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A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020

Forests absorb 30% of human emissions associated with fossil fuel burning. For this reason, forest disturbances monitoring is needed for assessing greenhouse gas balance. However, in several countries, the information regarding the spatio-temporal distribution of forest disturbances is missing. Remo...

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Autores principales: Francini, Saverio, Chirici, Gherardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149008/
https://www.ncbi.nlm.nih.gov/pubmed/35651671
http://dx.doi.org/10.1016/j.dib.2022.108297
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author Francini, Saverio
Chirici, Gherardo
author_facet Francini, Saverio
Chirici, Gherardo
author_sort Francini, Saverio
collection PubMed
description Forests absorb 30% of human emissions associated with fossil fuel burning. For this reason, forest disturbances monitoring is needed for assessing greenhouse gas balance. However, in several countries, the information regarding the spatio-temporal distribution of forest disturbances is missing. Remote sensing data and the new Sentinel-2 satellite missions, in particular, represent a game-changer in this topic. Here we provide a spatially explicit dataset (10-meters resolution) of Italian forest disturbances and magnitude from 2017 to 2020 constructed using Sentinel-2 level-1C imagery and exploiting the Google Earth Engine GEE implementation of the 3I3D algorithm. For each year between 2017 and 2020, we provide three datasets: (i) a magnitude of the change map (between 0 and 255), (ii) a categorical map of forest disturbances, and (iii) a categorical map obtained by stratification of the previous maps that can be used to estimate the areas of several different forest disturbances. The data we provide represent the state-of-the-art for Mediterranean ecosystems in terms of omission and commission errors, they support greenhouse gas balance, forest sustainability assessment, and decision-makers forest managing, they help forest companies to monitor forest harvestings activity over space and time, and, supported by reference data, can be used to obtain the national estimates of forest harvestings and disturbances that Italy is called upon to provide.
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spelling pubmed-91490082022-05-31 A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020 Francini, Saverio Chirici, Gherardo Data Brief Data Article Forests absorb 30% of human emissions associated with fossil fuel burning. For this reason, forest disturbances monitoring is needed for assessing greenhouse gas balance. However, in several countries, the information regarding the spatio-temporal distribution of forest disturbances is missing. Remote sensing data and the new Sentinel-2 satellite missions, in particular, represent a game-changer in this topic. Here we provide a spatially explicit dataset (10-meters resolution) of Italian forest disturbances and magnitude from 2017 to 2020 constructed using Sentinel-2 level-1C imagery and exploiting the Google Earth Engine GEE implementation of the 3I3D algorithm. For each year between 2017 and 2020, we provide three datasets: (i) a magnitude of the change map (between 0 and 255), (ii) a categorical map of forest disturbances, and (iii) a categorical map obtained by stratification of the previous maps that can be used to estimate the areas of several different forest disturbances. The data we provide represent the state-of-the-art for Mediterranean ecosystems in terms of omission and commission errors, they support greenhouse gas balance, forest sustainability assessment, and decision-makers forest managing, they help forest companies to monitor forest harvestings activity over space and time, and, supported by reference data, can be used to obtain the national estimates of forest harvestings and disturbances that Italy is called upon to provide. Elsevier 2022-05-21 /pmc/articles/PMC9149008/ /pubmed/35651671 http://dx.doi.org/10.1016/j.dib.2022.108297 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Francini, Saverio
Chirici, Gherardo
A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020
title A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020
title_full A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020
title_fullStr A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020
title_full_unstemmed A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020
title_short A Sentinel-2 derived dataset of forest disturbances occurred in Italy between 2017 and 2020
title_sort sentinel-2 derived dataset of forest disturbances occurred in italy between 2017 and 2020
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149008/
https://www.ncbi.nlm.nih.gov/pubmed/35651671
http://dx.doi.org/10.1016/j.dib.2022.108297
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