Lasse Keetz

Doctoral Research Fellow - Department of Geosciences

Norwegian version of this page

Email l.t.keetz@geo.uio.no

Room K07

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Visiting address Sars' gt./Monrads gt. Botanical Museum 0562 OSLO

Postal address 1172 Blindern / Natural History Museum 0318 OSLO

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Academic interests

Climate-vegetation interactions
Machine learning for vegetation modelling
Dynamic Global Vegetation Models
Distribution Modelling

Courses taught

GEO9915/5915 – Ecological Climatology (assistant teacher)
BIOS9211/5211 – Distribution Modelling (assistant teacher)
GEO2300 – Physical Processes in the Geosciences (assistant teacher)

Background

2019 – : Doctoral Research Fellow; Vegetation and Climate Dynamics, University of Oslo, Norway.

2019: Field Assistant, University of Gothenburg/ Tarfala Research Station, Sweden.
Vegetation inventory (Point Intercept method), laboratory analyses.

2017 – 2019: MSc in Physical Geography and Ecosystem Science, Lund University, Sweden.
Thesis: “Implications of a dynamic vertical root distribution for modelled water and carbon fluxes across selected ecosystems”.

2017: BSc thesis, Helmholtz-Centre for Environmental Research (UFZ), Leipzig, Germany.
Topic: “Estimating above ground biomass of a tropical rainforest from airborne LiDAR data for different scales”.

2016, 2018: Umeå University/ Abisko Scientific Research Station, Sweden.
Elective courses “Arctic Science” and “Arctic Ecosystems”.

2015 – 2016: Stockholm University, Sweden. Exchange semester at the Dept. of Computer and Systems Science.

Keetz, Lasse Torben; Lieungh, Eva; Karimi-Asli, Kaveh; Geange, Sonya Rita; Gelati, Emiliano & Tang, Hui [Show all 24 contributors for this article] (2023). Climate–ecosystem modelling made easy: The Land Sites Platform. Global Change Biology. ISSN 1354-1013. 29(15), p. 4440–4452. doi: 10.1111/gcb.16808. Full text in Research Archive Show summary
Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.
Naas, Adam Eindride; Halvorsen, Rune; Horvath, Peter; Wollan, Anders Kvalvåg; Bratli, Harald & Brynildsrud, Katrine Marie [Show all 16 contributors for this article] (2023). What explains inconsistencies in field-based ecosystem mapping? Applied Vegetation Science. ISSN 1402-2001. 26(1). doi: 10.1111/avsc.12715. Full text in Research Archive Show summary
Questions Field-based ecosystem mapping is prone to observer bias, typically resulting in a mismatch between maps made by different mappers, that is, inconsistency. Experimental studies testing the influence of site, mapping scale, and differences in experience level on inconsistency in field-based ecosystem mapping are lacking. Here, we study how inconsistencies in field-based ecosystem maps depend on these factors. Location Iškoras and Guollemuorsuolu, northeastern Norway, and Landsvik and Lygra, western Norway. Methods In a balanced experiment, four sites were field-mapped wall-to-wall to scales 1:5000 and 1:20,000 by 12 mappers, representing three experience levels. Thematic inconsistency was calculated by overlay analysis of map pairs from the same site, mapped to the same scale. We tested for significant differences between sites, scales, and experience-level groups. Principal components analysis was used in an analysis of additional map inconsistencies and their relationships with site, scale and differences in experience level and time consumption were analysed with redundancy analysis. Results On average, thematic inconsistency was 51%. The most important predictor for thematic inconsistency, and for all map inconsistencies, was site. Scale and its interaction with site predicted map inconsistencies, but only the latter were important for thematic inconsistency. The only experience-level group that differed significantly from the mean thematic inconsistency was that of the most experienced mappers, with nine percentage points. Experience had no significant effect on map inconsistency as a whole. Conclusion Thematic inconsistency was high for all but the dominant thematic units, with potentially adverse consequences for mapping ecosystems that are fragmented or have low coverage. Interactions between site and mapping system properties are considered the main reasons why no relationships between scale and thematic inconsistency were observed. More controlled experiments are needed to quantify the effect of other factors on inconsistency in field-based mapping.

View all works in Cristin

Lieungh, Eva; Keetz, Lasse Torben & Melteig, Elina (2023). Alle har gratis tilgang til en av verdens mest komplekse vitenskapelige modeller. Nå blir den enklere å bruke. [Internet]. Forskning.no.
Lieungh, Eva; Yilmaz, Yeliz; Tang, Hui; Keetz, Lasse Torben; Olsen, Siri Lie & Gya, Ragnhild [Show all 11 contributors for this article] (2023). Complementary field and modelling experiments: alpine vegetation under climate warming.
Lieungh, Eva; Melteig, Elina & Keetz, Lasse Torben (2023). Nå er det lettere å bli klimaforsker. [Internet]. titan.no.
Bryn, Anders; Brynildsrud, Katrine Marie; Horvath, Peter; Keetz, Lasse Torben; Lieungh, Eva & Naas, Adam Eindride [Show all 9 contributors for this article] (2023). “Ground-truth" from field-based ecosystem mapping.
Karimi-Asli, Kaveh; Keetz, Lasse Torben; Lieungh, Eva; Yilmaz, Yeliz A.; Tang, Hui & Torma, Michal [Show all 7 contributors for this article] (2022). NorESMhub/noresm-land-sites-platform: Release v1.0.0.
Bryn, Anders; Dalen, Thea Grobstok; Finne, Eirik Aasmo; Heiberg, Hanne; Keetz, Lasse Torben & Nilsen, Irene Brox [Show all 30 contributors for this article] (2021). Natur i endring - samspillet mellom klima og økosystemene.
Keetz, Lasse Torben; Bryn, Anders; Horvath, Peter; Skarpaas, Olav; Tallaksen, Lena M. & Zliobaite, Indre (2021). Using machine learning to model the distribution of Vegetation Types across Norway.