Dinámica estructural y área basal de bosques mixtos en dos áreas naturales protegidas de Jalisco

Figueroa-Navarro, C.; Pérez, Eduardo Salcedo; Gallegos-Rodríguez, Agustín; Vargas-Larreta, Benedicto; Huerta-Martínez, Francisco Martín; Ángeles‐Pérez, Gregorio

doi:10.29298/rmcf.v14i77.1318

Cited by 1 publication

(1 citation statement)

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“…This result is highly relevant as values of the CFL depend on tree structural variables [33]. Previous studies by Figueroa-Navarro et al (2023) in "Sierra de Quila" [88] obtained similar results to those achieved in the present work, with average densities of 556 trees per ha and a mean basal area of 20 m 2 /ha, with Pinus douglasiana being the most dominant species, regarding Pinus genus and Quercus resinosa as the most dominant species of the Quercus genus.…”

Section: Plots Forest Structurementioning

confidence: 70%

Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery

Chávez-Durán,

García,

Olvera-Vargas

et al. 2024

Forests

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Canopy fuels determine the characteristics of the entire complex of forest fuels due to their constant changes triggered by the environment; therefore, the development of appropriate strategies for fire management and fire risk reduction requires an accurate description of canopy forest fuels. This paper presents a method for mapping the spatial distribution of canopy fuel loads (CFLs) in alignment with their natural variability and three-dimensional spatial distribution. The approach leverages an object-based machine learning framework with UAV multispectral data and photogrammetric point clouds. The proposed method was developed in the mixed forest of the natural protected area of “Sierra de Quila”, Jalisco, Mexico. Structural variables derived from photogrammetric point clouds, along with spectral information, were used in an object-based Random Forest model to accurately estimate CFLs, yielding R2 = 0.75, RMSE = 1.78 Mg, and an average Biasrel = 18.62%. Canopy volume was the most significant explanatory variable, achieving a mean decrease in impurity values greater than 80%, while the combination of texture and vegetation indices presented importance values close to 20%. Our modelling approach enables the accurate estimation of CFLs, accounting for the ecological context that governs their dynamics and spatial variability. The high precision achieved, at a relatively low cost, encourages constant updating of forest fuels maps to enable researchers and forest managers to streamline decision making on fuel and forest fire management.

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Section: Plots Forest Structurementioning

confidence: 70%