Accurate Area Estimates Research Articles

Best practices to account for capture probability and viewable area in camera‐based abundance estimation

AbstractA suite of recently developed statistical methods to estimate the abundance and density of unmarked animals from camera traps require accurate estimates of the area sampled by each camera. Although viewshed area is fundamental to achieving accurate abundance estimates, there are no established guidelines for collecting this information in the field. Furthermore, while the complexities of the detection process from motion sensor photography are generally acknowledged, viewable area (the common factor between motion sensor and time lapse photography) on its own has been underemphasized. We establish a common set of terminology to identify the component parts of viewshed area, contrast the photographic capture process and area measurements for time lapse and motion sensor photography, and review methods for estimating viewable area in the field. We use a case study to demonstrate the importance of accurate estimates of viewable area on abundance estimates. Time lapse photography combined with accurate measurements of viewable area allow researchers to assume that capture probability equals 1. Motion sensor photography requires measuring distances to each animal and fitting a distance sampling curve to account for capture probability of <1.

A novel search and survey technique for unmanned aerial systems in detecting and estimating the area for wildfires

In recent years Unmanned Aerial Vehicles (UAVs) have progressively been utilized for wildfire management, and are especially in prevalent in forest fire monitoring missions. To ensure the fast detection and accurate area estimation of forest fires, a two-step search and survey algorithm for multi-UAV system is proposed to address these fire scenarios. Initially, a grid-based partition method is applied to divide the area-of-interest into several search areas. Then, an archetype search pattern is used to provide timely UAV exploration within those sub-areas. Once the fire zones are detected, a novel survey strategy is employed for UAVs to discover the boundary points of the fire zones, so that the area of the fire zones can be estimated using the sampled boundary points. In addition, the effect of wind is accounted for improving fire zone boundary estimates. The proposed search-and-survey procedure is validated on multiple simulated scenarios using the U.S. Air Force’s mission-realistic Aerospace Multi-Agent Simulation Environment (AMASE) software. Simulation results showcase that the proposed search pattern can effectively discover the seeded fire zones within 40 min of the mission. This is relatively faster than the other two well-known search patterns. Moreover, the proposed survey technique provides a coverage estimate with at least 85% accuracy for the area of interest within 90 min of the mission.

MAPPING TREES ON FARMLANDS USING OBIA METHOD AND HIGH RESOLUTION SATELLITE DATA: A CASE STUDY OF KORAPUT DISTRICT, ODISHA

Abstract. Agroforestry is an integrated self-sustainable land use management system that is not only capable of producing food from marginal agricultural land but also capable of maintaining and improving the quality of environment. Accurate assessment of trees on farmlands i.e. agroforestry would help in determining their contribution in meeting timber demand and also in carbon sequestration vis-a-vis climate change mitigation. In the present, high resolution multispectral satellite imagery (LISS-IV) has been used for mapping and estimating agroforestry area in Koraput district of Odisha. Both supervised and Object based Image Analysis (OBIA) classifications methods have been applied. In case of supervised maximum likelihood method, those pixels are fully captured where trees exist, whereas in OBIA captures trees according to their crown shapes. This proved OBIA method to be better in identification of trees on farmlands (scattered trees, boundary, and block plantations) than supervised method. This can lead to accurate estimation of area under trees in scattered form, in linear form and also in patch form. Improved results were obtained in case of OBIA classification with more than 90% accuracy. This research implies that remote sensing provide promising tools for evaluating and mapping of agroforestry at district level. Hence, the proposed approach of using high resolution remote sensing data in conjunction with OBIA method would be promising for mapping agroforestry area.

Mechanistic Insights of V2+/V3+ Reaction on Glassy Carbon for Vanadium Redox Flow Batteries

Energy demand is increasing due to the world’s growing population and rising quality of life, accompanied by increasing CO2 emissions. A shift towards renewable energy sources like solar and wind is needed to sustainably meet our growing energy requirements. However, the intermittent nature of renewable resources creates the need for energy storage technologies, which would store excess energy and then supply this stored energy when it is needed. Pumped hydroelectric is the most used electrical energy storage method, but is limited to specific geographical regions, leading to a push towards development of redox flow batteries (RFBs). Despite the unique ability of RFBs to decouple energy and power density while having high current densities, their commercial viability is limited by high costs. In particular, All Vanadium Redox Flow Batteries (VRFBs, i.e., (VO2+/VO2 +//V2+/V3+)) have received much interest due to the use of the common element vanadium that removes the issue of cross-contamination, leading to higher Coulombic efficiencies. The sluggish kinetics of V2+/V3+ redox couple on the negative electrode (Reaction 1), however, is found to limit VRFBs efficiency.1 Thus, improving V2+/V3+ kinetics can lead to higher current densities, reducing the overall cost of energy storage. Studies indicate the V2+/V3+ reaction is an inner sphere electron transfer based on differences in activity on certain surfaces,2 but there is a lack of the mechanistic understanding needed to further improve rates. V2+→ V3+ + e‾ (Reaction 1) Eº = −0.255 V vs SHE Traditionally, VRFBs use porous graphite felts with sulfuric acid (H2SO4) as an electrolyte, often without accurate area estimates needed for obtaining normalized current densities. Recent studies have shown increased stability of V ions and higher energy capacities for mixed H2SO4 and HCl electrolytes,3 but the effect of electrolyte on kinetics is also not well understood. Here, we study the V2+/V3+ reaction kinetics on a controlled electrode surface in H2SO4 and HCl, and show that the kinetics increase with State of Charge (SoC) and in the presence of Cl-, where Cl- also changes the V3+ coordination shell as detected by UV-Vis. We use a polished glassy carbon disk as the reaction surface, with high conductivity and controlled electrochemical surface area as compared to graphite felts. We find the V2+/V3+ reaction rates are independent of the total vanadium ion concentration, but instead depend on SoC. SoC was estimated by deconvoluting the individual V2+ and V3+ concentrations from UV-Vis. Exchange current densities for oxidation (iO,oxidation ) decrease with decreasing SoC, whereas Tafel slopes for oxidation (boxidation ) remain constant with SoC. Rate measurements at varied temperature (23.3 – 40.0 °C) were conducted to evaluate effective activation energies (Ea ) for the V2+/V3+ reaction at different SoC by two independent measurements, a) iO,oxidation and b) Charge Transfer Resistance (Rct ). The two independent methods for determining Ea matched closely, and show that Ea increases with decreasing SoC (Figure 1). This dependence of Ea on SoC along with high magnitudes (42 – 44 kJ mol-1 for 50% SoC, as compared to ∼13 kJ mol-1 for the outer sphere ferri-ferrocyanide redox couple4) confirms V2+/V3+ is an inner sphere reaction. This variation of iO,oxidation and Ea with SoC is indicative of a mechanism involving an adsorbed V intermediate affecting reaction kinetics. We measure higher iO,oxidation in HCl compared to H2SO4, which we hypothesize is due to a change in the coordination shell of the reacting specie, leading to formation of a different transition state with lower Ea . V2+ and V3+ ions in pure water typically exist as [V(H2O)6]2+ and [V(H2O)6]3+ (Oh symmetry) respectively with six labile water molecules constituting their first coordination sphere.5 We use UV-Vis to determine the structure of the coordination shell with anions present, by a shift in peak locations (based on ligand field strength) and absolute absorbance (depicting changes in molecular symmetry). The V2+ UV-Vis spectra remained unchanged in H2SO4, HCl, and mixed acid electrolytes of varied concentrations, indicating [V(H2O)6]2+ was the dominant species, with no anions in the first coordination sphere. However, the UV-Vis spectra of V3+ had a shift in peak locations with reduced absolute absorbance in HCl relative to H2SO4, indicating formation of a different V3+ complex, which we hypothesize is related to the change in redox kinetics.

Fast and accurate circuit delay model for FPGA architectural exploration

Field programmable gate arrays (FPGAs) are adopted in many electronic systems, due to their design flexibility and high performance. For providing right FPGAs for different applications, FPGA architectural exploration is needed. Accurate estimation of area and delay of low-level FPGA circuits is required to evaluate different architecture candidates during the exploration. In this study, the authors present a fast and accurate delay model by extracting the key parameters affecting FPGA delay and by combining the classical Elmore equivalent model and the powerful learning capability of neural network. The derived model can be integrated with the existing FPGA architecture exploration flow perfectly. Experimental results show that compared with circuit simulator tool HSPICE, this model speeds up the delay estimation by 2863 times with the average error of 1.9% during the architectural exploration process. This fast and accurate estimation allows FPGA architects to explore more architectural options in limited time, resulting in optimised FPGA architecture.

A new kernel density estimator for accurate home‐range and species‐range area estimation

SummaryKernel density estimators are widely applied to area‐related problems in ecology, from estimating the home range of an individual to estimating the geographic range of a species. Currently, area estimates are obtained indirectly, by first estimating the location distribution from tracking (home range) or survey (geographic range) data and then estimating areas from that distribution. This indirect approach leads to biased area estimates and difficulty in deriving reasonable confidence intervals.We introduce a new kernel density estimator (and associated confidence intervals) focused specifically on area estimation that applies to both independently sampled survey data and autocorrelated tracking data. We test our methods against simulated movement data and demonstrate its use with African buffalo data.The area‐corrected kernel density estimator produces much more accurate area estimates, particularly at small sample sizes, and the newly derived confidence intervals are more reliable than existing alternatives.This new method is the most efficient nonparametric home‐range estimator for animal tracking data and should also be considered when calculating nonparametric range estimates from survey data. This estimator is now the default method in thectmm rpackage.

Algorithm to select Optimal Spectral Bands for Hyperspectral Index of Feature Extraction

Background/Objectives: Water body management and food-grains supply will be challenging tasks. Selecting spectral bands for accurate area estimation of water body and selected vegetation (crop) is the objective of this study. Methods/ Statistical Analysis: Indices such as Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI) are used for extracting water and vegetation respectively from other features. Generally bands providing high index values for the target are utilized for extraction. In this study the bands giving high index value for selected target and low index for surrounding are selected. Bands selected in this method provide better extraction and accurate area estimation. Findings: The NDWI and NDVI are based on multispectral data and have less number of combinations for band sets selection. Though the proposed method is derived from NDWI, it utilizes the Hyperspectral data that has narrow and hundreds of bands. Another advantage of this method is it utilizes the index value of target and surrounding features. It selects suitable band set by iteration method and provides accurate extraction of the targets and area estimation. The performance of the bands selected in this method was tested with coastal and inland water bodies. Area estimated with this method matches with NDVI and MNDWI values. Applications/Improvements: This method selects suitable bands to estimate area of water body and vegetation. This estimation will be useful for water body management and food production forecasting.

Accurate area estimation model for FPGA based Implementation

Accurate area estimation model for FPGA based Implementation

Epidemiology and forecasting of insect-pests and diseases for value-added agro-advisory

Models are means to capture, condense and organize knowledge. These are expressions, which represent relationship between various components of a system. A well-tested weather-based model can be an effective scientific tool for forewarning insect-pests and diseases in advance so that timely plant protection measures could be taken up. Various types of techniques have been developed for the purpose. The simplest technique forms the class of thumb rules, which are based on experience. Though these do not have much scientific background but are extensively used to provide quick forewarning of the menace. Another tool in practice is regression model that represents relationship between two or more variables so that one variable can be predicted from the other (s). Linear and non-linear regression models have been widely used in studying relationship of insect-pests and diseases with time and weather variables (as such or in some transformed forms). With the advent of computers more sophisticated techniques such as simulation modelling and machine learning approach such as decision tree induction algorithms, genetic algorithms, neural networks, rough sets, etc. have been explored. A number of simulation models have been developed all over the world for quantifying effects of various factors including weather on agriculture. These may provide a good forecast but require detailed data base, which may not be available. Machine learning approach has recently received some attention. As opposed to traditional model-based methods, machine learning approach is self adaptive methods in that there are a few a priori assumptions about the models for problem(s) under study. This technique learns more from examples and captures subtle functional relationships among the data even if the underlying relationships are unknown or hard to describe. This modelling approach with ability to learn from experience is very useful for many practical problems provided enough data are available. Remotely sensed data can provide useful information relating to area under the crop and also the condition thereof. It has certain advantages over land use statistics due to multi-spectral, synoptic and repetitive coverage. An attempt has been made for accurate estimation of area affected by insect-pests and diseases in crops along with accurate assessment of damage due to the same are possible for providing compensation to farmers. In this study, an Integrated Decision Support System (IDSS) for Crop Protection Services is also discussed.

Study of Fuzzy based Classifier Parameter Across Spatial Resolution

Classification and interpretation of satellite images are complex processes and that may be affected by various factors. Most fuzzy based soft classification techniques have been used to provide a more appropriate and accurate area estimation when fine, medium and coarse spatial resolution data are being used. Spatial resolution determines the spatial details on the Earth surface and greatly reduces the problem of mixed pixel. This paper examines the effect of weighting exponent „m‟ parameter of fuzzy c-means (FCM) and possibilistic c-mean (PCM) classifiers with respect to entropy, an uncertainty indicator for different extracted classes. This paper measures uncertainty variations across spatial resolution for different class extraction. Uncertainty can be defined as skepticism wherein entropy is an absolute indicator of an uncertainty. In this research work, fuzzy c-means (FCM) and possibilistic c-mean (PCM) classifiers have been used and entropy is computed to visualize the uncertainty. For this research work Resourcesat-1 (IRS-P6) data sets from AWIFS, LISS-III and LISS-IV sensors of same date have been used. Accuracy assessment of a classified image is an integral part of image classification and in this research two things were involved first optimization of weighting exponent „m‟, and computation of entropy. From the resultant Table 1, 2, 3, 4, 5, 6, 7 and 8 shows that the optimum values of „m‟ for FCM classifier on homogenous land cover classes are 2.9 and for heterogeneous classes are 2.7 where the membership values are varying from 0.8 to 0.9 with lesser entropy values, i.e. 0.35. Similarly for PCM classifier the optimum value of „m‟ for homogenous land cover classes are 3.2 and for heterogeneous classes are 3.0 where the membership values are varying from 0.8 to 0.9 with lesser entropy values, i.e. 0.78. In the second phase of study, to analyze the effect of uncertain pixels in FCM and PCM classifiers, Euclidean norm has been chosen for both the classifiers whereas the values of weighting exponent „m‟ varies from 1.1 to 4.0 for Sal forest, Eucalyptus plantation, water bodies, agriculture land with crop, agriculture moist land without crop, and agriculture dry land without crop. It is observed from the result Table 1, 2, 3, 4, 5, 6, 7 and 8, that uncertainty ratio is almost equal to referential value 2.585, for FCM and PCM classifiers using Euclidean norm. This reflects that fuzzy based soft classifiers FCM and PCM are producing higher classification accuracy with minimum level of uncertainty.

An improved bathymetric model for the modern and palaeo Lake Eyre

Here we demonstrate the applicability of using altimetry data and Landsat imagery to provide the most accurate digital elevation model (DEM) of Australia's largest playa lake — Lake Eyre. We demonstrate through the use of geospatial techniques a robust assessment of lake area and volume of recent lake-filling episodes whilst also providing the most accurate estimates of area and volume for larger lake filling episodes that occurred throughout the last glacial cycle. We highlight that at a depth of 25m Lake Mega-Eyre would merge with the adjacent Lake Mega-Frome to form an immense waterbody with a combined area of almost 35,000km2 and a combined volume of ~520km3. This would represent a vast water body in what is now the arid interior of the Australian continent. The improved DEM is more reliable from a geomorphological and hydrological perspective and allows a more accurate assessment of water balance under the modern hydrological regime. The results presented using GLAS/ICESat data suggest that earlier historical soundings were correct and the actual lowest topographic point in Australia is −15.6m below sea level. The results also contrast nicely the different basin characteristics of two adjacent lake systems: Lake Eyre and Lake Frome.

PROBLEMS AND PROSPECTS OF PAPAYA CULTIVATION IN NORTHEASTERN STATES OF INDIA

Systematic and accurate estimate of area and production of different horticultural crops in the northeastern region are not available. But still papaya is the fourth important fruit crop next to citrus, banana and pineapple in the region and is cultivated in about 12,700 ha with an annual production of 158,900 mt with a productivity of 12.51 t ha -1 which is far below the national productivity of 31.69 t ha -1 . Papaya has good potential in the tropical and subtropical areas of the region, which is mostly in Assam, Tripura, Arunchal Pradesh, Meghalaya and Nagaland. Since, it has been grown for the last several years in backyard garden, large mixed population has resulted, which needs purification and also offers scope to select a superior clone. Low productivity recorded in northeastern states is due to cultivation of local varieties. Majority of the orchards are having the problem of drought stress from December to March leads moisture stress, heavy rainfall from April to October causes heavy soil erosion and nutrients loss and it also offers congenial environment for the attack of so many insect-pest and diseases. The collar rot is very serious problem at seedling stage and mosaic, leaf curl and mites are very destructive at flowering stage of plants. The new improved varieties of dwarfing nature are more susceptible to the leaf curl in compare to local and tall. Frost during December-January in some part of the region is another limiting factor for papaya cultivation. The soil of all the papaya growing area is acidic in nature, which hampered the availability of the important macro and micro nutrients to the plant and adversely affect on yield. The farmers of the region seldom use any soil amendments like lime in their orchards. Depletion of nutrients by washing of rich surface soil and leaching by heavy rains are causing nutritional deficiencies in certain areas. For increasing the productivity of papaya in the region introduction of area specific cultivars like 'Zinta' and 'Surya' in Mizoram, 'Pusa Delecious' and 'Ambasa Local' in Tripura, 'Pusa Nanaha' and 'Pusa Dwarf' in Assam may perform better than local genotypes. Being calciferous crop, papaya requires heavy amount of fertilizers specially calcium and potassium. Proper orchard management practices like INM, GAP (Good Agricultural practices), good irrigation management with adequate moisture conservation methods during acute dry period (December to March) and effective and timely control of pest and diseases through eco-friendly approaches (during rainy season) may boost the production and productivity of papaya in the region. Extraction of papain from local existing population can be taken up on commercial scale to obtain additional income.

Effects of Point Pattern Shape on Home‐Range Estimates

Abstract: Home‐range estimators are commonly tested with simulated animal locational data in the laboratory before the estimators are used in practice. Although kernel density estimation (KDE) has performed well as a home‐range estimator for simulated data, several recent studies have reported its poor performance when used with data collected in the field. This difference may be because KDE and other home‐range estimators are generally tested with simulated point locations that follow known statistical distributions, such as bivariate normal mixtures, which may not represent well the space‐use patterns of all wildlife species. We used simulated animal locational data of 5 point pattern shapes that represent a range of wildlife utilization distributions to test 4 methods of home‐range estimation: 1) KDE with reference bandwidths, 2) KDE with least‐squares cross‐validation, 3) KDE with plug‐in bandwidths, and 4) minimum convex polygon (MCP). For the point patterns we simulated, MCP tended to produce more accurate area estimates than KDE methods. However, MCP estimates were markedly unstable, with bias varying widely with both sample size and point pattern shape. The KDE methods performed best for concave distributions, which are similar to bivariate normal mixtures, but still overestimated home ranges by about 40–50% even in the best cases. For convex, linear, perforated, and disjoint point patterns, KDE methods overestimated home‐range sizes by 50–300%, depending on sample size and method of bandwidth selection. These results indicate that KDE does not produce home‐range estimates that are as accurate as the literature suggests, and we recommend exploring other techniques of home‐range estimation.

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.

Comparing MODIS and ETM+ data for regional and global land classification

Nearly simultaneous reflectance data sets from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), at 30-m resolution, and the Terra satellite instrument MODIS, at 500-m resolution, are compared for their ability to map fractional coverage of surface types over large areas. Lower spatial resolution MODIS classification results are generally comparable those of ETM+, with discrepancies for some regions with mixed surface types. Analysis of laboratory and field spectra suggests an ambiguity, the “brightness ambiguity”, which can prevent accurate area estimation of pixels having two or more surface types. This ambiguity, plus general mathematical inversion issues, can account for the discrepancy. Thus, occasional high-resolution measurements, as from Landsat 7, are necessary to refine estimations of large area surface types from MODIS and similar lower spatial resolution instruments.

Quantitative dynamic flood monitoring with NOAA AVHRR

The analysis of the spatial extent and temporal pattern of flood inundation from remotely sensed imagery is of critical importance to flood mitigation. With a high frequency of global coverage, NOAA/AVHRR has the advantage of detecting flood dynamics during devastating floods. In this paper, we describe a systematic approach to flood monitoring using AVHRR data. Four critical issues for successful flood monitoring with AVHRR were identified: correct identification of water bodies, effective reduction of cloud contamination, accurate area estimation of flood extent, and dynamic monitoring of flood processes. In accordance with the spectral characteristics of water and land in AVHRR channels, a simple but effective water identification method was developed with the ability to reduce cloud influences. The area of flooded regions was calculated with the consideration of areal distortion due to map projection, and mixed pixels at water/land boundaries. Flood dynamics were analysed from flood distributions in b...

Quantitative dynamic flood monitoring with NOAA AVHRR

The analysis of the spatial extent and temporal pattern of flood inundation from remotely sensed imagery is of critical importance to flood mitigation. With a high frequency of global coverage, NOAA/AVHRR has the advantage of detecting flood dynamics during devastating floods. In this paper, we describe a systematic approach to flood monitoring using AVHRR data. Four critical issues for successful flood monitoring with AVHRR were identified: correct identification of water bodies, effective reduction of cloud contamination, accurate area estimation of flood extent, and dynamic monitoring of flood processes. In accordance with the spectral characteristics of water and land in AVHRR channels, a simple but effective water identification method was developed with the ability to reduce cloud influences. The area of flooded regions was calculated with the consideration of areal distortion due to map projection, and mixed pixels at water/land boundaries. Flood dynamics were analysed from flood distributions in both space and time. The maximum spatial extent of floods, generated by compiling the time series of flood maps, was informative about flood damages. We report a successful use of this approach to monitor the Huaihe river flood, a centennial devastating disaster occurred in the Huaihe river basin of China in the summer of 1991.

Accurate area determination of complex leaves using digital image analysis

A recently described computer image analysis method was modified forquantification of leaf area. These areas were compared to those estimated by alower resolution method based on planimetry. Leaves ofEucalyptus nitens (Deane & Maiden) Maiden, pinnae ofDicksonia antarctica Labill. and leaves ofAcacia dealbata Link were used for analysis, offeringsurfaces of low, intermediate and high complexity, respectively.Low-resolution planimetry was found to be a suitable method for thecalculation of leaf area of simple broadleaves. However, for surfaces ofgreater complexity, the higher resolution of image analysis gave more accurateestimates of area. Overlapping of primary pinnae in the complexA. dealbata leaf proved to be a larger source of errorthan inadequacy of resolution.

Crop area monitoring within an advanced agricultural information system

Abstract This paper describes a framework for an image processing procedure for operational agricultural crop area estimation. This operational framework has been conceived within the development of an Advanced Agricultural Information System (AAIS) for the “Regione del Veneto “ (RdV ‐ Veneto Region) in northeastern Italy. The objective of this program is to develop the ability to generating timely and accurate area estimates and production information for four major agricultural crops: soybeans, sugar beets, corn, and small grains. AAIS uses state of the art methods in remote sensing and geographic information systems (GIS) technology and integrates a variety of data types including satellite imagery. This paper describes the methodology developed for image and ancillary data processing for the production of crop area statistics. Using a combination of standard unsupervised classification and GIS operations that incorporate knowledge about the agricultural system, a “sequential masking” classification pr...

Accuracy of d′ and A′ as estimates of sensitivity

In signal detection theory, sensitivity can be indexed as either the distance between distributions or the area under the isosensitivity curve. The statistic d′ is usually identified as a distance measure of sensitivity, while A′ is an estimate of the area. This article considers both statistics as estimates. As such, they can be evaluated against actual distances and areas calculated under specific assumptions about the variances of the distributions. The analysis shows that d′ is a better estimate of distance than A′ is of area only under the assumption that the variances of the hypothetical distributions are equal (i.e., the slope of the z-coordinate ROC is 1.0). When the slope takes on values other than 1.0, A′ is generally a more accurate estimate of area than d′ is of distance.