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

Chávez-Durán, Álvaro Agustín; García, Mariano; Olvera-Vargas, Miguel; Aguado, Inmaculada; Figueroa-Rangel, Blanca Lorena; Trucíos-Caciano, Ramón; Rubio-Camacho, Ernesto Alonso

doi:10.3390/f15020225

Cited by 4 publications

(1 citation statement)

References 68 publications

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“…Aside from serving as the material basis for surface fires, they also have a marked impact on the formation of canopy fires [3,4]. The fine dead fuel load is an important characteristic of surface fuels-calculated as the absolute dry mass per unit area-and has a significant impact on the occurrence and behavior of wildland fires [5]. Almost all wildland fire management systems worldwide require this load value as an input, making it the most important classification feature in fuel descriptions [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting Fine Dead Fuel Load of Forest Floors Based on Image Euler Numbers

Zhang,

Tian

2024

Forests

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The fine dead fuel load on forest floors is the most critical classification feature in fuel description systems, affecting several parameters in the manifestation of wildland fires. An accurate determination of this fine dead fuel load contributes substantially to effective wildland fire prevention, monitoring, and suppression. This study investigated the viability of incorporating image Euler numbers into predictive models of fine dead fuel load via the taking photos method. Pinus massoniana needles and Quercus fabri broad leaves—typical fuel types in karst areas—served as the research subjects. Accurate field data were collected in the Tianhe Mountain forests, China, while artificial fine dead fuelbeds of differing loads were constructed in the laboratory. Images of the artificial fuelbeds were captured and uniformly digitized according to various conversion thresholds. Thereafter, the Euler numbers were extracted, their relationship with fuel load was analyzed, and this relationship was applied to generate three load-prediction models based on stepwise regression, nonlinear fitting, and random forest algorithms. The Euler number had a significant relationship with both P. massoniana and Q. fabri fuel loads. At low conversion thresholds, the Euler number was negatively correlated with fuel load, whereas a positive correlation was recorded when this threshold exceeded a certain value. The random forest model showed the best prediction performance, with mean relative errors of 9.35% and 14.54% for P. massoniana and Q. fabri, respectively. The nonlinear fitting model displayed the next best performance, while the stepwise regression model exhibited the largest error, which was significantly different from that of the random forest model. This study is the first to propose the use of image features to predict the fine fuel load on a surface. The results are more objective, accurate, and time-saving than current fuel load estimates, benefiting fuel load research and the scientific management of wildland fires.

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Section: Introductionmentioning

confidence: 99%

Predicting Fine Dead Fuel Load of Forest Floors Based on Image Euler Numbers

Zhang,

Tian

2024

Forests

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Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

Reilly,

Clark,

Loechler

et al. 2024

Remote Sensing of Environment

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High-Resolution Mapping of Litter and Duff Fuel Loads Using Multispectral Data and Random Forest Modeling

Chávez-Durán,

Olvera-Vargas,

Aguado

et al. 2024

Fire

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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.

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

Cited by 4 publications

References 68 publications

Predicting Fine Dead Fuel Load of Forest Floors Based on Image Euler Numbers

Predicting Fine Dead Fuel Load of Forest Floors Based on Image Euler Numbers

Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

High-Resolution Mapping of Litter and Duff Fuel Loads Using Multispectral Data and Random Forest Modeling

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