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Forests
Special Issues
Spatio-Temporal Monitoring of Forest Fires and Vegetation
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Spatio-Temporal Monitoring of Forest Fires and Vegetation

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (23 January 2024) | Viewed by 3425

Special Issue Editors

Prof. Dr. Luis A. Ruiz

E-Mail Website
Guest Editor
Geo-Environmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: lidar for forest structure analysis; 3D fire behaviour models; object-based feature extraction and classification; land use/land cover change analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Pablo Crespo-Peremarch

E-Mail Website
Guest Editor
Geo-Environmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: remote sensing; discrete and full-waveform lidar; forest monitoring; machine and deep learning; time series; geoinformatics

Special Issue Information

Dear Colleagues,

Forest dynamics monitoring is crucial for forest management operations, ecosystem and biodiversity preservation, tracking climate change effects, and wildfire prevention, control, and recovery. The recent advances in remote sensing techniques are providing new perspectives and tools for the study of vegetation variations over time and space. The large variety of remote sensing platforms (satellite, aerial, UAV, and terrestrial), imagery (multispectral and hyperspectral), and resulting products (i.e., time series and photogrammetric point clouds) are contributing to both the large-scale and fine characterization of forests. The use of terrestrial, airborne, and even satellite-borne laser scanning systems is becoming more common when analyzing forest structures and the spatial distribution of vegetation. In this Special Issue, we aim to collect contributions about new advances in remote sensing laser scanning systems, data sets, methods, and tools used to map and monitor vegetation from a spatiotemporal perspective, with a special emphasis on the prevention and mitigation of forest fires and the interpretation and analysis of the evolution of forest landscapes through the application of these techniques.

Prof. Dr. Luis A. Ruiz
Dr. Pablo Crespo-Peremarch
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. Forests is an international peer-reviewed open access monthly 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 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

  • forest structure
  • wildfires
  • hyperspectral
  • imagery
  • time series
  • ALS
  • TLS
  • UAV
  • spatial analysis
  • vegetation monitoring

Published Papers (2 papers)

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Research

26 pages, 7833 KiB  
Article
Analyzing Independent LFMC Empirical Models in the Mid-Mediterranean Region of Spain Attending to Vegetation Types and Bioclimatic Zones
by María Alicia Arcos, Roberto Edo-Botella, Ángel Balaguer-Beser and Luis Ángel Ruiz
Forests 2023, 14(7), 1299; https://doi.org/10.3390/f14071299 - 24 Jun 2023
Viewed by 983
Abstract
This paper presents empirical models developed through stepwise multiple linear regression to estimate the live fuel moisture content (LFMC) in a Mediterranean area. The models are based on LFMC data measured in 50 field plots, considering four groups with similar bioclimatic characteristics and [...] Read more.
This paper presents empirical models developed through stepwise multiple linear regression to estimate the live fuel moisture content (LFMC) in a Mediterranean area. The models are based on LFMC data measured in 50 field plots, considering four groups with similar bioclimatic characteristics and vegetation types (trees and shrubs). We also applied a species-specific LFMC model for Rosmarinus officinalis in plots with this dominant species. Spectral indices extracted from Sentinel-2 images and their averages over the study time period in each plot with a spatial resolution of 10 m were used as predictors, together with interpolated meteorological, topographic, and seasonal variables. The models achieved adjusted R2 values ranging between 52.1% and 74.4%. Spatial and temporal variations of LFMC in shrub areas were represented on a map. The results highlight the feasibility of developing satellite-derived LFMC operational empirical models in areas with various vegetation types and taking into account bioclimatic zones. The adjustment of data through GAM (generalized additive models) is also addressed in this study. The different error metrics obtained reflect that these models provided a better fit (most adjusted R2 values ranged between 65% and 74.1%) than the linear models, due to GAMs being more versatile and suitable for addressing complex problems such as LFMC behavior. Full article
(This article belongs to the Special Issue Spatio-Temporal Monitoring of Forest Fires and Vegetation)
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17 pages, 2760 KiB  
Article
Characterizing Live Fuel Moisture Content from Active and Passive Sensors in a Mediterranean Environment
by Mihai A. Tanase, Juan Pedro Gonzalez Nova, Eva Marino, Cristina Aponte, Jose Luis Tomé, Lucia Yáñez, Javier Madrigal, Mercedes Guijarro and Carmen Hernando
Forests 2022, 13(11), 1846; https://doi.org/10.3390/f13111846 - 04 Nov 2022
Cited by 4 | Viewed by 1594
Abstract
Live fuel moisture content (LFMC) influences many fire-related aspects, including flammability, ignition, and combustion. In addition, fire spread models are highly sensitive to LFMC values. Despite its importance, LFMC estimation is still elusive due to its dependence on plant species traits, local conditions, [...] Read more.
Live fuel moisture content (LFMC) influences many fire-related aspects, including flammability, ignition, and combustion. In addition, fire spread models are highly sensitive to LFMC values. Despite its importance, LFMC estimation is still elusive due to its dependence on plant species traits, local conditions, and weather patterns. Although LFMC mapping from active synthetic aperture radar has increased over the past years, their utility for LFMC estimation needs further analysis to include additional areas characterized by different vegetation species and fire regimes. This study extended the current knowledge using medium spatial resolution (20 m) time series acquired by active (Sentinel-1) and passive (Sentinel-2) sensors. Our results show that optical-based LFMC estimation may achieve acceptable accuracy (R2 = 0.55, MAE = 15.1%, RMSE = 19.7%) at moderate (20 m) spatial resolution. When ancillary information (e.g., vegetation cover) was added, LFMC estimation improved (R2 = 0.63, MAE = 13.4%). Contrary to other studies, incorporating Sentinel-1 radar data did not provide for improved LFMC estimates, while the use of SAR data alone resulted in increased estimation errors (R2 = 0.28, MAE = 19%, RMSE = 25%). For increased fire risk scenarios (LFMC < 120%), estimation errors improved (MAE = 9.1%, RMSE = 11.8%), suggesting that direct LFMC retrieval from satellite data may be achieved with high temporal and spatial detail. Full article
(This article belongs to the Special Issue Spatio-Temporal Monitoring of Forest Fires and Vegetation)
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