Conference PaperPDF Available

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

August 2022

DOI:10.3390/environsciproc2022017062

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CC BY 4.0

Conference: ICFBR 2022

Authors:

A. Pepe

Italian National Research Council

Daniela Stroppiana

Italian National Research Council

Available via license: CC BY 4.0

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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.

https://doi.org/10.3390/

environsciproc2022017062

Academic Editors: Pierpaolo Duce,

Donatella Spano, Michele Salis,

Bachisio Arca, Valentina Bacciu,

Grazia Pellizzaro and Costantino

Sirca

Published: 10 August 2022

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Abstract

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

*Correspondence: pepe.a@irea.cnr.it

† 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. https://doi.org/10.3390/environsciproc2022017062 https://www.mdpi.com/journal/environsciproc

Environ. Sci. Proc. 2022,17, 62 2 of 2

References

Szpakowski, D.M.; Jensen, J.L.R. A Review of the Applications of Remote Sensing in Fire Ecology. Remote Sens.

2019

,11, 2638.

[CrossRef]

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]

ResearchGate has not been able to resolve any citations for this publication.

CNN-based burned area mapping using radar and optical data

Article

Full-text available

Jul 2021
REMOTE SENS ENVIRON

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.

A Review of the Applications of Remote Sensing in Fire Ecology

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Nov 2019

Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification.