Area Estimation Research Articles

Estimation of the Departmental Female Employment Rate: Towards a New Strategy Based on Combining Spatial and Non-spatial Small Area Estimators

Abstract Local statistics are in high demand in almost all countries due to their relevance for public policy monitoring and evaluation, decision-making, and strategic planning. However, traditional sources of local statistics, such as general censuses, administrative statistics, and national and local surveys, have limitations. This has led to the development of several techniques for estimating parameters over small areas. In this article, we use both spatial and non-spatial estimators for small domains to estimate the local employment rate of women in Algeria. We find that the empirical spatial estimator is the most appropriate and efficient estimator as more spatial correlations are considered. We also find that an approach based on the combination of spatial and non-spatial small area estimators is very beneficial because it allows for obtaining efficient estimates with lower Root Mean Squared Errors (RMSEs). This finding is significant for Algeria and similar countries, where traditional sources of local statistics are limited. The combination of spatial and non-spatial estimators can provide more accurate and reliable estimates of local statistics, which can be used to inform public policy and decision-making.

Noninvasive system for weight estimation in cactus crops: A YOLOv5-decision tree approach based on interval type-2 fuzzy sets

This study proposes a noninvasive system to estimate the weight of a prickly pear cactus (Opuntia ficus-indica), which combines a model based on deep learning to detect and estimate its area and a model based on supervised learning and developed under a type-2 interval fuzzy sets approach to estimate its weight. YOLOv5 was used for detection, which achieved results with an accuracy of 0.999, recall of 0.999, mAP_0.5 of 0.995, and mAP_0.5:0.95 of 0.990. Area estimation is performed based on the object's coordinates in the image. To perform the weight estimation task, a comparative analysis of models based on supervised learning was performed to identify the model that best described the response variable, selecting a decision tree model. The training and adjustment process included the uncertainty in a weight evaluation system using a granatary scale to enhance the weight estimation capability. Using a trapezoidal membership function, the type-2 interval fuzzy set approach was used to convert the collected data into fuzzy numbers. The model findings for the weight estimation task were also favorable, with a model-explained variance R2 of 0.98, a root mean square error (RMSE) of 10.02, and a mean absolute error (MEA) of 6.33 g. Finally, both models were incorporated into a graphical user interface to facilitate their use in the real weight estimation process. A real case application with the proposed system demonstrated its effectiveness in detecting and estimating weight dynamically with an error of 9 g. Therefore, its viability as an alternative system for weight estimation in cactus crops was demonstrated. Furthermore, a comparative analysis demonstrated the superiority of the proposed hybrid approach over similar approaches in the literature.

Digital surface model generation from high‐resolution satellite stereos based on hybrid feature fusion network

AbstractRecent studies have demonstrated that deep learning‐based stereo matching methods (DLSMs) can far exceed conventional ones on most benchmark datasets by both improving visual performance and decreasing the mismatching rate. However, applying DLSMs on high‐resolution satellite stereos with broad image coverage and wide terrain variety is still challenging. First, the broad coverage of satellite stereos brings a wide disparity range, while DLSMs are limited to a narrow disparity range in most cases, resulting in incorrect disparity estimation in areas with contradictory disparity ranges. Second, high‐resolution satellite stereos always comprise various terrain types, which is more complicated than carefully prepared datasets. Thus, the performance of DLSMs on satellite stereos is unstable, especially for intractable regions such as texture‐less and occluded regions. Third, generating DSMs requires occlusion‐aware disparity maps, while traditional occlusion detection methods are not always applicable for DLSMs with continuous disparity. To tackle these problems, this paper proposes a novel DLSM‐based DSM generation workflow. The workflow comprises three steps: pre‐processing, disparity estimation and post‐processing. The pre‐processing step introduces low‐resolution terrain to shift unmatched disparity ranges into a fixed scope and crops satellite stereos to regular patches. The disparity estimation step proposes a hybrid feature fusion network (HF2Net) to improve the matching performance. In detail, HF2Net designs a cross‐scale feature extractor (CSF) and a multi‐scale cost filter. The feature extractor differentiates structural‐context features in complex scenes and thus enhances HF2Net's robustness to satellite stereos, especially on intractable regions. The cost filter filters out most matching errors to ensure accurate disparity estimation. The post‐processing step generates initial DSM patches with estimated disparity maps and then refines them for the final large‐scale DSMs. Primary experiments on the public US3D dataset showed better accuracy than state‐of‐the‐art methods, indicating HF2Net's superiority. We then created a self‐made Gaofen‐7 dataset to train HF2Net and conducted DSM generation experiments on two Gaofen‐7 stereos to further demonstrate the effectiveness and practical capability of the proposed workflow.

Temporal-Like Bivariate Fay-Herriot Model: Leveraging Past Responses and Advanced Preprocessing for Enhanced Small Area Estimation of Growing Stock Volume

Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes. This study introduces an enhanced temporal-like bivariate Fay-Herriot model, improving upon its univariate counterpart. The model incorporates field data and auxiliary data, including canopy height metrics from WorldView stereo-imagery and past census data, sourced from the University Forest of Pertouli in Central Greece. The model aims to estimate the growing stock volume for 2008 and 2018, focusing on enhancing the precision of the 2018 estimates. The 2008 dependent variable is used as auxiliary information by the model for more reliable 2018 small area estimates. A novel preprocessing pipeline is also introduced, which includes outlier identification, cluster analysis, and variance smoothing. Compared to direct estimates and the standard univariate Fay-Herriot model, our bivariate approach shows a percentage variance reduction of 96.58% and 13.52%, respectively. The methodology not only offers more reliable estimates with reduced variance and bias but also contributes to more accurate decision-making for sustainable forest management.

Tree species classification improves the estimation of BVOCs from urban greenspace

Accurate estimation of biogenic volatile organic compounds (BVOCs) emissions from urban plants is important as BVOCs affect the formation of secondary pollutants and human health. However, uncertainties exist for the estimation of BVOCs emissions from urban greenspace due to the lack of tree species classification with high spatial resolution. Here, we generated a tree species classification dataset with 10 m resolution to estimate tree species-level BVOCs emissions and quantify their impact on air quality in Shenzhen in southern China. The results showed that for the entire city, the BVOCs emissions based on traditional plant functional types (PFTs) dataset were substantially underestimated compared with the tree species classification data (6.37 kt versus 8.23 kt, with 22.60 % difference). The underestimation is particularly prominent in urban built-up areas, where our estimation was 1.65 kt, nearly twice of that based on PFTs data (0.86 kt). BVOCs estimation in built-up areas contributed approximately 20.07 % to the total. These BVOCs contributed substantially to the increase of ambient O3, but had limited impacts to ambient fine particulate matter (PM2.5). Our results underscore the importance of high spatial resolution tree species-level classification in more accurate estimation of BVOCs, especially in highly developed urban areas. The enhanced understanding of the patterns of BVOCs emissions by urban trees and the impact on secondary pollutants can better support fine-scale tree planning and management for livable environments in urban areas.

Closing Reporting Gaps: A Comparison of Methods for Estimating Unreported Subgroup Achievement on NAEP

ABSTRACT The National Assessment of Educational Progress (NAEP) program only reports state-level subgroup results if it samples at least 62 students identifying with the subgroup. Since some subgroups constitute small proportions of many states’ general student populations, these minority subgroups are seldom sufficiently sampled to meet this sample size requirement. Consequently, education researchers and policymakers are regularly left without a comprehensive understanding of how states are supporting the learning and achievement of different subgroups of students, including underserved subgroups. Using grade 8 mathematics results in 2015, this study compares the performance of two separate techniques in predicting mean subgroup achievement on NAEP. Results demonstrate that a small area estimation technique, known as the Fay-Herriot (FH) model, represents a promising approach. Follow-up research should employ the FH technique with math achievement data from other testing years, as well as data from NAEP reading to evaluate the technique’s generalizability.

Generating fuel consumption maps on prescribed fire experiments from airborne laser scanning

Background Characterisation of fuel consumption provides critical insights into fire behaviour, effects, and emissions. Stand-replacing prescribed fire experiments in central Utah offered an opportunity to generate consumption estimates in coordination with other research efforts. Aims We sought to generate fuel consumption maps using pre- and post-fire airborne laser scanning (ALS) and ground measurements and to test the spatial transferability of the ALS-derived fuel models. Methods Using random forest (RF), we empirically modelled fuel load and estimated consumption from pre- and post-fire differences. We used cross-validation to assess RF model performance and test spatial transferability. Key results Consumption estimates for overstory fuels were more precise and accurate than for subcanopy fuels. Transferring RF models to provide consumption estimates in areas without ground training data resulted in loss of precision and accuracy. Conclusions Fuel consumption maps were produced and are available for researchers who collected coincident fire behaviour, effects, and emissions data. The precision and accuracy of these data vary by fuel type. Transferability of the models to novel areas depends on the user’s tolerance for error. Implications This study fills a critical need in the broader set of research efforts linking fire behaviour, effects, and emissions.

Site Suitability Classification for Saturated Bioenergy Buffers and Area Estimates in the U.S. Midwest

HighlightsSuitability criteria for saturated bioenergy buffer (SBB) sites were developed.A method to classify cropland areas as suitable SBB sites was demonstrated using public data.The criteria and method for classifying suitable SBBs were generally successful at U.S. Midwest sites.Areas were identified within U.S. Midwest states having the greatest potential for SBB implementation.Abstract.Nutrient loss from tile-drained, row-crop fields is a major component in the impact of agricultural production on surface water quality, because drain tiles provide a short-circuit for nutrient-rich drainage water to reach ditches and eventually downstream waterbodies (e.g., creeks, streams, rivers). Saturated buffers (SBs) help address the tile-drainage water quality problem, and incorporating bioenergy crops into SBs (saturated bioenergy buffers, or SBBs) could provide both environmental protection and an economic benefit to farmers. The objectives of this analysis were to: (1) develop a site suitability classification approach for rapid, cost-effective identification of SBB candidate sites; and, after validation, (2) use it to estimate the size of land in the U.S. Midwest considered suitable for SBB sites. The approach uses several biophysical parameters to identify sites that (1) are likely to be tile-drained, row-cropped agricultural land; (2) have conditions conducive for denitrification; and (3) have stable streambanks. Supporting data came from federally funded and maintained U.S. databases (e.g., Soil Survey Geographic Database). This classification approach was implemented in ArcGIS Desktop and validated using data from various SB field studies in the U.S. Midwest. This approach has proven to be robust because it identified SB sites considered to be either performing or promising in reducing nitrate losses in tile drainage water with an accuracy range of 57% to 63%. SBB candidate areas in the U.S. Midwest totaled up to 225,000 ha and 342,000 ha for buffer widths of 20 and 30 m, respectively. The proposed approach could assist in both local siting and regional-scale implementation of SBBs, which could significantly mitigate nitrate loss in tile-drained U.S. Midwest agricultural lands while providing other environmental benefits and economic incentives for farmers. Keywords: Biomass production, Multi-functional landscapes, Sustainable agricultural land management, Tile-drained agriculture, Water quality protection.

Fundamental study on estimation of tooth contact area based on combined image from temperature monitor images of tooth surface with a high-speed-camera thermography and on meshing vibration for high reduction ratio hypoid gear with a three-tooth pinion

Fundamental study on estimation of tooth contact area based on combined image from temperature monitor images of tooth surface with a high-speed-camera thermography and on meshing vibration for high reduction ratio hypoid gear with a three-tooth pinion

Experimental Comparison of Logic Circuit Synthesis Methods

This paper presents the results of an experimental comparison of methods for the synthesis of combinational logic circuits that implement specified Boolean functions. The following methods were considered: the method of Akers, bi-decomposition, the methods of cascades, Minato-Morreale, Reed-Muller and DSD-decomposition. The comparison was based on an estimate of power, delay and area of synthesized logic circuits. The evaluation was carried out without the process of technology mapping of the circuits. These parameters were chosen because they are the main criteria for technology-independent optimization, where these methods are widely used. Boolean functions with the number of arguments from 4 to 10 were used as input data. They were generated on the basis of information on the frequency of occurrence of various NPN-equivalence classes of Boolean functions of 4 variables. As a result of the study, it was found that the Minato-Morreale method is the most universal in solving technology-independent optimization problems and can be used for different criteria.

Estimation of Vulnerable Areas to Faults Caused by Tree Vegetation in Power Distribution Systems

Estimation of Vulnerable Areas to Faults Caused by Tree Vegetation in Power Distribution Systems

Construction Cost Estimation in Data-Poor Areas Using Grasshopper Optimization Algorithm-Guided Multi-Layer Perceptron and Transfer Learning

Construction Cost Estimation in Data-Poor Areas Using Grasshopper Optimization Algorithm-Guided Multi-Layer Perceptron and Transfer Learning

High-resolution imagery of the coral reefs of Gorgona Island, Colombian Pacific Ocean: An improved view for monitoring and research

The shape and size of a coral reef are both key factors influencing its ecology and biodiversity. These parameters play a fundamental role in the creation of specific habitats that support a rich biodiversity. They also greatly influence its resilience to climatic events and external influences. Here we present a practical example of photogrammetric reconstruction of the best developed coral reefs of the Colombian Pacific, located at Gorgona Island. Using unoccupied aerial vehicles (drones) and Geographic Information Systems (GIS), we produced high resolution orthophotomosaics and precise contour maps of four reefs and updated estimates of reef areas. We illustrate the improved accuracy of these maps in comparison with maps obtained from satellite imagery and conclude by highlighting the usefulness of drones to locate, measure, and monitor small coral reefs.

IN SITU MELT TEMPERATURE ANALYSIS DURING HIGH PRESSURE GAS ATOMIZATION OF LIQUID METALS

Imaging of the melt plume during high pressure gas atomization (HPGA) using consumer digital single lens reflex (DSLR) equipment provides useful information about the process. Color imaging and high spatial resolution can be a useful adjunct to the more widely reported imaging using specialist high frame rate cameras. With knowledge of the camera's color response curves, the ratio of the signals in the red, green, and blue channels can be used to make spatially resolved temperature estimates of the material within the melt plume. Moreover, by combining these temperature estimates, which depend only upon intensity ratios, with the actual intensity of the optical signal, we propose that it is possible to obtain estimates of the relative surface area of the melt within the plume. This in turn can be related to the local melt fragmentation rate within the atomization plume.

Leaf area estimation in Coffea canephora genotypes by neural networks and multiple regression

Leaf area estimation in Coffea canephora genotypes by neural networks and multiple regression

Estimation of Rice Yield in Haryana using Remote sensing data

The direct methods used in general agricultural yield estimation surveys are only acceptable at the national and state levels; however smaller scale yield estimations are not much hampered by it. However, due to their broad and varied application in the public and private sectors, the need for appropriate micro level (e.g., district, block, village level) statistics from sample surveys has increased dramatically over the past decade. However, with the help of satellite and crop yield data using the current sampling design it is possible to create small area estimates of agricultural production at the micro level. This study investigates the application of the small area estimating technique at the block level for the Kurukshetra district, Haryana, India. To generate a better crop yield estimate for paddy crop utilizing data from crop cutting trials and remote sensing data (MODIS/Terra data).

A Deepfake Video Detection Considering Sequential Keyframes and An Estimation of Fake Area by Input Attribution

A Deepfake Video Detection Considering Sequential Keyframes and An Estimation of Fake Area by Input Attribution

Small Area Estimation Using Empirical Bayes Poisson Gamma on Adolescent Fertility Rate in Indonesia

High population growth is one of the main population problems facing Indonesia. One of the government's efforts to overcome this is by preventing adolescent fertility. The Adolescence Fertility Rate (AFR) produced by the IDHS is designed until provincial level, whereas the availability of AFR at the district/city level is needed as an indicator of regional development measurement. The purpose of this research is to produce an estimation of AFR at the district/city level in Indonesia and find out which auxiliary variables significantly influence it and evaluate the performance of the model in estimating AFR. The analytical method used is descriptive analysis to explain the characteristics of adolescent fertility and auxiliary variables and also direct estimation and the indirect estimation method using Small Area Estimation Empirical Bayes Poisson Gamma. The results showed that the number of villages, school facilities, health facilities, health workers, telephone lines and operators significantly affected the fertility of adolescents and the results of the SAE EB Poisson Gamma estimation were better than the direct estimation method. Suggestions proposed are the government need to increase attention to districts/cities that have AFR that is higher than the average AFR or National AFR and increase the number of school facilities and the number of health workers.

Small Area Estimation of Multidimensional Poverty in East Java Province Using Satellite Imagery

The government has so far focused on a monetary approach to overcoming poverty, while poverty is multidimensional. Holistic and accurate poverty indicators are needed as material for policy formulation, such as the Multidimensional Poverty Index (IKM), which is calculated from raw data from the National Socioeconomic Survey (SUSENAS). However, the direct estimation of the multidimensional poverty headcount (AKM) is only accurate at the provincial level, as seen from the relative standard error (RSE) of several districts and cities, which is still above 25 percent. Increasing the sample size requires time, effort, and cost, so the Small Area Estimation (SAE) method can be an alternative. Apart from using official statistics for accompanying variables, satellite imagery has the advantage of being up-to-date and available up to a granular level. This study aims to estimate the AKM at the district/city level in East Java Province by utilizing satellite imagery and official statistics in SAE. The results showed that SAE HB Beta-logistics, with the accompanying variables combined with satellite imagery and official statistics, has a higher accuracy than direct estimation.

Assessing the spatial coherence of forest cover indicators from different data sources: A contribution to sustainable development reporting

The accuracy of forest area estimates has improved over time as a result of field/cadastral surveys, enhanced remote sensing techniques, and the effectiveness of algorithms for automatical recognition of land cover types. However, forest statistics seem to be less accurate in disaggregated spatial domains such as small administrative units. To evaluate the contribution of different data sources to small-area forest cover estimation in Europe, we compared seven indicators with different spatial coverage and resolution. The analysis considered multiple information sources from innovative initiatives, such as the Copernicus Land monitoring scheme, and traditional (national) surveys. More specifically, the study examined the spatial coherence of these indicators at the municipal scale in Italy to achieve two objectives: (i) assessing the overall precision of forest cover rates and (ii) identifying spatial variations in forest cover rates associated with the technical characteristics of each data source. A spatial econometric approach was used to identify the sources of spatial divergence in forest cover rates and determine the data providers best suited to meet the information requirements of environmental reporting at the desired spatial scale. The results reveal that the selected indicators show varying degrees of internal coherence, with some indices displaying strong correlations and others delineating heterogeneous spatial patterns. Our study highlights the importance of choosing the right information source assessing forest area at the municipal level and provides a valuable approach quantifying the coherence and reliability of environmental indicators in monitoring key aspects of sustainable development.