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Article

Characterization of Wildfires and Harvesting Forest Disturbances and Recovery Using Landsat Time Series: A Case Study in Mediterranean Forests in Central Italy

1
OpenGeoHub Foundation, 6708 PW Wageningen, The Netherlands
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Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, 6700 AA Wageningen, The Netherlands
3
Geolab Laboratory of Forest Geomatics, Department of Agriculture Food Environment and Forestry, University of Florence, 50145 Florence, Italy
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ForTech Laboratorio Congiunto, University of Florence, 50145 Florence, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Florent Mouillot
Received: 29 March 2022 / Revised: 18 May 2022 / Accepted: 20 May 2022 / Published: 24 May 2022
(This article belongs to the Special Issue Mediterranean Fires)
Large-scale forest monitoring benefits greatly from change detection analysis based on remote sensing data because it enables characterizing forest dynamics of disturbance and recovery by detecting both gradual and abrupt changes on Earth’s surface. In this study, two of the main disturbances occurring in Mediterranean forests, harvesting operations and forest fires, were analyzed through the analysis of Landsat Times Series images in a case study in Central Italy (Tuscany region). Disturbances were characterized based on their distinct temporal behaviors before and after the event: a period of 20 years (1999–2018) was used to extract and analyze at pixel level spectral trajectories for each disturbance and produce descriptive temporal trends of the phenomena. Recovery metrics were used to characterize both short- (5 years) and long-term aspects of recovery for harvested and burned areas. Spectral, recovery, and trend analysis metrics were then used with the Random Forest classifier to differentiate between the two disturbance classes and to investigate their potential as predictors. Among spectral bands, the Landsat SWIR 1 band proved the best to detect areas interested by harvesting, while forest fires were better detected by the SWIR 2 band; among spectral indices, the NBR scored as the best for both classes. On average, harvested areas recovered faster in both short- and long-term aspects and showed less variability in the magnitude of the disturbance event and recovery rate over time. This tendency is confirmed by the results of the classifier, which obtained an overall accuracy of 98.6%, and identified the mean of the post-disturbance values of the trend as the best predictor to differentiate between disturbances. View Full-Text
Keywords: wildfire; harvest; forests; forest fires; coppices; classification of forest disturbances; time series wildfire; harvest; forests; forest fires; coppices; classification of forest disturbances; time series
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MDPI and ACS Style

Bonannella, C.; Chirici, G.; Travaglini, D.; Pecchi, M.; Vangi, E.; D’Amico, G.; Giannetti, F. Characterization of Wildfires and Harvesting Forest Disturbances and Recovery Using Landsat Time Series: A Case Study in Mediterranean Forests in Central Italy. Fire 2022, 5, 68. https://doi.org/10.3390/fire5030068

AMA Style

Bonannella C, Chirici G, Travaglini D, Pecchi M, Vangi E, D’Amico G, Giannetti F. Characterization of Wildfires and Harvesting Forest Disturbances and Recovery Using Landsat Time Series: A Case Study in Mediterranean Forests in Central Italy. Fire. 2022; 5(3):68. https://doi.org/10.3390/fire5030068

Chicago/Turabian Style

Bonannella, Carmelo, Gherardo Chirici, Davide Travaglini, Matteo Pecchi, Elia Vangi, Giovanni D’Amico, and Francesca Giannetti. 2022. "Characterization of Wildfires and Harvesting Forest Disturbances and Recovery Using Landsat Time Series: A Case Study in Mediterranean Forests in Central Italy" Fire 5, no. 3: 68. https://doi.org/10.3390/fire5030068

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