High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modeling

Forest fuels are the core element of fire management; each fuel component plays an important role in fire behavior. Therefore, accurate determination of their characteristics and spatial distribution is crucial. This paper introduces a novel method for mapping the spatial distribution of litter and...

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Autores: Chávez Durán, Álvaro Agustín, Olvera Vargas, Miguel, Aguado Suárez, María Inmaculada|||0000-0002-9975-849X, Figueroa Rangel, Blanca Lorena, Trucíos Caciano, Ramón, Rubio Camacho, Ernesto Alonso, Xelhuantzi Carmona, Jaqueline, García Alonso, Mariano|||0000-0001-6260-5791
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/67640
Acceso en línea:http://hdl.handle.net/10017/67640
https://dx.doi.org/10.3390/fire7110408
Access Level:acceso abierto
Palabra clave:Fuel loads
High-resolution
Spatial distribution
Multispectral data
Random forest
Geografía
Geography
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spelling High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modelingChávez Durán, Álvaro AgustínOlvera Vargas, MiguelAguado Suárez, María Inmaculada|||0000-0002-9975-849XFigueroa Rangel, Blanca LorenaTrucíos Caciano, RamónRubio Camacho, Ernesto AlonsoXelhuantzi Carmona, JaquelineGarcía Alonso, Mariano|||0000-0001-6260-5791Fuel loadsHigh-resolutionSpatial distributionMultispectral dataRandom forestGeografíaGeographyForest fuels are the core element of fire management; each fuel component plays an important role in fire behavior. Therefore, accurate determination of their characteristics and spatial distribution is crucial. This paper introduces a novel method for mapping the spatial distribution of litter and duff fuel loads using data collected by unmanned aerial vehicles. The approach leverages a very high-resolution multispectral data analysis within a machine learning framework to achieve precise and detailed results. A set of vegetation indices and texture metrics derived from the multispectral data, optimized by a ?Variable Selection Using Random Forests? (VSURF) algorithm, were used to train random forest (RF) models, enabling the modeling of high-resolution maps of litter and duff fuel loads. A field campaign to measure fuel loads was conducted in the mixed forest of the natural protected area of ?Sierra de Quila?, Jalisco, Mexico, to measure fuel loads and obtain field reference data for calibration and validation purposes. The results revealed moderate determination coefficients between observed and predicted fuel loads with R2 = 0.32, RMSE = 0.53 Mg/ha for litter and R2 = 0.38, RMSE = 13.14 Mg/ha for duff fuel loads, both with significant p-values of 0.018 and 0.015 for litter and duff fuel loads, respectively. Moreover, the relative root mean squared errors were 33.75% for litter and 27.71% for duff fuel loads, with a relative bias of less than 5% for litter and less than 20% for duff fuel loads. The spatial distribution of the litter and duff fuel loads was coherent with the structure of the vegetation, despite the high complexity of the study area. Our modeling approach allows us to estimate the continuous high-resolution spatial distribution of litter and duff fuel loads, aligned with their ecological context, which dictates their dynamics and spatial variability. The method achieved acceptable accuracy in monitoring litter and duff fuel loads, providing researchers and forest managers with timely data to expedite decision-making in fire and forest fuel management.20242024-11-07journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/67640https://dx.doi.org/10.3390/fire7110408reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/676402026-06-18T11:13:07Z
dc.title.none.fl_str_mv High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modeling
title High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modeling
spellingShingle High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modeling
Chávez Durán, Álvaro Agustín
Fuel loads
High-resolution
Spatial distribution
Multispectral data
Random forest
Geografía
Geography
title_short High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modeling
title_full High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modeling
title_fullStr High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modeling
title_full_unstemmed High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modeling
title_sort High-resolution mapping of litter and duff fuel loads using multispectral data and random forest modeling
dc.creator.none.fl_str_mv Chávez Durán, Álvaro Agustín
Olvera Vargas, Miguel
Aguado Suárez, María Inmaculada|||0000-0002-9975-849X
Figueroa Rangel, Blanca Lorena
Trucíos Caciano, Ramón
Rubio Camacho, Ernesto Alonso
Xelhuantzi Carmona, Jaqueline
García Alonso, Mariano|||0000-0001-6260-5791
author Chávez Durán, Álvaro Agustín
author_facet Chávez Durán, Álvaro Agustín
Olvera Vargas, Miguel
Aguado Suárez, María Inmaculada|||0000-0002-9975-849X
Figueroa Rangel, Blanca Lorena
Trucíos Caciano, Ramón
Rubio Camacho, Ernesto Alonso
Xelhuantzi Carmona, Jaqueline
García Alonso, Mariano|||0000-0001-6260-5791
author_role author
author2 Olvera Vargas, Miguel
Aguado Suárez, María Inmaculada|||0000-0002-9975-849X
Figueroa Rangel, Blanca Lorena
Trucíos Caciano, Ramón
Rubio Camacho, Ernesto Alonso
Xelhuantzi Carmona, Jaqueline
García Alonso, Mariano|||0000-0001-6260-5791
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Fuel loads
High-resolution
Spatial distribution
Multispectral data
Random forest
Geografía
Geography
topic Fuel loads
High-resolution
Spatial distribution
Multispectral data
Random forest
Geografía
Geography
description Forest fuels are the core element of fire management; each fuel component plays an important role in fire behavior. Therefore, accurate determination of their characteristics and spatial distribution is crucial. This paper introduces a novel method for mapping the spatial distribution of litter and duff fuel loads using data collected by unmanned aerial vehicles. The approach leverages a very high-resolution multispectral data analysis within a machine learning framework to achieve precise and detailed results. A set of vegetation indices and texture metrics derived from the multispectral data, optimized by a ?Variable Selection Using Random Forests? (VSURF) algorithm, were used to train random forest (RF) models, enabling the modeling of high-resolution maps of litter and duff fuel loads. A field campaign to measure fuel loads was conducted in the mixed forest of the natural protected area of ?Sierra de Quila?, Jalisco, Mexico, to measure fuel loads and obtain field reference data for calibration and validation purposes. The results revealed moderate determination coefficients between observed and predicted fuel loads with R2 = 0.32, RMSE = 0.53 Mg/ha for litter and R2 = 0.38, RMSE = 13.14 Mg/ha for duff fuel loads, both with significant p-values of 0.018 and 0.015 for litter and duff fuel loads, respectively. Moreover, the relative root mean squared errors were 33.75% for litter and 27.71% for duff fuel loads, with a relative bias of less than 5% for litter and less than 20% for duff fuel loads. The spatial distribution of the litter and duff fuel loads was coherent with the structure of the vegetation, despite the high complexity of the study area. Our modeling approach allows us to estimate the continuous high-resolution spatial distribution of litter and duff fuel loads, aligned with their ecological context, which dictates their dynamics and spatial variability. The method achieved acceptable accuracy in monitoring litter and duff fuel loads, providing researchers and forest managers with timely data to expedite decision-making in fire and forest fuel management.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-11-07
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/67640
https://dx.doi.org/10.3390/fire7110408
url http://hdl.handle.net/10017/67640
https://dx.doi.org/10.3390/fire7110408
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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