Ten-years dataset of poplar inventory in northern Italy
DOI:
https://doi.org/10.12899/asr-2448Keywords:
Dendrometric data, Populus sp., clones , growth modelling, biotic and abiotic adversity data, georeferenced samplingAbstract
The data refer to several poplar plantations located in the plains of northern Italy. The information was collected during the vegetative rest of each year from 1987 to 1996. Dendrometric data were recorded, such as the diameter at breast height, the diameter at five meters height and the planting density, as well as damage caused by biotic and abiotic adversities using a three-level intensity scale. All data is raw, with only total volume and the volume of the first log (up to 5 meters height) calculated using dendrometric equations based on tree diameter and height. The availability of a continuous inventory with annual measurements for 10 years on the same trees in the permanent sample plots has allowed the creation of a particularly important database for the study of growth models and the influence of biotic and abiotic adversities on wood production. This dataset could be used to perform further investigations, such as CO2 sequestration, to assess the environmental sustainability of the poplar plantations. Furthermore, thanks to this database, it is possible to identify which areas of the northern Italian plains are more suitable for poplar cultivation based on wood biomass production, or to evaluate the impact of pests and diseases with respect to clone and land characteristics.
References
Chiarabaglio P.M., Bergante S., Scirè M., Coaloa D. 2018 - Low cost poplar inventory in the plain of Piemonte (Italy). Annals of Silvicultural Research 42 (1): 39-42.
Corona P., Chianucci F., Marcelli A., Gianelle D., Fattorini L., Grotti M., Puletti N., Mattioli W. 2020 - Probabilistic sampling and estimation for large-scale assessment of poplar plantations in northern Italy. European Journal of Forest Research 139 (6): 981-988.
D’Amico G., Francini S., Giannetti F., Vangi E., Travaglini D., Chianucci F., Chirici G. 2021 - A deep learning approach for automatic mapping of poplar plantations using sentinel-2 imagery. GIScience and Remote Sensing 58 (8): 1352-1368.
Nielsen A.B., Östberg J., Delshammar T. 2014 - Review of urban tree inventory methods used to collect data at single-tree level. Arboriculture & Urban Forestry 40 (2): 96-111.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2023 Daniele Rizza, Piermario Chiarabaglio, Domenico Coaloa, Alessandro Rocci

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).







