Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities

Abstract

Aims of study: To evaluate the potential use of canopy height and intensity distributions, determined by airborne LiDAR, for the estimation of crown, stem and aboveground biomass fractions.To assess the effects of a reduction in LiDAR pulse densities on model precision.Area of study: The study area is located in Galicia, NW Spain. The forests are representative of Eucalyptus globules stands in NW Spain, characterized by low-intensity silvicultural treatments and by the presence of tall shrub.Material and methods: Linear, multiplicative power and exponential models were used to establish empirical relationships between field measurements and LiDAR metrics.A random selection of LiDAR returns and a comparison of the prediction errors by LiDAR pulse density factor were performed to study a possible loss of fit in these models.Main results: Models showed similar goodness-of-fit statistics to those reported in the international literature. R2 ranged from 0.52 to 0.75 for stand crown biomass, from 0.64 to 0.87 for stand stem biomass, and from 0.63 to 0.86 for stand aboveground biomass. The RMSE/MEAN · 100 of the set of fitted models ranged from 17.4% to 28.4%.Models precision was essentially maintained when 87.5% of the original point cloud was reduced, i.e. a reduction from 4 pulses m–2 to 0.5 pulses m–2.Research highlights: Considering the results of this study, the low-density LiDAR data that are released by the Spanish National Geographic Institute will be an excellent source of information for reducing the cost of forest inventories.Key words: Eucalypt plantations; airborne laser scanning; aboveground biomass; carbon stocks; remote sensing; forest inventory.

Comparison of ALS based models for estimating aboveground biomass in three types of Mediterranean forest

“…Our results also demonstrate that a specific second metric related to the shape of ALS height distribution (h kurt ) is potentially useful for improving mixed forest models (Table 4). As in other studies using low-density ALS data [Thomas et al, 2006;García et al, 2010;González-Ferreiro et al, 2013], the crown component of biomass was the least accurately modelled of the components analysed. One of the reasons for this is the complexity of field estimation of foliage biomass and the limited penetrability of the low-density ALS in the canopy, particularly in mixed forest with relatively closed canopy.…”

Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

“…Thus, for pure P. radiata forests, the TV models used by González-Ferreiro et al [76] yielded R 2 = 0.69 and rRMSE = 30% for 0.5 points/m 2 , and R 2 = 0.79 and rRMSE = 25% for 8 points/m 2 ; the AGB models used by the same authors yielded R 2 = 0.75 and rRMSE = 26.8% for 0.5 points/m 2 , and R 2 = 0.80 and rRMSE = 23.7% for 8 points/m 2 . In a later study of pure E. globulus stands, the AGB model used by González-Ferreiro et al [77] yielded R 2 = 0.63-0.83 and rRMSE = 28.3-19.2% for 0.5 points/m 2 , and R 2 = 0.76-0.86 and rRMSE = 22.8-17.6% for 4 points/m 2 . The TV model used by Gonçalves-Seco et al [78] for E. globulus forests yielded R 2 = 0.81 for a density of 4 points/m 2 .…”

Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

“…In addition, some of them have compared different point densities (González-Ferreiro et al, 2013;Singh, Gang, James, & Ross, 2015). However, few studies have been focused on comparing different algorithms to estimate forest parameters and they were all applied to high-density point clouds (Gagliasso, Hummel, & Temesgen, 2014;García-Gutiérrez et al, 2015;Gleason & Im, 2012;Görgens, Montaghi, & Rodriguez, 2015;Latifi, Nothdurft, & Koch, 2010).…”