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Job tips, internships & thesis suggestions

Here you will find job tips, thesis and project suggestions and internships, both with our own researchers and from outside the department.

Quick links:

In business, research or organisations outside the department

In research groups within the department

PhD and postdoc positions worldwide


In business, research or organisations outside the department


PhD position within Climb-Forest and new EU project coordinated from LU, in Göttingen, Germany

Deadline for applications is 2023-01-02. Please see attached PDF for more information.



 

MSc thesis: Konnektivitetsanalys av landskapet, Helsingborgs stad

The Helsingborg municipality is currently offering a thesis suggestion for a student in GIS and Physical Geography or Biology.

Read all about this thesis suggestion here (in Swedish)


Landskapets förändring i Båstad över tid

Note: this job requires Swedish language proficiency.

Projektbeskrivning – Landskapets förändring i Båstad över tid

Bakgrund

I Båstad ser man en utveckling mot att allt mer jordbruksmark tas i anspråk för bebyggelse. Detta gäller både i tätortsnära bebyggelseutveckling, inom detaljplan, och på landsbygden där det lämnas positiva förhandsbesked för ny bebyggelse.

Syfte

Att över tid se hur landskapet har förändrats och hur det kan komma att förändras i framtiden. Resultatet kan användas för information och utbildning av tjänstemän och förtroendevalda.

Examensarbetet kan ha flera olika utgångspunkter för att belysa problematiken; ekologiskt, ekonomiskt, näringsliv eller teknik.

Exempel på frågeställningar:

- Hur har landskapet förändrats sedan 1970-talet? Förändringar i förhållandet mellan öppen mark (åker- och betesmark), skog och bebyggelse. Landskapets fragmentering och dess påverkan på spridningskorridorer.

- Hur mycket bebyggelse ”tål” jordbruksmarken?

- Vilken påverkan har bebyggelsen haft på biologiskt mångfald och biodiversitet?

- Finns det alternativa utvecklingar och hur ser dessa alternativen ut?

- Är tillväxten bra för Sverige? Var går gränsen för hållbar tillväxt?

- Kan man använda GIS för att reglera bostadsbyggande på åkermark?

Tillgänglig data

Se separat excel-fil.

Kontaktpersoner

Båstads kommun:

Catharina Arehög, bygglovschef, 0431-774 90, g [at] bastad [dot] se">catharina.arehög [at] bastad [dot] se

Annika Jern, geodatachef, 0431-776 41, annika [dot] jern [at] bastad [dot] se ()

Lunds universitet:

Helena Elvén Eriksson, Institutionen för Naturgeografi och Ekosystemvetenskap (INES) & Lunds universitets GIS-centrum, 0707 49 50 67, helena [dot] elven_eriksson [at] nateko [dot] lu [dot] se ()

Karin Larsson, Institutionen för Naturgeografi och Ekosystemvetenskap (INES) & Lunds universitets GIS-centrum, 046-2224093, karin [dot] larsson [at] nateko [dot] lu [dot] se


EUGLOH internships

Internships are at all levels, but most at the master's and PhD level. Many are lab places but there are also other opportunities such internships at Paris-Saclay's international office in Paris.

Read more:
https://www.eugloh.eu/research/internships

If you are interested, please send an email to eugloh[at]er.lu.se


Internship/bywork as Forest data analyst at Katam Technologies

What: Katam Technologies is a team of ambitious startup entrepreneurs who want to revolutionize the forest industry through game breaking technology. They deliver high precision data to the forest sector supporting operational forestry decisions as well as long-term strategic decisions.

Forest data is collected using various tools such as smartphone, drones, and other data sources and transformed into valuable decision-making facts for our customers around the world. This leads to more optimal usage of the global forest land that benefits both society and the climate.

They are now looking for two internships to strengthen their forest data analysis team. For more information see PDF:

 


Jobb på Länsstyrelsen

Länsstyrelsen har ofta jobb, internships och examensarbeten som handlar om t.ex miljöövervakning, naturvård och GIS. Listan på lediga jobb finns här:

Jobba hos oss | Länsstyrelsen Skåne (lansstyrelsen.se)


Internships worldwide

Announced at the Lund University carreer pages


BSc internship or project work: GIS mapping at Ekologigruppen

Ekologigruppen is looking for someone to carry out erosion risk mapping over a suitable catchment area in Skåne according to the ecologist group's model both in Mapinfo and in ArcGIS and compare the results to see if the programs' algorithms differ significantly. Contact wosel [dot] thoresen [at] ekologigruppen [dot] se (Wösel) at Ekologigruppen.


Volunteer and internship opportunities for students of Geography and Environmental Science

Offered by GoEco, note that you may need some funding for their activities.

Volunteering for Students of Geography and Environmental Science


Internship in research groups abroad

Are you considering a career in research? As a current student at Lund University, you can do a research internship at one of the LERU universities that is part of the STREAM project programme, on a full-time basis, for anything from one month up to a whole semester. STREAM (STudent REseArch Mobility) is a project aiming to facilitate research exchange opportunities for current students at institutions within LERU (the League of European Research Universities).

Find out more about STREAM and how to proceed.


Thesis suggestions from Sustainalink (previously Miljöbron)

Sustainalink is a matchmaker between students and companies, check out their web page sv /en and take the chance to contact them with your cv.

Current projects at Sustainalink


Internships and student jobs in sustainability via Sustainergies

Sustainergies keeps an eye open for student opportunities at many interesting employers in Sweden. They also organise case study workshops and courses.
Sustainabilities student jobs page (in Swedish)


Swedish environmental protection agency has many openings for students

Internships, summer employments, and thesis suggestions. Many student opportunities in climate, communication, environmental protection, environmental law and more.

To the Naturvårdsverket jobs page (in Swedish)

 


In research groups within the department


Master Thesis Topic: Quantifying impacts of winter warming on Arctic tundra ecosystem carbon and nitrogen cycle

Background

With a warmer Arctic, woody plant species are moving north. The increased plant biomass can increase carbon (C) storage in parts of the Arctic and provide a possible negative feedback to Arctic warming. However, some areas have experienced vegetation damage, which offsets the higher C uptake. An increase in extreme winter warming (WW) events in the Arctic—periods of 5–10 days with above-freezing temperatures that cause the snow to melt and the upper soil to thaw, can cause damage to tundra vegetation, particularly to evergreen shrubs. We hypothesize that plant nitrogen (N) limitation is a key link between WW and damaged vegetation because WW events create spring-like climate conditions and trigger the release of mineral N – nitrate (NO3-) and ammonium (NH4+) – into soil solution. Although plants display some capacity to take up nitrogen during the winter, their response time is uncertain, and they may be poor competitors for the released nitrogen. Nitrate (NO3-) and ammonium (NH4+) can thus be lost in the environment or absorbed by microbes. As a result, less N is available for plants in the actual spring and summer, potentially causing plant damage and decreasing the C sink. Functional plant traits such as fine root depth or leaf C/N ratio differ from species to species and could impact plant N competitiveness in the growing season, but we do not know how they are affected by WW events.

Aims

Your task will be to figure out which plant functional trait that, as consequence of WW events, impedes plant N uptake and C storage the most. Thereby, which plant functional types (evergreen shrub, deciduous shrub, and moss?) that is the most sensitive to WW events in terms of its C storage.

Methods

In order to do that, you will model an Arctic tundra ecosystem in West Greenland. In the spring of 2022, we simulated an extreme winter warming event on a field-scale in Western Greenland and traced the thaw-released mineral N around the ecosystem. In the summer of 2022, we sampled the aboveground biomass of the plants which had been through a WW event and discovered that one of the plant functional types was more N limited after the winter thaw event than the others. Thus, plant functional type thus appears to be important for sensitivity to WW events. You will be in charge of adapting a process-oriented ecosystem model (CoupModel) to the West Greenland tundra heath using measurements of released N, where in the ecosystem WW-released N moves, soil properties, vegetation C and N content, and meteorological variables. A nearby tundra heath has been modelled in the CoupModel before, so you can start from the previous parameterizations and use the adapted setup to test the effect of vegetation type on how WW events impact plant C storage. How far you plan to go about the modelling is up to you, but a way to start could be to specify vegetation in the model to resemble three different plant types, which have different functional traits (rooting depth, C/N ratio, litter quality). You could then simulate a WW event on the three different plant functional types and use the model outputs to quantify, for each plant functional trait, the sensitivity towards and changes in the net C budget as a consequence of the measured impacts of WW events on N availability.

Supervisors

Wenxin Zhang (INES) and Laura Rasmussen (University of Gothenburg).

Wenxin and Laura

Wenxin is one of the leading authorities on the CoupModel, and Laura has a thorough knowledge of the Arctic ecosystem and had performed the field work.

Winter warming experiments

 
Three Master thesis topics focusing on modelling peatland carbon dynamics in a warmer world

(1) Modelling peatland carbon dynamics at different scales

Peatlands comprise 30% of the present-day soil organic carbon pool and are one of the biggest carbon reserves in terrestrial ecosystems [1]. They play an important role in the global carbon cycle, as a persistent long-term CO2 sink and a moderate source of methane (CH4). Only a few large-scale dynamic global vegetation models (DGVM) and land surface models (LSM) have incorporated peatland dynamics to hindcast Holocene peat carbon accumulation and used them to predict how these ecosystems will respond in future climate conditions. In this project, we will perform hindcast and future experiments with the established state-of-the-art peatland-vegetation model (LPJ-GUESS) [2, 3] at three peat-dominated regions under different climate-warming scenarios. The main aim of this project is to determine the fate of peatland carbon stocks in a warming world. To be specific, the project will (1) evaluate the model results using observed datasets from northern latitudes; (2) identify temporal and spatial patterns of peatland carbon sink in these regions; and (3) quantify the importance of climatic drivers that are responsible for modulating carbon fluxes. The overall purpose of this study is to minimize the uncertainty surrounding peatland carbon balance at different spatial and temporal scales.

(2) Managing peatlands to mitigate climate warming

Currently, the majority of global efforts are focused on keeping global warming to 1.5-2°C [4]. To achieve this goal, early and large-scale carbon emission reductions are required to keep the global temperature rise within safe limits and in line with the Paris Climate Agreement. Peatlands are important carbon sinks on land [5]  that have been playing a significant role in buffering the effects of climate change, and they will play an important role in supporting climate mitigation in the coming decades. If peatlands continue to sequester carbon in the future, their conservation will be a simple, inexpensive, and reliable mitigation option [6]. However, currently, damaged and disturbed peatlands are emitting at least two billion tons of carbon annually. In addition, unplanned management practices, together with ongoing warming, have turned many of the intact peatlands into major carbon sources due to higher soil respiration.Curbing emissions from both disturbed and intact peatlands requires planned and informed peatland management strategies [6,7].. Any unplanned activities related to drainage, rewetting, and land cover change will alter the prevailing balance of CO2‒CH4 fluxes and enhance carbon and methane emissions, which will potentially trigger positive peatland-generated climate feedback and thereby accelerate global warming. This project will investigate the role of peatlands in mitigating climate change and assess the wider impacts of climate change on peatland carbon stocks. Different modelling experiments will be performed to identify ideal sets of management practices for moderating CO2‒CH4 fluxes during the restoration of degraded peatlands and the conservation of intact peatlands. Initially, the focus will be on peatlands in Sweden, and later, the scope of the study will be expanded to include the entire Nordic region.

(3) Representation of tropical peatlands in vegetation models

Tropical peatlands cover 90 to 170 million ha and store approximately 152 to 288 Pg C [8]. They make up only 6% of global peatlands but contain 11-14% of the global peat carbon pool [9,10]. A wealth of information is available on the functioning of northern peatlands, but studies focusing on tropical peatlands have just started emerging in the last few decades [8,11,12]. Although peat accumulation in all types of peatlands largely follows the same underlying principles, there are multiple processes and mechanisms that are strikingly different between tropical and other types of peatlands. One unique characteristic of tropical peatlands is that they are primarily dominated by trees and are commonly known as peat swamp forests. These peatland types contain a high proportion of woody material in their litter pool, which is mainly derived from dead roots and branches. In addition, the microbial communities that thrive on these unique peat materials feature inherently different characteristics than typical northern peatland microbial communities. These factors lead to a distinct system response to external forcings and changes. Therefore, it is critical to include new formulations accounting for woody material and new parameterizations related to recalcitrant peat characteristics and tropical microbial communities in the current generation of peatland models. In this project, we will incorporate important tropical peat processes and parametrizations in the LPJ-GUESS Peatland. The main purpose of this project is to understand the current and future rates of carbon exchange in tropical peatland ecosystems at the regional level.

Supervisors: Nitin Chaudhary and Wenxin Zhang.

References:
[1] Chaudhary et al., (2017). Modelling Holocene peatland dynamics with an individual-based dynamic vegetation model. Biogeosciences, 14(10), 2571–2596
[2] Chaudhary et al., (2022). Modeling pan-Arctic peatland carbon dynamics under alternative warming scenarios. Geophysical Research Letters, 49, e2021GL095276. 
[3] Yu (2012).  Northern peatland carbon stocks and dynamics:  A review.  Biogeosciences, 9(10), 4071–4085.
[4] Friedlingstein P et al: Nat Geosci 2014, 7(10):709-715. 14. Goldstein A et al: Nat Clim Change 2020, 10(4):287-295
[5] Yu ZC: Biogeosciences 2012, 9(10):4071-4085. 43. Yu ZC et al: Geophys Res Lett 2010, 37.  
[6] Leifeld J et al: Nat Clim Change 2019, 9(12):945-947. 24. Loisel J et al: Holocene 2014, 24(9):1028-1042
[7] Goldstein A et al: Nat Clim Change 2020, 10(4):287-295. 15. Gorham E: Ecol Appl 1991, 1(2):182-195.
[8] Ribeiro et al., (2021). Tropical peatlands and their contribution to the global carbon cycle and climate change. Glob Change Biol., 27, 489– 505. 
[9] Page et al., (2011). Global and regional importance of the tropical peatland carbon pool. Global Change Biology, 17(2), 798– 818.
[10] Xu et al., (2018). PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. CATENA, 160, 134– 140.
[11] Dargie et al., (2017). Age, extent and carbon storage of the central Congo Basin peatland complex. Nature, 542(7639),
[12] Kelly et al., (2017). The vegetation history of an Amazonian domed peatland. Palaeogeography, Palaeoclimatology, Palaeoecology, 468, 129– 141. 


MSc thesis: Integration of satellite retrievals of CO2/CH4 in an atmospheric inverse model

Atmospheric inverse modeling is an approach that consists in estimating the emissions (or removal) of an atmospheric species (e.g. CO2 or CH4) into the atmosphere, given the observed impact of these emissions on the atmospheric composition (changes in the atmospheric concentration of the modelled species). The approach relies on an atmospheric transport model, which establishes the link between the emissions and the concentrations, and on a fitting algorithm, that adjusts the emissions in order to optimize the fit to observations.

The capacity of an inversion system to estimate emissions depends largely on the accuracy of the transport model, and on the quantity of observations that can be used to constrain the emissions. The objective of this master thesis project will be to implement the capacity to model satellite retrievals of CH4 and/or CO2 into the LUMIA inversion system. LUMIA is our in-house inversion system, and can currently use in-situ observations such as those from the ICOS network, but lacks the capacity to assimilate satellite retrievals.

Satellite retrievals are column-measurement (they measure the CO2/CH4 content integrated over the whole atmospheric column, and not at a specific point). As such, their representation in transport models is not totally straightforward. The first task in the project will therefore be the validation of the implementation (comparison with in-situ observations and with other models, diagnostic of potential biases, etc.). Depending on the student interests, the work can be extended by analyzing at specific features of the data (e.g. estimation of CO2 emissions from a large city), or by using the data in actual atmospheric inversions.

The project requires a basic understanding of meteorology, as well as good programming skills (python, at least, and ideally also Fortran).

Contact Guillaume Monteil for more information.


Master thesis proposal: Climate impact on spruce growth in Sweden

Climate change will influence both average conditions and the likelihood of extreme weather and climate events. Consequences of changes in temperature and precipitation regimes for forest ecosystems and tree growth is a key concern, as forests are both influenced by ongoing climate change and contribute to mitigation via carbon sequestration and substitution effects.

This suggested master thesis contributes to a project focusing on extreme-weather impact on Swedish Spruce forests. The task for the master thesis is to evaluate regional differences in relationship between year-to-year weather variability and tree ring growth using climate data and tree ring data from the National Forest Inventory. The Master student should be used to handle large datasets and should have some knowledge of programming, preferably in R.

Supervisors: Maj-Lena Finnander Linderson and Anna Maria Jönsson.
 


Master thesis propsal: Estimation of the CH4 emissions from the NordStream leakage

The CH4 emissions from the NordStream leakage have been observed at several ground-based ICOS atmospheric observation sites and it has been shown that the peaks in the CH4 observations are linked to the NordStream pipeline damage (see e.g. https://www.icos-cp.eu/event/1221).

Instead of using atmospheric tracer transport models in a forward mode (relating emissions to concentrations) one can also use atmospheric modelling in an inverse mode that relates observed greenhouse gas (GhG) concentrations to emissions (or removals) of GhGs.

In this project the student will use pre-calculated atmospheric backward footprints from an atmospheric transport model over the leakage period to estimate an emissions profile the best fits the observed concentrations at the ICOS sites. These so-called footprints are essentially providing an estimate of how the emissions from regions upstream of the observation sites infuence the greenhouse gas concentrations at the sites (one can also think of them as the sensitivity of the concentration at the observation site to previous emissions from a pre-defined domain). Using assumptions on the possible amount and time profiles of the leaked gas the student will create an ensemble of prior emissions profiles that, together with a simple optimisation algorithm, will be used to estimate an emissions profile that best fits the observed concentrations.

The project requires a basic understanding of meteorology, a good mathematical understanding, as well as good programming skills (python, at least, and ideally also Fortran).

Supervisors: Marko Scholze and Guillaume Monteil
 


Estimating the global biospheric 14C isotopic disequilibrium with LPJ

What: MSc thesis
When: Fall 2022 / Spring 2023

Thesis topic: Nuclear bomb tests in the 1950s and 1960s introduced a huge amount of radiocarbon (14C)-enriched carbon to the atmosphere. The ocean and the biosphere rapidly absorb this carbon. However, the biosphere slowly releases it back into the atmosphere through heterotrophic respiration. The isotopic disequilibrium is the difference between the radiocarbon content entering and leaving a carbon pool. It can be used to diagnose the turnover time of carbon in terrestrial carbon pools. It is also an important quantity for the estimation of fossil fuel emission from atmospheric inverse models.

In this thesis, you will estimate the global biospheric isotopic disequilibrium from 1901 to 2020 using the isotope-enabled LPJ model version developed by Scholze et al. (2003). You will also perform atmospheric transport simulations with the lagrangian model FLEXPART to compare the modeled concentrations with atmospheric ∆14C observations from the ICOS network and evaluate the feasibility of using the isotopic disequilibrium fluxes in atmospheric inverse modeling applications. You should have some programming skills in Python and FORTRAN.

Supervisors: Marko Scholze & Carlos Gomez


Master thesis propsal: Modelling vegetation dynamics and vegetation feedbacks under tropical climate extremes

Background: Terrestrial ecosystems play a key role in the global carbon cycle and climate. However, due to the complicated vegetation-climate interactions, both vegetation dynamics and climate are highly uncertain in the tropical regions and they have become one of the main challenges for further understanding the vegetation and climate dynamics in the future Anthropocene, in particular under climate extremes. Global warming along with the continuous changes in land use and land cover further complicate the overall picture. Will climate become more extreme if we consider the role of vegetation in the future climate?—a question remains largely unexplored. For addressing this question, we need a sophisticated research strategy to further understand the vegetation-climate relationships in the Earth system.

Master projects Modelling vegetation dynamics and vegetation feedbacks under tropical climate extremes

 
Aim: This degree project will allow you to explore the vegetation dynamics and vegetation feedback (e.g., how the ecosystems could affect climate) under tropical regions, which include but are not limited to Africa, South America (with a focus on the Amazonian region) and Australia. Special attention will be paid to vegetation responses and feedback under extreme climate events (e.g., droughts and heat waves).

Methods: You’ll have access to the interesting modelling data from the state-of-the-art coupled vegetation-climate Earth system models over the aforementioned tropical regions, with the assistance of multiple observational datasets (e.g., vegetation indices NDVI, EVI, EVI2, climate data from ERA5 and CRU) for exploring the research questions. The use of ecosystem model LPJ-GUESS and the coupled model RCA-GUESS is possible in a later stage.

Examples of research questions:

  1. Vegetation responses (e.g., LAI, GPP and ET) to the intensity and duration of climate extremes (e.g. Wu et al., 2022)
  2. Will vegetation feedback amplify or buffer climate extremes (e.g., heatwaves and droughts)? (e.g. Wu et al., 2016, 2021)

We are a supportive supervision team looking for independent students who are interested in modelling vegetation dynamics and terrestrial carbon cycles from an Earth science perspective. We expect you have good analytical skills using programming-based software such as Matlab or Python, which will be fundamental to the project tasks. You will have the opportunity to promote your research findings in a wide scientific community (e.g., SMHI, EGU, AGU).

Should you be interested in these challenging tasks in Earth sciences, please contact Dr. Minchao Wu and Prof. Ben Smith for more information.

References

  • Wu, M., Schurgers, G., Rummukainen, M., Smith, B., Samuelsson, P., Jansson, C., et al. (2016). Vegetation-climate feedbacks modulate rainfall patterns in Africa under future climate change. Earth System Dynamics, 7(3), 627–647.
  • Wu, M., Smith, B., Schurgers, G., Ahlström, A., & Rummukainen, M. (2021). Vegetation‐climate feedbacks enhance spatial heterogeneity of pan‐amazonian ecosystem states under climate change. Geophysical Research Letters, 48(8). https://doi.org/10.1029/2020gl092001
  • Wu, M., Manzoni, S., Vico, G., Bastos, A., de Vries, F. T., & Messori, G. (2022). Drought legacy in sub‐seasonal vegetation state and sensitivity to climate over the northern hemisphere. Geophysical Research Letters, 49(15). https://doi.org/10.1029/2022gl098700

Master thesis project: comparison and reconciliation of phenology estimation using LAI, GPP, SIF, and different vegetation indices

What: MSc thesis

When: 2023

Thesis topic: Vegetation phenology is the study of seasonal variation of vegetation growth and its interaction with climate. Remote sensing of vegetation phenology uses satellite-derived vegetation indices (NDVI, EVI, NIRv, PPI, etc.) or other biophysical variables (LAI, fAPAR, etc.), or GPP (gross primary production) and SIF (solar-induced fluorescence) data. These different variables may lead to different phenology estimates and, thus, different meanings of vegetation pheno-phase transitions or stages. How these estimates are interrelated, and their biophysical meaning needs to be investigated.

This thesis project aims to investigate vegetation phenology using various already-available satellite-derived vegetation products (LAI, VIs, GPP, SIF, etc.; platforms may include MODIS, SPOT vegetation, ProbaV, Sentinel-3, VIIRS, AVHRR, Sentinel-2, etc.) over small patches around target flux sites from FLUXNET. Sub-goals of the thesis project include but are not limited to investigations of

  1. How are phenological metrics estimated from different variables interrelated?
  2. What is the meaning of these phenology estimates? Are they inconsistent? If not, how to reconcile or evaluate these estimates?
  3. Investigation of possible spatial phenology variability
  4. Investigate possible temporal phenology variability
  5. Tentatively explain these variabilities and/or biological significance

Supervisors: Lanhui Wang and Hongxiao Jin. Contact us for more information.


Observe the phenology of European giant trees from satellites

What: MSc thesis

When: 2023

Thesis topic: Phenology is the study of the timing of life-cycle events at the population level, most often focusing on how they respond to climate change. It often uses long-term records and includes events such as flowering, leaf fall, hatching, and annual migration. Among them, the phenological response of trees to ongoing climate change has received central scientific attention because of its potentially significant and broad implications for the carbon cycle, water and energy balance, and biodiversity.

Traditional population-level phenology studies rely on long-term observations with human eyes, such as the large amounts of data recorded in the Pan European Phenology Project (PEP725), which are, however, labour intensive and costly to obtain and maintain. However, with the newly available high-resolution (10-m) vegetation phenology product based on Sentinel-2 satellites and locations of large amounts of giant trees in Europe mapped by citizen science, an observation-methodological revolution of tree phenology studies is in front of us.

This master thesis will use these available data to explore and demonstrate the possibility of studying the population-level phenology of giant trees in Europe with satellite data. In addition to this methodological advance, novel ecological questions, such as the phenological diversity and plasticity of trees within and between different species, can also be explored.

Please contact us if you are interested.

Supervisors: Lanhui WangLars Eklundh or Hongxiao Jin.


Monitoring crop phenology stages using different vegetation indices

What: MSc thesis

When: 2023

Thesis topic: The Lönnstorp Research Station has been carried out crop experiments for many decades and has accumulated rich field data. In recent years more near-surface remote sensing measurements (NDVI, digital camera, UAV, LAI-2000) are added to monitor crop growth.

Lönnstorp Research Station

 
In this master project, you will have the privilege to explore first-hand crop data and remote sensing data to estimate crop growth phenology, LAI, yield, etc. The sub-tasks include

  • Comparing PPI vs other indices for monitoring crop phenology;
  • Comparing PPI and field-measured LAI;
  • comparing near-surface remote sensing data with satellite measurements, e.g. Sentinel-2;
  • Estimation of crop biomass and yield using PPI.

Supervisors: El Houssaine Bouras and Hongxiao Jin. Contact us for more information.


Effects of solar-viewing geometry and soil background on estimation vegetation phenology/production using plant phenology index (PPI)

What: MSc thesis

When: 2023

Thesis topic: The plant phenology index (PPI) is a physically based vegetation index to gain a better representation of vegetation growth dynamics. This project aims to investigate how soil background and solar-viewing geometry affect PPI, and possibly to explore possible improvements in PPI formulation.

The thesis project will use

  1. Pro4sail radiative transfer model to simulate canopy reflectance under different conditions, so as to investigate solar angle, viewing direction, soil background influence PPI. Other vegetation indices (e. g. NDVI, EVI, and etc.) may also be compared.
  2. Field spectral measurement data from Sweden SITES Spec to investigate solar-viewing geometry impacts over daily or seasonal time series.

Soil spectral reflectance can be explored following the paper https://doi.org/10.1016/j.rse.2022.113182.

Supervisors: Hongxiao Jin and Lars Eklundh. Contact us for more information.


Estimation of vegetation growth using plant phenology index (PPI) over different land covers

What: MSc thesis

When: 2023

Thesis topic: The plant phenology index (PPI, Jin and Eklundh, 2014) is a physically based vegetation index proposed by the Earth observation research group of Lund University (https://www.nateko.lu.se/research/remote-sensing-and-earth-observation/…) using the solution to a radiative transfer equation, with simplified assumptions for soil background in red and near-infrared reflectance:

Equation

where DVI (= NIR - red) is the difference vegetation index, M is the temporal maximum DVI of a pixel/site, K is a factor calculated from solar diffuse fraction, and M is the temporal maximum DVI of a site or a pixel.

PPI has been shown to have superior performance in representing vegetation seasonal growth status over other indicators (see Tian et al. 2021). PPI is currently used as an indicator for vegetation productivity by European Environment Agency (EEA) using moderate resolution satellite data (MODIS) and by the Copernicus land monitoring services in high-resolution vegetation phenology and productivity (HR-VPP) product using Sentinel 2 data.

PPI is formulated as
Whereas thorough tests of PPI performance over different land covers, like desert with sparse or no vegetation, rainforest, croplands, grassland, etc. is necessary. PPI may eventually need further improvements in the formulation to achieve even better performance.

This thesis project aims to test PPI performance in the estimation of vegetation growth over different land cover types using either MODIS or Sentinel-2 top-of-canopy reflectance against flux tower-derived GPP data (such as ICOS or FLUXNET 2015) or LAI measurements, or any other possible ground truth available, and to explore the possible further improvement of PPI formulation to achieve better performance.

Supervisors: Hongxiao Jin and Lars Eklundh. Contact us for more information.


 

MSc thesis project: A spatio-temporal graph neural network framework for predicting usage efficiency of e-scooter sharing services

Shared e-scooter services, as one of the environment-friendly and sustainable transport modes,  have shown great potential to help relieve urban mobility issues, such as greenhouse gas (GHG) emission reduction in the transport sector, supporting public transport by dealing with first- and last-mile problems, etc. While e-scooter sharing services as a transport mode to enhance urban mobility, one main challenge is to improve the usage efficiency of the services since the usage demands are spatially and temporally unbalanced. Therefore, real-time forecasting of usage efficiency of e-scooter sharing services is required and beneficial for city planners and micro-mobility operators for the development of sustainable transportation.

Deep learning models provide an opportunity to achieve this research task. Motivated by this, the goal of the thesis is to develop a spatio-temporal graph neural network (STGNN) to predict the usage efficiency of e-scooter sharing services. Compared with traditional convolutional neural networks (CNN), STGNN will take into account spatial and temporal dependency to achieve higher prediction performance. E-scooter trip data will be used to evaluate the historical usage efficiency. If you are interested in geodata-driven urban studies using GeoAI techniques, this is for you!

The student is supposed to be familiar with machine learning /  deep learning. Python programming skills are required. A more specific introduction and data for the topic will be given by the supervisor upon request. 

Supervisor: Pengxiang Zhao


MSc thesis project: What dominates the increase in CO2 emissions from rivers on the Tibet Plateau?

When: Spring 2023

Introduction

The CO2 effluxes from rivers on the Tibet Plateau has been increasing from 1980s to 2010s (15.8±7.4 - 18.6±7.9 Tg/yr ) though the overall CO2 emissions in China has declined for the same period (Ran et al.,2021). Such increase was attributed to increased terrestrial deliveries of organic carbon and expanded surface area due to climate change(Ran et al.,2021). However, in the southwest of Tibet Plateau where the increase in CO2 emissions from rivers is evident and high, we found a significant decrease in the lateral export of dissolved organic carbon (DOC) from terrestrial ecosystems to rivers (Yan, unpublished). Given that the CO2 emissions from rivers on the Tibet Plateau are related with dissolved carbon (i.e., organic carbon and inorganic carbon)(Qu et al.,2017a), it raises several fun questions for us to think about:

  1. Riverine DOC might be quite biodegradable and thus can be decomposed rapidly before flowing downstream. The rapid decomposition of riverine DOC results in the increase in CO2 emissions and consequently the decline in riverine DOC export?
  2. The increase of CO2 emissions might result from the riverine dissolved inorganic carbon (DIC) since DIC concentration in the rivers on the Tibet Plateau is extremely high (on average, 30.7 mg/L)(Qu et al.,2017b), leading to supersaturation of CO2 in rivers and CO2 emissions to the atmosphere. In contrast, DOC concentration in rivers on the Tibet Plateau roughly ranges between 1-3 mg/L (Yan, unpublished).
  3. Both DOC and DIC in rivers contribute to the increase in CO2 emissions. But we don’t know yet how much they have contributed and how different the contributions vary in space and time, respectively.

To fill the knowledge gap mentioned above, it is crucial to get long time series of estimates of riverine DOC fluxes and DIC fluxes, and CO2 emissions based on which it is possible to source the drivers of the changes in CO2 emissions.  

Materials and methods

Most of the riverine measurements are collected already for the analysis. More measurements may be added if necessary. Modeling techniques e.g., machine learning methods, are required to estimate the carbon in rivers. 

Please contact us if you are interested. 

Contact: Yanzi Yan, Martin Berggren

References

  • Qu, B., Aho, K. S., Li, C., Kang, S., Sillanpää, M., Yan, F., & Raymond, P. A. (2017a). Greenhouse gases emissions in rivers of the Tibetan Plateau. Scientific Reports, 7(1), pp.16573. doi:10.1038/s41598-017-16552-6
  • Qu, B., Sillanpää, M., Kang, S., Yan, F., Li, Z., Zhang, H., & li, C. (2017b). Export of dissolved carbonaceous and nitrogenous substances in rivers of the “Water Tower of Asia”. Journal of Environmental Sciences, 65. doi:10.1016/j.jes.2017.04.001
  • Ran, L., Butman, D. E., Battin, T. J., Yang, X., Tian, M., Duvert, C., . . . Liu, S. (2021). Substantial decrease in CO2 emissions from Chinese inland waters due to global change. Nature communications, 12(1), pp.1-9. 

MSc thesis project: Capturing Carbon in Perennial Cropping Systems

Capturing Carbon in Perennial Cropping Systems (pdf, 100 kB)

Contact: Jonas Ardö (INES), Patrik Vestin (INES), Lennart Olsson (LUCSUS)


MSc thesis project: How different are old-growth primary forests from managed forests in Sweden?

Old mixed forest in autumn colours with beard lichen in the foreground.

Do you want to help us understand primary forests in Sweden (pristine forests)?

We have recently produced a digital (GIS) map of old-growth forests in Sweden and performed a nationwide inventory of vegetation and soils. One of the motivations behind this extensive data collection was to understand how human activity affects the forests, in terms of biomass and carbon storage, in terms of structure and biodiversity, etc. There are several opportunities to dig into our dataset for a thesis project. Two obvious topics would be measures of diversity, species or structural (i.e. tree sizes) and the influence of the largest trees for carbon storage. Students interested in these topics, or other related topics are welcome to contact me for potential thesis work.

Contact: Anders Ahlström


Master thesis proposal: Shrub encroachment classification on Öland using RGBI image data

Shrub encroachment

The Alvar on Öland is a world natural heritage with a very high biodiversity. However it is also a cultural site since the current high biodiversity vegetation can only sustain when managed in a proper way. Over the last decades shrub encroachment has been a major threat due to a decrease in grazing intensity.

The public administration is trying to optimize their effort and to clean the areas which are mot in need. In this thesis the student was using an automatic classification scheme, trying to use RGBI images to generate a map of shrub encroachment. However the map proved to have a rather low accurracy as well as computational issues only allowed to generate classifcations for some rather small areas. Two reasons were identified for not having a better classification.

One was that for an RGB(I) classification more validation sites would be needed and the second was that not all available data was used. The final goal of the work on Öland is not only to generate a shrub map for the Alvar now but also to be able to use earlier aerial photos to identify areas with high increase in shrub encroachment over time. Therefore in this thesis you are not only using RGBI data for the classification but also vegetation height data based on stereometric analyses of RGBI data provided by Lantmäteriet.

The first step is to generate a method to classify shrubs using RGBI+Digital surface model + ground height model. Once this is done this should be used as training and validation data to generate a classification method based only on RGBI for historical orthophotos.

This thesis includes a field trip to Öland.

Contact: Veiko Lehsten


Master thesis proposal: Using remote sensing to evaluate the effect of sports events on ground vegetation in the Swedish Mountains

Effect of sports events on ground vegetation

Medium to large scale sports events in the Swedish Mountains are becoming more and more common and the number of participants is rising steeply. These events are causing a new type of damage by trampling or cycling of the vegetation. In this thesis you are evaluating how well the effects of previous sports events can be monitored using remote sensing. You are using drone images (the flights can either be done by the student or can be arranged) and aerial photographs to evaluate the effect of the event on the vegetation. While this  thesis contains a number of field visits and vegetation evaluation (mainly estimation of bare ground), the majority of the work lies on the processing of remotely sensed data.

Contact: Veiko Lehsten

 


Master thesis proposal:  Using remote sensing to estimation of the use of plastic fields for the production of early potatoes. 

Estimation of the use of plastic fields for the production of early potatoes

The use of plastic in agricultural applications is a large problem since the plastic is often staying in the field or in the surrounding, or at least parts of it and it may take a very long time until it decomposes. Currently companies are developing alternatives for the plastic (sheets of organic origin which decompose easily) used to increase temperatures for early potatoes, however one of the hinder of market introduction is that there is no knowledge about how large the market for these products are. In this study you will use Sentinel data to make an assessment of how much plastic is used for the production of early potatoes in Sweden. You will develop an automatic classification method to perform the analysis at a country scale.

Contact: Veiko Lehsten


Master thesis proposal: Finding potential field sites of Dryas octopetala rings

Dryas octopetala rings

Dryas octopetala is a common dwarf shrub in the alpine areas of both Sweden and Switzerland. While it typically forms a complete cover of the area or cushions, in rare cases it grows in the shape of rings. As of now 3 sites are known where these rings were found, one on Svalbard and two in the Swiss national park. In all sites the special growth forms are found on an area with very small slopes, well above the tree line, and on a ground which is mainly dominated by pebbles. Also the total vegetation cover of the area is rather low (<20%). In this thesis you are supposed to generate maps of potential occurrences of this special growth form both for the Swiss Alps as well as for the Swedish fjäll area. The study is mainly focusing on the remote sensing and analysis of spatial data by using GIS, but if there is an interest, a field work part can be added in which some of the sites can be visited to evaluate the model performance.

Contact: Veiko Lehsten


MSc thesis project: Seasonal crop yield forecasting by combining Sentinel-1 and 2 data

When: Spring 2023

Accurate and reliable seasonal forecasts of crop yield at field and reginal scales are important decision support tool that help stakeholders such as farmers, commodity traders, and government officials to make strategic decisions.

Besides field surveys, crop yield forecasting is mainly carried out using empirical regression-based models between historical yield and in-season variables (agrometeorological, and /or remotely sensed data), or crop growth models based on biophysiological processes. The latter are able to describe crop growth and yield response to weather conditions, soil, and management practices and can provide good estimates of final crop yield when accurate values of input parameters and meteorological forcing data are available. However, the challenges of using these models for seasonal yield forecasting are related to the large number of input data that are difficult to provide accurately on a large scale (soil characteristics, sowing date, fertilization, irrigation...) as well as the uncertainty related to seasonal weather forecasts between the forecasting date and the harvest date. Given these limitations, the empirical regression-based models have been widely used to forecast crop yield over large areas. These models rely on the use of some selected variables (agrometeorological, and/or remotely sensed data) as independent variables to forecast crop yield.

Both optical and synthetic aperture radar (SAR) vegetation indices (VIs) have been extensively used for crop yield forecasting, e.g. NDVI and EVI. SAR data has advantages on the crop yield forecasting due to the independent on weather conditions and can supplement optical data under adverse weather conditions.

This thesis project aims to investigate the synergetic use of Sentinel-1 and 2 data through Multiple Linear Regression and machine learning algorithm to forecast crop yield as early as possible during the growing season. The sub-goals of this work are:

  1. to identify the optimal growth stage for which the selected VIs can be used for the forecasting of crop yield.
  2. to determine if the combination of sentinel 1 and 2 data will improve the forecasting of crop yield.
  3. to validate the estimation performance of developed models with in-situ measurement.

The student is supposed to have passed the remote sensing course NGEN08 and have good ability to work with geospatial data in GIS.

Contact: El Houssaine Bouras, Lars Eklundh


Student code-developer position

Part-time, alongside studies, up to 300 hours. Start as soon as possible.
 
Position for part-time code developer/user for a new benchmarking system that we are developing for vegetation models, such as LPJ-GUESS global vegetation model, to compare it to the latest observations. We want to add extra functionality in terms of the observations that the model is compared against and develop the system so that it is flexible to deal with multiple types of vegetation models. The benchmarking system is written in the R language. There may also be the requirement to run the LPJ-GUESS vegetation model. We are looking for someone with experience in coding (in R or another similar language) and interest in code development for scientific purposes.
 
To apply, please email  stefan [dot] olin [at] nateko [dot] lu [dot] se, including a CV and a paragraph describing your coding experience. Applications will be evaluated continuously until the position is filled.
 
Stefan, Thomas and Maj-Lena


Remote sensing of soil moisture in Sweden

What: MSc thesis
When: Fall 2022/spring 2023

Thesis topic

Soil moisture is an important of the Earth system. It plays an essential role in the exchange of water, energy and biogeochemical fluxes between the land surface and atmosphere.

In general, in-situ measurements of soil moisture are often limited in number of sites and available data length. Continuous efforts across the communities have been made to collect and share in-situ measurements. ICOS Carbon Portal (https://www.icos-cp.eu/) provides free and open access to all ICOS (Integrated Carbon Observation System) data. Several ICOS ecosystem stations across Sweden are collecting in-situ measurements of soil moisture. Meanwhile, many different satellite-based and model-simulated soil moisture products have been generated over the globe at varying spatial resolution. However, the accuracy of individual products would vary from regions to regions (e.g. Tavakol et al., 2019).

This Master thesis aims to evaluate multiple gridded soil moisture products against ICOS measurements in Sweden, and further to optimally merge multiple products to generate an improved dataset of soil moisture in Sweden. This thesis will involve processing large-size satellite data in different formats (e.g. NetCDF, HDF, GRIB), implementing merging algorithms,  and evaluating data and model results. This thesis is suitable for students with a good knowledge of programming (ideally in Python) and good skills in spatial analysis using GIS tools.

Supervisor: Zheng Duan. Contact him for for more information.


Modelling surface water temperature in lakes

What: MSc thesis
When: Fall 2022/spring 2023

Thesis topic

Lake surface water temperature (LSWT) is an important physical property of lakes. LSWT is a critical parameter for evaluating the water quality and biodiversity in lakes. It is also an indicator of climate change. Therefore, it is crucial to monitor LSWT to improve our understanding of the spatiotemporal dynamics of LSWT for many applications.

Conventionally and ideally, we can install in-situ gauge stations or monitoring sites with devices to measure surface water temperature in lakes, and these in-situ measurements are generally the most accurate. However, in-situ measurements in lakes are often sparse and limited in terms of spatial coverage (many lakes have no measurements) and temporal length (long-term continuous measurements are lacking). Thus, in-situ measurements cannot sufficiently capture the spatiotemporal dynamics of LSWT in lakes, particularly large lakes. Satellite remote sensing and lake models are two main methods to estimate LSWT, which can complement the limited in-situ measurements for monitoring spatiotemporal dynamics of LSWT.

This Master thesis aims to estimate/model lake surface water temperature in Lake Vänern, Sweden (other lakes could also be considered). Satellite products and classic lake models would be explored. This thesis will involve processing large-size satellite data, implementing models and evaluating data and model results. This thesis is suitable for students with a good knowledge of programming (ideally in Python) and good skills in spatial analysis using GIS tools.

Supervisor: Zheng Duan. Contact him for for more information.


MSc project: Spatiotemporal simulation of dust storm changes by integrating machine learning with cellular automata

Dust storm studies have been receiving notable attention over the past decades. Dust storm is a meteorological phenomenon that commonly occurs in arid and semi-arid regions. It can influence water cycle, climate, and vegetation, and also cause damage to human environment and health. Therefore, it is important and necessary to simulate dust storm changes to design dust storm control strategies.

Previous studies have indicated that dust storm occurrence and development are dependent on various driving factors, including surface conditions (e.g., land cover, soil properties, topographic factors, hydrological factors), meteorological factors (e.g., precipitation, temperature, humidity, wind), and human activities (e.g., agricultural practices, water management). In addition, cellular automata (CA), as a computational model represented by grids (or cells) with values, has been commonly used to model complex geographic processes and simulate geographic phenomena,  including urban sprawl, land use change, fire spread, desertification, flooding, etc. However, considering the complicated relationships between dust storm changes and the above-mentioned driving factors, conventional CA models have problems in defining transition rules and determining transition probabilities. Machine learning/deep learning methods provide an opportunity to model such complicated relationships.

In this context, the goal of the thesis is to develop an approach on spatiotemporal simulation of dust storm changes by integrating machine learning with cellular automata. The Middle East (ME) region is experiencing severe social-political-economic challenges, and is highly vulnerable to climate and human-induced environmental changes. The region is responsible for approximately 20-25% of the global dust emission. Hence, one region in the Middle East will be selected as a case study area.

Challenges to be addressed include:

  • The occurrence and development of dust storms will be modeled and simulated over time and space.
  • The strategies will be developed for the control and reduction of dust storms, e.g., developing green areas, or transporting water.

The student should be familiar with spatial data analysis and machine learning. Programming skills (Python or R) are beneficial. A more specific introduction to the topic will be given by the supervisors upon request. 

Supervisors: Ali Mansourian and Pengxiang Zhao


MSc project: Are shared e-scooters complementing or competing with public transport? A spatiotemporal perspective analysis

Shared e-scooters

As concerns about global warming and climate change have been growing over the past decades, shared micro-mobility systems have emerged as one of the alternative transportation modes that are environment-friendly and sustainable. In particular, the shared e-scooter systems have shown great potential to help relieve urban mobility issues, such as first- and last-mile problems, and greenhouse gas (GHG) emission reductions. Here, one open question is whether e-scooter sharing systems are complementing or competing with the existing public transport system.

With regards to the relationships between e-scooter sharing systems and public transport, on the one hand, shared e-scooters can complement the public transport system by improving accessibility to public transit. On the other hand, some short-distance trips by public transport can also be replaced by e-scooter sharing systems, which implies that shared e-scooters can also compete with public transport to some extent. Although there are already some studies that have explored the relationships between e-scooter sharing / bike-sharing systems and bus systems, little attention has been paid to the spatiotemporal dynamics of such relationships, and how they are influenced by the urban built environment. This research will be beneficial for planners and policymakers to better make evidence-based policies regarding the integration of shared e-scooter systems with public transport.

Motivated by this, the goal of the thesis is to explore and analyze the complicated relationships between shared e-scooters and public transport from a spatiotemporal perspective. Several cities in Sweden will be selected as study areas to support a comparison study. If you are interested in geodata-driven urban studies, this is for you.

The student should be familiar with spatial data processing, analysis, and mining. Programming skills (Python or R) are beneficial. A more specific introduction and data for the topic will be given by the supervisors upon request. 

Supervisors: Pengxiang Zhao and Ali Mansourian


Analysis of organic matter composition using fluorescence

What: MSc thesis
When: autumn 2022 and/or spring 2023

Optical characterizations of colored dissolved organic matter, including fluorescence Excitation-Emission Matrix analysis (EEM), offers a breadth of information about the source, bioavailability, diagenetic state and chemical composition of dissolved compounds in both soil and water. It is an established tool to study carbon dynamics in response to changes in vegetation, hydrology, cryology and other factors.

The aquatic biogeochemistry group at INES is currently generating more EEM samples than we have time to analyze using our “Aqualog” instrument. This brings a golden opportunity for Master’s projects to utilize these samples to address links between organic matter optical characteristics (EEM) and e.g. the cycling of organic carbon and nutrients.

Samples have been collected from natural streams, soils with different fire history, and experiments in soils, wetlands and coastal waters. There is up to ~1000 frozen/conserved or recently collected samples that can be analyzed and used for a wide range of thesis topics that relate to organic matter cycling or water quality.

Contact us to discuss possibilities.

Contact: Martin Berggren
 


Estimating biomass and phenology in African Rangelands using Earth Observation

Background: INES (Lars Eklundh and Jonas Ardö) recently got a contract for a project (RAMONA, staring in December 2021) with the European Space Agency (ESA). This project aims to develop and apply methodology to assess grazing resources in African Rangelands using (mainly) data from the Sentinels (Sentinel 1,2, and 3 in this case). The project includes assessment of rangeland type and extent, herbaceous biomass, biomass anomalies and rangeland phenology.

Thesis topics: As a party of this project we provide opportunity for 2-3 master thesis projects during 2022 (both spring an autumn) focusing on

  1. estimation of herbaceous rangeland biomass,
  2. assessment of rangeland phenology and
  3. quantification of biomass anomalies. 

In the document below you will find descriptions of these three topics from the original project plan. Based on information below we will define suitable topics, subtopics, study regions, study periods etc. and develop testable hypotheses, in cooperation with interested students.

When: Spring semester 2022 (potentially autumn semester 2022 as well).
Contact: Jonas Ardö, jonas [dot] ardo [at] nateko [dot] lu [dot] se, 072-2025028 or Lars Eklundh, Lars [dot] Eklundh [at] nateko [dot] lu [dot] se for more information


Bark Beetles Attack predictive risk mapping using machine learning

This master thesis topic is in line with a project related to monitoring and risk mapping of Bark Beetle attacks in Southern part of Sweden.

The aim of this master thesis is to model the association between Bark Beetle attacks with the environmental factors using Machin Learning. The contribution of the study is to study how spatial auto-correlation and spatial errors can be integrated into the ML modeling and if the new developed model increases the accuracy of Bark Beetle attack prediction.

The topic is suitable for students who like to research and learn more about Geo-data Science and Machin Learning. A good knowledge of Python programming is required for the implementation of the model, while available ML libraries will be used.

Contact Ali Mansourian if you are interested in the topic.

Supervisors: Ali Mansourian, Pengxiang Zhao


MSc project: Chanterelle GIS - where are they?

Background
Chanterelles are a common and popular edible fungus in many countries. In Sweden, it is perhaps the mushroom that is most sought after. Increasing the probability of finding chanterelles would be positive for many mushroom pickers.

Purpose
To investigate whether searching for chanterelles can be made more efficient using geodata and climate data.

Method
The first step is to characterize the environments (both in time and in space) where chanterelles grow, thrive and are picked through to collect data on existing "chanterelle places", theory from mushroom literature etc.

The second step is to use existing geodata (Lantmäteriet, skogsstyrelsen, SLU with several suppliers) and climate data (SMHI) to identify areas with an increased probability of finding chanterelles based on these geodata. This can be done with various overlay operations, fuzzy-GIS methods or deep learning (however, this requires large amounts of training data).

In the third step, Chanterelle-GIS performance is evaluated empirically through a number of standardized mushroom picking sessions, with and without the support of Chanterelle-GIS.

Read more:
https://cdnsciencepub.com/doi/abs/10.1139/b11-058
http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A295603&dswid=9562
http://svampguiden.com/rapporter/

When: Autumn 2022 (should include the mushroom season)
Contact: Jonas Ardö


MSc project: Spruce bark beetle - remote sensing and/or modelling

The hot drought in 2018 triggered a large spruce bark beetle outbreak in south Sweden. The outbreak has killed several million m2 of spruce trees and is still ongoing. We have recently started a project to develop methods to monitor spruce bark beetle attacks with Sentinel-2 data.

In the project we have work packages focusing on:

  1. studying risk factors for bark beetle outbreaks,
  2. detection of bark beetle attacked trees in Sentinel-2 data and
  3. modelling of bark beetle phenology.

We welcome master students to work in the project and have different suggestions for thesis topics. The thesis can be focusing on remote sensing, how can we detect bark beetle outbreaks in remote sensing data?

It is also possible to write a thesis directed towards modelling e.g. bark beetle phenology or with a focus on management strategies. A thesis can also be more related to GIS with a focus on risk factors for outbreaks.

Contact: Per-Ola Olsson


Master thesis: 3D city models - the "new" CityGML version 3.0

The number of 3D city models in the Worlds is growing and the most common standard for 3D city models is CityGML. A new version, CityGML 3.0 (Conceptual model) was recently approved as an official OGC Standard with several changes compared to CityGML 2.0 that opens up for interesting studies. Some changes are that the core-, building- and transportation modules are updated and that a module for versioning of city models is introduced. One major difference compared to CityGML 2.0 is the space concept that is used to "build" the objects in the city model. Spaces can be either physical spaces or logical spaces. For example the physical parts of a building (building, building parts) consists of physical spaces but there is also a possibility to divide the building into logical spaces based on e.g. ownership or function. One thing to study is if the logical spaces can be used to divide a building into different (logical) parts based on the function. This is something that will be tested in 2022 by Lantmäteriet for a new national specifications for buildings.
 
There are also other aspects of CityGML 3.0 that can be studied, related to transportation and versioning of city models.

Contact: Per-Ola Olsson
 


Will elevated CO2 help us feed the world?

BSc/MSc project
When: 2022

An increasing global population, combined with the effects of climate change, are expected to put extreme stress on the global food system over the coming decades. One piece of good news, amidst the bad, is that elevated atmospheric carbon dioxide concentrations have been shown in field experiments to increase crop yields substantially. A big challenge for scientists is to take this information from experiments and translate it to projections of yield change across whole regions or the globe. Over the last few years, we have developed a novel process-based crop model designed to do exactly this scaling. However, initial projections of future yield with this model appear extremely optimistic and to better understand if they are realistic, careful evaluation of the model against observations is required.

In this Master’s project you will work with the latest development version of the LPJ-GUESS crop model, running simulations of historical and potential future crop yields, evaluating them against observations and investigating the reasons behind the simulated strong increases in yield. You will receive training in running a state-of-the-art scientific model and in data processing. In the process you will also learn about the processes that govern how crop yield is likely to change in the future. Your research will contribute towards improving the LPJ-GUESS crop model, itself an international collaborative effort between institutions in Sweden and Germany, and which contributes to a range of international assessments on the future food system (Alexander et al., 2018Rabin et al., 2020).

Advanced programming skills are not a pre-requisite, as the LPJ-GUESS modelling can be done without changing the underlying C++ code (only requiring some basic commands in Linux), but ability to analyse datasets using a programming-based software such as Matlab or R will be fundamental to the project, as well as a basic understanding of the concepts of ecosystem modelling. Key references: Rosenzweig et al. (2014), Olin et al. (2015).

Contact: Thomas Pugh
 


Can we simulate the giant trees of the rainforest?

MSc project
When: Spring 2023

What: Tropical rainforests not only have an almost mystical allure, but also an enormous impact on the world's climate, taking up as much as a quarter of annual anthropogenic carbon emissions (Pan et al., 2011). Much of the carbon that they hold is stored within the trunks of the largest trees (Bastin et al., 2015). This means that being able to correctly simulate the range of tree sizes and types within the rainforest is fundamental to being able to predict how the ability of the forest to take up carbon might change in the future. The LPJ-GUESS vegetation model developed at INES is regularly used to make assessments of forest carbon uptake across the world (Pugh et al., 2019; Friedlingstein et al., 2020). We have updated LPJ-GUESS with a novel new scheme that allows it to simulate tree size structure within a forest in a simplified way. What is unclear is whether the updated model is actually able to successfully capture the dynamics and structure of real forests.

Your challenge in this project will be to use observations of forest structure from across the tropics to evaluate this new version of LPJ-GUESS and explore its behaviour. Sensitivity studies, changing the values of difficult-to-observe parameters, such as vulnerability to mortality, can be used to give insight into how differences in the strategies of tropical trees can lead to the complex size structures found in reality.

The project will contribute towards a wider development agenda working to improve our ability to simulate tree mortality (http://more.bham.ac.uk/treemort), linking with researchers across the world.

Advanced programming skills are not a pre-requisite, as the LPJ-GUESS modelling can be done without changing the underlying C++ code (only requiring some basic commands in Linux). But ability to analyse datasets using a programming-based software such as Matlab or R will be fundamental to the project, as well as a basic understanding of the concepts of ecosystem modelling.

Contact: Thomas PughDavid Wårlind
 


How do forests respond to drought? 

BSc/MSc project
When: 2022

What: When trees die, they stop absorbing carbon. Tree mortality is increasing and linked in parts to droughts, driven by climate change (Hartmann et al., 2018).

With more trees dying, less carbon will be absorbed, exacerbating climate change impacts (such as droughts!) - and more trees will likely die.
More accurate simulations on forest responses to drought, are urgently needed to decrease uncertainty in climate change projections. However, key questions on mechanisms that govern tree death remain unknown to ecosystem modellers.
Until now, most models have lacked of one of the crucial processes that govern tree mortality - failure of the tree’s hydraulic system.

Our research group has addressed this issue and recently added this crucial process into the state-of-the-art LPJ-GUESS model. However we don’t yet know whether the model is able to provide improved representations of larger scale key ecosystem processes such as primary productivity and evapotranspiration.

We need someone to evaluate the new model against observations. Your role in this project will be to use observations from eddy-covariance flux towers in the FLUXNET network to evaluate this new version of LPJ-GUESS and explore its behaviour. You will be able to explore how different functional strategies employed by trees affect the function of the forest. There is flexibility to focus on a world region of your choice, dependent on the availability of suitable FLUXNET data. 

The project will contribute towards a wider development agenda working to improve our ability to simulate tree mortality (http://more.bham.ac.uk/treemort), linking with researchers across the world .Advanced programming skills are not a pre-requisite, as the LPJ-GUESS modelling can be done without changing the underlying C++ code (only requiring some basic commands in Linux). But ability to analyse datasets using a programming-based software such as Matlab or R will be fundamental to the project, as well as a basic understanding of the concepts of ecosystem modelling.

Contact: Thomas Pugh, Annemarie Eckes-Shephard
 


Co-design your own thesis project on forests using modelling or data analysis?

BSc/MSc project
When: Anytime

What: We have available a range of possibilities for projects related to understanding the form and function of the world’s forests, from local to global scale. Possible techniques include:

Contact: Thomas Pugh to discuss your interests and match to possibilities.
 


Monitoring clear cuts of old forests

What: Preliminary analysis suggests that a large share of forest harvest (clear cuts) in Sweden is harvest of old forest stands that have never been clear cut before. There is however no official data on what types of forests are being cut, but we do know the location of past cuts for the last 10 years and planned cuts for each coming year. We have made the first steps towards a homepage that indicate when panned cuts are cuts of old forests, but our estimates are surrounded by large and unquantified uncertainties.

Our ambition is to create a high-quality webpage that tracks planned cuts of old forests that store much carbon and may have high biodiversity and recreational values. Our current estimate is based on a forest age map from year 2010, but it has not been evaluated and the uncertainties increase with forest age (i.e. it is difficult to distinguish an 80 year old forest from a forest that is 200 years old).

There are multiple projects that may contribute to this goal, ranging from technical and visual developments of the webpage to scientific predictions of forest age and diversity. Multiple datasets are available on forest type, species composition, tree height from airborne laser scanning, and around 100 thousand ground plots. There are also a multitude of other maps available.

To summarize, there are multiple projects available for driven and independent students that want to contribute to the project. These include:

  1. Technical implementations that may involve automatic updates of the map or adaptations for users to find potentially old forests that are reported to be cut.
  2. Developments of the visual appearance of the homepage.
  3. Evaluation and corrections of the existing forest age map using plot data.
  4. Creation of a new forest age map and/or an index indicating the age and values of forests marked for harvest.

We are in the beginning of this work, and other ideas are welcome.

Please contact me if you are interested.

Contact: Anders Ahlström
 


Master thesis project - Remote sensing of trends in pristine forests in Western Russia

Pristine forests are rare in boreal Eurasia and it is debated if they sequester carbon or if they are in equilibrium with no net growth. One way to investigate that is to use remote sensing to investigate trends in vegetation indices from the 1980s until present. We have done this for Swedish pristine forests and found strong trends, in this project a similar study would be done for western Russia.

We have a copy of the original GIS files used for the map and the report (http://old.forest.ru/eng/publications/intact/). It was first produced around the turn of the century but it isn’t fully clear what is included and what definitions were used. The first part of this project would be to read up on the production of the map and compare to other available datasets on intact forests. Most information is probably given in the publications for which the map was used. When that is done trends can be extracted from e.g. Google Earth engine to investigate if these forests has become greener and if productivity has increased.

The project is suited for a driven student with very good skills in GIS and some programming.

Please contact me if you are interested.

Contact: Anders Ahlström

 


Master thesis suggestion: Developing methods for linking precipitation time-series data in hydrological models

When: 2021

What: If you have good programing skills, and want to try solving one of the hottest problems in hydrological modelling then maybe this is a good opportunity for you.

Rainfall-Runoff hydrological models are very important tools for accurate prediction of the time and location of floods and droughts as well as spatiotemporal variation of water quality and other hydrologically factors that are crucial for our life and the environment. Moreover, a good Rainfall-Runoff model helps saving the environment but also reducing the costs. All Rainfall-Runoff hydrological models (e.g. HYPE, SWAT, MIKE basin, etc.) need precipitation observations as input data to run and produce results. One of the main problems in catchments modeling is the method used for linking model forcing climate data (precipitation & Temperature timeseries data) to sub catchments. HYPE (Hydrological Predictions for the Environment) is a widely used hydrological model developed at the Swedish Metrological and Hydrological Institute (SMHI) is to be used in this research project (https://www.smhi.se/en/research/research-departments/hydrology/hype-our-hydrological-model-1.7994).

The main aim of this research project is therefore to test new methods utilizing e.g. time series analysis and machine learning methods) for linking precipitation observations to sub-catchments in hydrological models. The developed methodology may improve the performance of all Rainfall-Runoff hydrological models, which will lead to more accurate flood forecasting, drought analysis, water quality assessments, as well as all other modeling results.

References

Arheimer, Berit ; Pimentel, Rafael ; Isberg, Kristina ; Crochemore, Louise ; Andersson, Jafet ; Hasan, Abdulghani ; Pineda, Luis. / Global catchment modelling usingWorld-Wide HYPE (WWH), open data, and stepwise parameter estimation. In: Hydrology and Earth System Sciences. 2020 ; Vol. 24, No. 2. pp. 535-559.

Contact: Abdulghani Hasan (abdulghani [dot] hasan [at] nateko [dot] lu [dot] se), Sadegh Jamali (sadegh [dot] jamali [at] tft [dot] lth [dot] se)


Master thesis suggestion: Analyzing urban trees in Helsingborg with Sentinel-2 satellite data

When: as soon as possible

What: Spectral remote sensing can be used to discriminate between different types of forest. The optical characteristics of trees are determined by properties such as the canopy arrangement, and the sizes, shapes and internal structures of their leaves. Many of these properties vary between tree species, which makes it possible to map different tree species with remotely sensed spectral data. However, because many tree species have relatively similar optical characteristics, there is a high risk of confusing species, particularly if only one remotely sensed image is used.
Tree species also differ in their phenological characteristics, such as the dates of bud burst in the spring, and the change in coloration and the dropping of leaves during autumn. These phenological characteristics of trees influence their physical appearance, and can be monitored with the help of earth observation data, if the data has short revisiting intervals such as those acquired by the Copernicus Sentinel-2 satellite mission.

In order to compare the spectral reflectance of different tree species, field-collected validation data are needed. A common limitation in remote sensing studies is a lack of high quality validation data. Luckily, some cities regularly make inventories of trees in parks, along streets, and other urban areas. In Helsingborg, the municipality have stored data at the level of individual trees, including information on the species identity of several thousands of trees, and in some cases also information on the tree height and crown width. Can these data be used to increase our knowledge about the spectral characteristics of different tree species, or to monitor changes in the status of individual trees over time? 

Contact: Oskar Löfgren
 


MSc Project: Breakpoints in global vegetation productivity during recent decades

Gross primary production, the uptake of CO2 by the vegetation, is an essential climate variable for monitoring of the terrestrial biosphere and its response to climate change. We recently produced a novel satellite based product on global-scale gross primary production of the vegetation 1982-2015. However, the dynamics of this data set have not yet been fully analyzed. Recent advances in time-series and breakpoint analysis open new possibilities, as they allow for the detection of nonlinear trends and turning points in these data. Hence, the overarching objective of this thesis is to apply novel time-series analysis to study dynamics and possible reasons for trends and breakpoints in the vegetation productivity 1982-2015. 
Contact: Torbern Tagesson
 


M.Sc. Project: Linked geospatial data

When: Spring 2022

Semantic web techniques and linked data (knowledge graphs) are increasingly used in the geospatial domain and are interesting for e.g. the next generation of spatial data infrastructure. At the department, we have conducted some research in linked geospatial data and are open for master thesis in the topic. The thesis could either be devoted technical issues (ontologies, RDF stores, etc.) or have a more applied focus.

Contact: Lars Harrie

 

Geo spatial linked data schematic illustration

 

 


Fragmentation of snow in collisions with graupel/hail observed in a lab experiment

 

The ice phase of storms is the cause of many aspects of severe weather. Freezing rain, hail and lightning from ice in clouds together cause many billions of euros of damage per year in Europe and USA. Yet the processes of ice initiation in storm clouds are still uncertain. There is evidence from aircraft observations that fragmentation of ice somehow can generate most of the ice particles in such clouds. Thus, lab experiments are required to quantify some mechanisms of fragmentation.

The prospective student will perform an experiment in our laboratory at INES to characterise one such mechanism. The student will create apparatus to observe collisions between a graupel particle grown by riming and a snow particle from by accretion of crystals using a cold box in an open-top freezer. Video imagery will be analysed computationally so as to count the numbers of fragments from snow-graupel collisions in the cold-box at various temperatures. An existing theoretical formulation will be adapted to fit the results (e.g. for application in a numerical model of clouds). The student should have an interest in the physics of materials and have experience in programming and in practical experimentation.

Contact: Vaughan Phillips
 


MSc Project: Generating a state-of-the-art satellite product of vegetation productivity

 

Given the importance of gross primary production (GPP) for monitoring of the terrestrial biosphere and its response to climate change, a consortium earth observation-based GPP products is of high societal relevance. Today, the publicly available satellite based products derives from just a few satellite sensors. In order for spatially explicit GPP to be properly assessed, it is extremely important that monitoring products are independent and based on different input data. Sentinel is a series of newly launched satellites having global coverage of reflectance in high spatial and spectral resolution, but there are still no GPP products generated from the Sentinel data. The overarching objective of this thesis is to combine information from the Sentinel-3 satellite with light use efficiency estimates from a dynamic global vegetation model to generate a state-of-the-art satellite product of GPP. The product will thereafter be evaluated against ground observations.


Contact: Torbern Tagesson


M.Sc. project: Impact of ecosystem properties of semi-arid savannas on dynamics in ground surface reflectance

The Dahra field site in Senegal, West Africa, was established as an in-situ research site to improve our knowledge regarding properties of semi-arid savanna ecosystems and their responses to climatic and environmental changes. A strong focus of the instrumental setup is to gain insight into the relationships between ground surface reflectance and savanna ecosystem properties for earth observation upscaling purposes using satellite data. This master thesis will make use of a unique in-situ data set of reflectance between 350-1800 nm and investigate how ecosystem properties of semi-arid savanna ecosystems influence the reflectance from the ground surface.


Contact: Torbern Tagesson


M.Sc. Project: Modelling long-term carbon dynamics for High-Arctic dry and wet ecosystems


The ongoing rapid warming over the Arctic has caused dramatic changes in environmental conditions (e.g. snow cover thickness and duration, active layer depth and soil moisture and temperature) and ecosystem dynamics. The carbon cycle in dry and wet ecosystems may respond to climatic warming in contrasting ways.

This project aims to elucidate the long-term dynamics of CO2 fluxes for the neighboring High-Arctic dry and wet ecosystems respectively and identify their biotic and abiotic drivers. The results will improve our understanding or prediction on how dry and wet Arctic ecosystems contribute to greenhouse gas emissions due to the present or future climate change.

The main research work will be based on an integration of a process-oriented model and fluxes measurement data sets (the method is similar to Zhang et al., 2018). 

This project should not necessarily require the candidate to have advanced programming skills, but the candidate should know about ecosystem modelling and be able to make statistical analysis and plotting using Matlab or R or other similar tools.


Contact: Wenxin Zhang


M.Sc. Project: Cartography and text setting to promote urban walking

When: Spring 2022

High quality maps are indispensable for wayfinding in e.g. urban environment. Therefore, main cities (such as London in the example below) are setting up maps within the city to inform citizens and tourists with navigation information to promote urban walking. To create this high quality maps there is a need for better methods to automate the cartographic work, especially in text setting. So, if you are interested in promoting urban walk in London, NY, Tokyo and other main cities this is a master thesis for you. You should ideally be a geomatics student or LTH student or LUMA student with interest in maps and programming. The thesis is a cooperation with the company T-kartor with its head office situated in Kristianstad, Skåne, but with activities all around the world.

Contact: Lars Harrie

 

M.Sc. Project: Cartography and text setting to promote urban walking.png


 


M.Sc. Project: Building and city models

When: Spring 2022

There is an increasing amount of 3D building models (BIM) and 3D city models. There are currently several research and innovation projects of the usage of these models in the digitalization of planning and building processes in the society, concentrating on issues such as automation of building permits, 3D cadaster issues and environmental modeling. The department is involved in several of those projects (many as part of the Smart Built Environment program – see http://www.smartbuilt.se/). We are open for M.SC. projects in several topics concerning the usage of BIM and city models connected to our projects.

Contact: Lars Harrie

 

 

Building and city models illustration

 


 

Internship at Hyltemossa and Norunda Research Station (http://www.icos-sweden.se).

The Internship offers the opportunity to learn about greenhouse gases measurements within ICOS but also to get involved within field work of the various projects running at each of the Stations. The internship is organized in the course NGEA51 (https://www.nateko.lu.se/education/courses-basic/physical-geography-and…), timing of the course is flexible and can also be taken over the summer. Even though it is a Bachelor level course it can be taken during the Master as well, especially as the internship offers the opportunity to produce a data set that can later be analyzed within a thesis. 
For further information contact the respective Station personnel:

Hyltemossa: michal [dot] heliasz [at] cec [dot] lu [dot] se (Michal Heliasz)

Norunda: Meelis Mölder


Master thesis work: Carbon storage and ecosystem functioning in old-growth forests

 

Do you want to help us understand old-growth forests in Sweden (gammelskogar)? This project is suitable on for both MSc and BSc level. It can start at any time.

We have recently produced a digital (GIS) map of old-growth forests in Sweden. In a larger long term project we aim to investigate if old-growth forests store more carbon than production forests do. In the suggested project the student would perform carbon inventory in a single or a few old-growth forests and in the surrounding managed ecosystems (forest plantations, pastures or croplands). The work would be performed in collaboration with a PhD student. Several old-growth forests are located in Skåne and in Southern Sweden, but there are sites all over the country. 
Because there are multiple forests there are multiple projects, and two or more students could collaborate. Because the project involves field work the ideal candidate(s) would be willing to organize and perform field trips and measurements. The project also involves analysis of collected data and existing inventory data.

We also welcome other ideas that are related to this topic.

Please contact Anders Ahlström if you are interested.

 

Carbon storage and ecosystem functioning in old-growth forests


 


Master thesis work: Detection and analysis of e large scale ecosystem functional relationships

 

Detection and analysis of e large scale ecosystem functional relationships

Functional relationships are powerful tools to analyze large datasets, from dynamic models and from upscaled observations. The aim of the project would be to find new regions where a functional relationship between environmental drivers and ecosystem response can be found. Suitable regions vary mainly along a single environmental variable, such as water availability or temperature, but not both. That way the ecosystem response to a single environmental driver can be analyzed.

The proposed workflow involves scientific programming to identify potential regions, regions that mainly vary in one variable across space. The second step would be to compare the functional relationships of empirical datasets on ecosystem carbon cycle (GPP, NPP, biomass, soil carbon etc) as well as outputs from climate models and ecosystem models. The project therefore involves handling of large datasets, something that is common in global analysis and climate change research.

The ideal candidate has some knowledge in scientific programming and spatial data.

For more information see our previous publication on a functional relationship in Amazonia (https://www.nature.com/articles/s41467-017-00306-z)

Please contact Anders Ahlström for more information.

 


Master thesis work: Map and analyze pristine ecosystems in various regions

Map and analyze pristine ecosystems in various regions

   

Map and analyze pristine ecosystems in various regions

This project is suitable on for both MSc and BSc level. The project also works for E-learning program theses. Any semester, there are multiple projects that can start at any time.
There are still large uncertainties on how land use, such as forestry and agriculture affects the carbon cycle and biodiversity. A larger long term project at the department aims to further our knowledge on this by contrasting pristine ecosystems (ecosystems that are more or less natural, such as old growth forests) to surrounding ecosystems under land use. Right now we have produced a digital map (GIS map) of old growth forests in Sweden, but we are also interested in expanding the map to more countries. Data also exists for European Russia, but we have not analyzed it.
This project can therefore take many shapes, e.g.;

  1. Produce a map for an unmapped region. This can be done by collecting information from known sources, digitizing older maps or using remote sensing information such as aerial or satellite images to digitize known forests. Our long term aim is to build a global map, you would contribute to this.
  2. Analyze our already mapped old-growth forests in Sweden. There are several questions here; (a) if old-growth forests differ in terms of their topography or soil characteristics from surrounding ecosystems. This is important to know when performing paired analysis, if the old growth forest has been left unused because it is a mountain or a wetland, then the paired analysis of that forest will need to be modified. This mainly involves GIS analysis. (b) Use forest inventory data or remote sensing data to compare carbon storage between the old growth forests and surrounding ecosystems under land use.
  3. Perform the first analyses on old growth forests in European Russia, all of the above types analysis can be performed.
  4. A topic of your own that uses our new old growth forest map.

The student will work with our project on these questions. There are many things to do, the projects can be varied to focus on carbon cycle, ecology or technical GIS / remote sensing aspects.
The scope of the project and analysis can be adjusted to the type of thesis (bachelor or master) and it is also possible to divide the work between multiple students. The project is not limited to the allocated time allocated for the thesis; it could start earlier and continue after. Multiple students are also welcome, working in groups or focusing on different regions.

Please email Anders Ahlström if you are interested.

 


Charge separation in ice-ice collisions in the absence of liquid:  simulations of published lab experiments


When: any time

What: Lightning in storms is caused by electric fields arising from charge separation in re-bounding ice-ice collisions. The project aims at evaluating laboratory observations of charge separation in ice-ice collisions in the absence of liquid. This is a weaker form of charging than that usually studied but may be significant owing to the prevalence of ice-only cloud in storms.

The prospective student will simulate the ice crystal growth and collisions in a specific experiment observing charging that was performed years ago and described in a published paper in 2016. The student will create a simple model to replicate the experiment and then do variational analysis to deduce a formulation for the charge separation that can be applied in cloud models. The student should have an interest in computer modelling and programming (Fortran or C) and should be familiar with a LINUX-based operating system.

Contact: Vaughan Phillips

 

 


PhD and postdoc positions worldwide

 

 

  • Ph D position. The newly established Plant Ecology Modelling group in the Department of Botany at Trinity College Dublin invites for applications for a fully-funded 4-year PhD project. The project aims to address this challenge by developing a novel model representation of the physiology and ecology of herbaceous plants. Read more

 

  • Postdoc position in modelling plant-environment and soil-plant interactions (deadline for applications Sep 15th, 2022). We are looking for candidates interested in developing, evaluating, and applying mathematical models based on ecological and ecophysiological mechanisms to explore the tradeoffs between sustainability and reduced vulnerability to climate change, as part of an exciting project assessing the potential of perennial grain crops. Read more and apply for the postdoc position here.
     

 

  • PhD position in crop production adaptations to climate change (deadline for applications Aug 22nd, 2022). We are looking for candidates interested in digging into a wide range of land-use, crop yield, and climatic data and quantifying how climatic conditions and crop and landscape diversity affect the reliability of crop yields in the face of more variable and extreme climatic conditions, via statistical and stochastic models. This is a fully funded 4-yr PhD project. Read more and apply for the PhD position here.

 


 

 

Want to add or remove suggestions?