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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: closed (15 June 2023) | Viewed by 8068

Special Issue Editors


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Guest Editor
GeoLAB—Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura 13, 50145 Firenze, Italy
Interests: application of geomatics to forestry; remote sensing; forest inventories and monitoring; sustainable forest management; land planning; landscape ecology; biodiversity; forest fires and climate change; bio-geo-chemical models; decision support systems; forest ecology
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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

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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 (2 papers)

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Research

21 pages, 17528 KiB  
Article
The Effect of Surrounding Vegetation on Basal Stem Measurements Acquired Using Low-Cost Depth Sensors in Urban and Native Forest Environments
by James McGlade, Luke Wallace, Bryan Hally, Karin Reinke and Simon Jones
Sensors 2023, 23(8), 3933; https://doi.org/10.3390/s23083933 - 12 Apr 2023
Viewed by 5251
Abstract
Three colour and depth (RGB-D) devices were compared, to assess the effect of depth image misalignment, resulting from simultaneous localisation and mapping (SLAM) error, due to forest structure complexity. Urban parkland (S1) was used to assess stem density, and understory vegetation (≤1.3 m) [...] Read more.
Three colour and depth (RGB-D) devices were compared, to assess the effect of depth image misalignment, resulting from simultaneous localisation and mapping (SLAM) error, due to forest structure complexity. Urban parkland (S1) was used to assess stem density, and understory vegetation (≤1.3 m) was assessed in native woodland (S2). Individual stem and continuous capture approaches were used, with stem diameter at breast height (DBH) estimated. Misalignment was present within point clouds; however, no significant differences in DBH were observed for stems captured at S1 with either approach (Kinect p = 0.16; iPad p = 0.27; Zed p = 0.79). Using continuous capture, the iPad was the only RGB-D device to maintain SLAM in all S2 plots. There was significant correlation between DBH error and surrounding understory vegetation with the Kinect device (p = 0.04). Conversely, there was no significant relationship between DBH error and understory vegetation for the iPad (p = 0.55) and Zed (p = 0.86). The iPad had the lowest DBH root-mean-square error (RMSE) across both individual stem (RMSE = 2.16cm) and continuous (RMSE = 3.23cm) capture approaches. The results suggest that the assessed RGB-D devices are more capable of operation within complex forest environments than previous generations. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Forest Remote Sensing)
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14 pages, 2217 KiB  
Article
Early Stage Forest Fire Detection from Himawari-8 AHI Images Using a Modified MOD14 Algorithm Combined with Machine Learning
by Naoto Maeda and Hideyuki Tonooka
Sensors 2023, 23(1), 210; https://doi.org/10.3390/s23010210 - 25 Dec 2022
Cited by 4 | Viewed by 2206
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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