Santos, M.M.; Batista, A.C.; Rezende, E.H.; Da Silva, A.D.P.; Cachoeira, J.N.; Dos Santos, G.R.; Biondi, D.; Giongo, M. Estimating the Surface Fuel Load of the Plant Physiognomy of the Cerrado Grassland Using Landsat 8 OLI Products. Remote Sens.2023, 15, 5481.
Santos, M.M.; Batista, A.C.; Rezende, E.H.; Da Silva, A.D.P.; Cachoeira, J.N.; Dos Santos, G.R.; Biondi, D.; Giongo, M. Estimating the Surface Fuel Load of the Plant Physiognomy of the Cerrado Grassland Using Landsat 8 OLI Products. Remote Sens. 2023, 15, 5481.
Santos, M.M.; Batista, A.C.; Rezende, E.H.; Da Silva, A.D.P.; Cachoeira, J.N.; Dos Santos, G.R.; Biondi, D.; Giongo, M. Estimating the Surface Fuel Load of the Plant Physiognomy of the Cerrado Grassland Using Landsat 8 OLI Products. Remote Sens.2023, 15, 5481.
Santos, M.M.; Batista, A.C.; Rezende, E.H.; Da Silva, A.D.P.; Cachoeira, J.N.; Dos Santos, G.R.; Biondi, D.; Giongo, M. Estimating the Surface Fuel Load of the Plant Physiognomy of the Cerrado Grassland Using Landsat 8 OLI Products. Remote Sens. 2023, 15, 5481.
Abstract
Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining fuel load and composition spatially and temporally, are pretty scarce. The need to have fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment makes a constant search for the improvement of the use of remote sensing techniques in determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations; (2) to estimate fuel load through Random Forest and k-Nearest Neighbor (k-NN) algorithms in comparison with re-gression analyses; and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 x 3 pixel window surrounding the reference pixel. For the multiple linear regression analyses, the R² values ranged from 0.63 to 0.78, and the models fitted by the Random Forest al-gorithm ranged from 0.52 to 0.83, while those fitted by k-NN ranged from 0.30 to 0.68. Adopting the Random Forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for Cerrado grassland relative to the multiple linear regression analyses.
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