Analysing of frozen ground in Finland:affecting environmental factors, trends in northern Finland and applicability of satellite data

Abstract. The warming climate will lead to major changes in cold regions in the future. These changes will be more rapid and more severe in high latitudes. This would eventually also affect the seasonal soil frost depth, which has a significant impact on water and energy cycle between atmosphere and soil surface in cold regions. The frozen ground is understood as the soil layer which freezes and thaws annually. In this study, we investigate the seasonal soil frost depth in Finland 1981–2010 in open, forest and bog environments with three main aims: 1) To study which variables affect the overall thickness of soil frost layer and its changes most 2) To explore are there trends or major changes in the past 30 years on soil frost time series in northern Finland 3) To evaluate the applicability of satellite data against in-situ data in Finland. The main data in this study are frost tube in-situ measurements conducted by the Finnish Environmental Institute in 1981–2010. As a satellite data, we are using National Aeronautics and Space Administration’s Earth System Data Record for Land Surface Freeze/Thaw State (FT-ESDR) and European Centre for Medium-Range Weather Forecast’s (ECMWF) reanalysis model of soil temperature ERA-Interim datasets. For the first objective, we apply GAM (generalized additive model) and LME (linear mixed-effects model) statistical models in multivariate analysis. In the second objective, we are using the Mann-Kendall trend test and the Sen’s slope estimate to conduct trend analysis. In the third objective, we evaluate the satellite-based measurement against in-situ observations with contingency tables. Based on the multivariate analysis, the most statistically significant factors were air temperature, snow depth, precipitation and north coordinate. The interaction plots revealed that the effect of air temperature and snow depth to the maximum depth of soil frost is not a linear and varied in open, forest and bog environments. The yearly average of maximum depth of soil frost had decreased 2.12 cm/year on open, 2.75 cm/year on forest and 0.5 cm/year on bog sites in 1981–2010. The most distinct decreases were experienced in May in all three site types. The FT-ESDR and ERA Interim had the highest error rate percentages (avg. 68% and 56%) during shallow snow cover and soil frost depth. The accuracy increased steadily with the increasing soil frost and snow layer. The study revealed that the seasonal soil frost depth has been decreasing between 1981 and 2010 in Finland. This study aimed to give more insight about the multidimensional process of frozen ground. Results can be applied in future research planning. The way to improve the current setting would require information about factors like soil moisture, groundwater, and extensive data from a longer period of time

Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500-900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3-61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between -14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits.Peer reviewe