Spatial variability of organic layer thickness and carbon stocks in mature boreal forest stands--implications and suggestions for sampling designs

Mineral Soils Are an Important Intermediate Storage Pool of Black Carbon in Fennoscandian Boreal Forests

Approximately 40% of earth's carbon (C) stored in land vegetation and soil is within the boreal region. This large C pool is subjected to substantial removals and transformations during periodic wildfire. Fire-altered C, commonly known as pyrogenic carbon (PyC), plays a significant role in forest ecosystem functioning and composes a considerable fraction of C transport to limnic and oceanic sediments. While PyC stores are beginning to be quantified globally, knowledge is lacking regarding the drivers of their production and transport across ecosystems. This study used the chemo-thermal oxidation at 375°C (CTO-375) method to isolate a particularly refractory subset of PyC compounds, here called black carbon (BC), finding an average increase of 11.6 g BC m^-2 at 1 year postfire in 50 separate wildfires occurring in Sweden during 2018. These increases could not be linked to proposed drivers, however BC storage in 50 additional nearby unburnt soils related strongly to soil mass while its proportion of the larger C pool related negatively to soil C:N. Fire approximately doubled BC stocks in the mineral layer but had no significant effect on BC in the organic layer where it was likely produced. Suppressed decomposition rates and low heating during fire in mineral subsoil relative to upper layers suggests potential removals of the doubled mineral layer BC are more likely transported out of the soil system than degraded in situ. Therefore, mineral soils are suggested to be an important storage pool for BC that can buffer short-term (production in fire) and long-term (cross-ecosystem transport) BC cycling.

Evaluating different spatial interpolation methods and modeling techniques for estimating spatial forest site index in pure beech forests: a case study from Turkey

Spatial interpolation methods are widely used to estimate some ecological and environmental parameters that are difficult to measure. One of these parameters is forest site index, which is a demonstration of forest productivity. The aim of this study was to estimate forest site index in a beech forest ecosystem in Turkey. In this context, soil characteristics, stand parameters, and topographic features were measured in 70 temporary sample plots of beech forest stands. Forest site index of beech forest stands was predicted using different modeling techniques such as multiple regression analysis (MLR), multilayer perceptron (MLP), radial basis function (RBF), multiple regression kriging (MLRK), multilayer perceptron kriging (MLPK), and radial basis function kriging (RBFK). The results showed that the RBFK (R² = 0.98) and MLRK (R² = 0.96) outperformed the others to predict forest site index in the study area. The greatest improvement occurred when krigged residual used with MLR, which increase from 0.23 to 0.96. Thus, MLRK method significantly improved the prediction accuracy for site index. The models combined with krigged residuals were more successful than those used without krigged residuals. The results of this study suggest that the combined methods may help obtaining improved site index maps for forest management.

Mapping Above- and Below-Ground Carbon Pools in Boreal Forests: The Case for Airborne Lidar

A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (Picea abies) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m²) towards individual grid cells (1 m²) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C.