Enrico Pompei

Modelling natural forest expansion on a landscape level by multinomial logistic regression

Abstract Natural forest expansion is one of the most relevant landscape changes in many temperate countries. Although large areas are involved, relatively few studies have been carried out with the objective of unravelling the specific impact of the individual factors characterising the sites prone to such a process. The aim of this article is to present a research tool for assessing the factors characterising farmland sites prone to natural conversion from crop growing and pasture to forests and other wooded land (OWL), and for predicting the probability of such a land-use change. The methodological approach is based on multinomial logistic regression. As a case study, the approach was applied to land-use classification repeated on the same sites in a large area of central Italy on two successive occasions, spanning two decades, from the beginning of the 1980s up to 2002. Of all the factors assessed, landscape attributes were identified as a sufficient subset for quantitative prediction of change from farmland to OWL or to forest. The tested modelling approach is explicitly empirical and planning-oriented. From a quantitative point of view, the precision of the models may be only indicative for assessing land-use change probability for single observations, while it is appropriate for predicting mean probabilities at a landscape mapping level, where it is possible to sample a number of sites. At this level, the approach is a useful tool for simulating future landscape scenarios related to natural forest expansion.

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 so-called 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 large-scale 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.

Quality control procedures in the Italian national forest inventory

National forest inventories represent a fundamental source of data and knowledge for forestry and environmental policy and allow for the production of national and regional level statistics on forests. The value of these statistics confirms the need for a sampling design that adequately delivers representation by reducing sampling error, but also for a data quality process that limits the non-sampling errors. The article summarizes the quality control procedures of the three sampling phases adopted in the Italian national forest inventory, carried out between 2003 and 2006. The development of an integrated system of actions and controls which are able to limit subjective interpretations, in order to guarantee harmonized information all over the country, was a considerable effort within the overall project. Critical points to be considered were the consistent number of measures and evaluations undertaken during the three inventory phases, the high variability of observed attributes, the consistent number of surveyors involved, and costs of quality control, especially those related to fieldwork. At the end, examples on the overall quality of the classification performed on land cover and vegetation are discussed, as well as the impact of classification errors on the total forest area estimates.