Genetic variability and juvenile–adult correlations of Norway spruce (Picea abies) provenances, tested in multisite comparative trials

Variation in aboveground biomass carbon accumulation in Scots pine seed orchards progeny

Increasing growth and biomass accumulation in forest stands may positively contribute to carbon (C) sequestration and climate change mitigation. Tree improvement programs develop planting material with enhanced growth and biomass accumulation. Scots pine is commonly planted in Europe, and provides a potential for increased C accumulation in forest biomass when using improved seed origins. Our objective was to investigate variation in standing aboveground C accumulation among the progeny of Scots pine seed orchards in climatically variable environments, where we also compared the amount of accumulated C between the tested populations and commercial stands. The aboveground biomass of trees in two series of replicated common garden trials was estimated with eight allometric equations, converted into C, and expressed per unit area. For each trial site we selected reference stands matching the age, stand composition and forest site type, where the same measurements and calculations were done on sample plots. We specifically expected to find the progeny that would express better growth and greater accumulation of C in their biomass when compared to the reference stands.
Significant and large variation was found among the examined seed sources and trial sites. On average, aboveground C accumulation varied among sites from 31.0 to 60.4 Mg ha-1 (age 22) and from 25.5 to 34.0 Mg ha-1 (age 17). Differences between populations at individual sites ranged from 41% to 55% (age 22), and from 29% to 54% (age 17). However, only a few of the investigated progeny had C accumulation significantly greater than the reference stands, and some had a lower C accumulation, depending on the study site.
This study for the first time quantifies the amount of and variation in aboveground C accumulation among the progeny of Scots pine clonal and seedling seed orchards in Poland. It also contributes to the knowledge of the patterns of within-species variation in growth and biomass accumulation. Variation we found is promising for the potential to enhance C sequestration in forest stands through tree improvement. However, the lower C accumulation or non-significant differences between research trials and reference stands, indicate that the level of growth enhancement from phenotypic selection practiced so far in Polish forestry is limited. For increased C sequestration in planted forests, selection would need to be intensified.

A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data

As a forest structural parameter, leaf area index (LAI) is crucial for efficient intensive plantation management. Leaf area is responsible for the energy absorption needed for photosynthetic production and transpiration, both affecting growth. Currently, LAI can be estimated either by remote-sensing methods or ground-based methods. However, unlike ground-based methods, remote estimation provides a cost-effective and ecologically significant advance. The aim of our study was to evaluate whether machine learning algorithms can be used to quantify LAI, using either optical remote sensing or LiDAR metrics in Eucalyptus dunnii and Eucalyptus grandis stands. First, empirical relationships between LAI and remote-sensing data using LiDAR metrics and multispectral high-resolution satellite metrics, were assessed. Selected variables for LAI estimation were: forest canopy cover, laser penetration index, canopy relief ratio (from among the LiDAR data), the green normalized difference vegetation index, and normalized difference vegetation index (from among spectral vegetation indices). We compared the accuracy of three machine learning algorithms: artificial neural networks (ANN), random forest (RF) and support vector regression (SVR). The coefficient of determination ranged from 0.60, for ANN, to 0.84, for SVR. The SVR regression methods showed the best performance in terms of overall model accuracy and RMSE (0.60). The results show that the remote sensing data applied through machine learning algorithms provide an effective method to estimate LAI in eucalypt plantations. The methodology proposed is directly applicable for operational forest planning at the landscape level.