Poplar is one of the most widespread fast-growing forest species. In Northern Italy, plantations are characterized by large interannual fluctuations, requiring frequent monitoring to inform on wood supply and to manage the stands. The use of radar satellite data is proving useful for forest monitoring, being weather independent and sensitive to the changes in forest canopy structure, but it has been scarcely tested in the case of poplar. Here, L-band ALOS2 (Advanced Land Observing Satellite-2) dual-pol data were tested to detect clear-cut plantations in consecutive years. ALOS2 quad-pol data were used to discriminate among different age classes, a much complex task than detecting poplar plantations extent. Results from different machine learning algorithms indicate that with dual-pol data, poplar forest can be discriminated from clear-cut areas with 80% overall accuracy, similar to what is usually obtained with optical data. With quad-pol data, four age classes were classified with moderate overall accuracy (73%) based on polarimetric decompositions, three 3 age classes with higher accuracy (87%) based on HV band. Sources of error are represented by poplar areas of intermediate age when stems, branches and leaves were not developed enough to detect by scattering mechanisms. This study demonstrates the feasibility of monitoring poplar plantations with satellite radar, which represents a growing source of information thanks to already-planned future satellite missions.

Volume tables and terrestrial laser scanning: a technology innovation supporting forest mensuration Ecologically and economically sustainable planning of forest resources requires tools capable of providing estimates with adequate accuracy on volume, biomass and woody increments. Interest in these attributes has increased since the United Nations Framework Convention on Climate Change has been given a further boost by the birth of the carbon credit market in the early 2000s. However, the data collection necessary to formulate allometric models for estimating wood volume is challenging, both due to the considerable amount of data required and because the necessary destructive measurements are very laborious. Furthermore, given the great structural, managerial and environmental diversity that characterizes the Italian forests, the sample size for the development of allometric models must necessarily be large. Over the years, all these aspects have led to a progressive abandonment of measurements in the forests for the production of volume tables. Recent applications of the terrestrial laser scanner (TLS) for collecting dimensional information on trees have demonstrated their effectiveness. In this study we present the work carried out in the autumn/winter 2022-2023 for the creation of new volume tables for the black pine forests in Vallombrosa (FI -Central Italy), based on data collected with a TLS. The study involved the same pine forests studied in 1969 for the production of volume tables in Vallombrosa. After showing the methods and analysis needed to obtain the volume tables, the paper discusses the results in comparison with those produced in 1969.