Design-Based Treatment of Unit Nonresponse by the Calibration Approach

The use of nonresponse calibration weighting is considered in a complete design-based frameworkto account for the cases in which nonresponse is a fixed characteristic of the units, just like the interest variable. Approximate expressions of design-based bias and variance of the calibration estimator are derived and some estimators of the sampling variance are proposed. The choice of auxiliary variables is discussed from theoretical and practical point of view. The results of an extensive simulation study demonstrate how the reliability of the procedure is mainly determined by the capability of selecting auxiliary variables in such a way that their relationship with the interest variable is similar for both the respondent and nonrespondent sub-populations.auxiliary variables, calibration estimator, variance estimator, simulation study.

A Permutation-based Combination of Sign Tests for Assessing Habitat Selection

The analysis of habitat use in radio-tagged animals is approached by comparing the portions of use vs the portions of availability observed for each habitat type. Since data are linearly dependent with singular variance-covariance matrices, standard multivariate statistical test cannot be applied. To overcome the problem, compositional data analysis is customary performed via log-ratio transform of sample observations. The procedure is criticized in this paper, emphasizing the many drawbacks which may arise from the use of compositional analysis. An alternative nonparametric solution is proposed in the framework of multiple testing. The habitat use is assessed separately for each habitat type by means of the sign test performed on the original observations. The resulting p-values are combined in an overall test statistic whose significance is determined permuting sample observations. The theoretical findings of the paper are checked by simulation studies. Applications to some case studies are considered.compositional data analysis, Johnson’s second order selection, Johnson’s third order selection, Monte Carlo studies, multiple testing, random habitat use.

Aerial assessment of landscape net change by means of two-phase network sampling: an application to central Italy

A design-based procedure for estimating the land cover net change between two dates in time is considered by means of aerial information. The aerial information in the first date consists of analogical photos while the aerial information in the second date consists of digital photos or satellite imagery. In the first phase of sampling a set of points is selected on the study area by means of unaligned systematic sampling. The digital imagery available for the second date may be readily adopted for spotting and classifying the first-phase points, thus obtaining a firstphase estimator of the areal extents in the second date. On the other hand, owing to the operational difficulties of spotting all the first-phase points on the analogical photos, a second-phase sample selected from these points is necessary to estimate the areal extents of the land categories in the first date. Finally, the land cover net changes may be estimated by means of the difference between the resulting estimates in the two dates. The use of stratified network sampling is considered in the second phase in order to handle the presence of analogical photos overlapping each other at their boundaries. An application of the procedure is considered for assessing the land cover net change in the Abruzzo Region (Central Italy) between the years 1954 and 2002.L'articolo è disponibile sul sito dell'editore www.interscience.wiley.co

Estimating the volume of forest growing stock using auxiliary information derived from relascope or ocular assessments

The aim of this study is to demonstrate the potential of integrating probabilistic sampling and estimation with the conventional technique referred to as forest inventory by compartments. The objective of this paper is to propose two strategies for the assessment of growing stock volume using two-phase sampling, namely: (i) relascope basal area estimation performed on first-phase sampling points followed by volume estimation performed on a sub-sample of points selected in the second phase; (ii) ocular evaluation of growing stock volume performed on first-phase sampling plots of fixed size followed by volume estimation performed on a sub-sample of plots selected in the second phase. The effectiveness of using the auxiliary information gathered in the first phase is assessed by comparing the double-expansion estimator of total volume which depends solely on the second-phase sample with the two-phase ratio estimator which instead calibrates the double-expansion estimator on the basis of first-phase information. Conservative estimators of sampling variances and confidence intervals are derived for both the estimators. As is usual in forest inventories, first-phase sampling is assumed to be performed on a systematic random grid while three different schemes are considered for drawing the second-phase sample: simple random sampling without replacement, stratified sampling and 3-P sampling. The performance of double-expansion and ratio estimators under the three schemes adopted in the second phase is empirically checked by means of a simulation study performed on a real compartment in a beech forest of Central Italy. Simulation results show that the use of auxiliary information generated in the first phase constitutes a very effective way of increasing the accuracy of volume estimation at the compartment level, with a moderate increase of fieldwork

Assessing the attributes of scattered trees outside the forest by a multi-phase sampling strategy

A sampling strategy to be used with multi - phase forest inventories is proposed for assessing scattered trees outside the forest on large territories. The fi rst phase is carried out by means of a systematic search over the area to be inventoried. The area is partitioned into regular polygons of the same size and points are randomly located, one per polygon. Subsequently, in the second phase, the land cover class of the fi rst-phase points is determined by very high - resolution remotely sensed imagery and a sample of points are selected from each land cover stratum. Then, the number of trees outside the forest lying within plots at the sampled points is recorded on the imagery. Finally, in the third phase, a subsample is selected from the second-phase samples of each stratum and the biophysical attributes of trees within plots are measured in the fi eld. Approximately unbiased estimators of abundance and of totals and averages of biophysical attributes are achieved in the second and third phase , respectively, together with the estimators of the corresponding variances. A simulation study is performed in order to assess the accuracy of the strategy under random and aggregated distributions of trees. The sampling errors achieved in the second phase using sampling fractions of ~ 0.3 per cent of trees vary from 6 to 13 per cent , whereas the errors achieved in the third phase using sampling fractions of ~ 0.15 per cent vary from 15 to 31 per cent . The results obtained from three case studies carried out in Italy confi rm the accuracy levels achieved in the simulation.L'articolo è disponibile sul sito dell'editore http://forestry.oxfordjournals.org

A matching procedure to improve k-NN estimation of forest attribute maps

The integration of forest inventory and mapping has emerged as a major issue for assessing forest attributes and multiple environmental functions. Associations between remotely sensed data and the biophysical attributes of forest vegetation (standing wood volume, biomass increment, etc.) can be exploited to estimate the attribute values for sampled and non-sampled pixels, thus producing maps for the entire region of interest. Among the available procedures, the k-nearest neighbours (k-NN) technique is becoming popular, even for practical applications. However, the k-NN estimates at the pixel level tend to average towards the population mean and to have suppressed variance, since large values are usually underestimated and small values overestimated. This tendency may be detrimental for k-NN applications in forest resource management planning and scenario analysis where the representation of the spatial variability of each attribute of interest across the surveyed territory is fundamental. The present paper proposes a procedure to tackle such an issue by modifying k-NN estimates via a post-processing procedure of distribution matching. The empirical distribution function of the population values is estimated from the sample of ground data by using the 0-inflated beta distribution as the assisting model and the k-NN estimates are subsequently modified in such a way as to match the estimated distribution. The statistical properties of the distribution matching estimators for totals and averages are theoretically derived, while the performance of the distribution matching estimator at the pixel level are empirically evaluated by a simulation study.L'articolo è disponibile sul sito dell'editore www.Journals.elsevier.co