseases. The periodontal inflamed surface area (PISA) is useful to clinically assess periodontal diseases. However, PISA is difficult to calculate and PISA-compatible screening kits are required. We aimed to investigate the association between salivary LD levels, using a test kit, and PISA and PISA-Japanese and determine the feasibility and reliability of the salivary LD test kit for evaluation of periodontal status. Methods: This study included 110 patients (66.4% female, median and 25-75 percentiles of age were 66.5 and 53.0-75.0 years, respectively) who visited the Dental University Clinic in Japan. Resting saliva samples were collected from each participant and LD levels were evaluated in real time using a kit featuring an integer scale ranging from 1 to 10. PISA and PISA-Japanese were calculated using periodontal parameters. Results: The median salivary LD level was 4.0. The medians of PISA and PISA-Japanese were 46.9 and 61.0, respectively. Salivary LD levels were positively correlated with the bleeding on probing rate (r = 0.626, p < 0.001), PISA (r = 0.560, p < 0.001), and PISA-Japanese (r = 0.581, p < 0.001). Conclusions: Our results suggest that salivary LD levels assessed using the salivary LD kit showed a significantly positive correlation with PISA and PISA-Japanese. In addition, we developed the PISA estimation formula using salivary LD levels measured with a test kit, sex, and age.