Transferability of Empirical Models Derived from Satellite Imagery for Live Fuel Moisture Content Estimation and Fire Risk Prediction
<p>Study area (black rectangle) within the region of Andalusia (red) in Spain (green), with details on the location of the 27 sampling plots (INIA + INFOCA) and the historical wildfires (burnt areas for each year in different colours) in the pilot areas (Z1, Z2 and Z3).</p> "> Figure 2
<p>Observed LFMC values in INIA (green) and INFOCA (orange) field sampling plots during the study period (June 2021 to September 2022).</p> "> Figure 3
<p>Pearson correlation coefficients for spectral indices and LFMC in the calibration dataset (values highlighted in dark blue denote stronger correlations, r > 0.90).</p> "> Figure 4
<p>Extrapolation of the best empirical models previously fitted for the region of Madrid to the sampling plots in Andalusia, NLR-exp (<b>left</b>) and NLR-sqr (<b>right</b>), depicting validation results of original models (<b>upper</b>) and after recalibration (<b>lower</b>) with the linear regression (Y = a + bX) between observed and predicted values: NLR-exp (a = 17.33, b = 0.712); NLR-sqr (a = 7.659, b = 0.832). Y = recalibrated LFMC; X = predicted LFMC.</p> "> Figure 5
<p>Observed vs. predicted LFMC values in the best models for each formulation tested with the calibration dataset (n = 224) for field plots in Andalusia.</p> "> Figure 5 Cont.
<p>Observed vs. predicted LFMC values in the best models for each formulation tested with the calibration dataset (n = 224) for field plots in Andalusia.</p> "> Figure 6
<p>Observed vs. predicted LFMC values in the independent validation (n = 111) of the best models fitted for field plots in Andalusia.</p> "> Figure 6 Cont.
<p>Observed vs. predicted LFMC values in the independent validation (n = 111) of the best models fitted for field plots in Andalusia.</p> "> Figure 7
<p>Performance of the best empirical model for LFMC estimation before the 7 selected historical wildfire events in the pilot areas (Z1, Z2 and Z3), depicting the mean value of reference plots available for each wildfire (blue) and the LFMC range for each date (vertical bars). Headings indicate the wildfire name, with the ignition date in brackets and depicted as red symbols.</p> "> Figure A1
<p>Changes in LFMC estimation obtained with the best empirical model derived from Sentinel-2 data in the Z2 pilot area before the Almonaster wildfire (ignited on 27 August 2020), which burned an area of 14,957 ha in 12 days (perimeter in black). This wildfire was the biggest event in the region of Andalusia during the study period (2018–2022). LFMC is only shown for pixels corresponding to shrubland. Reference plots used in <a href="#fire-07-00276-f007" class="html-fig">Figure 7</a> for LFMC value calculations are depicted as green triangles.</p> "> Figure A1 Cont.
<p>Changes in LFMC estimation obtained with the best empirical model derived from Sentinel-2 data in the Z2 pilot area before the Almonaster wildfire (ignited on 27 August 2020), which burned an area of 14,957 ha in 12 days (perimeter in black). This wildfire was the biggest event in the region of Andalusia during the study period (2018–2022). LFMC is only shown for pixels corresponding to shrubland. Reference plots used in <a href="#fire-07-00276-f007" class="html-fig">Figure 7</a> for LFMC value calculations are depicted as green triangles.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and LFMC from Field Data
2.2. Remote Sensing Data
2.3. Data Analysis
2.3.1. Database Generation
2.3.2. Statistical Modelling
2.3.3. Validation and Recalibration of Empirical Models
2.3.4. LFMC Estimation in Historical Wildfires
3. Results
3.1. LFMC Data from Field Monitoring
3.2. Correlation between LFMC and Spectral Indices
3.3. Extrapolation of LFMC Models from a Different Geographical Area
3.4. Calibration and Independent Validation of New LFMC Models Fitted in the Study Area
3.5. Correlation between Historical Wildfire Occurrence and LFMC Estimated from Remote Sensing Data
4. Discussion
4.1. Spatial Transferability vs. Site-Specific LFMC Empirical Models
4.2. Temporal Transferability of LFMC Empirical Models for Wildfire Risk Assessment
4.3. Future Research
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Comparison of Empirical Models in Terms of Error Level in the Validation with LFMC Field Monitoring Data Available in the Region of Andalusia
Model | Formulation | All LFMC Range | LFMC < 100% | ||
---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | ||
LR | LFMC = a + bVARI | 22.01 | 17.65 | 19.75 | 15.71 |
MLR | LFMC = a + b·VARI + c·SAVI | 22.21 | 17.18 | 18.43 | 13.69 |
NLR-log | LFMC = a + b·log(1 + VARI) + c·log(SAVI) | 21.91 | 17.07 | 19.74 | 15.04 |
NLR-exp | LFMC = a·exp(b·VARI)·exp(c·SAVI) | 23.52 | 18.25 | 16.28 | 12.24 |
NLR-sqr | LFMC = a + b·(VARI)0.5 + c·(SAVI)0.5 | 21.98 | 17.03 | 18.78 | 14.12 |
Model | Formulation | All LFMC Range | LFMC < 100% | ||
---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | ||
MLR | LFMC = a + b·VARI + c·EVI | 19.92 | 15.73 | 17.88 | 13.23 |
MLR-pot * | LFMC = a·(1 + VARI)b·EVIc | 19.75 | 15.40 | 15.65 | 11.38 |
MLR-exp ** | LFMC = a·exp(b·VARI)·exp(c· EVI) | 19.78 | 15.26 | 14.64 | 10.64 |
NLR-log | LFMC = a + b·log(1 + VARI) + c·log(EVI) | 20.25 | 16.10 | 19.61 | 14.80 |
NLR-exp | LFMC = a·exp(b·VARI)·exp(c· EVI) | 19.89 | 15.53 | 15.67 | 11.53 |
NLR-sqr | LFMC = a + b·(VARI)0.5 + c·(EVI)0.5 | 19.86 | 15.71 | 18.85 | 14.04 |
Appendix B. Example of Extrapolation of an Empirical Model for LFMC Monitoring with Sentinel-2 Data in Historical Wildfire Events
References
- Jolly, W.M.; Cochrane, M.A.; Freeborn, P.H.; Holden, Z.A.; Brown, T.J.; Williamson, G.J.; Bowman, D.M.J.S. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 2015, 6, 7537. [Google Scholar] [CrossRef] [PubMed]
- Boer, M.M.; Nolan, R.H.; De Dios, V.R.; Clarke, H.; Price, O.F.; Bradstock, R.A. ChangingWeather Extremes Call for Early Warning of Potential for Catastrophic Fire. Earth’s Futur. 2017, 5, 1196–1202. [Google Scholar] [CrossRef]
- Ruffault, J.; Curt, T.; Martin-StPaul, N.K.; Moron, V.; Trigo, R.M. Extreme wildfire events are linked to global-change-type droughts in the northern Mediterranean. Nat. Hazards Earth Syst. Sci. 2018, 18, 847–856. [Google Scholar] [CrossRef]
- Turco, M.; Rosa-Cánovas, J.J.; Bedia, J.; Jerez, S.; Montávez, J.P.; Llasat, M.C.; Provenzale, A. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nat. Commun. 2018, 9, 3821. [Google Scholar] [CrossRef] [PubMed]
- Dupuy, J.-L.; Fargeon, H.; Martin-StPaul, N.; Pimont, P.; Ruffault, J.; Guijarro, M.; Hernando, C.; Madrigal, J.; Fernandes, P. Climate change impact on future wildfire danger and activity in Southern Europe: A Review. Ann. For. Sci. 2020, 77, 35. [Google Scholar] [CrossRef]
- Jolly, W.M.; Johnson, D.M. Pyro-Ecophysiology: Shifting the Paradigm of Live Wildland Fuel Research. Fire 2018, 1, 8. [Google Scholar] [CrossRef]
- Aguado, I.; Chuvieco, E.; Borén, R.; Nieto, H. Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment. Int. J. Wildland Fire 2007, 16, 390–393. [Google Scholar] [CrossRef]
- Weise, D.R.; Zhou, X.; Sun, L.; Mahalingam, S. Fire spread in chaparral-‘go or no go’? Int. J. Wildland Fire 2005, 14, 99–106. [Google Scholar] [CrossRef]
- Marino, E.; Dupuy, J.L.; Pimont, F.; Guijarro, M.; Hernando, C.; Linn, R. Fuel bulk density and fuel moisture content effect on fire rate of spread: A comparison between FIRETEC model predictions and experimental results in shrub fuels. J. Fire Sci. 2012, 30, 277–299. [Google Scholar] [CrossRef]
- Rossa, C.G.; Veloso, R.; Fernandes, P.M. A laboratory-based quantification of the effect of live fuel moisture content on fire spread rate. Int. J. Wildland Fire 2016, 25, 569–573. [Google Scholar] [CrossRef]
- Chuvieco, E.; Aguado, I.; Cocero, D.; Riaño, D. Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR images in forest fire danger studies. Int. J. Remote Sensing 2003, 24, 1621–1637. [Google Scholar] [CrossRef]
- Luo, K.; Quan, X.; Binbin, H.; Yebra, M. Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China. Forests 2019, 10, 887. [Google Scholar] [CrossRef]
- Ruffault, J.; Martin-StPaul, N.; Pimont, F.; Dupuy, J.-L. How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems. Agric. For. Meteorol. 2018, 262, 391–401. [Google Scholar] [CrossRef]
- Nolan, R.H.; Hedo, J.; Arteaga, C.; Sugai, T.; Resco de Dios, V. Physiological drought responses improve predictions of live fuel moisture dynamics in a Mediterranean forest. Agric. For. Meteorol. 2018, 263, 417–427. [Google Scholar] [CrossRef]
- Pivovaroff, A.L.; Emery, N.; Sharifi, M.R.; Witter, M.; Keeley, J.E.; Rundel, P.W. The effect of ecophysiological traits on live fuel moisture content. Fire 2019, 2, 28. [Google Scholar] [CrossRef]
- Ferrer, A.; Sánchez, P.; Borrego, C.; Jiménez, J.A.; Rodríguez y Silva, F. Estimation of moisture in live fuels in the mediterranean: Linear regressions and random forests. J. Environ. Manag. 2022, 322, 116069. [Google Scholar] [CrossRef] [PubMed]
- Nolan, R.H.; Foster, B.; Griebel, A.; Choat, B.; Medlyn, B.E.; Yebra, M.; Younes, N.; Boer, M.M. Drought-related leaf functional traits control spatial and temporal dynamics of live fuel moisture content. Agric. For. Meteorol. 2022, 319, 108941. [Google Scholar] [CrossRef]
- Peñuelas, J.; Rutishauser, T.; Filella, I. Phenology Feedbacks on Climate Change. Science 2009, 324, 887–888. [Google Scholar] [CrossRef]
- Yebra, M.; Dennison, P.E.; Chuvieco, E.; Riaño, D.; Zylstra, P.; Hunt, E.R., Jr.; Danson, F.M.; Qi, Y.; Jurdao, S. A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. Remote Sens. Environ. 2013, 136, 455–468. [Google Scholar] [CrossRef]
- Chuvieco, E.; Aguado, I.; Salas, J.; García, M.; Yebra, M.; Oliva, P. Satellite Remote Sensing Contributions to Wildland Fire Science and Management. Curr. For. Rep. 2020, 6, 81–96. [Google Scholar] [CrossRef]
- Marino, E.; Yebra, M.; Guillén-Climent, M.; Algeet, N.; Tomé, J.L.; Madrigal, J.; Guijarro, M.; Hernando, C. Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations. Remote Sens. 2020, 12, 2251. [Google Scholar] [CrossRef]
- Costa-Saura, J.M.; Balaguer-Beser, Á.; Ruiz, L.A.; Pardo-Pascual, J.E.; Soriano-Sancho, J.L. Empirical Models for Spatio-Temporal Live Fuel Moisture Content Estimation in Mixed Mediterranean Vegetation Areas Using Sentinel-2 Indices and Meteorological Data. Remote Sens. 2021, 13, 3726. [Google Scholar] [CrossRef]
- Arcos, M.A.; Edo-Botella, R.; Balaguer-Beser, Á.; Ruiz, L.Á. Analyzing Independent LFMC Empirical Models in the Mid-Mediterranean Region of Spain Attending to Vegetation Types and Bioclimatic Zones. Forests 2023, 14, 1299. [Google Scholar] [CrossRef]
- Fan, L.; Wigneron, J.P.; Xiao, Q.; Al-Yaari, A.; Wen, J.; Martin-St Paul, N.; Dupuy, J.L.; Pimont, F.; Al Bitar, A.; Fernandez-Moran, R.; et al. Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region. Remote Sens. Environ. 2018, 205, 210–223. [Google Scholar] [CrossRef]
- Tanase, M.A.; Nova, J.P.G.; Marino, E.; Aponte, C.; Tomé, J.L.; Yáñez, L.; Madrigal, J.; Guijarro, M.; Hernando, C. Characterizing Live Fuel Moisture Content from Active and Passive Sensors in a Mediterranean Environment. Forests 2022, 13, 1846. [Google Scholar] [CrossRef]
- Yebra, M.; Chuvieco, E.; Riaño, D. Estimation of live Fuel Moisture Content from MODIS images for fire risk assessment. Agric. For. Meteorol. 2008, 148, 523–536. [Google Scholar] [CrossRef]
- Yebra, M.; Chuvieco, E. Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem. Remote Sens. Environ. 2009, 113, 2403–2411. [Google Scholar] [CrossRef]
- Jurdao, S.; Yebra, M.; Guerschman, J.P.; Chuvieco, E. Regional estimation of woodland moisture content by inverting Radiative Transfer Models. Remote Sens. Environ. 2013, 132, 59–70. [Google Scholar] [CrossRef]
- Shu, Q.; Quan, X.; Yebra, M.; Liu, X.; Wang, L.; Zhang, Y. Evaluating the Sentinel-2A satellite data for fuel moisture content retrieval. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; pp. 9416–9419. [Google Scholar]
- Hernández-Rodríguez, M.; Martín-Pinto, P.; Oria-de-Rueda, J.A.; Diaz-Balteiro, L. Optimal management of Cistus ladanifer shrublands for biomass and Boletus edulis mushroom production. Agrofor. Syst. 2017, 91, 663–676. [Google Scholar] [CrossRef]
- Montero, G.; López-Leiva, C.; Ruiz-Peinado, R.; López-Senespleda, E.; Onrubia, R.; Pasalodos, M. Producción de Biomasa y Fijación de Carbono por los Matorrales Españoles y por el Horizonte Orgánico Superficieal de los Suelos Forestales; Ministerio de Agricultura, Pesca y Alimentación: Madrid, Spain, 2020; 225p. [Google Scholar]
- R Core Team. R: The R Project for Statistical Computing. Available online: https://www.r-project.org/ (accessed on 13 May 2024).
- Lai, G.; Quan, X.; Yebra, M.; He, B. Model-driven estimation of closed and open shrublands live fuel moisture content. GIScience Remote Sens. 2022, 59, 1837–1856. [Google Scholar] [CrossRef]
- Arganaraz, J.P.; Landi, M.A.; Bravo, S.J.; Gavier-Pizarro, G.I.; Scavuzzo, C.M.; Bellis, L.M. Estimation of Live Fuel Moisture Content from MODIS Images for Fire Danger Assessment in Southern Gran Chaco. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 5339–5349. [Google Scholar] [CrossRef]
- Myoung, B.; Kim, S.H.; Nghiem, S.V.; Jia, S.; Whitney, K.; Kafatos, M.C. Estimating Live Fuel Moisture from MODIS Satellite Data for Wildfire Danger Assessment in Southern California USA. Remote Sens. 2018, 10, 87. [Google Scholar] [CrossRef]
- Adab, H.; Kanniah, K.D.; Beringer, J. Estimating and up-scaling fuel moisture and leaf dry matter content of a temperate humid forest using multi resolution remote sensing data. Remote Sens. 2016, 8, 961. [Google Scholar] [CrossRef]
- Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [Google Scholar] [CrossRef]
- Marino, E.; Guillén-Climent, M.; Algeet, N.; Tomé, J.L.; Hernando, C. Estimation of live fuel moisture content of shrubland using MODIS and Sentinel-2 images. In Advances in Forest Fire Research 2018; Chapter 2—Fuel Management; Viegas, D.X., Ed.; Imprensa da Universidade de Coimbra: Coimbra, Portugal, 2018; pp. 218–226. [Google Scholar]
- Dennison, P.E.; Roberts, D.A.; Peterson, S.H.; Rechel, J. Use of Normalized Difference Water Index for monitoring live fuel moisture. Int. J. Remote Sens. 2005, 26, 1035–1042. [Google Scholar] [CrossRef]
- Martin-StPaul, N.; Pimont, F.; Dupuy, J.L.; Rigolot, E.; Ruffault, J.; Fargeon, H.; Cabane, E.; Duché, Y.; Savazzi, R.; Toutchkov, M. Live Fuel Moisture Content (LFMC) Time Series for Multiple Sites and Species in the French Mediterranean Area since 1996. Ann. For. Sci. 2018, 75, 57. [Google Scholar] [CrossRef]
- Salis, M.; Del Giudice, L.; Jahdi, R.; Alcasena-Urdiroz, F.; Scarpa, C.; Pellizzaro, G.; Bacciu, V.; Schirru, M.; Ventura, A.; Casula, M.; et al. Spatial Patterns and Intensity of Land Abandonment Drive Wildfire Hazard and Likelihood in Mediterranean Agropastoral Areas. Land 2022, 11, 1942. [Google Scholar] [CrossRef]
Spectral Index | Abbreviation | S2 Bands |
---|---|---|
Normalized Difference Vegetation Index | NDVI | B8, B4 |
NDVI 8A | B8A, B4 | |
Normalized Difference Moisture Index | NDMI | B8, B11 |
NDMI 8A | B8A, B11 | |
Global Vegetation Moisture Index | GVMI | B8, B11 |
GVMI 8A | B8A, B11 | |
Green Normalized Difference Vegetation Index | GNDVI | B8, B3 |
Normalized Difference Water Index | NDWI | B8, B12 |
Normalized Difference Red-Edge Index | NDI45 | B5, B4 |
Normalized Difference Glacier Index | NDGI | B3, B4 |
Enhanced Vegetation Index | EVI | B8, B4, B2 |
EVI 8A | B8A, B4, B2 | |
Soil Adjusted Vegetation Index | SAVI | B8, B4 |
SAVI 8A | B8A, B4 | |
Visible Atmospherically Resistant Index | VARI | B3, B4, B2 |
Model | Formulation | Predicted Values | Validation (n = 335) | |||
---|---|---|---|---|---|---|
Min | Max | R2adj | RMSE | MAE | ||
LR * | LFMC = a + bVARI | 50.5 | 183.2 | 0.651 | 26.54 | 22.50 |
MLR * | LFMC = a + b·VARI + c·SAVI | 50.5 | 222.2 | 0.644 | 26.38 | 20.35 |
NLR-log * | LFMC = a + b·log(1+VARI) + c·log(SAVI) | 19.6 | 205.9 | 0.654 | 24.80 | 19.35 |
NLR-exp | LFMC = a·exp(b·VARI)·exp(c·SAVI) | 65.8 | 279.5 | 0.601 | 31.06 | 23.38 |
NLR-sqr | LFMC = a + b·(VARI)0.5 + c·(SAVI)0.5 | 40.2 | 213.5 | 0.652 | 25.45 | 19.77 |
Model | Formulation | Predicted Values | Validation (n = 335) | |||
---|---|---|---|---|---|---|
Min | Max | R2adj | RMSE | MAE | ||
LR | LFMC = a + bVARI | 15.2 | 194.4 | 0.651 | 22.01 | 17.65 |
MLR | LFMC = a + b·VARI + c·SAVI | 50.3 | 189.4 | 0.644 | 22.21 | 17.18 |
NLR-log | LFMC = a + b·log(1+VARI) + c·log(SAVI) | 23.6 | 182.0 | 0.654 | 21.91 | 17.07 |
NLR-exp | LFMC = a·exp(b·VARI)·exp(c·SAVI) | 64.1 | 215.7 | 0.601 | 23.52 | 18.25 |
NLR-sqr | LFMC = a + b·(VARI)0.5 + c·(SAVI)0.5 | 41.0 | 184.9 | 0.652 | 21.98 | 17.03 |
Model | Formulation | Predicted Values | Calibration (n = 224) | |||
---|---|---|---|---|---|---|
Min | Max | R2adj | RMSE | MAE | ||
MLR | LFMC = a + b·VARI + c·EVI | 52.0 | 181.6 | 0.708 | 19.71 | 15.57 |
MLR-pot * | LFMC = a·(1 + VARI)b·EVIc | 51.5 | 198.0 | 0.699 | 19.76 | 15.26 |
MLR-exp ** | LFMC = a·exp(b·VARI)·exp(c·EVI) | 55.9 | 205.3 | 0.706 | 20.02 | 15.11 |
NLR-log | LFMC = a + b·log(1 + VARI) + c·log(EVI) | 36.6 | 181.8 | 0.709 | 20.33 | 16.17 |
NLR-exp | LFMC = a·exp(b·VARI)·exp(c·EVI) | 56.6 | 210.9 | 0.722 | 19.89 | 15.40 |
NLR-sqr | LFMC = a + b·(VARI)0.5 + c·(EVI)0.5 | 40.4 | 183.2 | 0.717 | 20.07 | 15.77 |
Model | Formulation | Predicted Values | Validation (n = 111) | |||
---|---|---|---|---|---|---|
Min | Max | R2adj | RMSE | MAE | ||
MLR | LFMC = a + b·VARI + c·EVI | 49.7 | 173.4 | 0.686 | 19.92 | 15.73 |
MLR-pot * | LFMC = a·(1 + VARI)b·EVIc | 56.0 | 184.5 | 0.693 | 19.75 | 15.40 |
MLR-exp ** | LFMC = a·exp(b·VARI) ·exp(c·EVI) | 58.4 | 186.9 | 0.694 | 19.78 | 15.26 |
NLR-log | LFMC = a + b·log(1 + VARI) + c·log(EVI) | 44.0 | 174.4 | 0.677 | 20.25 | 16.10 |
NLR-exp | LFMC = a·exp(b·VARI) ·exp(c·EVI) | 59.9 | 193.2 | 0.686 | 19.89 | 15.53 |
NLR-sqr | LFMC = a + b·(VARI)0.5 + c·(EVI)0.5 | 46.4 | 174.6 | 0.689 | 19.86 | 15.71 |
Pilot Area | Province | Wildfire Name | Ignition Date | Area Burned (ha) | Reference Plots |
---|---|---|---|---|---|
Z1 | Córdoba | Alcaracejos | 2021/08/16 | 379 | 3 |
Espiel | 2022/07/25 | 160 | 3 | ||
Z2 | Huelva | Nerva | 2018/08/02 | 1628 | 3 |
Almonaster | 2020/08/27 | 14,957 | 4 | ||
Z3 | Sevilla | El Ronquillo | 2021/07/28 | 43 | 3 |
Guillena | 2022/07/10 | 416 | 3 | ||
Cantillana | 2022/07/25 | 249 | 3 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Marino, E.; Yáñez, L.; Guijarro, M.; Madrigal, J.; Senra, F.; Rodríguez, S.; Tomé, J.L. Transferability of Empirical Models Derived from Satellite Imagery for Live Fuel Moisture Content Estimation and Fire Risk Prediction. Fire 2024, 7, 276. https://doi.org/10.3390/fire7080276
Marino E, Yáñez L, Guijarro M, Madrigal J, Senra F, Rodríguez S, Tomé JL. Transferability of Empirical Models Derived from Satellite Imagery for Live Fuel Moisture Content Estimation and Fire Risk Prediction. Fire. 2024; 7(8):276. https://doi.org/10.3390/fire7080276
Chicago/Turabian StyleMarino, Eva, Lucía Yáñez, Mercedes Guijarro, Javier Madrigal, Francisco Senra, Sergio Rodríguez, and José Luis Tomé. 2024. "Transferability of Empirical Models Derived from Satellite Imagery for Live Fuel Moisture Content Estimation and Fire Risk Prediction" Fire 7, no. 8: 276. https://doi.org/10.3390/fire7080276