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Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progress...

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Autores principales: Lazzeri, Giacomo, Frodella, William, Rossi, Guglielmo, Moretti, Sandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230056/
https://www.ncbi.nlm.nih.gov/pubmed/34207736
http://dx.doi.org/10.3390/s21123982
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author Lazzeri, Giacomo
Frodella, William
Rossi, Guglielmo
Moretti, Sandro
author_facet Lazzeri, Giacomo
Frodella, William
Rossi, Guglielmo
Moretti, Sandro
author_sort Lazzeri, Giacomo
collection PubMed
description Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.
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spelling pubmed-82300562021-06-26 Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy Lazzeri, Giacomo Frodella, William Rossi, Guglielmo Moretti, Sandro Sensors (Basel) Article Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth. MDPI 2021-06-09 /pmc/articles/PMC8230056/ /pubmed/34207736 http://dx.doi.org/10.3390/s21123982 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lazzeri, Giacomo
Frodella, William
Rossi, Guglielmo
Moretti, Sandro
Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy
title Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy
title_full Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy
title_fullStr Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy
title_full_unstemmed Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy
title_short Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy
title_sort multitemporal mapping of post-fire land cover using multiplatform prisma hyperspectral and sentinel-uav multispectral data: insights from case studies in portugal and italy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8230056/
https://www.ncbi.nlm.nih.gov/pubmed/34207736
http://dx.doi.org/10.3390/s21123982
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