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Julia Wagner

About me

  • PhD student at the Department of Physical Geography since June 2018, Main Supervisor: Gustaf Hugelius
  • Member of Bolin Centre for Climate Reserach, research theme 2 (Water, biogeochemistry and climate)
  • Member of the PhD council at Department of Physical Geography (2020 and 2021)
  • Board member of APECS Sweden (since 2019)
  • Student representative in the Environment and Equality group ("RALV") (2022 & 2023)

In my research I am combining field sampling, laboratory analysis, remote sensing and digital soil mapping methods to map landcover/landforms and Permafrost Soils. I am involved in the EU Horizon 2020 Project Nunataryuk which investigates the impacts of thawing Arctic Permafrost coasts and involves natural and social scientists.




I am mainly teaching methods in Geography and Geographic Information Systems (GIS) on Bachelor and Master level. I supervise labs and end of course projects. During 2019/20 I was co-supervisor of 2 MSc Theses.


I am currently involved in teaching the following course (2023):

Geografiska informationssystem (GIS), 7,5 hp, distanskurs



I was involved in teaching the following courses (2018-2022)


Geografiska informationssystem (GIS), 7,5 hp, distanskurs


Geografiska informationssystem (GIS), 7,5 hp, distanskurs

Geografiska informationssystem (GIS), 7,5 hp

Geografi I (30hp) - Geografisk metodik I

Geographic Analysis and Visualization in GIS






A selection from Stockholm University publication database

  • Dissolved organic matter characterization in soils and streams in a small coastal low-Arctic catchment

    2022. Niek Jesse Speetjens (et al.). Biogeosciences 19 (12), 3073-3097


    Ongoing climate warming in the western Canadian Arctic is leading to thawing of permafrost soils and subsequent mobilization of its organic matter pool. Part of this mobilized terrestrial organic matter enters the aquatic system as dissolved organic matter (DOM) and is laterally transported from land to sea. Mobilized organic matter is an important source of nutrients for ecosystems, as it is available for microbial breakdown, and thus a source of greenhouse gases. We are beginning to understand spatial controls on the release of DOM as well as the quantities and fate of this material in large Arctic rivers. Yet, these processes remain systematically understudied in small, high-Arctic watersheds, despite the fact that these watersheds experience the strongest warming rates in comparison. Here, we sampled soil (active layer and permafrost) and water (porewater and stream water) from a small ice wedge polygon (IWP) catchment along the Yukon coast, Canada, during the summer of 2018. We assessed the organic carbon (OC) quantity (using dissolved (DOC) and particulate OC (POC) concentrations and soil OC content), quality (δ13C DOC, optical properties and source apportionment) and bioavailability (incubations; optical indices such as slope ratio, Sr; and humification index, HIX) along with stream water properties (temperature, T; pH; electrical conductivity, EC; and water isotopes). We classify and compare different landscape units and their soil horizons that differ in microtopography and hydrological connectivity, giving rise to differences in drainage capacity. Our results show that porewater DOC concentrations and yield reflect drainage patterns and waterlogged conditions in the watershed. DOC yield (in mg DOC g−1 soil OC) generally increases with depth but shows a large variability near the transition zone (around the permafrost table). Active-layer porewater DOC generally is more labile than permafrost DOC, due to various reasons (heterogeneity, presence of a paleo-active-layer and sampling strategies). Despite these differences, the very long transport times of porewater DOC indicate that substantial processing occurs in soils prior to release into streams. Within the stream, DOC strongly dominates over POC, illustrated by DOC/POC ratios around 50, yet storm events decrease that ratio to around 5. Source apportionment of stream DOC suggests a contribution of around 50 % from permafrost/deep-active-layer OC, which contrasts with patterns observed in large Arctic rivers (12 ± 8 %; Wild et al., 2019). Our 10 d monitoring period demonstrated temporal DOC patterns on multiple scales (i.e., diurnal patterns, storm events and longer-term trends), underlining the need for high-resolution long-term monitoring. First estimates of Black Creek annual DOC (8.2 ± 6.4 t DOC yr−1) and POC (0.21 ± 0.20 t yr−1) export allowed us to make a rough upscaling towards the entire Yukon Coastal Plain (34.51 ± 2.7 kt DOC yr−1 and 8.93 ± 8.5 kt POC yr−1). Rising Arctic temperatures, increases in runoff, soil organic matter (OM) leaching, permafrost thawing and primary production are likely to increase the net lateral OC flux. Consequently, altered lateral fluxes may have strong impacts on Arctic aquatic ecosystems and Arctic carbon cycling.

    Read more about Dissolved organic matter characterization in soils and streams in a small coastal low-Arctic catchment
  • Arctic Tundra Land Cover Classification on the Beaufort Coast Using the Kennaugh Element Framework on Dual-Polarimetric TerraSAR-X Imagery

    2021. Willeke A'Campo (et al.). Remote Sensing 13 (23)


    Arctic tundra landscapes are highly complex and are rapidly changing due to the warming climate. Datasets that document the spatial and temporal variability of the landscape are needed to monitor the rapid changes. Synthetic Aperture Radar (SAR) imagery is specifically suitable for monitoring the Arctic, as SAR, unlike optical remote sensing, can provide time series regardless of weather and illumination conditions. This study examines the potential of seasonal backscatter mechanisms in Arctic tundra environments for improving land cover classification purposes by using a time series of HH/HV TerraSAR-X (TSX) imagery. A Random Forest (RF) classification was applied on multi-temporal Sigma Nought intensity and multi-temporal Kennaugh matrix element data. The backscatter analysis revealed clear differences in the polarimetric response of water, soil, and vegetation, while backscatter signal variations within different vegetation classes were more nuanced. The RF models showed that land cover classes could be distinguished with 92.4% accuracy for the Kennaugh element data, compared to 57.7% accuracy for the Sigma Nought intensity data. Texture predictors, while improving the classification accuracy on the one hand, degraded the spatial resolution of the land cover product. The Kennaugh elements derived from TSX winter acquisitions were most important for the RF model, followed by the Kennaugh elements derived from summer and autumn acquisitions. The results of this study demonstrate that multi-temporal Kennaugh elements derived from dual-polarized X-band imagery are a powerful tool for Arctic tundra land cover mapping.

    Read more about Arctic Tundra Land Cover Classification on the Beaufort Coast Using the Kennaugh Element Framework on Dual-Polarimetric TerraSAR-X Imagery
  • Aboveground biomass patterns across treeless northern landscapes

    2021. Aleksi Räsänen (et al.). International Journal of Remote Sensing 42 (12), 4532-4557


    Aboveground vegetation biomass in northern treeless landscapes - peatlands and Arctic tundra - has been modelled with spectral information derived from optical remote sensing in several studies. However, synthesized overviews of biomass patterns across circumpolar sites have been limited. Based on data from eight study sites in Europe, Siberia and Canada, we ask (1) how biomass is divided between plant functional types (PFTs) and (2) how well biomass patterns can be detected with widely available, moderate spatial resolution (3-10 m) satellite imagery and topographic data. We explain biomass patterns using random forest regressions with the predictors being spectral bands and indices calculated from multi-temporal Sentinel-2 and PlanetScope imagery and topographic information calculated from ArcticDEM data. Our results indicate that there are notable differences in vegetation composition between northern landscapes with mosses, graminoids and deciduous shrubs being the most dominant PFTs. Remote sensing data detects biomass patterns, but regression performance varies between sites (explained variance 36-70%, normalized root mean square error 9-19%). There is also variability between sites whether Sentinel-2 or PlanetScope data is more suitable to detect biomass patterns and which the most important predictors are. Topographic information has a minor or negligible importance in most of the sites. Our results suggest that there is no easily generalizable relationship between satellite-derived vegetation greenness and biomass.

    Read more about Aboveground biomass patterns across treeless northern landscapes
  • Above-ground biomass estimates based on active and passive microwave sensor imagery in low-biomass savanna ecosystems

    2018. Andreas Braun, Julia Wagner, Volker Hochschild. Journal of Applied Remote Sensing 12 (4)


    Although many studies exist on the estimation and monitoring of above-ground biomass (AGB) of forest ecosystems by methods of remote sensing, very little research has been carried out for ecosystems of low primary production, such as grasslands, steppes, or savannas. Our study intends to approach this gap and investigates the correlation between space-borne radar information and AGB at the scale of 10 tons per hectare and below. Additionally, we introduce the integration of passive brightness temperature as an additional covariate for biomass estimation, based on the hypothesis that it contains information complementary to microwave backscatter of the active sensors. Our findings show that large-scale estimates of AGB can be conducted for grasslands and savannas at high accuracy (R-2 up to 0.52). Additionally, we found that the integration of passive radar can increase the quality of AGB estimates in terms of explained variance for selected cases. We hope that these indications are a starting point for more integrated approaches toward biomass estimations based on Earth observation methods.

    Read more about Above-ground biomass estimates based on active and passive microwave sensor imagery in low-biomass savanna ecosystems

Show all publications by Julia Wagner at Stockholm University