RT info:eu-repo/semantics/article
T1 Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data
A1 Alonso Rego, Cecilia
A1 Arellano Pérez, Stéfano
A1 Guerra Hernández, Juan
A1 Molina Valero, Juan Alberto
A1 Martínez Calvo, Adela
A1 Pérez Cruzado, César
A1 Castedo Dorado, Fernando
A1 González Ferreiro, Eduardo Manuel
A1 Álvarez González, Juan Gabriel
A1 Ruiz González, Ana Daría
A2 Ingenieria Agroforestal
K1 Ingeniería forestal
K1 Forest fuel modeling
K1 ALS/TLS
K1 Canopy fuel characterization
K1 Understory fuel characterization
AB [EN] In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand-and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing crossvalidation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales
PB MDPI
LK https://hdl.handle.net/10612/18770
UL https://hdl.handle.net/10612/18770
NO Alonso-Rego, C., Arellano-Pérez, S., Guerra-Hernández, J., Molina-Valero, J. A., Martínez-Calvo, A., Pérez-Cruzado, C., Castedo-Dorado, F., González-Ferreiro, E., Álvarez-González, J. G., & Ruiz-González, A. D. (2021). Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data. Remote Sensing, 13(24). https://doi.org/10.3390/RS13245170
DS BULERIA. Repositorio Institucional de la Universidad de León
RD Jul 6, 2024