Estimation of Individual Tree Stem Biomass in an Uneven-Aged Structured Coniferous Forest Using Multispectral LiDAR Data

Stem biomass is a fundamental component of the global carbon cycle that is essential for forest productivity estimation. Over the last few decades, Light Detection and Ranging (LiDAR) has proven to be a useful tool for accurate carbon stock and biomass estimation in various biomes. The aim of this study was to investigate the potential of multispectral LiDAR data for the reliable estimation of single-tree total and barkless stem biomass (TSB and BSB) in an uneven-aged structured forest with complex topography. Destructive and non-destructive field measurements were collected for a total of 67 dominant and co-dominant Abies borisii-regis trees located in a mountainous area in Greece. Subsequently, two allometric equations were constructed to enrich the reference data with non-destructively sampled trees. Five different regression algorithms were tested for single-tree BSB and TSB estimation using height (height percentiles and bicentiles, max and average height) and intensity (skewness, standard deviation and average intensity) LiDAR-derived metrics: Generalized Linear Models (GLMs), Gaussian Process (GP), Random Forest (RF), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). The results showcased that the RF algorithm provided the best overall predictive performance in both BSB (i.e., RMSE = 175.76 kg and R2 = 0.78) and TSB (i.e., RMSE = 211.16 kg and R2 = 0.65) cases. Our work demonstrates that BSB can be estimated with moderate to high accuracy using all the tested algorithms, contrary to the TSB, where only three algorithms (RF, SVR and GP) can adequately provide accurate TSB predictions due to bark irregularities along the stems. Overall, the multispectral LiDAR data provide accurate stem biomass estimates, the general applicability of which should be further tested in different biomes and ecosystems.

Generic Additive Allometric Models and Biomass Allocation for Two Natural Oak Species in Northeastern China

Current models for oak species could not accurately estimate biomass in northeastern China, since they are usually restricted to Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) on local sites, and specifically, no biomass models are available for Liaodong oak (Quercuswutaishanica Mayr). The goal of this study was, therefore, to develop generic biomass models for both oak species on a large scale and evaluate the biomass allocation patterns within tree components. A total of 159 sample trees consisting of 120 Mongolian oak and 39 Liaodong oak were harvested and measured for wood (inside bark), bark, branch and foliage biomass. To account for the belowground biomass, 53 root systems were excavated following the aboveground harvest. The share of biomass allocated to different components was assessed by calculating the ratios. An aboveground additive system of biomass models and belowground equations were fitted based on predictors considering diameter (D), tree height (H), crown width (CW) and crown length (CL). Model parameters were estimated by jointly fitting the total and the components’ equations using the weighted nonlinear seemingly unrelated regression method. A leave-one-out cross-validation procedure was used to evaluate the predictive ability. The results revealed that stem biomass accounts for about two-thirds of the aboveground biomass. The ratio of wood biomass holds constant and that of branches increases with increasing D, H, CW and CL, while a reverse trend was found for bark and foliage. The root-to-shoot ratio nonlinearly decreased with D, ranging from 1.06 to 0.11. Tree diameter proved to be a good predictor, especially for root biomass. Tree height is more prominent than crown size for improving stem biomass models, yet it puts negative effects on crown biomass models with non-significant coefficients. Crown width could help improve the fitting results of the branch and foliage biomass models. We conclude that the selected generic biomass models for Mongolian oak and Liaodong oak will vigorously promote the accuracy of biomass estimation.

Aboveground Biomass Allocation of Boreal Shrubs and Short-Stature Trees in Northwestern Canada

In this follow-on study on aboveground biomass of shrubs and short-stature trees, we provide plant component aboveground biomass (herein ‘AGB’) as well as plant component AGB allometric models for five common boreal shrub and four common boreal short-stature tree genera/species. The analyzed plant components consist of stem, branch, and leaf organs. We found similar ratios of component biomass to total AGB for stems, branches, and leaves amongst shrubs and deciduous tree genera/species across the southern Northwest Territories, while the evergreen Picea genus differed in the biomass allocation to aboveground plant organs compared to the deciduous genera/species. Shrub component AGB allometric models were derived using the three-dimensional variable volume as predictor, determined as the sum of line-intercept cover, upper foliage width, and maximum height above ground. Tree component AGB was modeled using the cross-sectional area of the stem diameter as predictor variable, measured at 0.30 m along the stem length. For shrub component AGB, we achieved better model fits for stem biomass (60.33 g ≤ RMSE ≤ 163.59 g; 0.651 ≤ R2 ≤ 0.885) compared to leaf biomass (12.62 g ≤ RMSE ≤ 35.04 g; 0.380 ≤ R2 ≤ 0.735), as has been reported by others. For short-stature trees, leaf biomass predictions resulted in similar model fits (18.21 g ≤ RMSE ≤ 70.0 g; 0.702 ≤ R2 ≤ 0.882) compared to branch biomass (6.88 g ≤ RMSE ≤ 45.08 g; 0.736 ≤ R2 ≤ 0.923) and only slightly better model fits for stem biomass (30.87 g ≤ RMSE ≤ 11.72 g; 0.887 ≤ R2 ≤ 0.960), which suggests that leaf AGB of short-stature trees (<4.5 m) can be more accurately predicted using cross-sectional area as opposed to diameter at breast height for tall-stature trees. Our multi-species shrub and short-stature tree allometric models showed promising results for predicting plant component AGB, which can be utilized for remote sensing applications where plant functional types cannot always be distinguished. This study provides critical information on plant AGB allocation as well as component AGB modeling, required for understanding boreal AGB and aboveground carbon pools within the dynamic and rapidly changing Taiga Plains and Taiga Shield ecozones. In addition, the structural information and component AGB equations are important for integrating shrubs and short-stature tree AGB into carbon accounting strategies in order to improve our understanding of the rapidly changing boreal ecosystem function.

Fibre and Seed Productivity of Industrial Hemp (Cannabis sativa L.) Varieties under Mediterranean Conditions

Farmers’ interest in renewable raw materials such as hemp (Cannabis sativa L.) fibres has recently increased, but hemp productivity is strongly affected by genotype and environment conditions. A 3-year field experiment was conducted under Mediterranean environment in northern Greece to evaluate the productivity (regarding fibres and seeds) of six monoecious hemp varieties. The vars. Futura 75 and Bialobrzeskie provided the greatest (p < 0.01) fibre productivity (4.57 and 4.27 t ha−1, respectively), which were 77.1% and 65.5%, respectively, greater than that of the least productive var. Fedora 17. However, the vars. Santhica 27, Tygra and Bialobrzeskie provided the highest (p < 0.05) seed yield (2.7, 2.9 and 2.6 t ha−1, respectively), which were 28.6%, 38.1% and 23.8%, respectively, greater than that of the least productive var. Futura 75. Hemp fibre yield was strongly positively correlated with total biomass (R2 = 0.8612) and stem biomass yield (R2 = 0.9742), while it was inversely correlated with fibre strength (R2 = 0.424). Hemp seed yield was not correlated with the hemp plant density, height, total biomass or stem biomass yield. The six hemp genotypes evaluated in the study had Δ9-tetrahydrocannabinol (THC) content lower than 0.2% satisfying the European legislation requirements for industrial hemp varieties. The results of the study indicated that, under Mediterranean conditions (northern Greece), the var. Bialobrzeskie showed high productivity, as averaged across years, for both fibres and seeds. This result is very helpful for farmers which should prefer hemp varieties of dual-purpose production (stems and inflorescences or stems and seeds) adapted best to their local environment.

Vine mealybugs disrupt biomass allocation in grapevine

Vine mealybug Planococcus ficus Signoret (Hemiptera: Pseudococcidae) is an important phloem-feeding pest species in many grapevine producing areas worldwide. The economic damage of P. ficus is thought to be mainly caused by sooty mould on infested grape clusters and transmission of plant viruses. Direct damage caused by mealybug feeding to grapevine plants (Vitis vinifera, L.) has only been vaguely described or otherwise completely discarded. The present study is the first to give an insight into the direct impacts of P. ficus on vegetative growth and biomass dynamics of grapevine plants. In a screenhouse, three-year-old, potted grapevine plants were infested with mealybugs at two different densities, imitating high and low field infestation levels. Mealybug numbers, plant biomass, leaf area, leaf size and leaf number were monitored over six months and compared to a control treatment without mealybugs. High infestation levels reduced leaf and stem biomass by one third, while low levels of P. ficus impacted only stem biomass, indicating a higher sensibility of the perennial parts of the plant or a reallocation of biomass. Leaf area, size and number were not affected by mealybug feeding.In conclusion, grapevine response to P. ficus is gradual and involves different plant parts depending on the severity of the attack. Contrary to previous assumptions, this study demonstrates considerable direct impacts of mealybug feeding on temporal and perennial parts of grapevine plants.