Spatial patterns of biomass change across Finland in 2009–2015

Abstract

Forest characteristics vary largely at the regional level and in smaller geographic areas in Finland. The amount of greenhouse gas emissions is related to changes in biomass and the soil type (e.g. upland soils vs. peatlands). Forest characteristics affect forest management practices and how vulnerable single stands are to disturbances. Spatially accurate map data of forests and biomass changes could improve the ability to suggest optimal management alternatives for any patch of land, e.g. in terms of climate change mitigation. In our study, estimating and explaining spatial patterns of biomass change across Finland was the main interest. We analysed biomass changes on different soil and site types on forested land using the Finnish Multi-Source National Forest Inventory (MS-NFI) map layers of the 2009 and 2015 datasets. Silvicultural management and treatment units larger than individual pixels were created by automatic segmentation of the thematic maps. The segmented MS-NFI biomass maps of growing stocks, including above and below ground biomass resulted an average estimate of 77.7 tons ha-1 compared to the national forest inventory estimate of 76.5 tons ha-1 in 2015 for Finland. Forest soil type had a similar effect on average biomass in segmented MS-NFI and NFI data. Despite good regional and country-level results, at the segment level the biomass distributions were narrowed and averaging of biomass estimates was observed. Hence, biomass changes on segments can be considered only approximate values; also, those small differences in average biomass may accumulate when map layers from more than one time point are compared. MS-NFI classification results depend on the satellite images and field data used, causing variation in successive inventories. In addition, to avoid false biomass change observations due to the low growth rate of boreal forests, a six-year study period was set. A kappa of 0.44 was achieved for precision when comparing undisturbed and disturbed forest stands in the Global Forest Cover layer and MS-NFI segmented map, indicating the low ability of the global forest map to identify land cover changes for Finland. The segmented biomass maps provide a useful tool for forest owners to analyse carbon stock changes in their forests and how to affect the amount of carbon by forest management.

Effect of forest management choices on carbon sequestration and biodiversity at national scale

“…These data are available in a 16 × 16 m grid which has been further elaborated into segments combining adjacent pixels representing similar stand characteristics. The segments form unified units or “stands” of variable size (mean 3926 m 2 ) (Haakana et al 2022 ). In addition, we used information about the field-based NFI on region-specific age-class distributions to correct the MSNFI-based distribution which has been found to be slightly skewed (Haakana et al 2022 ) (Fig.…”

“…The segments form unified units or “stands” of variable size (mean 3926 m 2 ) (Haakana et al 2022 ). In addition, we used information about the field-based NFI on region-specific age-class distributions to correct the MSNFI-based distribution which has been found to be slightly skewed (Haakana et al 2022 ) (Fig. S4.1 , Supplementary Information 4).…”

Role of data uncertainty when identifying important areas for biodiversity and carbon in boreal forests

“…To initiate PREBAS simulations, we used information on the initial state and type of the forest from the Multi Source National Forest Inventory 12 (MS-NFI 12, 2014–2018) maps, which describe the forest parameters across Finland at 16 × 16 m resolution (Tomppo et al 2008 ). To reduce computational effort, the simulation was done on homogeneous segments consisting of multiple 16 m pixels (Haakana et al 2022 ). Within segments, the initial value and growth in forest were assumed the same for all pixels.…”

Role of land cover in Finland’s greenhouse gas emissions

“…Forest area was based on the 12th Multi-Source National Forest Inventory (MS-NFI 2019) estimates (Mäkisara et al 2022 ). Forest pixels (16 m) on mineral soil and drained peatland were classified using National Land Survey data (Haakana et al 2022 ). Areas of rivers and lakes were obtained from the river network data set of Finnish Environment Institute.…”