Conference PaperPDF Available

Mapping Burned Areas from Sentinel-1 and Sentinel-2 Data

Citation: Pepe, A.; Sali, M.; Boschetti,
M.; Stroppiana, D. Mapping Burned
Areas from Sentinel-1 and Sentinel-2
Data. Environ. Sci. Proc. 2022,17, 62.
Academic Editors: Pierpaolo Duce,
Donatella Spano, Michele Salis,
Bachisio Arca, Valentina Bacciu,
Grazia Pellizzaro and Costantino
Published: 10 August 2022
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Mapping Burned Areas from Sentinel-1 and Sentinel-2 Data
Antonio Pepe * , Matteo Sali , Mirco Boschetti and Daniela Stroppiana
Istituto per il Rilevamento Elettromagnetico dell’Ambiente (CNR-IREA), Consiglio Nazionale delle Ricerche,
80124 Napoli, Italy
Presented at the Third International Conference on Fire Behavior and Risk, Sardinia, Italy, 3–6 May 2022.
Keywords: optical and multi-spectral satellite data; SAR satellite data; fire monitoring
Fires devastated Europe during the summer of 2021, with hundreds of events burn-
ing across the Mediterranean, causing unprecedented damage to people, properties, and
ecosystems. Remote sensing (RS) is widely recognized as a key source of data for monitor-
ing wildfires [
], exploiting both optical/multi-spectral and microwave satellite sensors [
Optical/multi-spectral and microwave satellite observations can provide information on
areas affected by fires as well as on fire severity, which is the damage that affects vegetation.
The major advantage of RS technology is the consistent and operative availability of data
over large areas; these data can also be provided in near real-time for a fast assessment
of fire damage. In this work, we exploit both Sentinel-1 (S1) and Sentinel-2 (S2) data
from the Sicily region, Italy, to map and monitor the burned areas of the summer 2021
season. Coherent/incoherent change detection approaches have been applied to extract
areas where the RS signal has registered a significant change that could have been induced
by the occurrence of fire. Cross-comparison analyses between the results obtained using
optical and microwave images have been carried out to characterize the performance of the
exploited RS methods. To this aim, the fire perimeters available from the European Forest
Fire Information System (EFFIS) were used.
Author Contributions:
Conceptualization: A.P. and D.S.; Development and application of the
Methodology: M.B.; Validation: M.S. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external fundings.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in
the study
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
Environ. Sci. Proc. 2022,17, 62.
Environ. Sci. Proc. 2022,17, 62 2 of 2
Szpakowski, D.M.; Jensen, J.L.R. A Review of the Applications of Remote Sensing in Fire Ecology. Remote Sens.
,11, 2638.
Belenguer-Plomer, M.A.; Tanase, M.A.; Chuvieco, E.; Bovolo, F. CNN-based burned area mapping using radar and optical data.
Remote. Sens. Environ. 2021,260, 112468. [CrossRef]
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Full-text available
In this paper, we present an in-depth analysis of the use of convolutional neural networks (CNN), a deep learning method widely applied in remote sensing-based studies in recent years, for burned area (BA) mapping combining radar and optical datasets acquired by Sentinel-1 and Sentinel-2 on-board sensors, respectively. Combining active and passive datasets into a seamless wall-to-wall cloud cover independent mapping algorithm significantly improves existing methods based on either sensor type. Five areas were used to determine the optimum model settings and sensors integration, whereas five additional ones were utilised to validate the results. The optimum CNN dimension and data normalisation were conditioned by the observed land cover class and data type (i.e., optical or radar). Increasing network complexity (i.e., number of hidden layers) only resulted in rising computing time without any accuracy enhancement when mapping BA. The use of an optimally defined CNN within a joint active/passive data combination allowed for (i) BA mapping with similar or slightly higher accuracy to those achieved in previous approaches based on Sentinel-1 (Dice coefficient, DC of 0.57) or Sentinel-2 (DC 0.7) only and (ii) wall-to-wall mapping by eliminating information gaps due to cloud cover, typically observed for optical-based algorithms.
Full-text available
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