Comparison of the local pivotal method and systematic sampling for national forest inventories

Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran

Direct measurement of snow water equivalent(SWE)in snow-dominated mountainous areas is difficult,thus its prediction is essential for water resources management in such areas.In addition,because of nonlinear trend of snow spatial distribution and the multiple influencing factors concerning the SWE spatial distribution,statistical models are not usually able to present acceptable results.Therefore,applicable methods that are able to predict nonlinear trends are necessary.In this research,to predict SWE,the Sohrevard Watershed located in northwest of Iran was selected as the case study.Database was collected,and the required maps were derived.Snow depth(SD)at 150 points with two sampling patterns including systematic random sampling and Latin hypercube sampling(LHS),and snow density at 18 points were randomly measured,and then SWE was calculated.SWE was predicted using artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and regression methods.The results showed that the performance of ANN and ANFIS models with two sampling patterns were observed better than the regression method.Moreover,based on most of the efficiency criteria,the efficiency of ANN,ANFIS and regression methods under LHS pattern were observed higher than the systematic random sampling pattern.However,there were no significant differences between the two methods of ANN and ANFIS in SWE prediction.Data of both two sampling patterns had the highest sensitivity to the elevation.In addition,the LHS and the systematic random sampling patterns had the least sensitivity to the profile curvature and plan curvature,respectively.

Comparison of fixed area and distance sampling methods in open forests:case study of Zagros Forest,Iran

The main aim of this study was to evaluate methods for fixed area and distance sampling in the Zagros open forest area in western Iran. Basic forest management and planning required appropriate quantitative and qualitative information. Two sampling methods were compared on the basis of the actual means of characteristics derived from the 100 % survey. In total, 37 sampling plots were systematically installed with a grid of 100 m × 100 m in the study area. Density, crown canopy, and basal area of the stands were measured. The 100 % survey showed that tree density above 12.5 cm diameter at breast height was 68.04 stem ha-1, basal area was 15.16 m2 ha-1 and crown canopy percentage was 35.71% ha-1. The values for the traits determined by the two sampling methods differed significantly （P = 0.05）. When the time required for the methods was compared, transect sampling required less than systematic-random sampling. Therefore, the transect sampling method was the more economical method for the Zagros open forests. The transect sampling method was statistically defensible and practical for quantitating characteristics of the Zagros open forests.

Subsampling bias and the best-discrepancy systematic cross validation

Statistical machine learning models should be evaluated and validated before putting to work.Conventional k-fold Monte Carlo cross-validation(MCCV)procedure uses a pseudo-random sequence to partition instances into k subsets,which usually causes subsampling bias,inflates generalization errors and jeopardizes the reliability and effectiveness of cross-validation.Based on ordered systematic sampling theory in statistics and low-discrepancy sequence theory in number theory,we propose a new k-fold cross-validation procedure by replacing a pseudo-random sequence with a best-discrepancy sequence,which ensures low subsampling bias and leads to more precise expected-prediction-error(EPE)estimates.Experiments with 156 benchmark datasets and three classifiers(logistic regression,decision tree and na?ve bayes)show that in general,our cross-validation procedure can extrude subsampling bias in the MCCV by lowering the EPE around 7.18%and the variances around 26.73%.In comparison,the stratified MCCV can reduce the EPE and variances of the MCCV around 1.58%and 11.85%,respectively.The leave-one-out(LOO)can lower the EPE around 2.50%but its variances are much higher than the any other cross-validation(CV)procedure.The computational time of our cross-validation procedure is just 8.64%of the MCCV,8.67%of the stratified MCCV and 16.72%of the LOO.Experiments also show that our approach is more beneficial for datasets characterized by relatively small size and large aspect ratio.This makes our approach particularly pertinent when solving bioscience classification problems.Our proposed systematic subsampling technique could be generalized to other machine learning algorithms that involve random subsampling mechanism.

Forest Structure Analysis in the Oban Division of Cross River National Park, Nigeria

Information on forest structure is important for forest management decisions. This is inadequate in many situations, especially where timber is not of primary interest. We analyzed the structure of two forest types in the Oban Division of Cross River National Park, Nigeria. Systematic sampling technique was used to establish two transects measuring 2,000 x 2 m, at 600 m interval in the two forest types in four locations. Four 50 m x 50 m plots were located alternately at 500 m intervals along each transect, constituting 32 plots per forest type and 64 plots in all, Diameters at breast height （DBH）, base; middle and top; crown diameter; total height and crown length were measured on all trees with DBH 〉_ 10 cm. There were 159 stems/ha in the close-canopy forest and 132 stems/ha in the secondary forest. The mean DBH were 34.5 cm and 33.62 cm respectively. The mean heights were 24.79 m and 23.97 m, respectively. Basal area/ha were 41.59 m2 ha~ and 27.38 m2 hal for the two forest types. Majority of the trees encountered in the two forest types belonged to the middle stratum which has implication for small mammals＇ populations. Emergent trees which are otherwise scarce in other parts of the country were recorded, which also has implications for density thinning and seed supplies.

Wine and Its Legality -A Survey to Know the Consumer Opinions on the Activities of Sicilian Wine Companies Operating on Lands Confiscated from the Mafia

The current guidelines of the European Union Common Agricultural Policy face the agricultural sector in the position of the backbone for the economic development of rural areas and regions with difficult economic differentiation. The EU Common Agricultural Policy defines agriculture as ＂multifunctional＂ and among the different roles and functions expected, there is also the ＂social function＂, defined as the ability that the farm has to generate services with respect to a population with risk of social exclusion. This paper investigated all the agricultural initiatives with social impacts that were carried in the lands confiscated from the organized crime （called mafia）, mainly in the South of Italy and especially in Sicily. Through an Italian law, these lands could be used with social purposes by a particular kind of associations which might exercise an agricultural activity with the aim to produce food products, sell them in the market and offer employment opportunities in the agricultural sector. In particular, in Sicily, the activity of the ＂Social Cooperative Placido Rizzotto-Libera Terra＂ and its winery ＂Cantina Centopassi＂ which received honors and awards tbr its production of wine obtained from the earliest harvests and for its social work in that territory were well known. This work, which was part of a much broader study on ＂wine and legality＂, aimed to know the opinion of Sicilian wine consumers and their knowledge about this topic with particular reference to the Cantina Centopassi.

Comparison of Cost Incurred in Two Survey Methodologies for Measles Vaccine Coverage

Background: The World Health Organization (WHO) initiated the Expanded Program on Immunization (EPI) in 1974. It has been widely used in different studies. Along with this, other survey methodologies have been compared to study immunization coverage at different regions. To consider different survey methodologies, one of the most important factors is the cost incurred that survey methodology. A survey method is considered as more efficient or better than the other survey method if the cost incurred in a particular method is less than the other one. Methods: In this study, cost incurred in two stage (30 × 30) cluster sampling and systematic sampling methods have been compared using a cost function for measles vaccine coverage. Measles vaccine coverage data has been taken from the survey “Comparison of Two Survey Methodologies to Estimates Total Vaccination Coverage” sponsored by Indian Council of Medical Research (ICMR), New Delhi. Results: The results show that there are no significant differences between the point estimates of measles vaccine coverage under the considered survey methodologies. But the cost incurred in systematic sampling is more than that of two stage cluster sampling. Conclusion: It can be concluded that systematic sampling survey is costlier than that of two stage cluster sampling for this study population.

A spatially-explicit count data regression for modeling the density of forest cockchafer(Melolontha hippocastani) larvae in the Hessian Ried(Germany)

Background： In this paper, a regression model for predicting the spatial distribution of forest cockchafer larvae in the Hessian Ried region （Germany） is presented. The forest cockchafer, a native biotic pest, is a major cause of damage in forests in this region particularly during the regeneration phase. The model developed in this study is based on a systematic sample inventory of forest cockchafer larvae by excavation across the Hessian Ried. These forest cockchafer larvae data were characterized by excess zeros and overdispersion. Methods： Using specific generalized additive regression models, different discrete distributions, including the Poisson, negative binomial and zero-inflated Poisson distributions, were compared. The methodology employed allowed the simultaneous estimation of non-linear model effects of causal covariates and, to account for spatial autocorrelation, of a 2-dimensional spatial trend function. In the validation of the models, both the Akaike information criterion （AIC） and more detailed graphical procedures based on randomized quantile residuals were used. Results： The negative binomial distribution was superior to the Poisson and the zero-inflated Poisson distributions, providing a near perfect fit to the data, which was proven in an extensive validation process. The causal predictors found to affect the density of larvae significantly were distance to water table and percentage of pure clay layer in the soil to a depth of I m. Model predictions showed that larva density increased with an increase in distance to the water table up to almost 4 m, after which it remained constant, and with a reduction in the percentage of pure clay layer. However this latter correlation was weak and requires further investigation. The 2-dimensional trend function indicated a strong spatial effect, and thus explained by far the highest proportion of variation in larva density. Conclusions： As such the model can be used to support forest practitioners in their decision making for regeneration and forest protection planning in the Hessian predicting future spatial patterns of the larva density is still comparatively weak. Ried. However, the application of the model for somewhat limited because the causal effects are

Relationship between sampling intensity and precision for estimating damage to maize caused by rodents

In this study we aimed to determine the relationship between sampling intensity and precision for estimating rodentdamage. We used the systematic row sampling technique to provide data to achieve precision and accuracy inestimations of rodent damage in maize fields at the planting and seedling stages. The actual rodent damage to maizein 15 fields, each 0.5 ha in size, in Morogoro, Tanzania, was established at the seedling stage. These data were usedto simulate the sampling intensities that would provide precision and accuracy. The variations between estimateswere plotted against the sampling intervals. The results of this study show that the relationship between averagestandardized variances and sampling intervals is linear. The heterogeneous distribution of damage in some plotscaused variations in the accuracy of the estimates between plots, but a sampling interval of five rows consistentlyproduced estimates with a variance of less than 10%. We provide a standard curve that will allow a decision to bemade on the sampling intensity as a function of required precision using the systematic row sampling technique inmaize fields.

Validation of GIS layers in the EU:getting adapted to available reference data

An optimal validation of a thematic map would ideally require in-situ observations of a large sample of units specifically conceived for the map under validation.This is often not possible due to budget limitations.The alternative can be using photo-interpretation of high or very high resolution images instead of in-situ observations or using available data sets that do not fully comply with the ideal characteristics:unit size,reference date or sampling plan.This paper illustrates some examples of use of available data in the European Union.For land cover maps,the best existing data set is probably Land Use/Cover Areaframe Survey(LUCAS)that has been conducted by Eurostat on four occasions since 2001.Because LUCAS is based on systematic sampling,advantages and limitations of systematic sampling are discussed.A fine-scale population density map is presented as an example of a situation in which reference data on a statistical sample cannot be collected.