Ryan Bright
Seniorforsker
Biografi
Sammendrag
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Sammendrag
Forests, especially in the northern latitudes, are vulnerable ecosystems to climate change, and tree-ring data offer insights into growth-climate relationships as an important effect. Using the National Forest Inventory plot network, we analysed these correlations for the two dominant conifer species in Norway – Norway spruce and Scots pine – for the 1960–2020 period. For both species, the June climate was an important driver of radial growth during this period. Countrywide, the climate-growth correlations divided the Norwegian forests into spatial clusters following a broad shift from temperature- to water-sensitivity of growth with latitude and altitude. The clusters were delineated by a mean 1960–2020 June temperature of ca. 12°C for Norway spruce and Scots pine. The annual mean growing season and July temperatures – but not June temperature – has increased by 1.0 °C between the 1960–1990 and 1990–2020 periods, with a slight increase in precipitation. Despite this warming and wetting trend, the long-term growth-climate relationship has remained relatively stable between 1960 and 1990 and 1990–2020 for both species. The threshold between temperature and water-sensitive growth has not changed in the last two 31-year periods, following the stability of the June temperature compared with other months during the growing season. These findings highlight geographically coherent regions in Norway, segregating between temperature- and water-sensitive radial growth for the two major conifer species, temporally stable in the long-term for the 1960–2020 period studied.
Sammendrag
Monitoring surface albedo at a fine spatial resolution in forests can enrich process understanding and benefit ecosystem modeling and climate-oriented forest management. Direct estimation of surface albedo using 10 m reflectance imagery from Sentinel-2 is a promising research avenue to this extent, although questions remain regarding the representativeness of the underlying model of surface reflectance anisotropy originating from coarser-resolution imagery (e.g., MODIS). Here, using Fennoscandia (Norway, Sweden, Finland) as a case region, we test the hypothesis that systematic stratification of the forested landscape into similar species compositions and physical structures prior to the step of carrying out angular bin regressions can lead to improved albedo estimation accuracy of direct estimation algorithms. We find that such stratification does not lead to statistically meaningful improvement over stratification based on conventional land cover classification, suggesting that factors other than forest structure (e.g., soils, understory vegetation) may be equally important in explaining within-forest variations in surface reflectance anisotropy. Nevertheless, for Sentinel-2-based direct estimation based on conventional forest classification, we document total-sky surface albedo errors (RMSE) during snow-free and snow-covered conditions of 0.015 (15 %) and 0.037 (21 %), respectively, which align with those of the coarser spatial resolution products in current operation.
