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Forests
Special Issues
Modeling, Measuring, and Mapping Wildland Fuels
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Modeling, Measuring, and Mapping Wildland Fuels

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 (12 September 2021) | Viewed by 8357

Special Issue Editors

Dr. Russ Parsons

E-Mail Website
Guest Editor
US Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, Missoula, MT, USA
Interests: 3D fuels; prescribed fire; fuel treatments; fuel modeling; physics-based fire modeling; fuel mapping; remote sensing
Special Issues, Collections and Topics in MDPI journals
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. Francois Pimont

E-Mail Website
Guest Editor
French National Institute for Agriculture, Food, and Environment (INRAE), Paris, France
Interests: remote sensing; wildland fire; fire modelling; fuel modelling; FIRETEC; LiDAR

Special Issue Information

Dear colleagues,

In many places across the globe, climate change has led to increased fire occurrence, with larger and more damaging fires. The intersection of such fires with areas of increasing population density has caused high-fatality disasters in numerous countries. Amid this growing crisis, fire managers need accurate fuel maps and fire modeling for decision support, risk assessment and strategic planning efforts. However, developing fuel maps can be challenging, as wildland fuels are complex in distribution, structure and characteristics. They are dynamic, changing over time and space in response to environmental factors, disturbances and management action. This complexity has fostered a rapidly growing, diverse and multidisciplinary field, linking biology, physiology, physics, remote sensing and computer modeling. In this Special Issue of Forests, entitled “Modeling, Measuring and Mapping Wildland Fuels”, we aim to collect timely and emerging research in wildland fuels science, with a focus on new developments, applications and technologies in mapping, measuring and modeling fuels. We encourage researchers to submit articles in a wide range of themes in this arena. Examples might include modeling fuel dynamics, novel mapping approaches, classification algorithms and image processing, new approaches for cross-scale linkages, and remote sensing methods and applications characterizing 3D fuel structure.

Dr. Russell A. Parsons
Prof. Luis A. Ruiz
Dr. Francois Pimont
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

  • Fuel modeling
  • Fuel mapping
  • ALS
  • TLS
  • Imagery
  • Fuel structure
  • Fuel characteristics
  • Flammability
  • Heterogeneity
  • Modeling

Published Papers (3 papers)

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Research

18 pages, 2719 KiB  
Article
Fuel Modelling Characterisation Using Low-Density LiDAR in the Mediterranean: An Application to a Natural Protected Area
by Aurora Ferrer Palomino and Francisco Rodríguez y Silva
Forests 2021, 12(8), 1011; https://doi.org/10.3390/f12081011 - 29 Jul 2021
Cited by 4 | Viewed by 1806
Abstract
Fuel structure and characteristics are important to better understand and predict wildfire behaviour. The aim of the present study was to develop a methodology for characterising fuel models using low-density and free LiDAR data that facilitate the work of managers of protected territories. [...] Read more.
Fuel structure and characteristics are important to better understand and predict wildfire behaviour. The aim of the present study was to develop a methodology for characterising fuel models using low-density and free LiDAR data that facilitate the work of managers of protected territories. Field inventories were carried out in order to understand the characteristics of the stand and the variables that fuel models must include. This information, together with the use of the intensity and structure provided by LiDAR, was used to perform statistical analyses. The linear regressions obtained to characterise the stand of the mixed Quercus spp.–Pinus ssp.-dominated stand had an R2 value ranging from 0.4393 to 0.66. While working with low-density LiDAR data (which has more difficulties crossing the canopy), in addition to the obtained results, we performed the statistical analysis of the dominant stand to obtain models with R2 values ranging from 0.8201 to 0.8677. The results of this research show that low-density LiDAR data are significant; however, in mixed stands, it is necessary to only use the dominant stratum because other components generate noise, which reduces the predictive capacity of the models. Additionally, by using the decision tree developed in combination, it is possible to update the mapping of fuel models in inaccessible areas, thereby significantly reducing costs. Full article
(This article belongs to the Special Issue Modeling, Measuring, and Mapping Wildland Fuels)
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16 pages, 24933 KiB  
Article
Analysis of Mediterranean Vegetation Fuel Type Changes Using Multitemporal LiDAR
by Alba García-Cimarras, José Antonio Manzanera and Rubén Valbuena
Forests 2021, 12(3), 335; https://doi.org/10.3390/f12030335 - 12 Mar 2021
Cited by 8 | Viewed by 3055
Abstract
Increasing fire size and severity over the last few decades requires new techniques to accurately assess canopy fuel conditions and change over larger areas. This article presents an analysis on vegetation changes by mapping fuel types (FT) based on conditional rules according to [...] Read more.
Increasing fire size and severity over the last few decades requires new techniques to accurately assess canopy fuel conditions and change over larger areas. This article presents an analysis on vegetation changes by mapping fuel types (FT) based on conditional rules according to the Prometheus classification system, which typifies the vertical profile of vegetation cover for fuel management and ecological purposes. Using multi-temporal LiDAR from the open-access Spanish national surveying program, we selected a 400 ha area of interest, which was surveyed in 2010 and 2016 with scan densities of 0.5 and 2 pulses·m−2, respectively. FTs were determined from the distribution of LiDAR heights over an area, using grids with a cell size of 20 × 20 m. To validate the classification method, we used a stratified random sampling without replacement of 15 cells per FT and made an independent visual assessment of FT. The overall accuracy obtained was 81.26% with a Kappa coefficient of 0.73. In addition, the relationships among different stand structures and ecological factors such as topographic aspect and forest vegetation cover types were analyzed. Our classification algorithm revealed that stands lacking understory vegetation usually appeared in shady slopes, which were mainly covered by beech stands, whereas sunny areas were preferentially covered by oak stands, where the understory reached greater height thanks to more light availability. Our analysis on FT changes during that 6 year time span revealed potentially hazardous transitions from cleared forests towards a vertical continuum of canopy fuels, where wildfire events would potentially reach tree crowns, especially in oak forests and southern slopes with higher sun exposure for lower fuel moistures and increased flammability. Accurate methods to characterize forest canopy fuels and change over time can help direct forest management activities to priority areas with greater fire hazard. Multi-date canopy fuel information indicated that while some forest types experienced a growth of the shrub layer, others presented an understory decrease. On the other hand, loss of understory was more frequently detected in beech stands; thus, those forests place lower risk of wildfire spread. Our approach was developed using low-density and publicly available datasets and was based on direct canopy fuel measurements from multi-return LiDAR data that can be accurately translated and mapped according to standard fuel type categories that are familiar to land managers. Full article
(This article belongs to the Special Issue Modeling, Measuring, and Mapping Wildland Fuels)
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19 pages, 3764 KiB  
Article
Estimation of 10-Hour Fuel Moisture Content Using Meteorological Data: A Model Inter-Comparison Study
by HoonTaek Lee, Myoungsoo Won, Sukhee Yoon and Keunchang Jang
Forests 2020, 11(9), 982; https://doi.org/10.3390/f11090982 - 11 Sep 2020
Cited by 18 | Viewed by 2848
Abstract
Forest fire modeling often requires estimates of fuel moisture status. Among the various fuel variables used for fire modeling studies, the 10-h fuel moisture content (10-h FMC) is a promising predictor since it can be automatically measured in real time at study sites, [...] Read more.
Forest fire modeling often requires estimates of fuel moisture status. Among the various fuel variables used for fire modeling studies, the 10-h fuel moisture content (10-h FMC) is a promising predictor since it can be automatically measured in real time at study sites, yielding more information for fire models. Here, the performance of 10-h FMC models based on three different approaches, including regression (MREG), machine learning algorithms (MML) with random forest and support vector machine, and a process-based model (MFSMM), were compared. In addition, whole-year models of each type were compared with their respective seasonal models to explore whether the development of separate seasonal models yielded better estimates. Meteorological conditions and 10-h FMC were measured each minute for 18 months in and near a forest site and used for constructing and examining the 10-h FMC models. In the assessments, MML showed the best performance (R2 = 0.77–0.82 and root mean squared error [RMSE] = 2.05–2.84%). The introduction of the correction coefficient into MREG improved its estimates (R2 improved from 0.56–0.58 to 0.68–0.70 and RMSE improved from 3.13–3.85% to 2.64–3.27%) by reducing the errors associated with high 10-h FMC values. MFSMM showed the worst performance (R2 = 0.41–0.43 and RMSE = 3.70–4.39%), which could possibly be attributed to the lack of radiation input from the study sites as well as the particular fuel moisture stick sensor that was used. Whole-year models and seasonal models showed almost equal performance because 10-h FMC varied in response to atmospheric moisture conditions rather than specific seasonal patterns. The adoption of a hybrid modeling approach that blends machine-learning and process-based approaches may yield better predictability and interpretability. This study provides additional evidence of the lagged response of 10-h FMC after rainfall, and suggests a new way of accounting for this response in a regression model. Our approach using comparisons among models can be utilized for other fire modeling studies, including those involving fire danger ratings. Full article
(This article belongs to the Special Issue Modeling, Measuring, and Mapping Wildland Fuels)
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