Classification and mapping of fuels in Mediterranean forest landscapes using a UAV-LiDAR system and integration possibilities with Handheld Mobile Laser Scanner systems

Hoffrén, Raúl (Universidad de Zaragoza) ; Lamelas, María Teresa (Universidad de Zaragoza) ; Riva, Juan de la (Universidad de Zaragoza)

Resumen: In this study, we evaluated the capability of an unmanned aerial vehicle with a LiDAR sensor (UAV-LiDAR) to classify and map fuel types based on the Prometheus classification in Mediterranean environments. UAV data were collected across 73 forest plots located in NE of Spain. Furthermore, data collected from a handheld mobile laser scanner system (HMLS) in 43 out of the 73 plots were used to assess the extent of improvement in fuel identification resulting from the fusion of UAV and HMLS data. UAV three-dimensional point clouds (average density: 452 points/m2) allowed the generation of LiDAR metrics and indices related to vegetation structure. Additionally, voxels of 5 cm3 derived from HMLS three-dimensional point clouds (average density: 63,148 points/m2) facilitated the calculation of fuel volume at each Prometheus fuel type height stratum (0.60, 2, and 4 m). Two different models based on three machine learning techniques (Random Forest, Linear Support Vector Machine, and Radial Support Vector Machine) were employed to classify the fuel types: one including only UAV variables and the other incorporating HMLS volume data. The most relevant UAV variables introduced into the classification models, according to Dunn’s test, were the 99th and 10th percentile of the vegetation heights, the standard deviation of the heights, the total returns above 4 m, and the LiDAR Height Diversity Index (LHDI). The best classification using only UAV data was achieved with Random Forest (overall accuracy = 81.28%), with confusion mainly found between similar shrub and tree fuel types. The integration of fuel volume from HMLS data yielded a substantial improvement, especially in Random Forest (overall accuracy = 95.05%). The mapping of the UAV model correctly estimated the fuel types in the total area of 55 plots and at least part of the area of 59 plots. These results confirm that UAV-LiDAR systems are valid and operational tools for forest fuel classification and mapping and show how fusion with HMLS data refines the identification of fuel types, contributing to more effective management of forest ecosystems.

Idioma: Inglés

DOI: 10.3390/rs16183536

Año: 2024

Publicado en: Remote Sensing 16, 18 (2024), 3536 [20 pp.]
ISSN: 2072-4292

Factor impacto JCR: 4.1 (2024)

Categ. JCR: GEOSCIENCES, MULTIDISCIPLINARY rank: 47 / 258 = 0.182 (2024) - Q1 - T1
Categ. JCR: IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY rank: 14 / 36 = 0.389 (2024) - Q2 - T2
Categ. JCR: REMOTE SENSING rank: 20 / 65 = 0.308 (2024) - Q2 - T1
Categ. JCR: ENVIRONMENTAL SCIENCES rank: 127 / 374 = 0.34 (2024) - Q2 - T2

Factor impacto SCIMAGO: 1.019 - Earth and Planetary Sciences (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/S51-23R
Financiación: info:eu-repo/grantAgreement/ES/MCIU/FPU18-05027

Tipo y forma: Article (Published version)

Área (Departamento): Área Geografía Humana (Dpto. Geograf. Ordenac.Territ.)
Área (Departamento): Área Análisis Geográfico Regi. (Dpto. Geograf. Ordenac.Territ.)