Accuracy Of Area Estimation Research Articles

Lung area estimation using functional tidal electrical impedance variation images and active contouring

Objective. Electrical impedance tomography is a valuable tool for monitoring global and regional lung mechanics. To evaluate the recorded data, an accurate estimate of the lung area is crucial. Approach. We present two novel methods for estimating the lung area using functional tidal images or active contouring methods. A convolutional neural network was trained to determine, whether or not the heart region was visible within tidal images. In addition, the effects of lung area mirroring were investigated. The performance of the methods and the effects of mirroring were evaluated via a score based on the impedance magnitudes and their standard deviations in functional tidal images. Main results. Our analyses showed that the method based on functional tidal images provided the best estimate of the lung area. Mirroring of the lung area had an impact on the accuracy of area estimation for both methods. The achieved accuracy of the neural network’s classification was 94%. For images without a visible heart area, the subtraction of a heart template proved to be a pragmatic approach with good results. Significance. In summary, we developed a routine for estimation of the lung area combined with estimation of the heart area in electrical impedance tomography images.

An assessment of Planet satellite imagery for county-wide mapping of rice planting areas in Jiangsu Province, China with one-class classification approaches

ABSTRACT There is a growing demand of the spatial distribution of major crops at the field level in recent years for the precision management of farmlands in developing countries. However, most previous studies used low or medium resolution images (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat) to map the planting areas of rice in various regions of China. As a new source of satellite imagery, the 3 m Planet satellite imagery with daily revisit frequency has rarely been used for mapping crop types. In particular, it is still unclear how well the Planet imagery would perform in mapping the rice planting areas in the Lower Reaches of the Yangtze River Plain of China where image acquisition is strongly limited by rainy and cloudy weather conditions. To overcome the spectral confusion between rice and non-rice vegetation types, this study proposed one-class classification approaches with Planet imagery from the early and late seasons of rice crops over three counties (Guanyun, Rugao and Wujiang) across Jiangsu Province in eastern China. Apart from the traditional use of mono-temporal imagery from the late season (Strategy I), we developed two strategies to make use of the bi-temporal imagery from the early and late seasons, with one being direct classification using stacked images (Strategy II) and the other being stepwise classification (Strategy III). All classifications were performed with the popular machine learning classifier one-class support vector machine (OCSVM) and evaluated from the aspects of classification accuracy and area estimation accuracy. The results showed Strategy I could achieve an overall classification accuracy of 83.02% and kappa coefficient (ƙ) of 0.65. The overall accuracy (OA) can be significantly improved by Strategy II (OA = 84.44% and ƙ = 0.69) and further by Strategy III (OA = 90.42% and ƙ = 0.81). In terms of area estimation, the estimates from Strategy III were consistent with the government reported statistical data with a relative error (RE) within 10% at the county level. At the town level, the correspondence between Planet-derived rice planting areas and the Landsat-8 derived reference data was significantly improved with Strategy III as compared to the other two for Guanyun and Wujiang. The stepwise classification approach (Strategy III) developed with Planet imagery has great values in monitoring rice paddies over the regions with fragmentized distribution of rice fields.

Extraction of Leaf Biophysical Attributes Based on a Computer Graphic-based Algorithm Using Terrestrial Laser Scanning Data

Leaf attribute estimation is crucial for understanding photosynthesis, respiration, transpiration, and carbon and nutrient cycling in vegetation and evaluating the biological parameters of plants or forests. Terrestrial laser scanning (TLS) has the capability to provide detailed characterisations of individual trees at both the branch and leaf scales and to extract accurate structural parameters of stems and crowns. In this paper, we developed a computer graphic-based 3D point cloud segmentation approach for accurately and efficiently detecting tree leaves and their morphological features (i.e., leaf area and leaf angle distributions (leaf azimuthal angle and leaf inclination angle)) from single leaves. To this end, we adopted a sphere neighbourhood model with an adaptive radius to extract the central area points of individual leaves with different morphological structures and complex spatial distributions; meanwhile, four auxiliary criteria were defined to ensure the accuracy of the extracted central area points of individual leaf surfaces. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to cluster the central area points of leaves and to obtain the centre point corresponding to each leaf surface. We also achieved segmentation of individual leaf blades using an advanced 3D watershed algorithm based on the extracted centre point of each leaf surface and two morphology-related parameters. Finally, the leaf attributes (leaf area and leaf angle distributions) were calculated and assessed by analysing the segmented single-leaf point cloud. To validate the final results, the actual leaf area, leaf inclination and azimuthal angle data of designated leaves on the experimental trees were manually measured during field activities. In addition, a sensitivity analysis investigated the effect of the parameters in our segmentation algorithm. The results demonstrated that the segmentation accuracy of Ehretia macrophylla (94.0%) was higher than that of crape myrtle (90.6%) and Fatsia japonica (88.8%). The segmentation accuracy of Fatsia japonica was the lowest of the three experimental trees. In addition, the single-leaf area estimation accuracy for Ehretia macrophylla (95.39%) was still the highest among the three experimental trees, and the single-leaf area estimation accuracy for crape myrtle (91.92%) was lower than that for Ehretia macrophylla (95.39%) and Fatsia japonica (92.48%). Third, the method proposed in this paper provided accurate leaf inclination and azimuthal angles for the three experimental trees (Ehretia macrophylla: leaf inclination angle: R 2 = 0.908, RMSE = 6.806° and leaf azimuth angle: R 2 = 0.981, RMSE = 7.680°; crape myrtle: leaf inclination angle: R 2 = 0.901, RMSE = 8.365° and leaf azimuth angle: R 2 = 0.938, RMSE = 7.573°; Fatsia japonica: leaf inclination angle: R 2 = 0.849, RMSE = 6.158° and leaf azimuth angle: R 2 = 0.947, RMSE = 3.946°). The results indicate that the proposed method is effective and operational for providing accurate, detailed information on single leaves and vegetation structure from scanned data. This capability facilitates improvements in applications such as the estimation of leaf area, leaf angle distribution and biomass.

A GRAPHIC TOOL FOR ESTIMATION OF THE AREA OF BURNS IN CHILDREN

The severity of burns depends both on their size and depth of affected tissues. The existing simple methods for estimation of the injured skin area are not precise, especially in children. During last decades, attempts were made to employ information technologies for more precise estimation of the skin area affected by the burn. Usually, a special template («skitsa») is used to represent a human body in a schematic way, allowing automatic calculation of affected area with high accuracy. It is especially important in children, since proportions of their bodies vary greatly at different ages.A computer graphic tool for estimation of area of burns was designed for the Department of Pediatric Burns. It uses an algorithm that allows to adjust burn data displayed on a two-dimensional template, taking into account the three-dimensional nature of the human body. Such correction increases burn area estimation accuracy. Additionally, this system allows using digital photographs of burn area for dynamic assessment of skin condition during treatment. A special algorithm is used to compensate for possible color distortions of the photographs in series, enabling better possibility of patient’s skin condition comparison at different times.

Estimation of mango growing areas using remote sensing

An attempt was made to estimate acreage and to depict the spatial distribution of mango growing areas using linear imaging self-scanning image of remote sensing data for Saharanpur district of Uttar Pradesh. The image was processed using ENVI software. Unsupervised, supervised and decision tree classification were used for the acreage estimation. The study has clearly demonstrated the usefulness of remote sensing image for identifying and acreage estimation of mango orchards. The results also indicated that mango acreage estimation was more accurate in decision tree approach (92.92%) as compared to other two methods (81.73 and 80.43%, respectively). The overall result revealed that the finer resolution the time series data, better the accuracy of area estimation. The above study proves that the remote sensing data could be effectively used for other perennial horticultural crops with finer resolution time series data.

Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method

Using remote sensing data to estimate crop area is efficient to a wide range of end-users, including government agencies, farmers and researchers. Moderate spatial resolution (MSR) image data are widely used to estimate crop area. But its accuracy can’t meet the demands of precision. Spatial sampling techniques integrated the strengths of remote sensing and sampling survey are being widely used. This method need large sample size which is cannot be guaranteed by remote sensing due to weather. The Unmanned Aerial Vehicle (UAV) can be used as an effective way to guarantee enough sample size. This paper proposed a spatial sampling method using MSR image classification results and UAV transects, a stratified random sampling method was proposed, area-scale (from MSR image classification) was used as auxiliary variable to guide the distribution of UAV transects, which had proved that 2% sampling ratio can make the crop area estimation accuracy more than 95% with a 95% confidence interval.

Planting area estimation of Chinese Tibetan medicine Herpetospermum pedunculosum based on RS&GIS-by case study of Lengqi and Xinglong town in Luding county

The herbs used as the material for traditional Chinese medicine are mostly planted at mountain areas where there is appropriate weather condition. Due to the complex terrain condition in mountain area, the planting at such places is always associated with a scattered pattern, which inevitably bring difficulty to the work of accurate measurement for the planting area. Advanced research about monitoring the planting area for medicine-herb under complex environmental condition is so critical and meaningful for developing the medicine-herb planting to a large scale and an industry. Therefore, this research, taking the planting of Herpetospermum pedunculosum in Luding county as an example, utilizes RS&GIS: using the image data from TM and ETM datum as the remote sensing information source, integrating GPS data from quadrat survey on the spots, and analyzing on a GIS platform, to extract the planting area of H. pedunculosum in Luding county. The results indicate that: (1) the method and technology used to estimate the planting area of H. pedunculosum are feasible and can be applied to monitoring the planting area of medicine-herb at mountain area under complex environmental conditions: (2) the planting area of H. pedunculosum in Luding county is estimated to be 857. 246 acres by the methods utilized in this research, which is closed to the value from local statistical data with an error range less than 5%; (3) in consideration of those inevitable errorrs generating from the procedures such as remote sensing data acquisition, data preprocessing, data interpretation, and so on, and the fact that the classification of remote sensing data, which must be implemented by those above procedures, was used directly to extract the herb type, the accuracy of area estimation for the herb is significantly influenced by the accuracy of classification for herb type, and the estimated area is inevitably associated with some error.

Parcel-Level Identification of Crop Types Using Different Classification Algorithms and Multi-Resolution Imagery in Southeastern Turkey

This research investigates the accuracy of pixel- and object-based classifi cation techniques across varying spatial resolutions to identify crop types at parcel level and estimate the area at six test sites to fithe optimum data source for the identifi cation of crop parcels. Multi-sensor data with spatial resolutions of 2.5 m, 5 m and 10 m from SPOT5 and 30 m from Landsat-5 TM were used. Maximum Likelihood (ML), Spectral Angle Mapper (SAM), and Support Vector Machines (SVM) were used as pixel-based methods in addition to object-based image classifi cation (OBC). Post-classifi cation methods were applied to the output of pixel-based classifi cation to minimize the noise effects and heterogeneity within the agricultural parcels. In addition, processing-time performance of the algorithms was evaluated for the test sites and district scale classifi cation. OBC results provided comparatively the best performance for both parcel identifi cation and area estimation at 10 m and fi ner spatial resolution levels. SVM followed OBC at 2.5 m and 5 m resolutions but accuracies decreased dramatically with coarser resolutions. ML and SAM results were worse up to 30 m resolution for both crop type identifi cation and area estimation. In general, parcel identifi cation effi ciency was strongly correlated with spatial resolution while the classifi cation algorithm was a more effective factor than spatial resolution for area estimation accuracy. Results also provided an opportunity to discuss the effects of image resolution and the classifi cation algorithm independent factors such as parcel size, spatial distribution of crop types and crop patterns.

Characteristics of quazi-likelihood estimation of the image area in the presence of spatial noise

Asymptotic expressions for the characteristics of quasi-likelihood estimation of image area have been obtained by using the local Markov approximation method. It was shown that the accuracy of area estimation is determined by the magnitude of intensity jump over the boundary limiting the area occupied by image.

Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method: a Simulation Experiment

This paper proposed a spatial sampling method, this method had proved that using stratified random sampling, choosing area-scale index as auxiliary variable, using simulated MSR image and UAV image can improve the crop area estimation accuracy over large area, the crop area estimation accuracy can be more than 95% under a 95% CI.

Acoustic Rhinometry in Healthy Humans: Accuracy of Area Estimates and Ability to Quantify Certain Anatomic Structures in the Nasal Cavity

We evaluated the accuracy of acoustic rhinometry (AR) measurements in healthy humans and assessed the ability of AR in quantifying the dimensions of the paranasal sinuses and certain anatomic structures in the nasal cavity. Twenty nasal passages of 10 healthy adults were examined by AR and computed tomography (CT) before and after decongestion. Actual cross-sectional areas of the nasal cavity and actual locations of the nasal valve, the head of the inferior turbinate, the head of the middle turbinate, the ostia of the frontal and maxillary sinuses, and the choana were determined from CT sections perpendicular to the curved acoustic axis of the nasal passage. The AR-measured cross-sectional areas in the anterior nasal cavity were in reasonable agreement with the corresponding areas determined from CT, whereas AR consistently overestimated the passage areas at locations posterior to the paranasal sinus ostia. The nasal valve was identified as a pronounced minimum on the AR area-distance curve. However, AR did not discretely identify the head of the inferior turbinate, the head of the middle turbinate, or the choana. The local minima on the AR area-distance curve beyond the nasal valve are caused by acoustic resonances in the nasal cavity, and do not correspond to any anatomic structure. The AR area overestimation beyond the paranasal sinus ostia is due to the interaction between the nasal cavity and the paranasal sinuses, rather than to sound loss into the sinuses. Acoustic rhinometry provides no quantitative information on ostium size or sinus volume in either non-decongested or decongested nasal cavities.

Resolution dependent errors in remote sensing of cultivated areas

Remote sensing has become a common and effective method for estimating the areal coverage of land cover classes. One class of particular interest is agriculture as area estimates of cultivated lands are important for purposes such as estimating yields or irrigation needs. The synoptic coverage of satellite imagery and the relative ease of automated analysis have led to widespread mapping of agriculture using remote sensing. The accuracy of area estimates derived from these maps is known to be related to the accuracy of the maps. However, even in the situation where the map is very accurate, errors in area estimates may occur. These errors result from the behavior of the distribution of subpixel proportions of cultivated areas, and how that behavior changes as a result of sensor spatial resolution and class definitions. The sensitivity of estimates of cultivated areas to sensor spatial resolution and to the choice of threshold used to define cultivated land is explored in six agriculturally distinct locations around the world. Using a beta model for the distribution of subpixel proportions that is parameterized using variograms, it is possible to model the distribution of subpixel proportions for any spatial resolution. When the spatial resolution is small with respect to the spatial structure of the landscape (as measured by the variogram range) use of any class definition threshold produces an estimate very close to the true area coverage. On the other hand, as the resolution becomes coarse in relation to the variogram range, the subpixel proportions are no longer concentrated at the extremes of the distribution and the difference between the estimated and the true area has greater sensitivity to the selected threshold used to define classes. Thus, for the cases examined here, both the resolution and the class definition threshold have a strong influence on area estimates. The spatial resolutions where errors can be large depend on landscape spatial structure, which can be quantified using variograms. The net effect is that for the same spatial resolution, some places will exhibit much larger errors in area estimates than others. For the site in the Anhui province of China, where agricultural fields are very small (0.07 ha on the average), area estimates are highly sensitive to class definition thresholds even at the relatively fine resolution of 45 m. Conversely, in California (USA) spatial resolutions as coarse as 500 m can be used to reliably estimate cultivated areas. Results also suggest that the proportion of the total area that is cultivated significantly influences the accuracy of area estimates. When the area proportion is low, the class definition threshold must also be low to achieve an accurate area estimate. Conversely, in areas dominated by agriculture, a very stringent class definition of cultivated lands is required for accurate area estimates. While explored in the context of estimation of cultivated areas, the findings presented here are generic to the problem of area estimation using remote sensing.

The effect of spatial resolution on measurement of vegetation cover in three Mojave Desert shrub communities

Moderate resolution remotely sensed imagery (20–30 m) has lacked sufficient spatial resolution to accurately measure total vegetative cover in arid environments because the density of plant cover is generally too low to significantly influence spectral reflectance. New and emerging imagery is now available at significantly higher spatial resolutions, resulting in pixel sizes equal to or smaller than many dominant desert shrubs. This had led to an opposing problem, where desert shrubs now exhibit multi-modal probability functions associated with the individual elements of the plant, such as foliage, woody stems and branches, and shadows within and around shrub canopies. High resolution imagery was collected at three spatial resolutions for study sites in the south-central Mojave Desert of California to (1) determine the effect of spatial resolution on the detection of four common species of shrubs ( Chilopsis linearis, Psorothamnus spinosus, Larrea tridentata, and Ambrosia dumosa), (2) assess the affect of increasing pixel size on the accuracy of area estimates of shrub cover, and (3) assess the accuracy of vegetation cover measurements when compared to vegetation cover measured in the field. Spatial resolution greater than 1.0 m was not capable of discerning a large percentage of the shrubs in this region of the Mojave Desert. Yet, high correlation ( r 2 = 0.71 – 0.93 ) was found between field measurements of percent cover and those derived from imagery at spatial resolutions ranging from 0.6 to 1.0 m. The results indicated that a spatial resolution of 1.0 m or smaller was necessary to estimate percent cover and the area of individual shrubs from high-resolution imagery. Spatial resolution is dependent on the abundance and size of species of desert shrubs within plant communities, however 1.0 m resolution was found to be adequate for this region of the Mojave Desert.

A local nearest‐neighbor convex‐hull construction of home ranges and utilization distributions

We describe a new method for estimating the area of home ranges and constructing utilization distributions (UDs) from spatial data. We compare our method with bivariate kernel and α‐hull methods, using both randomly distributed and highly aggregated data to test the accuracy of area estimates and UD isopleth construction. The data variously contain holes, corners, and corridors linking high use areas. Our method is based on taking the union of the minimum convex polygons (MCP) associated with the k−1 nearest neighbors of each point in the data and, as such, has one free parameter k. We propose a “minimum spurious hole covering” (MSHC) rule for selecting k and interpret its application in terms of type I and type II statistical errors. Our MSHC rule provides estimates within 12% of true area values for all 5 data sets, while kernel methods are worse in all cases: in one case overestimating area by a factor of 10 and in another case underestimating area by a factor of 50. Our method also constructs much better estimates for the density isopleths of the UDs than kernel methods. The α‐hull method does not lead directly to the construction of isopleths and also does not always include all points in the constructed home range. Finally we demonstrate that kernel methods, unlike our method and the α‐hull method, does not converges to the true area represented by the data as the number of data points increase.

Assessing the Accuracy of National Land Cover Dataset Area Estimates at Multiple Spatial Extents

Site-specific accuracy assessments evaluate fine-scale accuracy of land-use/land-cover (LULC) datasets but provide little insight into accuracy of area estimates of LULC classes derived from sampling units of varying size. Additionally, accuracy of landscape structure metrics calculated from area estimates cannot be determined solely from site-specific assessments. We used LULC data from Rhode Island and Massachusetts as reference to determine the accuracy of area measurements from the National Land Cover Dataset (NLCD) within spatial units ranging from 0.1 to 200 km 2 . When regressed on reference area, NLCD area of developed land, agriculture, forest, and water had positive linear relationships with high r2, suggesting acceptable accuracy. However, many of these classes also displayed mean differences (NLCD - REFERENCE), and linear relationships between the NLCD and reference were not one-to-one (i.e., low r 2 , β 0 ¬= 0, β 1 ¬= 1), suggesting mapped area is different from true area. Rangeland, wetland, and barren were consistently, poorly classified.

Broad-scale mapping of temporary wetlands in arid Australia

We describe a robust methodology to detect and estimate the size of temporary wetlands using AVHRR satellite data within large areas (10 6 km 2) of the arid zone. The methodology uses spectral matching and exclusion of salt surfaces to discriminate between water and non-water surfaces. The status of 115 lakes was used to evaluate the methodology. A classification accuracy of 98% was achieved for wetlands >120 ha, with accuracy of area estimates varying with the size and shape of individual lakes. Spectral matching of AVHRR data has methodological and cost advantages over the use of higher spatial resolution data for understanding changes in the distribution of water at broad scales.

A generalized confusion matrix for assessing area estimates from remotely sensed data

The formulation of a generalized area-based confusion matrix for exploring the accuracy of area estimates is presented. The generalized confusion matrix is appropriate for both traditional classification algorithms and sub-pixel area estimation models. An error matrix, derived from the generalized confusion matrix, allows the accuracy of maps generated using area estimation models to be assessed quantitatively and compared to the accuracies obtained from traditional classification techniques. The application of this approach is demonstrated for an area estimation model applied to Landsat data of an urban area of the United Kingdom.

Crop monitoring in Sweden

Abstract This paper deals with the most important Swedish agriculture crops in relation to (i) Landsat-based spectral differences, (ii) mono- and multi-temporal Landsat classification measured as producer's, user's and Kappa accuracy and (iii) Landsat classification determined as area estimation accuracy. The potential of satellite data for crop monitoring in Sweden was found to be rather low for most crops except rape seed and sugar beet. Concerning these crops results indicated a possible operational applicability.

The accuracy of quantification from 1D NMR spectra using the PIQABLE algorithm

The ability to produce accurate estimates of peak parameters in 1 D NMR spectra is of critical importance in interpreting experimental results, particularly for the analysis of in vivo spectra, where low signal to noise is common. The accuracy of the quantification obtainable using the automatic algorithm PIQABLE is reported here. The original version of PIQABLE produces reliable estimates of the areas of isolated peaks in low signal-to-noise spectra with a variable baseline. The algorithm has been extended to treat partially overlapping peaks and automatically estimate constant and linear phase corrections. A variety of simulated spectra has been analyzed in order to address four different topics: the accuracy of area estimates as a function of peak signal-to-noise ratio, the influence of variable baseline on area estimates, the effect of partially overlapping peaks, and the performance of the automatic phasing routines. The results underline the limitations imposed on any quantification method by the magnitude of random noise in the spectrum and the importance of employing statistical techniques to identify peaks and predict the accuracy of parameter estimates.

An assessment of Landsat data acquisition history on identification and area estimation of corn and soybeans

In the past decade, numerous studies have demonstrated the potential of satellite remote sensing for providing accurate timely crop area information. This study assessed the impact of Landsat data acquisition history on classification and area estimation accuracy of corn and soybeans in the U.S. Corn Belt. The results illustrate the importance of selecting Landsat acquisitions based on spectral differences in crops at certain development stages. Although early season information can provide estimates of total corn and soybean areas, acquisitions from about emergence and after tasseling of the corn seem to provide a minimal set for accurate identification of corn and soybeans in the U.S. Corn Belt. Additional acquisitions provide only marginally greater separability for corn and soybeans.