Special Issue "Remote Sensing of Forest Fire: Data, Science and Operational Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 15 December 2020.

Special Issue Editor

Dr. Rosa Lasaponara
E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche, Rome, Italy
Interests: remote sensing, satellite time series analysis, risk monitoring, archaeology, fire
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technologies have long been considered as a key tool for fire data, science, modelling, management, and monitoring. The most recent developments in computer technology, data processing, artificial intelligence (AI), deep learning approaches, and geospatial data mining techniques, enable advanced dynamic modelling, tools, data integration, and assimilation schemes, and are expected to significantly support and improve fire science and operational applications. Recently, the availability of new sensors from satellite, aerial, drone, and ground, along with the free access to large archives of data, has opened new perspectives for both fire science and applications. We invite you to submit articles on topics including, but not limited to, the following:

  • Earth observation (optical, SAR, UAV, and LiDAR) as a tool for data science and operational applications
  • Advanced geospatial data mining techniques
  • Integration of satellite, aerial/drone, and in situ observation in the Copernicus Era
  • Fire disturbance monitoring at multiple spatio-temporal scales
  • Deep learning approaches for fire science and applications
  • Advances in remote sensing of forest fire fuel mapping
  • Data integration for fire and post fire geo-hazards risk mitigation and management
  • Earth big data for monitoring and mapping fire and post-fire induced risk
  • Fusion and integration of data and information from multiple sources
  • Integration of RS with climate and meteorological data and forecasting
  • New tools and methods for fire monitoring and mitigation strategies

Dr. Rosa Lasaponara
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Fire 
  • Earth observation 
  • Copernicus
  • Data integration

Published Papers (1 paper)

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Research

Open AccessArticle
Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas
Remote Sens. 2019, 11(22), 2661; https://doi.org/10.3390/rs11222661 - 14 Nov 2019
Abstract
Burned area algorithms from radar images are often based on temporal differences between pre- and post-fire backscatter values. However, such differences may occur long past the fire event, an effect known as temporal decorrelation. Improvements in radar-based burned areas monitoring depend on a [...] Read more.
Burned area algorithms from radar images are often based on temporal differences between pre- and post-fire backscatter values. However, such differences may occur long past the fire event, an effect known as temporal decorrelation. Improvements in radar-based burned areas monitoring depend on a better understanding of the temporal decorrelation effects as well as its sources. This paper analyses the temporal decorrelation of the Sentinel-1 C-band backscatter coefficient over burned areas in Mediterranean ecosystems. Several environmental variables influenced the radar scattering such as fire severity, post-fire vegetation recovery, water content, soil moisture, and local slope and aspect were analyzed. The ensemble learning method random forests was employed to estimate the importance of these variables to the decorrelation process by land cover classes. Temporal decorrelation was observed for over 32% of the burned pixels located within the study area. Fire severity, vegetation water content, and soil moisture were the main drivers behind temporal decorrelation processes and are of the utmost importance for areas detected as burned immediately after fire events. When burned areas were detected long after fire (decorrelated areas), due to reduced backscatter coefficient variations between pre- to post-fire acquisitions, water content (soil and vegetation) was the main driver behind the backscatter coefficient changes. Therefore, for efficient synthetic aperture radar (SAR)-based monitoring of burned areas, detection, and mapping algorithms need to account for the interaction between fire impact and soil and vegetation water content. Full article
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