RT info:eu-repo/semantics/article
T1 What is the most suitable height range of ALS point cloud and LiDAR metric for understorey analysis? A study case in a mixed deciduous forest, Pokupsko basin, Croatia
A1 Martín García, Saray
A1 Balenović, Ivan
A1 Jurjević, Luka
A1 Lizarralde, Iñigo
A1 Buján, Sandra
A1 Alonso Ponce, Rafael
K1 Understorey
K1 Forests and forestry
K1 Bosques y silvicultura
K1 Arbustos
K1 Deciduous forest
K1 Forest fires - Prevention and control
K1 Bosques - Incendios - Prevención y control
K1 Forest ecology
K1 Ecología forestal
K1 Forest management
K1 Bosques - Gestión
K1 Sustainable development
K1 Desarrollo sostenible
K1 Croatia
K1 3106 Ciencia Forestal
K1 3106.08 Silvicultura
AB Understorey evaluation is essential in wildlife habitat management, biomass storage and wildfire suppression, among other areas. The lack of a standardised methodology in the field measurements, and in their subsequent analysis, forces researchers to look for procedures that effectively extract understorey data to make management decisions corresponding to actual stand conditions. In this sense, when analysing the understorey characteristics from LiDAR data, it is very usual to ask: “what value should we set the understorey height range to?” It is also usual to answer by setting a numeric value on the basis of previous research. Against that background, this research aims to identify the optimal height to canopy base (HCB) filter–LiDAR metric relationship for estimating understorey height (UH) and understorey cover (UC) using LiDAR data in the Pokupsko Basin lowland forest complex (Croatia). First, several HCB values per plot were obtained from field data (measured HCBi—HCBM-i, where i ϵ (minimum, maximum, mean, percentiles)), and then they were modelled based on LiDAR metrics (estimated HCBi—HCBE-i). These thresholds, measured and estimated HCBi per plot, were used as point cloud filters to estimate understorey parameters directly on the point cloud located under the canopy layer. In this way, it was possible to predict the UH with errors (RMSE) between 0.90 and 2.50 m and the UC with errors (RMSE) between 8.8 and 18.6 in cover percentage. Finally, the sensitivity analysis showed the HCB filter (the upper threshold to select the understorey LiDAR points) is the most important factor affecting the UH estimates, while this factor and the LiDAR metric are the most important factors affecting the UC estimates.
PB MDPI
SN 2072-4292
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/63147
UL https://uvadoc.uva.es/handle/10324/63147
LA eng
NO Remote Sensing, 2022, Vol. 14, Nº. 9, 2095
NO Producción Científica
DS UVaDOC
RD 30-jun-2024