Comparing different models for fuel load estimation in rockrose shrubland in the Mediterranean region from LiDAR data
Submitted: 2024-11-15
|Accepted: 2025-03-17
|Published: 2025-04-03
Copyright (c) 2025 Stéfano Arellano-Pérez, Eva Marino del Amo, José L. Tomé Morán, Santiago Martín Alcón
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Keywords:
Biomass, forest fires, Cistus ladanifer L., remote sensing, LiDAR-PNOA
Supporting agencies:
Junta de Andalucía
Fondo Europeo de Desarrollo Regional FEDER dentro del programa Interreg V A España – Portugal (POCTEP) 2014-2020
Instrumento de Recuperación Europeo (EU Next Generation)
Dirección General de Desarrollo Rural, Innovación y Formación Agroalimentaria (DGDRIFA)
Agencia Estatal de Investigación
Abstract:
Shrub communities of Cistus ladanifer L. (gum rockrose) are one of the most characteristic, extensive, and prone to wildfire of Mediterranean ecosystems. In addition, these shrublands have a remarkable potential for the extraction of subproducts, which are highly valuable in the pharmaceutical, food and cosmetic industries. Therefore, estimating and their biomass is essential to manage and prioritize their use, calculate their carbon content and CO2 capture as well as predict their fire behaviour and possible emissions. In this study, we aim to estimate the fuel load of gum rockrose shrublands in southern Spain based on airborne LIDAR data from PNOA. For this purpose, non-destructive field inventories were carried out with measurements of mean height and shrub cover in 143 circular plots in Andalusia region. These two fuel variables were used as inputs in an existing specific equation to estimate the fuel load for C. ladanifer.
Two different approaches were compared to estimate the fuel load of these gum rockrose by linear regression analysis: (i) direct estimation (DE), consisting of the adjustment that directly relates fuel load to ALS data; and (ii) indirect estimation in two steps (IE) based on the adjustment of equations to estimate the input variables (shrub height and cover) of the gum rockrose from LiDAR data. Better goodness-of-fit statistics were obtained in the direct estimation model than in the indirect estimation model, explaining 70% and 72% of the observed variability, respectively. These results can be valuable for the development of gum rockrose biomass mapping for use in fire prevention and suppression and in the planning of harvesting for the extraction of their products.
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