Morphophysiological parameters of needles have been studied in Scots pine ( Pinus sylvestris L.) and Siberian spruce ( Picea obovataLedeb.) trees of different life states growing under conditions of...

The morphological, biochemical, and physiological characteristics of silver birch ( Betula pendula Roth), curly birch ( B. pendula Roth var. carelica), and downy birch ( B. pubescensEhrh.) at early...

This study explores the potential of using Unmanned Aircraft Vehicles (UAVs) as a measurement platform for estimating greenhouse gas (GHG) fluxes over complex terrain. We proposed and tested an inverse modeling approach for retrieving GHG fluxes based on two-level measurements of GHG concentrations and airflow properties over complex terrain with high spatial resolution. Our approach is based on a three-dimensional hydrodynamic model capable of determining the airflow parameters that affect the spatial distribution of GHG concentrations within the atmospheric boundary layer. The model is primarily designed to solve the forward problem of calculating the steady-state distribution of GHG concentrations and fluxes at different levels over an inhomogeneous land surface within the model domain. The inverse problem deals with determining the unknown surface GHG fluxes by minimizing the difference between measured and modeled GHG concentrations at two selected levels above the land surface. Several numerical experiments were conducted using surrogate data that mimicked UAV observations of varying accuracies and density of GHG concentration measurements to test the robustness of the approach. Our primary modeling target was a 6 km² forested area in the foothills of the Greater Caucasus Mountains in Russia, characterized by complex topography and mosaic vegetation. The numerical experiments show that the proposed inverse modeling approach can effectively solve the inverse problem, with the resulting flux distribution having the same spatial pattern as the required flux. However, the approach tends to overestimate the mean value of the required flux over the domain, with the maximum errors in flux estimation associated with areas of maximum steepness in the surface topography. The accuracy of flux estimates improves as the number of points and the accuracy of the concentration measurements increase. Therefore, the density of UAV measurements should be adjusted according to the complexity of the terrain to improve the accuracy of the modeling results.

This paper presents the results of studies on soil carbon stock dynamics, CO₂ emission, and soil microbial respiration during post-industrial succession upon reforestation of a sand–gravel quarry in southern Karelia. In July 2021 and 2022, soils in the areas with different reforestation practices were studied. In 1991, one-year-old Scots pine (Pinus sylvestris L.) seedlings were planted on the plots with (1) sandy–gravelly mineral soil (sample plots SP 1 and SP 2) and (2) soil with added peat (SP 3). Native podburs (Entic Podzols) under undisturbed lingonberry pine forest (SP 4) were used as the control. We determined the total (organic) carbon, the microbial activity (using the basal and substrate-induced respiration methods in laboratory), and the CO₂ emission from the soil surface (in the field). The assessment of the properties of post-industrial soils indicated that the studied parameters in replantozem on SP 3 were close to those in the podzolized podbur on SP 4, which attested to a positive effect of peat-enriched substrate on the soil and plant recovery in the abandoned quarry. Considerably higher values of carbon stocks (by 6–10 times) and CO₂ fluxes (by 2–4 times) in the replantozem (Umbric Leptosols (Novic)) on SP 3 in comparison with psammozems (Skeletic Leptosols) on SP 1 and SP 2 were due to intensive organic matter accumulation and the application of peat substrate rich in nutrients and plant diaspores during planting. Soil carbon stocks, CO₂ emission, microbial activity, and stand productivity at SP 1 and SP 2 were lower than those at SP 3 and SP 4. To make more accurate predictions of the dynamics of the analyzed parameters of disturbed soils, it is necessary to take into account their spatial and temporal variability.