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

Extending large-scale forest inventories to assess urban forests

Urban areas are continuously expanding today, extending their influence on an increasingly large proportion of woods and trees located in or nearby urban and urbanizing areas, the socalled urban forests. Although these forests have the potential for significantly improving the quality the urban environment and the well-being of the urban population, data to quantify the extent and characteristics of urban forests are still lacking or fragmentary on a large scale. In this regard, an expansion of the domain of multipurpose forest inventories like National Forest Inventories (NFIs) towards urban forests would be required. To this end, it would be convenient to exploit the same sampling scheme applied in NFIs to assess the basic features of urban forests. This paper considers approximately unbiased estimators of abundance and coverage of urban forests, together with estimators of the corresponding variances, which can be achieved from the first phase of most largescale forest inventories. A simulation study is carried out in order to check the performance of the considered estimators under various situations involving the spatial distribution of the urban forests over the study area. An application is worked out on the data from the Italian NFI.L'articolo è disponibile sul sito dell'editore www.springer.co