Differences In Estimation Accuracy Research Articles

Effectiveness and Characteristics of Virtual Antennas in the Multiple Signal Classification Algorithm

This paper proposes a novel direction of arrival (DoA) estimation method based on the Multiple Signal Classification (MUSIC) algorithm using virtual antennas. The MUSIC method is a widely used DoA estimation technique known for its high accuracy and high resolution. However, it has a fundamental limitation: when the necessary condition of having more antenna elements at the base station than users is not met, DoA estimation becomes infeasible. To address this limitation, we introduce the concept of virtual antennas, allowing DoA estimation, even under restricted conditions. Virtual antennas are not physically present but can be virtually arranged, enabling the generation of virtual-received signals similar to those of real array antennas. Through simulation experiments, we demonstrate that our approach enables DoA estimation with the MUSIC algorithm even when its necessary conditions are not satisfied. Additionally, this paper explores the characteristics of virtual antennas in detail. We conduct simulation experiments to examine the differences in estimation accuracy between real and virtual antennas, as well as the impact of virtual antenna arrangement and count on estimation accuracy. The results show that, although virtual antennas provide lower estimation accuracy compared to real antennas, their flexible arrangement allows for improved resolution when signal sources are closely spaced by increasing the spacing between virtual antennas. Furthermore, under Additive White Gaussian Noise (AWGN) conditions, increasing the number of virtual antennas enhances estimation accuracy.

The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model

The assimilation of remote sensing data into mechanistic models of crop growth has become an available method for estimating yield. The objective of this study was to explore an effective assimilation approach for estimating maize grain protein content and yield using a canopy remote sensing data and crop growth model. Based on two years of field experiment data, the remote sensing inversion model using assimilation intermediate variables, namely leaf area index (LAI) and leaf nitrogen accumulation (LNA), was constructed with an R2 greater than 0.80 and a low root-mean-square error (RMSE). The different data assimilation approaches showed that when the LAI and LNA variables were used together in the assimilation process (VLAI+LNA), better accuracy was achieved for LNA estimations than the assimilation process using single variables of LAI or LNA (VLAI or VLNA). Similar differences in estimation accuracy were found in the maize yield and grain protein content (GPC) simulations. When the LAI and LNA were both intermediate variables in the assimilation process, the estimation accuracy of the yield and GPC were better than that of the assimilation process with only one variable. In summary, these results indicate that two physiological and biochemical parameters of maize retrieved from hyperspectral data can be combined with the crop growth model through the assimilation method, which provides a feasible method for improving the estimation accuracy of maize LAI, LNA, GPC and yield.

Psychology Faculty Overestimate the Magnitude of Cohen’s d Effect Sizes by Half a Standard Deviation

In this experiment, we recruited 261 psychology faculty to determine the extent to which they were able to visually estimate the overlap of two distributions given a Cohen’s d effect size; and vice-versa estimate d given two distributions of varying overlap. In a pre-test, participants in both conditions over-estimated effect sizes by half a standard deviation on average. No significant differences in estimation accuracy by psychology sub-field were found, but having taught statistics coursework was a significant predictor of better performance. After a short training session, participants improved substantially on both tasks on the post-test, with 63% reduction in absolute error and negligible overall bias (98% bias reduction). Furthermore, post-test performance indicated that learning transferred across answering modes. Teachers of statistics might find it beneficial to include a short exercise (less than 10 minutes) requiring the visual estimation of effect sizes in statistics coursework to better train future psychology researchers.

The number line estimation task is a valid tool for assessing mathematical achievement: A population-level study with 6484 Luxembourgish ninth-graders

The number line estimation task is an often-used measure of numerical magnitude understanding. The task also correlates substantially with broader measures of mathematical achievement. This raises the question of whether the task would be a useful component of mathematical achievement tests and instruments to diagnose dyscalculia or mathematical giftedness and whether a stand-alone version of the task can serve as a short screener for mathematical achievement. Previous studies on the relation between number line estimation accuracy and broader mathematical achievement were limited in that they used relatively small nonrepresentative samples and usually did not account for potentially confounding variables. To close this research gap, we report findings from a population-level study with nearly all Luxembourgish ninth-graders (N = 6484). We used multilevel regressions to test how a standardized mathematical achievement test relates to the accuracy in number line estimation on bounded number lines with whole numbers and fractions. We also investigated how these relations were moderated by classroom characteristics, person characteristics, and trial characteristics. Mathematical achievement and number line estimation accuracy were associated even after controlling for potentially confounding variables. Subpopulations of students showed meaningful differences in estimation accuracy, which can serve as benchmarks in future studies. Compared with the number line estimation task with whole numbers, the number line estimation task with fractions was more strongly related to mathematical achievement in students across the entire mathematical achievement spectrum. These results show that the number line estimation task is a valid and useful tool for diagnosing and monitoring mathematical achievement.

Effects of delayed weather radar images on pilots’ spatial awareness

Data-linked Next Generation Weather Radar (NEXRAD) images can be delayed up to 20 min in the cockpit. Pilots' underappreciating or ignoring the time delay may be the major cause of two fatal accidents. No studies have connected spatial awareness with accidents. This study evaluated how delayed radar information affects the spatial awareness of pilots at three levels of analysis. Thirty-one student pilots and flight instructors completed three sequential estimation tasks (i.e., the current location of storms, the current relative distance to storms, and the future relative distance to storms). Fifty-four weather scenarios were developed for three factors (storm speeds, delays, displays) and presented to pilots. The results indicated that delays and the storm speed significantly affected the three levels of spatial awareness. Participants' estimation accuracy was the lowest under long delay and fast speed in the current location estimation, under medium delay and speed in the current distance estimation, and under short delay and slow speed in the future distance estimation. Spatial awareness could be high under the long delay and fast speed conditions if pilots had no time limits. Thus, pilots can process 20-min delayed radar information. However, there were no differences in estimation accuracy between the static and animation displays in any of the conditions. Well-designed features on displays, such as scale or distance measuring tools, can aid pilots’ spatial estimation and support all levels of spatial awareness.

Extraction of Spectral Information from Airborne 3D Data for Assessment of Tree Species Proportions

With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous.

Development and evaluation of image-series for portion size estimation in dietary assessment among adults.

Portion size images are advantageous in dietary assessment. The aim of the present study was to develop and validate new culturally specific image-series for portion size estimation to be used in a new Norwegian version of a British web-based dietary assessment tool (myfood24). Twenty-three image-series of different foods, each containing seven portion size images, were created and validated in a group of adults (n 41, 58 % female) aged 19-44 (median 23), out of which 63 % had higher (tertiary) education. The participants compared 46 portions of pre-weighed foods to the portion size images (1886 comparisons in total). Portion size estimations were either classified as correct, adjacent or misclassified. The weight discrepancy in percentage between the chosen and the correct portion size image was also calculated. Mann-Whitney U tests were used to explore if portion size estimation accuracy differed across sample characteristics, or if it depended on how the foods were presented. For thirty-eight of the forty-six presented food items, the participants selected the correct or adjacent portion size image 98 % on average. The remaining eight food items were on average misclassified by 27 % of the participants. Overall, a mean weight discrepancy of 2⋅5 % was observed between the chosen and the correct portion size images. Females estimated portion size more accurately than males (P = 0⋅019). No other significant differences in estimation accuracy were observed. In conclusion, the new image-series performed satisfactorily, except for the image-series depicting bread, caviar spread and marzipan cake, which will be altered. The present study demonstrates the importance of validating portion size estimation tools.

To Add or Not to Add Game Elements? Exploring the Effects of Different Cognitive Task Designs Using Eye Tracking

Research on instructional design provides inconsistent results on the use of game elements in cognitive tasks or learning. Cognitive load theory suggests that game elements increase extraneous cognitive load and, thus, may distract the users. In contrast, from an emotional design perspective, the use of game elements is argued to increase performance by providing a more interesting and motivating task environment. To contribute to this debate, the current study investigated the effect of game elements on behavioral performance, attention, and motivation. We designed two versions of the number line estimation task-one with game elements and one without. Participants completed both versions of the task while their eye-fixation behavior was recorded. Results indicated that participants paid attention to game elements, that is, they fixated them, although they were not necessary to complete the task. However, no difference in estimation accuracy was observed between the two task versions. Moreover, the task version with game elements was rated to be more attractive, stimulating, and novel, and participants reported experiencing greater flow. In sum, these data indicate that game elements seem to capture attention but also increase motivational aspects of learning tasks rather than decreasing performance.

Cost Awareness of Common Supplies Is Severely Impaired Among All Members of the Surgical Team

BackgroundIncreased health care spending concerns have generated interest in reducing operating room (OR) costs, but the cost awareness of the surgical team selecting intraoperative supplies remains unclear. This work characterizes knowledge of supply cost among surgeons and OR staff in a large academic hospital and seeks to examine the role of experience and training with regards to cost insight. MethodsThis work is a cross-sectional study of surgeons, trainees, nurses, and surgical technicians (n = 372) across all surgical specialties at a large academic hospital. Participants completed a survey reporting frequency of use and estimated cost for 11 common surgical supplies as well as opinions on access to cost information in the OR. Cost estimation error was expressed as the ratio of estimated-to-actual cost, and groups were compared with one-way analysis of variance and chi-squared testing. Spearman correlation (ρ) was used to describe the relationship between monotonic variables. ResultsOverestimation error was universal and ranged widely (3.80-49.79). There was no significant difference in estimation accuracy when stratified by role or years of experience. Less expensive items had higher rates of estimation error than more expensive items (P < 0.001), and a moderately strong relationship was found between decreased item cost and increased estimation error (ρ: 0.49). The overwhelming majority (91%) of respondents expressed a desire to learn more about supply pricing. ConclusionsPrice knowledge of common supplies is globally impaired for entire surgical team but coexists with a strong desire to augment cost awareness. Improved access to cost information has a high potential to inform surgical decision-making and decrease OR waste.

Evaluating Different Non-Destructive Estimation Methods for Winter Wheat (Triticum aestivum L.) Nitrogen Status Based on Canopy Spectrum

Compared to conventional laboratory testing methods, crop nitrogen estimation methods based on canopy spectral characteristics have advantages in terms of timeliness, cost, and practicality. A variety of rapid and non-destructive estimation methods based on the canopy spectrum have been developed on the scale of space, sky, and ground. In order to understand the differences in estimation accuracy and applicability of these methods, as well as for the convenience of users to select the suitable technology, models for estimation of nitrogen status of winter wheat were developed and compared for three methods: drone equipped with a multispectral camera, soil plant analysis development (SPAD) chlorophyll meter, and smartphone photography. Based on the correlations between observed nitrogen status in winter wheat and related vegetation indices, green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI) were selected as the sensitive vegetation indices for the drone equipped with a multispectral camera and smartphone photography methods, respectively. The correlation coefficients between GNDVI, SPAD, and VARI were 0.92 ** and 0.89 **, and that between SPAD and VARI was 0.90 **, which indicated that three vegetation indices for these three estimation methods were significantly related to each other. The determination coefficients of the 0–90 cm soil nitrate nitrogen content estimation models for the drone equipped with a multispectral camera, SPAD, and smartphone photography methods were 0.63, 0.54, and 0.81, respectively. In the estimation accuracy evaluation, the method of smartphone photography had the smallest root mean square error (RMSE = 9.80 mg/kg). The accuracy of the smartphone photography method was slightly higher than the other two methods. Due to the limitations of these models, it was found that the crop nitrogen estimation methods based on canopy spectrum were not suitable for the crops under severe phosphate deficiency. In addition, in estimation of soil nitrate nitrogen content, there were saturation responses in the estimation indicators of the three methods. In order to introduce these three methods in the precise management of nitrogen fertilizer, it is necessary to further improve their estimation models.

Analysis of Hybrid State Estimators: Accuracy and Convergence of Estimator Formulations

This paper provides a comprehensive analysis of several hybrid state estimators (HSEs) to assess their performance and identify their unique characteristics in transmission network. The results reveal that the formulation of an HSE can have a significant impact on both the estimation accuracy and the convergence properties of the estimator, which are the two most important performance factors for an HSE. Furthermore, the analysis performed has identified the formulation that maximizes these factors. The assessment can be divided into three parts. The first part conducts comprehensive simulations for the IEEE 14 bus test system for every possible phasor measurement units (PMU) placement. The second part validates the conclusions made in the first part using simulations of the IEEE 118 bus test system with both random PMU placements and optimally and suboptimally selected placements. The third part further validates these conclusions and directly explains the reasons for the differences in estimation accuracy observed for the different formulations of HSEs through mathematical analysis.

Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe.

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343–2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties.

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( &lt; 50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.

Estimation of Uterine Size: How Accurate Are We?

To evaluate the accuracy of gynecologic surgeons at estimating uterine dimensions and weight. Six model uteri of various sizes were created to simulate the size and consistency of a uterus and displayed at 3 stations. The visual station (VS) comprised 2 specimens placed on an unmarked table. The laparoscopic station (LS) consisted of 2 model uteri, each placed in a separate simulated abdomen with a 0 degree laparoscope and 2 operative trocars with standard instruments. The blind weight station (BWS) consisted of blind palpation of 2 separately weighted models (heavy model [HM] and light model [LM]). Participants visually estimated the dimensions of each VS and LS models and blindly palpated the BWS models to estimate weight. Participants included 15 residents, 27 attendings, and 6 medical students. There was no difference in estimation accuracy regarding gender and age. For the VS and LS groups, participants underestimated all dimensions (VS variance = -15.0%; P < 0.001 and LS variance = -31.9%; P < 0.0001). Laparoscopic estimation was less accurate than direct vision (P < 0.0001). Attendings and residents equally underestimated the 3 dimensions visually (P = 0.46), but attendings were more accurate at estimating laparoscopic dimensions (-25.8% vs -41.1%; P = 0.0001). All groups overestimated model weights (HM variance, 92.5%; P < 0.001 and LM variance, 132.0%; P < 0.0001), with attendings more accurate than residents (39.7% vs 167.6%; P = 0.015 for HM and 52.0% vs 238.5%; P = 0.035 for LM). Gynecologic surgeons at all levels of training are inaccurate at estimating dimensions and weights. With surgical decisions often predicated on estimates, education is needed to improve estimation methods.

Distance perception in an open water environment: Analysis of individual differences.

We investigated whether distance estimation accuracy over open water is influenced by the viewing direction of the observer. Twenty-two healthy students (9 male, 13 female) made 10 distance estimates ranging between 50 and 950 m actual distance in 2 viewing conditions: (1) from shore to boat and (2) from boat to shore. There were no consistent differences in estimation accuracy between viewing directions. The group data revealed a general tendency to underestimate actual distances (74%), but there was considerable interindividual variance (mean error of 74% ± 27%, range = 31% to 145%). A multilevel regression model of estimate accuracy suggests there were three subgroups of participants. One subgroup (N = 4, 18%) were consistent underestimaters, regardless of distance, whereas another subgroup (N = 5, 23%) consistently overestimated. However, the majority (N = 13, 59%) tended to underestimate at shorter distances (less than 400 m) and then overestimate at longer distances. These findings have important implications in survival situations in open water where an individual may need to judge an estimated distance against their perceived swimming capacity in order to self-rescue.

Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters

Abstract The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for parameters irrespective of inversion approaches was leaf area index &gt; canopy chlorophyll content &gt; leaf chlorophyll content &gt; equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

Collective landmarks for deep time: a new tool for evolution education

Evolution is a fundamental, organising concept in biology, yet there is widespread resistance to evolution among US students and there are rising creationist challenges in Europe. Resistance to evolution is linked to lack of understanding of the age of the Earth. An understanding of deep time is thus essential for effective biology education. Landmarks – well-known events of known absolute age – are important in developing a mental framework for deep time. ‘Collective landmarks’ are important events of which a group of students has accurate and precise knowledge. The collective landmarks for time were determined in several semesters of an interdisciplinary undergraduate course at a US public research university. A written survey was employed that asked students to select the appropriate ten-fold time range for ten important cosmological, biological and historical events. The collective landmarks were very similar across classes, including the Big Bang and the Cold War, and there was progress developing additional landmarks. No statistically significant differences in accuracy of estimation were detected across science, technology, engineering and mathematics (STEM) and social sciences majors. The potential utility of collective landmarks within a single biology course, and across a university biology programme, is described.

Runners Greatly Underestimate Sweat Losses Before and After a 1-hr Summer Run

The purpose of this study was to determine how accurately runners estimate their sweat losses. Male (n = 19) and female (n = 20) runners (41 ± 10 yr, VO2max 57 ± 9 ml · kg(-1) · min(-1) from the southeastern U.S. completed an ~1-hr run during late summer on a challenging outdoor road course (wet bulb globe temperature 24.1 ± 1.5 °C). Runs began at ~6:45 a.m. or p.m. Before and after running, participants filled race-aid-station paper cups with a volume of fluid they felt would be equivalent to their sweat losses. Total sweat losses and losses by percent body weight differed (p < .01) between men (1,797 ± 449 ml, 2.3% ± 0.6%) and women (1,155 ± 258 ml, 1.9% ± 0.4%). Postrun estimates (738 ± 470 ml) were lower (p < .001) than sweat losses (1,468 ± 484 ml), equaling underestimations of 50% ± 23%, with no differences in estimation accuracy by percentage between genders. Runners who reported measuring changes in pre- and postrun weight to assess sweat losses within the previous month (n = 9, -54% ± 18%) were no more accurate (p = .55) than runners who had not (n = 30, -48% ± 24%). These results suggest that inadequate fluid intake during runs or between runs may stem from underestimations of sweat losses and that runners who do assess sweat-loss changes may be making sweat-loss calculation errors or do not accurately translate changes in body weight to physical volumes of water.

Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM

The rapid development in aerial digital cameras in combination with the increased availability of high-resolution Digital Elevation Models (DEMs) provides a renaissance for photogrammetry in forest management planning. Tree height, stem volume, and basal area were estimated for forest stands using canopy height, density, and texture metrics derived from photogrammetric matching of digital aerial images and a high-resolution DEM. The study was conducted at a coniferous hemi-boreal site in southern Sweden. Three different data-sets of digital aerial images were used to test the effects of flight altitude and stereo overlap on an area-based estimation of forest variables. Metrics were calculated for 344 field plots (10 m radius) from point cloud data and used in regression analysis. Stand level accuracy was evaluated using leave-one-out cross validation of 24 stands. For these stands the tree height ranged from 4.8 to 26.9 m (17.8 m mean), stem volume 13.3 to 455 m3 ha−1 (250 m3 ha−1 mean), and basal area from 4.1 to 42.9 m2 ha−1 (27.1 m2 ha−1 mean) with mean stand size of 2.8 ha. The results showed small differences in estimation accuracy of forest variables between the data-sets. The data-set of digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet), showed Root Mean Square Errors (in percent of the surveyed stand mean) of 8.8% for tree height, 13.1% for stem volume and 14.9% for basal area. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry.

Students' Accuracy of Measurement Estimation: Context, Units, and Logical Thinking

This study examined students' accuracy of measurement estimation for linear distances, different units of measure, task context, and the relationship between accuracy estimation and logical thinking. Middle school students completed a series of tasks that included estimating the length of various objects in different contexts and completed a test of logical thinking ability. Results found that the students were not able to give accurate estimations for the lengths of familiar objects. Students were also less accurate in estimating in metric units as compared to English or novel units. Estimation accuracy was dependent on the task context. There were significant differences in estimation accuracy for two‐ versus three‐dimensional estimation tasks. There were no significant differences for estimating objects with different orientations or embedded objects. For the tasks requiring the students to estimate in English units, the embedded task and the three‐dimensional tasks were correlated with logical thinking. For estimation tasks with novel units, three‐dimensional and two‐dimensional estimation tasks were significantly correlated with the logical thinking.In order to interact effectively with our environment it is essential to possess an intuitive grasp of both dimension and scale and to be able to manipulate such information. Estimation, approximating and measuring are all components of such intuition (Forrester, Latham, &amp; Shire, 1990, p. 283).