Mitgliedschaften und Aktivitäten in Gremien
Projekte
- Dist-KISS – Distance-Keeping: Influence of the StreetScape ( – )
Gefördertes Einzelprojekt: VolkswagenStiftung - Corona Crisis and Beyond – Perspectives for Science, Scholarship and Society | Förderkennzeichen: 99 714 - Future land-use-change induced subsidence in the Mekong delta (seit )
Eigenmittelprojekt - LCS – Land Change Science Education ( – )
Gefördertes Einzelprojekt: Universität Utrecht - FAPESP SPRINT 2016/50495-4 – Land use change impacts of increased bioenergy demand in Brazil ( – )
Eigenmittelprojekt
- Dist-KISS – Distance-Keeping: Influence of the StreetScape ( – )
Publikationen
Auswahl
- 10.1016/j.ecolind.2019.04.053. . ‘Recent and projected impacts of land use and land cover changes on carbon stocks and biodiversity in East Kalimantan, Indonesia.’ Ecological Indicators 103: 563–575. doi:
- 10.1016/j.cities.2019.01.006. . ‘A computational approach to The Image of the City.’ Cities 89: 14–25. doi:
- 10.1016/j.envsoft.2017.08.006. . ‘How a Pareto frontier complements scenario projections in land use change impact assessment.’ Environmental Modelling & Software 97: 287–302. doi:
- 10.1111/gcbb.12270. . ‘What can and can't we say about indirect land-use change in Brazil using an integrated economic - land-use change model?’ GCB Bioenergy 8, Nr. 3: 561–578. doi:
- 10.1016/j.envsoft.2015.02.013. . ‘Detecting systemic change in a land use system by Bayesian data assimilation.’ Environmental Modelling and Software 75, Nr. null: 424–438. doi:
- 10.1016/j.envsoft.2013.11.009. . ‘Identifying a land use change cellular automaton by Bayesian data assimilation.’ Environmental Modelling and Software 53, Nr. null: 121–136. doi:
- 10.1016/j.compenvurbsys.2011.08.003. . ‘Spatio-temporal uncertainty in Spatial Decision Support Systems: A case study of changing land availability for bioenergy crops in Mozambique.’ Computers, Environment and Urban Systems 36, Nr. 1: 30–42. doi:
Gesamtliste
- . . ‘Quantifying uncertainty in Pareto fronts arising from spatial data.’ Environmental Modelling and Software 141, Nr. 105069. doi: 10.1016/j.envsoft.2021.105069.
- 10.1016/j.compenvurbsys.2020.101573. . ‘Modelling the effect of landmarks on pedestrian dynamics in urban environments.’ Computers, Environment and Urban Systems 86: 101573. doi:
- 10.1371/journal.pone.0244099. . ‘Perception of urban subdivisions in pedestrian movement simulation.’ PloS one 15, Nr. 12: e0244099. doi:
- 10.1088/1748-9326/abb428. . ‘Brazilian Amazon indigenous peoples threatened by mining bill.’ Environmental Research Letters 15, Nr. 10. doi:
- 10.3390/land9020052. . ‘Quantifying the Effect of Land Use Change Model Coupling.’ Land 9, Nr. 52. doi:
- 10.3390/rs12030411. . ‘Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner.’ Remote Sensing 12, Nr. 3: 411. doi:
- 10.1038/s43016-020-0062-5. . ‘Spatial Optimization - The local versus global food debate.’ Nature Food 1: 198–199. doi:
- 10.1088/1748-9326/ab5aab. . ‘Global ecosystem service values in climate class transitions.’ Environmental Research Letters 15, Nr. 2. doi:
- 10.4230/LIPIcs.GIScience.2021.I.0. (Eds.): . 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. : Dagstuhl Publishing. doi:
- In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 705–7011. doi: 10.5194/isprs-archives-XLIII-B3-2020-705-2020. . ‘Exploring NASA's harmonized Landsat and Sentinel-2 (HLS) dataset to monitor deforestation in the Amazon rainforest.’
- In 14th International Conference on Spatial Information Theory (COSIT 2019), edited by , 5:1 – 5:8. Dagstuhl, Germany: Dagstuhl Publishing. doi: 10.4230/LIPIcs.COSIT.2019.5. . ‘Route Choice Through Regions by Pedestrian Agents.’
- Contributed to the Spatial Data Science symposium, Santa Barbara, USA. . ‘A plea for statistical analyses of geosimulation model projections.’
- 10.1016/j.ecolind.2019.04.053. . ‘Recent and projected impacts of land use and land cover changes on carbon stocks and biodiversity in East Kalimantan, Indonesia.’ Ecological Indicators 103: 563–575. doi:
- 10.1016/j.cities.2019.01.006. . ‘A computational approach to The Image of the City.’ Cities 89: 14–25. doi:
- 10.1111/gcbb.12594. . ‘Exploring the emergence of a biojet fuel supply chain in Brazil: an agent‐based modeling approach.’ Global Change Biology Bioenergy 00: 1–18. doi:
- 10.1111/gcbb.12534. [online first] . ‘Mapping land use changes resulting from biofuel production and the effect of mitigation measures.’ Global Change Biology Bioenergy x. doi:
- Contributed to the The 21st AGILE Conference on Geographic Information Science, Lund, Sweden. . ‘Mental representation of space in Agent Based Models for pedestrian movement in urban environments: A conceptual model.’
- 10.1016/j.enpol.2018.09.015. . ‘Exploring policy options to spur the expansion of ethanol production and consumption in Brazil: An agent-based modeling approach.’ Energy Policy 123: 619–641. doi:
- 10.3390/land7030108. . ‘Analyses of Land Cover Change Trajectories Leading to Tropical Forest Loss: Illustrated for the West Kutai and Mahakam Ulu Districts, East Kalimantan, Indonesia.’ Land 7, Nr. 3. doi:
- contributed to the The 9th Ecosystem Services Partnership (ESP9) conference, Shenzhen, China, . [accepted / in Press (not yet published)] „Spatial Evaluation of Global Climate Class Transitions and Ecosystem Service Values.“
- 10.1016/j.envsoft.2017.08.006. . ‘How a Pareto frontier complements scenario projections in land use change impact assessment.’ Environmental Modelling & Software 97: 287–302. doi:
- 10.1002/bbb.1803. . ‘Modeling the impacts of wood pellet demand on forest dynamics in southeastern United States.’ Biofuels, Bioproducts and Biorefining 11, Nr. 6: 1007–1029. doi:
- contributed to the The 20th AGILE Conference on Geographic Information Science, Wageningen, The Netherlands, . „A framework to monitor, model, and actively manage crowd behaviour.“
- Contributed to the The 20th AGILE Conference on Geographic Information Science, Wageningen, The Netherlands. . ‘Locating the position of a scenario projection in solution space.’
- . . Quantifying and reducing uncertainty in land use change model projections - Case studies on the implications of increasing bioenergy demands Dissertationsschrift, Utrecht University. Utrecht, The Netherlands: Selbstverlag / Eigenverlag.
- 10.1016/j.jenvman.2016.08.055. . ‘Linking carbon stock change from land-use change to consumption of agricultural products: A review with Indonesian palm oil as a case study.’ Journal of Environmental Management 184, Nr. 2: 340–352. doi:
- 10.1111/gcbb.12270. . ‘What can and can't we say about indirect land-use change in Brazil using an integrated economic - land-use change model?’ GCB Bioenergy 8, Nr. 3: 561–578. doi:
- 10.1016/j.apenergy.2016.04.069. . ‘Supply chain optimization of sugarcane first generation and eucalyptus second generation ethanol production in Brazil.’ Applied Energy 173, Nr. null: 494–510. doi:
- 10.1016/j.envsoft.2015.02.013. . ‘Detecting systemic change in a land use system by Bayesian data assimilation.’ Environmental Modelling and Software 75, Nr. null: 424–438. doi:
- 10.1111/gcbb.12176. . ‘Model collaboration for the improved assessment of biomass supply, demand, and impacts.’ GCB Bioenergy 7, Nr. 3: 422–437. doi:
- 10.1002/bbb.1471. . ‘Integrated spatiotemporal modelling of bioenergy production potentials, agricultural land use, and related GHG balances; demonstrated for Ukraine.’ Biofuels, Bioproducts and Biorefining 8, Nr. 3: 391–411. doi:
- 10.1016/j.envsoft.2013.11.009. . ‘Identifying a land use change cellular automaton by Bayesian data assimilation.’ Environmental Modelling and Software 53, Nr. null: 121–136. doi:
- 10.1016/j.rser.2014.02.040. . ‘Combining empirical and theory-based land-use modelling approaches to assess economic potential of biofuel production avoiding iLUC: Argentina as a case study.’ Renewable and Sustainable Energy Reviews 34, Nr. null: 208–224. doi:
- . . Impacts of Biofuel Production, Case Studies: Mozambique, Argentina and Ukraine – Final Report . Utrecht, The Netherlands: UNIDO and GEF, .
- 10.1016/j.compenvurbsys.2011.08.003. . ‘Spatio-temporal uncertainty in Spatial Decision Support Systems: A case study of changing land availability for bioenergy crops in Mozambique.’ Computers, Environment and Urban Systems 36, Nr. 1: 30–42. doi:
- 10.1111/j.1757-1707.2011.01147.x. . ‘Spatiotemporal land use modelling to assess land availability for energy crops - illustrated for Mozambique.’ GCB Bioenergy 4, Nr. 6: 859–874. doi: