Sources and sinks of greenhouse gases in the landscape: Approach for spatially explicit estimates

Holmberg, Maria; Akujärvi, Anu; Anttila, Saku; Autio, Iida; Haakana, Markus; Junttila, Virpi; Karvosenoja, Niko; Kortelainen, Pirkko; Mäkelä, Annikki; Minkkinen, Kari; Minunno, Francesco; Ранкинен, Катри; Ojanen, Paavo; Paunu, Ville-Veikko; Peltoniemi, Mikko; Rasilo, Terhi; Sallantaus, Tapani; Savolahti, Mikko; Tuominen, Sakari; Tuominen, Seppo; Vanhala, Pekka; Forsius, Martin

doi:10.1016/j.scitotenv.2021.146668

Cited by 11 publications

(8 citation statements)

References 63 publications

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“…Scots pine, Norway spruce and Silver birch (Minunno et al 2016(Minunno et al : 2019. The model has shown robust performances in predicting forest growth and primary production of managed forests at landscape (Holmberg et al 2021) and country level (Holmberg et al 2019). However, the model reliability should be better tested for forest ecosystem characterized by more complex structure such us uneven aged mixed stands.…”

Section: Discussionmentioning

confidence: 99%

Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Miettinen

Carlier²,

Häme³

et al. 2021

International Journal of Remote Sensing

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Section: Discussionmentioning

confidence: 99%

Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Miettinen

Carlier²,

Häme³

et al. 2021

International Journal of Remote Sensing

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“…However, a point density underneath forest canopy can be much lower than 5 m -2 on terrain level, which may influence on flow routing under dense vegetation. In any case, this study can promote further precision forestry-related applications such as dynamic terrain trafficability prediction (Salmivaara et al 2020), spatially dynamic forest resource planning and management (Mönkkönen et al 2014), more detailed ditch maintenance planning in peatlands (Laurén et al 2021), and carbon-optimized landscape management (Holmberg et al 2021). From global perspective this research offers useful tools for mapping headwater streams and man-made ditches (Ågren and Lidberg 2019).…”

Section: Discussionmentioning

confidence: 93%

Improvements to stream extraction and soil wetness mapping within a forested catchment by increasing airborne LiDAR data density – a case study in Parkano, western Finland

Niemi¹

2021

Silva Fenn.

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The pulse density of airborne Light Detection and Ranging (LiDAR) is increasing due to technical developments. The trade-offs between pulse density, inventory costs, and forest attribute measurement accuracy are extensively studied, but the possibilities of high-density airborne LiDAR in stream extraction and soil wetness mapping are unknown. This study aimed to refine the best practices for generating a hydrologically conditioned digital elevation model (DEM) from an airborne LiDAR -derived 3D point cloud. Depressionless DEMs were processed using a stepwise breaching-filling method, and the performance of overland flow routing was studied in relation to a pulse density, an interpolation method, and a raster cell size. The study area was situated on a densely ditched forestry site in Parkano municipality, for which LiDAR data with a pulse density of 5 m were available. Stream networks and a topographic wetness index (TWI) were derived from altogether 12 DEM versions. The topological database of Finland was used as a ground reference in comparison, in addition to 40 selected main flow routes within the catchment. The results show improved performance of overland flow modeling due to increased data density. In addition, commonly used triangulated irregular networks were clearly outperformed by universal kriging and inverse-distance weighting in DEM interpolation. However, the TWI proved to be more sensitive to pulse density than an interpolation method. Improved overland flow routing contributes to enhanced forest resource planning at detailed spatial scales.â2

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“…Emissions of GHG from lakes (CO 2 , CH 4 ) and rivers (CO 2 ) were calculated using area-specific empirical emission coefficients (Holmberg et al 2021 ). Lake emissions were calculated for five different size classes of lakes: 0.01–0.1 km 2 , 0.1–1 km 2 , 1–10 km 2 , 10–100 km 2 and larger than 100 km 2 .…”

Section: Methodsmentioning

confidence: 99%

“…Many Finnish regions and communities have their own climate roadmaps and local level C neutrality goals (Saikku et al 2022 ). Comprehensive regional-level information on the sources and sinks of GHGs from different land use forms supports regional GHG emission mitigation efforts (Buffam et al 2011 ; Vanhala et al 2016 ; Holmberg et al 2021 ). Kangas and Ollikainen ( 2022 ) presented a policy tool promoting both climate and biodiversity targets.…”

Section: Introductionmentioning

confidence: 96%

“…Finnish lakes cover ca 10% of the surface area and have been shown to be important as sources of GHGs in the landscape (Kortelainen et al 2004 , 2006 , 2013 , 2020 ). A recent study on the GHG balance of a large river basin in SW Finland reported net emission intensity 0.16 GgCO 2 eq km −2 yr −1 , and per capita emissions of 5.6 MgCO 2 eq yr −1 (Holmberg et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Role of land cover in Finland’s greenhouse gas emissions

Holmberg,

Junttila,

Schulz

et al. 2023

Ambio

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We present regionally aggregated emissions of greenhouse gases (GHG) from five land cover categories in Finland: artificial surfaces, arable land, forest, waterbodies, and wetlands. Carbon (C) sequestration to managed forests and unmanaged wetlands was also assessed. Models FRES and ALas were applied for emissions (CO2, CH4, N2O) from artificial surfaces and agriculture, and PREBAS for forest growth and C balance. Empirical emission coefficients were used to estimate emissions from drained forested peatland (CH4, N2O), cropland (CO2), waterbodies (CH4, CO2), peat production sites and undrained mires (CH4, CO2, N2O). We calculated gross emissions of 147.2 ± 6.8 TgCO2eq yr−1 for 18 administrative units covering mainland Finland, using data representative of the period 2017–2025. Emissions from energy production, industrial processes, road traffic and other sources in artificial surfaces amounted to 45.7 ± 2.0 TgCO2eq yr−1. The loss of C in forest harvesting was the largest emission source in the LULUCF sector, in total 59.8 ± 3.3 TgCO2eq yr−1. Emissions from domestic livestock production, field cultivation and organic soils added up to 12.2 ± 3.5 TgCO2eq yr−1 from arable land. Rivers and lakes (13.4 ± 1.9 TgCO2eq yr−1) as well as undrained mires and peat production sites (14.7 ± 1.8 TgCO2eq yr−1) increased the total GHG fluxes. The C sequestration from the atmosphere was 93.2 ± 13.7 TgCO2eq yr−1. with the main sink in forest on mineral soil (79.9 ± 12.2 TgCO2eq yr−1). All sinks compensated 63% of total emissions and thus the net emissions were 53.9 ± 15.3 TgCO2eq yr−1, or a net GHG flux per capita of 9.8 MgCO2eq yr−1.

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Sources and sinks of greenhouse gases in the landscape: Approach for spatially explicit estimates

Cited by 11 publications

References 63 publications

Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Improvements to stream extraction and soil wetness mapping within a forested catchment by increasing airborne LiDAR data density – a case study in Parkano, western Finland

Role of land cover in Finland’s greenhouse gas emissions

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