Thanks to their ability to produce ecosystem services, forest ecosystems have a significant social, economic and environmental impact on the development of many regions in theworld, especially when located in urban and peri-urban areas. Today, increased forest vulnerability is being reflected in a larger number of severe decline episodes associated mainly with drought. In this context, the Mediterranean area shows high forest vulnerability and a subsequent decline in its natural renewal rate. In this scenario, the aim of the present research is to assess sustainability of a protected pristine deciduous oak forest near Rome, developing a forest health condition monitoring tool based on the application of multispectral satellite data, and through the identification of silvicultural models appropriate for promoting natural forest renewal. The preliminary results of this research indicate how the exclusion of wildlife through fenced areas significantly favors the natural renewal of the forest, especially when silvicultural actions such as thinning are carried out. It has also been ascertained through the use of multispectral satellite data that there is a widespread decline in the health vegetative state of the deciduous oak surfaces, due to a widespread senescence of the forest. In the addressed study area, the Natural State Reserve of Castelporziano (Rome), data and results from this research can work as an important decision tool for sustainable forest management.

The Contribution of Remote Sensing and Silvicultural Treatments to the Assessment of Decline in an Oak Deciduous Forest: The Study Case of a Protected Area in Mediterranean Environment / Recanatesi, Fabio; Piccinno, Matteo; Cucca, Benedetta; Rossi, Carlo Maria; Ripa, Maria Nicolina. - (2020), pp. 36-49. - LECTURE NOTES IN ARTIFICIAL INTELLIGENCE. [10.1007/978-3-030-58814-4_3].

The Contribution of Remote Sensing and Silvicultural Treatments to the Assessment of Decline in an Oak Deciduous Forest: The Study Case of a Protected Area in Mediterranean Environment

Piccinno, Matteo
Secondo
;
2020

Abstract

Thanks to their ability to produce ecosystem services, forest ecosystems have a significant social, economic and environmental impact on the development of many regions in theworld, especially when located in urban and peri-urban areas. Today, increased forest vulnerability is being reflected in a larger number of severe decline episodes associated mainly with drought. In this context, the Mediterranean area shows high forest vulnerability and a subsequent decline in its natural renewal rate. In this scenario, the aim of the present research is to assess sustainability of a protected pristine deciduous oak forest near Rome, developing a forest health condition monitoring tool based on the application of multispectral satellite data, and through the identification of silvicultural models appropriate for promoting natural forest renewal. The preliminary results of this research indicate how the exclusion of wildlife through fenced areas significantly favors the natural renewal of the forest, especially when silvicultural actions such as thinning are carried out. It has also been ascertained through the use of multispectral satellite data that there is a widespread decline in the health vegetative state of the deciduous oak surfaces, due to a widespread senescence of the forest. In the addressed study area, the Natural State Reserve of Castelporziano (Rome), data and results from this research can work as an important decision tool for sustainable forest management.
2020
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
978-3-030-58813-7
978-3-030-58814-4
Protected area · Risk assessment · Oak decline · Remote sensing · NDVI · GIS · Sentinel-2 · Landsat 5
02 Pubblicazione su volume::02a Capitolo o Articolo
The Contribution of Remote Sensing and Silvicultural Treatments to the Assessment of Decline in an Oak Deciduous Forest: The Study Case of a Protected Area in Mediterranean Environment / Recanatesi, Fabio; Piccinno, Matteo; Cucca, Benedetta; Rossi, Carlo Maria; Ripa, Maria Nicolina. - (2020), pp. 36-49. - LECTURE NOTES IN ARTIFICIAL INTELLIGENCE. [10.1007/978-3-030-58814-4_3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1447471
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