ABSTRACT
In the quest to advance fire prevention and control strategies, new techniques for quantifying combustible material are being explored. Among these, the use of multispectral sensors and red, green, and blue (RGB) cameras has emerged as promising solutions to streamline both cost and time expenditures in the field. This study aimed to assess the feasibility of employing an airborne multispectral sensor and an airborne RGB digital camera, mounted on a multirotor drone, to estimate combustible material load in a Cerrado area through linear regression analysis. Conducted within a savanna formation, the study involved evaluating 40 samples of combustible material load in 1 m² plots. Aerial surveys were conducted to capture images, facilitating the derivation of reflectance variables, vegetation indices, and descriptive parameters of the three-dimensional model. The resulting equation, customized to predict total combustible material within the study area, exhibited considerable significance (p < 0.001), accompanied by a determination coefficient (R²) of 0.70 and an adjusted R² of 0.65. Upon analyzing the variables’ impact on the model, it became evident that while the point density of the model showed higher correlations, the normalized difference vegetation index wielded notable influence, as indicated by its prominent weight within the adjusted equation.
Keywords
Remote sensing; UAV; Prediction
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Source: Authors (2022)