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Special Issue "Editorial Board Members' Collection Series: Forest Remote Sensing"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 15 June 2023 | Viewed by 1103

Special Issue Editors

Special Issue Information

Dear Colleagues,

Remote sensing has been used in a diverse range of fields, such as forest monitoring and ecology, supporting management applications from mapping spatio-temporal forest dynamics (compositions and structures), to the estimation of forest variables or their integration into modeling systems. Technological development, integration and adoption in forestry continues to grow; therefore, the application of advanced forest remote sensing technology has become the current focus in the research into the development of forest observation and information systems.

The aim of this Special Issue is to present the new research and developments in the field. We invite original contributions that demonstrate the current research trends. The topics of this Special Issue may include the following:

  • Hydroclimatic and ecological models and simulations driven by satellite data in forests;
  • Integration of EO data with in situ field observations;
  • Monitoring and spatial estimation of ecosystem services (carbon, biodiversity, landscape, soil protection, etc.);
  • Development of new methods for the processing and analysis of EO data;
  • Applications of Earth observation techniques for monitoring forest disturbances and dynamics;
  • Applications of remote sensing to support sustainable forest resource management;
  • Big data processing for large-scale relevant EO data analysis;
  • Artificial Intelligence frameworks for the extraction of valuable signals for disaster risk management and forecasting in forestry using large sets of EO data.

Prof. Dr. Gherardo Chirici 
Dr. Antonio Pepe
Guest Editors

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 submissions that pass pre-check are 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. Sensors 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 2400 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

  • earth observation
  • remote sensing
  • forest monitoring
  • ecological modeling

Published Papers (1 paper)

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Research

Article
Early Stage Forest Fire Detection from Himawari-8 AHI Images Using a Modified MOD14 Algorithm Combined with Machine Learning
Sensors 2023, 23(1), 210; https://doi.org/10.3390/s23010210 - 25 Dec 2022
Viewed by 822
Abstract
The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal-resolution images, observed by the Advanced Himawari Imager (AHI) [...] Read more.
The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal-resolution images, observed by the Advanced Himawari Imager (AHI) onboard the geostationary meteorological satellite Himawari-8. In order to not miss early stage forest fire pixels with low temperature, we omit the potential fire pixel detection from the MOD14 algorithm and parameterize four contextual conditions included in the MOD14 algorithm as features. The proposed method detects fire pixels from forest areas using a random forest classifier taking these contextual parameters, nine AHI band values, solar zenith angle, and five meteorological values as inputs. To evaluate the proposed method, we trained the random forest classifier using an early stage forest fire data set generated by a time-reversal approach with MOD14 products and time-series AHI images in Australia. The results demonstrate that the proposed method with all parameters can detect fire pixels with about 90% precision and recall, and that the contribution of contextual parameters is particularly significant in the random forest classifier. The proposed method is applicable to other geostationary and polar-orbiting satellite sensors, and it is expected to be used as an effective method for forest fire detection. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Forest Remote Sensing)
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