Class3Dp: A supervised classifier of vegetation species from point clouds

Carbonell-Rivera, Juan Pedro; Estornell, Javier; Ruiz, Luis Ángel; Crespo-Peremarch, Pablo; Almonacid-Caballer, Jaime; Torralba, Jesús

doi:10.1016/j.envsoft.2023.105859

Cited by 1 publication

(1 citation statement)

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“…Thus, from overlapping images, it is possible to obtain a spectral point cloud, which provides both geometric Fire 2024, 7, 132 3 of 20 and spectral information of the analyzed environment [23,24]. The integration of spectral and geometric information in photogrammetric point clouds increases the diversity of variables and can improve classifications and segmentations compared to the exclusive use of orthophotography or traditional LiDAR data [25,26]. However, compared to LiDAR data, UAV-DAP is more sensitive to light conditions and does not have the same levels of accuracy and penetration capability [22].…”

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confidence: 99%

Relationships of Fire Rate of Spread with Spectral and Geometric Features Derived from UAV-Based Photogrammetric Point Clouds

Carbonell-Rivera,

Moran,

Seielstad

et al. 2024

Fire

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Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models.

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