Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

Abstract

The account of total biomass can assist with the evaluation of climate regulation policies from local to global scales. This study estimates total biomass (TB), including tree and shrub biomass fractions, in Pinus halepensis Miller forest stands located in the Aragon Region (Spain) using Airborne Laser Scanning (ALS) data and fieldwork. A comparison of five selection methods and five regression models was performed to relate the TB, estimated in 83 field plots through allometric equations, to several independent variables extracted from ALS point cloud. A height threshold was used to include returns above 0.2 m when calculating ALS variables. The sample was divided into training and test sets composed of 62 and 21 plots, respectively. The model with the lower root mean square error (15.14 tons/ha) after validation was the multiple linear regression model including three ALS variables: the 25th percentile of the return heights, the variance, and the percentage of first returns above the mean. This study confirms the usefulness of low-density ALS data to accurately estimate total biomass, and thus better assess the availability of biomass and carbon content in a Mediterranean Aleppo pine forest.Forests 2018, 9, 158 2 of 17 tools due to its capability to provide 3-D information of vegetation structure. Vertical forest structure has been estimated with ALS data for several applications, such as forest inventory [16][17][18], forest structural heterogeneity [19][20][21][22], fuel type mapping [23,24] fuel modelling [23][24][25][26] or tree damage detection after natural disasters [27][28][29] for several height strata. However, few studies have focused on shrub biomass characterization with ALS data [30][31][32][33]. Some studies have used low density ALS data to estimate forest biomass [25,[34][35][36][37][38], but little research has been performed including shrub vegetation because of the inherent difficulty in the estimation related to its low height and uniform surface [30]. Several studies state that ALS data tends to underestimate shrub vegetation [39][40][41][42]. Besides, when shrub and tree vegetation cover is high [43] and density of ALS data is low, the accuracy of digital elevation models (DEM) used to normalize return heights decreases [30]. The performed studies use an approach that combines ALS data and harvesting field measurements for biomass estimation [30,33]. In this sense, the lack of more studies to characterize shrub vegetation might have been associated with the necessary destructive sampling to generate forest structure equations, the assumption of simple geometric shapes [44,45], and the additional difficulty to estimate biomass at a regional scale using low-density ALS data. However, the presence of shrub biomass in the understory or the existence of shrubland areas constitutes an important land use in the Mediterranean basin. In this sense, the availability of shrub allometric equations for the main Spanish shrub species [46] have opened new opportunities. These equations allow the estimat...

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

“…For example, the TV model used by Navarro et al [80] in Madrid yielded R 2 = 0.79 and rRMSE = 25.6% for a laser pulse density of 2.96 points/m 2 in research plots of P. pinaster forests. Domingo et al [81] in Aragon reported that the AGB model yielded R 2 values of 0.78-0.87 for 1.5 points/m 2 for research plots of Pinus halepensis. The results obtained in Extremadura by Guerra-Hernández et al [38] for AGB model yielded R 2 = 0.57-0.74 and rRMSE = 25.9-33.1% in pure Pinus pinea forests for a laser pulse density of 0.5 points/m 2 and plots belonging to the Extremadura Forest Service, and Hernando et al [82] estimated the AGB yield R 2 = 0.64 and rRMSE = 16.72% for 1.15 pulses m 2 in research plots of Pinus sylvestris-dominated forest located in Spain.…”

Assessment of the Carbon Stock in Pine Plantations in Southern Spain through ALS Data and K-Nearest Neighbor Algorithm Based Models

“…Here, kNN algorithms were used to generate spatially explicit forest C-stock models using ALS and inventory data [25,40,61]. We selected the RF model because it was proved to perform better than multivariate linear regression and other nonparametric statistical methods in the prediction of W t -S [40,62].…”

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

“…The use of low-density ALS data has been successfully used for estimating forestry attributes in different forest ecosystems (e.g., [33][34][35]), being also the case in Mediterranean forests of Spain (e.g., [36][37][38]). The analysis of the influence of point density on model predictions have been analyzed by several authors (e.g., [39][40][41]), who established that point density has little or no effect on predictions as statistical metrics remain stable [42].…”

“…In this sense, individual-tree growth models are powerful tools to update stand variables to the Light Detection and Ranging (LiDAR) mission date. For example, the use of diameter at breast height (dbh) and the height growth values from general yield tables of the Spanish National Forest Inventory have been applied for estimating total tree biomass in Aleppo pine forests [36]. However, specific single-tree growth models calibrated with tree rings are more accurate, particularly for improving model consistency when working at regional scale.…”