Carbon dioxide exchange in subarctic ecosystems measured by a micrometeorological technique

Helsinki, for public criticism in Auditorium E204, Physicum (Gustaf Hällströmin kat

Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling

Remote sensing based biomass estimates in Arctic areas are usually produced using coarse spatial resolution satellite imagery, which is incapable of capturing the fragmented nature of tundra vegetation communities. We mapped aboveground biomass using field sampling and very high spatial resolution (VHSR) satellite images (QuickBird, WorldView-2 and WorldView-3) in four different Arctic tundra or peatland sites with low vegetation located in Russia, Canada, and Finland. We compared site-specific and cross-site empirical regressions. First, we classified species into plant functional types and estimated biomass using easy, non-destructive field measurements (cover, height). Second, we used the cover/height-based biomass as the response variable and used combinations of single bands and vegetation indices in predicting total biomass. We found that plant functional type biomass could be predicted reasonably well in most cases using cover and height as the explanatory variables (adjusted R-2 0.21-0.92), and there was considerable variation in the model fit when the total biomass was predicted with satellite spectra (adjusted R-2 0.33-0.75). There were dissimilarities between cross-site and site-specific regression estimates in satellite spectra based regressions suggesting that the same regression should be used only in areas with similar kinds of vegetation. We discuss the considerable variation in biomass and plant functional type composition within and between different Arctic landscapes and how well this variation can be reproduced using VHSR satellite images. Overall, the usage of VHSR images creates new possibilities but to utilize them to full potential requires similarly more detailed in-situ data related to biomass inventories and other ecosystem change studies and modelling.Peer reviewe

Modeling Above-Ground Biomass Across Multiple Circum-Arctic Tundra Sites Using High Spatial Resolution Remote Sensing Data

Non peer reviewe

Detecting peatland vegetation patterns with multi-temporal field spectroscopy

doi: 10.1080/15481603.2022.2152303Peatlands are one of the most significant terrestrial carbon pools, and the processes behind the carbon cycle in peatlands are strongly associated with different vegetation patterns. Handheld spectroradiometer data has been widely applied in ecological research, but there is a lack of studies on peatlands assessing how the temporal and spectral resolution affect the detectability of vegetation patterns. We collected field spectroscopy and vegetation inventory data at two northern boreal peatlands, Lompolojankka and Halssiaapa, between late May and August 2019. We conducted multivariate random forest regressions to examine the appropriate periods, benefits of multi-temporal data, and optimal spectral bandwidth and sampling interval for detecting plant communities and the two-dimensional (2D) %-cover, above-ground biomass (AGB) and leaf area index (LAI) of seven plant functional types (PFTs). In the best cross-site regression models for detecting plant community clusters (PCCs), R-2 was 42.6-48.0% (root mean square error (RMSE) 0.153-0.193), and for PFT 2D %-cover 53.9-69.8% (RMSE 8.2-17.6%), AGB 43.1-61.5% (RMSE 86.2-165.5 g/m(2)) and LAI 46.3-51.3% (RMSE 0.220-0.464 m(2)/m(2)). The multi-temporal data of the whole season increased R-2 by 13.7-24.6%-points and 10.2-33.0%-points for the PCC and PFT regressions, respectively. There was no single optimal temporal window for vegetation pattern detection for the two sites; in Lompolojankka the early growing season between late May and mid-June had the highest regression performance, while in Halssiaapa, the optimal period was during the peak season, from July to early August. In general, the spectral sampling interval between 1 to 10 nm yielded the best regression performance for most of the vegetation characteristics in Lompolojankka, whereas the optimal range extended to 20 nm in Halssiaapa. Our findings underscore the importance of fieldwork timing and the use of multi-temporal and hyperspectral data in detecting vegetation in spatially heterogeneous landscapes.Peer reviewe

A Simple Method to Determine the Timing of Snow Melt by Remote Sensing with Application to the CO2 Balances of Northern Mire and Heath Ecosystems

Peer reviewe

Ohraa kasvavan turvepellon vuotuinen hiilidioksiditase

ei saatavill

A semi-empirical model of boreal-forest gross primary production, evapotranspiration, and soil water — calibration and sensitivity analysis

201

Carbon dioxide and energy flux measurements in four northern-boreal ecosystems at Pallas

201

Impact of reindeer herding on vegetation biomass and soil carbon content : a case study from Sodankylä, Finland

Peer reviewe