Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

Premio extraordinario de Trabajo Fin de Máster curso 2021/2022. Máster en Geomática, Teledetección y Modelos Espaciales Aplicados a la Gestión ForestalRemotely sensed vegetation indices have been widely used to estimate live fuel moisture content (LFMC), an important driver of wildfire risk, due to broad data availability. However, marked differences in vegetation structure affect the relationship between field-measured LFMC and reflectance, which limits spatial extrapolation of these indices. To overcome this limitation, I explored the potential of Random Forests (RF), a machine learning technique based on the ensemble of multiple decision trees, to estimate LFMC at the subcontinental scale in the Mediterranean basin wildland. I built RF models using a combination of MODIS spectral bands, vegetation indices, surface temperature, and the day of year as predictors. I used the Globe-LFMC and the Catalan LFMC monitoring program databases as ground-truth samples (10,374 samples). The modelling process consisted in a feature selection and two step spatial cross-validation in order to avoid spatial overfitting. The final LFMCRF model was calibrated and evaluated with samples collected between 2000 and 2014, and independently tested with samples from 2015 to 2019 reporting an overall root mean square errors (RMSE) of 19.9% and 16.4%, respectively. The results from LFMCRF were comparable to current approaches based on radiative transfer models (RMSE ~74–78%), introducing a reliable alternative for large-scale applications. This study fills an important research gap by creating a homogeneous approach to estimate LFMC across the Western Mediterranean basin. I used the final model to generate a public database with weekly LFMC maps extended to the fire-prone Mediterranean basin.Los índices de vegetación derivados de la teledetección se han utilizado ampliamente para estimar el contenido de humedad del combustible vivo (LFMC por sus siglas en inglés), un factor importante del riesgo de incendios forestales, debido a la amplia disponibilidad de datos. Sin embargo, marcadas diferencias en la estructura de la vegetación afectan la relación entre LFMC medido en campo y la reflectancia captada por los sensores de los satélites, lo que limita la extrapolación espacial de estos índices. Para superar esta limitación, exploré el potencial de Random Forests (RF), una técnica de aprendizaje automático basada en la agregación de múltiples árboles de decisión, para estimar LFMC a escala subcontinental en la cuenca Mediterránea. Probé distintos modelos de RF usando una combinación de bandas espectrales de MODIS, índices de vegetación, la temperatura superficial terrestre y el día del año como predictores. Utilicé las bases de datos del Globe-LFMC y del programa catalán de seguimiento de LFMC como muestras de verdad-terreno (10.374 muestras). El proceso de modelado consistió en una selección de predictores y una validación cruzada espacial para evitar el sobreajuste espacial. El modelo final de LFMCRF se calibró y evaluó con muestras recolectadas entre 2000 y 2014, y se probó de forma independiente con muestras de 2015 a 2019, reportando valores generales de raíz del error cuadrático medio (RMSE por sus siglas en inglés) de 19,9% y 16,4%, respectivamente. Los resultados de LFMCRF fueron comparables a los enfoques actuales basados en modelos de transferencia radiativa (RMSE ~74–78%), introduciendo una alternativa confiable para aplicaciones a gran escala. Este estudio llena un importante vacío de investigación al crear un enfoque homogéneo para estimar LFMC en toda el área occidental de la cuenca Mediterránea. El modelo final fue usado para generar una base de datos pública con mapas de LFMC semanales extendidos a toda la cuenca Mediterránea propensa a incendios forestales

Obtención de mapas de humedad del combustible a partir de variables meteorológicas para la predicción del riesgo de incendios forestales a escala regional: nuevo enfoque a los actuales índices de peligro de incendio

Els incendis forestals constitueixen una de les majors pertorbacions ecològiques a nivell mundial i, especialment, a les regions mediterrànies. Per anticipar-se a situacions de perill, aquest treball proposa l’aplicació espacial d’un índex de risc d’incendis forestals definit pel contingut mínim d’humitat dels combustibles fins morts (FM) i la relació existent amb l’àrea cremada després d’un incendi. El model emprat per predir FM requereix, únicament, del dèficit de pressió de vapor, llur valor s’obté de les dades en quadrícula de temperatura i humitat relativa. Per Universitat de Lleida obtenir això, es van provar les següents tècniques d’interpolació: regressió lineal (RL), ponderació per l’invers de la distància (IDW) i kriging ordinari (OK). Dins de la RL es van avaluar dues funcions diferents (RL1 i RL2) amb l’elevació, la latitud i la longitud com a variables d’entrada. L’estudi es va realitzar en el territori espanyol de la Península Ibèrica i les Illes Balears, i durant juny i juliol de 2018. Els millors resultats es van obtenir amb la tècnica d’OK amb enfocament localitzat. El producte final és una aplicació automatitzada de predicció de FM que busca ser operativa entre els cossos d’extinció d’incendis forestals.Los incendios forestales constituyen una de las mayores perturbaciones ecológicos a nivel mundial y, en especial, en las regiones mediterráneas. Para anticiparse a situaciones de peligro, este trabajo propone la aplicación espacial de un índice de riesgo de incendios forestales definido por el contenido mínimo de humedad de los combustibles finos muertos (FM) y la relación existente con el área quemada tras un incendio. El modelo empleado para predecir FM requiere únicamente del déficit de presión de vapor, cuyo valor se obtiene de los datos en cuadrícula de temperatura y humedad relativa. Para ello, se probaron las siguientes técnicas de interpolación: regresión lineal (RL), ponderación por el inverso de la distancia (IDW) y kriging ordinario (OK). Dentro de la RL se evaluaron dos funciones diferentes (RL1 y RL2) con la elevación, la latitud y la longitud como variables de entrada. El estudio se realizó en el territorio español de la Península Ibérica y las Islas Baleares, y durante junio y julio de 2018. Los mejores resultados se obtuvieron con la técnica de OK con enfoque localizado. El producto final es un aplicativo automatizado de predicción de FM que busca ser operativo entre los cuerpos de extinción de incendios forestales.Forest fires constitute one of the greatest ecological disturbances worldwide and, especially, in the Mediterranean regions. To anticipate situations of danger, this project suggests the spatial application of a forest fire risk index defined by the minimum moisture content of dead fine fuels (FM) and the existing relationship with the area burned after a fire. The model used to predict FM only requires the vapor pressure deficit, which value is obtained from the gridded temperature and relative humidity data. For this, the following interpolation techniques were tested: linear regression (RL), inverse distance weighted (IDW) and ordinary kriging (OK). Within the RL, two different functions (RL1 and RL2) were evaluated with elevation, latitude and longitude as input variables. The study was conducted in the Spanish territory of the Iberian Peninsula and the Balearic Islands during June and July 2018. The best results were obtained with the OK technique through localized approach. The final product is an automated FM prediction application that seeks to be operative among forest firefighting bodies

VPD-based models of dead fine fuel moisture provide best estimates in a global dataset

Dead fine fuel moisture content (FM) is one of the most important determinants of fire behavior. Fire scientists have attempted to effectively estimate FM for nearly a century, but we are still lacking broad scale evaluations of the different approaches for prediction. Here we tackle this problem by taking advantage or a recently compiled global fire behavior database (BONFIRE) gathering 1603 records of 1h (i.e., <6 mm diameter or thickness) dead fuel moisture content from measurements before experimental fires. We compared the results of models routinely used by different agencies worldwide, empirical models, semi-mechanistic models and also non-linear and machine learning approaches based on either temperature and relative humidity or vapor pressure deficit (VPD). A semi-mechanistic model based on VPD showed the best performance across all FM ranges and a historical model developed in Australia (MK5) was additionally recommended for low fuel moisture estimations. We also observed significant differences in FM dynamics between vegetation types with FM in grasslands more responsive to changes in atmospheric dryness than woody ecosystems. The addition of computational complexity through machine learning is not recommended since the gain in model fit is small relative to the increase in complexity. Future research efforts should concentrate on predictions at low FM (<10 %) as this is the range most significant for fire behavior and where the poorest model performance was observed. Model predictions are available from https://hcfm.shinyapps.io/shinyfmd/.This work was supported by the Portuguese Foundation for Science and Technology (projects UIDB/04033/2020 and PTDC/AAG-MAA/ 2656/2014), the Spanish MICINN (RTI2018-094691-B-C31, PID2020- 116556RA-I00) and EU H2020 (grant agreements 101003890-FirEUrisk, and 101037419-FireRES).info:eu-repo/semantics/publishedVersio

A semi-mechanistic model for predicting daily variations in species-level live fuel moisture content

Live Fuel Moisture Content (LFMC) is one of the main factors affecting forest ignitability as it determines the availability of existing live fuel to burn. Currently, LFMC is monitored through spectral vegetation indices or inferred from meteorological drought indices. While useful, neither approach provides mechanistic insights into species-specific LFMC variation and they are limited in the ability to forecast LFMC under altered future climates. Here, we developed a semi-mechanistic model to predict daily variation in LFMC across woody species from different functional types by adjusting a soil water balance model which estimates predawn leaf water potential (Ψpd). Our overarching goal was to balance the trade-off between biological realism, which enhances model applicability, and parameterization complexity, which may limit its value within operational settings. After calibration, model predictions were validated against a dataset comprising 1659 LFMC observations across peninsular Spain, belonging to different functional types and from contrasting climates. The overall goodness of fit for our model (R2 = 0.5) was better than that obtained by an existing models based on drought indices (R2 = 0.3) or spectral vegetation indices (R2 = 0.1). We observed the best predictive performance for seeding shrubs (R2 = 0.6) followed by trees (R2 = 0.5) and resprouting shrubs (R2 = 0.4). Through its relatively simple parameterization, the approach developed here may pave the way for a new generation of process-based models that can be used for operational purposes within fire risk mitigation scenarios.This work was partly founded by the Spanish Government, grant number RTI2018-094691-B-C31 (MCIU/AEI/FEDER, EU) . R.B-R. ac-knowledges the Community of Madrid for the predoctoral contract PEJD-2019-PRE/AMB-15,644 funded by the Youth Employment Initia-tive (YEI) . M. De C. was supported by the Spanish Ministry of Science and Innovation via competitive grant CGL2017-89149-C2-2-R. UNED founding for open access publishing

Convergence in critical fuel moisture and fire weather thresholds associated with fire activity in the pyroregions of Mediterranean Europe

Wildfires are becoming an increasing threat to many communities worldwide. There has been substantial progress towards understanding the proximal causes of increased fire activity in recent years at regional and national scales. However, subcontinental scale examinations of the commonalities and differences in the drivers of fire activity across different regions are rare in the Mediterranean zone of the European Union (EUMed). Here, we first develop a new classification of EUMed pyroregions, based on grouping different ecoregions with similar seasonal patterns of burned area. We then examine the thresholds associated with fire activity in response to different drivers related to fuel moisture, surface meteorology and atmospheric stability. We document an overarching role for variation in dead fuel moisture content (FMd), or its atmospheric proxy of vapor pressure deficit (VPD), as the major driver of fire activity. A proxy for live fuel moisture content (EVI), wind speed (WS) and the Continuous Haines Index (CH) played secondary, albeit important, roles. There were minor differences in the actual threshold values of FMd (10–12%), EVI (0.29–0.36) and CH (4.9–5.5) associated with the onset of fire activity across pyroregions with peak fire seasons in summer and fall, despite very marked differences in mean annual burned area and fire size range. The average size of fire events increased with the number of drivers exceeding critical thresholds and reaching increasingly extreme values of a driver led to disproportionate increases in the likelihood of a fire becoming a large fire. For instance, the percentage of fires >500 ha increased from 2% to 25% as FMd changed from the wettest to the driest quantile. Our study is among the first to jointly address the roles of fuel moisture, surface meteorology and atmospheric stability on fire activity in EUMed and provides novel insights on the interactions across fire activity triggers.Peer ReviewedPostprint (author's final draft