Estimation of carbon dioxide fluxes on a ridge-hollow bog complex using a high resolution orthophotoplan

Abstract

The use of unmanned aerial vehicles for detailed mapping of ecosystems has become increasingly important in recent years. As one of the main terrestrial carbon reserves, peatland ecosystems are of the great interest in obtaining highly detailed orthophotoplans. At the same time, there is a lack of publications devoted to the total carbon dioxide fluxes in each type of bog microforms. This paper presents the results of our study, which aimed to develop methods for mapping peatland microlandscapes and for estimation of integral carbon dioxide fluxes between the peatland surface and the atmosphere. Based on a highly detailed orthophotoplan compiled using unmanned aerial vehicles, we assessed the areas of major microform groups (swamps, hollows, and ridges) in a bog located in the Central Forest State Nature Biosphere Reserve (European Russia). The classification accuracy ranged from 79% to 93%. The areas of ridges, hollows, and swamps were 0.16 km 2 , 0.32 km 2 , and 0.12 km 2 , respectively. To make an integral estimation of carbon dioxide fluxes, we used earlier data on carbon dioxide emissions (ecosystem respiration), uptake (gross ecosystem exchange), and balance (net ecosystem exchange) measured by soil chamber method on representative experimental plots of respective microform types. After recalculating fluxes to areas of microforms, the integral values for different classes in the summer seasons of 2014, 2016 and 2017 were 15-91 kg CO 2 × h-1 for ecosystem respiration, 21-190 kg CO 2 × h-1 for gross ecosystem exchange, and from-122 kg CO 2 × h-1 to 41 kg CO 2 × h-1 for net ecosystem exchange. The results of the study confirmed that highly detailed orthophotoplans, obtained with the use of unmanned aerial vehicles, make it possible to distinguish the boundaries of such bog microforms as swamps, hollows and ridges with a high accuracy, despite the presence of some errors in the classification. The study of the structural and functional organisation of the bog should be carried out with considering its seasonal and interannual dynamics as well as all microform types.

Interannual variability of the ecosystem CO2 fluxes at paludified spruce forest and ombrotrophic bog in southern taiga

“…The "Staroselsky Mokh" site (OB) named as RU-Fy4 in FLUXNET database (56.4727° N, 33.0413° E, 240 m a.s.l.) is an old (up to 9000 years) ombrotrophic bog and has an area about 6 km 2 (Ivanov et al, 2021) (Ivanov et al, 2021). Bog edges are covered by trees, predominantly by Scots pine (Pinus sylvestris) with tree heights up to 10-13 m. In the central part of the bog small pine trees (within 2-5 m height) grow on some ridges.…”

“…Average peat layer thickness at OB site is 3.2 m with maximum of 5.5 m (Ivanov et al, 2021). Ground water level is typically 30 cm below the surface at ridges and hummocks and 10 cm above the surface at hollows.…”

Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects

“…An article by Ivanov et al (2021) demonstrates the potential of UAV remote sensing for mapping CO 2 fluxes and carbon (C) stocks in peatland ecosystems. Their findings highlight the potential of UAV remote sensing for accurately assessing microform groups and estimating CO 2 fluxes [116].…”

“…An article by Ivanov et al (2021) demonstrates the potential of UAV remote sensing for mapping CO 2 fluxes and carbon (C) stocks in peatland ecosystems. Their findings highlight the potential of UAV remote sensing for accurately assessing microform groups and estimating CO 2 fluxes [116]. Those findings are consistent with previous research by Beyer et al (2019), who successfully mapped vegetation communities in rewetted fen sites using UAV-based multisensory data [117].…”

“…The processing and analysis of large volumes of remote sensing data can be complex and time-consuming. The study by Ivanov et al (2021) involves advanced data processing techniques, such as orthophoto plan compilation and structural equation modelling, which require expertise and computational resources [116]. Additionally, Díaz-Varela et al ( 2018) mention the need for accurate georeferencing, image overlap, and external control data to ensure data quality and precise analysis [48].…”