Article Analysis of Mediterranean Vegetation Fuel Type Changes Using Multitemporal LiDAR Alba García-Cimarras 1, * , José Antonio Manzanera 1 and Rubén Valbuena 2   Citation: García-Cimarras, A.; Manzanera, J.A.; Valbuena, R. Analysis of Mediterranean Vegetation Fuel Type Changes Using Multitemporal LiDAR. Forests 2021, 12, 335. https://doi.org/10.3390/ f12030335 Academic Editor: Russell Andrew Parsons Received: 16 December 2020 Accepted: 9 March 2021 Published: 12 March 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Research Group SILVANET, Universidad Politécnica de Madrid (UPM), ETSI Montes, Forestal y del Medio Natural, 28040 Madrid, Spain; joseantonio.manzanera@upm.es 2 Thoday Building, School of Natural Sciences, Bangor University, Bangor LL57 2UW, UK; r.valbuena@bangor.ac.uk * Correspondence: alba.gcimarras@upm.es Abstract: Increasing fire size and severity over the last few decades requires new techniques to accurately assess canopy fuel conditions and change over larger areas. This article presents an analysis on vegetation changes by mapping fuel types (FT) based on conditional rules according to the Prometheus classification system, which typifies the vertical profile of vegetation cover for fuel management and ecological purposes. Using multi-temporal LiDAR from the open-access Spanish national surveying program, we selected a 400 ha area of interest, which was surveyed in 2010 and 2016 with scan densities of 0.5 and 2 pulses·m −2 , respectively. FTs were determined from the distribution of LiDAR heights over an area, using grids with a cell size of 20 × 20 m. To validate the classification method, we used a stratified random sampling without replacement of 15 cells per FT and made an independent visual assessment of FT. The overall accuracy obtained was 81.26% with a Kappa coefficient of 0.73. In addition, the relationships among different stand structures and ecological factors such as topographic aspect and forest vegetation cover types were analyzed. Our classification algorithm revealed that stands lacking understory vegetation usually appeared in shady slopes, which were mainly covered by beech stands, whereas sunny areas were preferentially covered by oak stands, where the understory reached greater height thanks to more light availability. Our analysis on FT changes during that 6 year time span revealed potentially hazardous transitions from cleared forests towards a vertical continuum of canopy fuels, where wildfire events would potentially reach tree crowns, especially in oak forests and southern slopes with higher sun exposure for lower fuel moistures and increased flammability. Accurate methods to characterize forest canopy fuels and change over time can help direct forest management activities to priority areas with greater fire hazard. Multi-date canopy fuel information indicated that while some forest types experienced a growth of the shrub layer, others presented an understory decrease. On the other hand, loss of understory was more frequently detected in beech stands; thus, those forests place lower risk of wildfire spread. Our approach was developed using low-density and publicly available datasets and was based on direct canopy fuel measurements from multi-return LiDAR data that can be accurately translated and mapped according to standard fuel type categories that are familiar to land managers. Keywords: vegetation change; fuel type; fuel models; Prometheus classification system; LiDAR 1. Introduction Longitudinal studies using multitemporal remote sensing series can be very helpful for the purpose of monitoring and analyzing the dynamics of ecosystem structural fea- tures [1]. Traditionally, most remote sensing-assisted studies on the vegetation dynamics were based on spectral sensors. For instance, vegetation phenology has been studied using Moderate Resolution Imaging Spectroradiometer (MODIS) [2], land cover with Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM) and Sentinel-2 [3], fire risks with Landsat or National Oceanic and Atmospheric Administration-Advanced Very High Forests 2021, 12, 335. https://doi.org/10.3390/f12030335 https://www.mdpi.com/journal/forests