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GIS modelling

Landscape-level analyses with GIS software and probabilistic approaches increase the realism of exposure assessments.

Geo-Information Systems (GIS)

As standard environmental fate modelling does not always result in a realistic exposure analysis of surface water or land ecosystems, the use of landscape geo-data can increase the realism of exposure scenarios and allows to develop landscape-level and probabilistic approaches in the environmental risk assessment (FOCUS, 2007).

Landscape analysis with Geographical Information Systems (GIS) is a suitable tool to evaluate key factors like proximity of surface waters or plant ecosystems to the application areas, interception by riparian vegetation or wind speed and direction.

Typical tasks in landscape analysis include (compare Kubiak et al., 2014, Schad, 2006, Thomas et al., 2016):

  • Identify regions of interests (ROI) regarding e.g. distribution of target crop, water bodies, etc.
  • Determine areas under investigation (AUI) based on available geo-data or by generating new geo-data (manual digitising or semi-automated recognition)
  • Establish and maintain geo-databases for further geo-processing analyses
  • Perform proximity analysis to identify “hot spots” of exposure by creating, evaluating and mapping spatial statistics (such as spatial exposure percentiles)
  • Develop landscape based spray drift or runoff model (see Wang and Rautmann, 2008, Schad, 2013)
  • Standardise GIS analyses with custom python scripts, to facilitate otherwise time consuming, repetitive tasks

Landuse data from Open Street Map ( can be used to identify ROI and AUI.

Digitised high-resolution data are capable to identify smaller habitats like riparian vegetation shielding streams from adjacent arable land.


  • FOCUS 2007. Landscape And Mitigation Factors In Aquatic Risk Assessment. Volume 1. Extended Summary and Recommendations. Report of the FOCUS Working Group on Landscape and Mitigation Factors in Ecological Risk Assessment, EC Document Reference SANCO/10422/2005 v2.0. 169pp. Available online:
  • Kubiak, R. Hommen, U., Bach, M., Classen, S., Gergs, A., Golla, B., Guerniche, D., Klein, M., Krumpe, J., Preuss, TG., Ratte, H.T., Roß-Nickol, M., Schäfers, C., Strauss, T., Toschki, A., Trapp, M. 2014. Georeferenced Probabilistic Risk Assessment of Pesticides – Further Advances in Assessing the Risk to Aquatic Ecosystems by Spray Drift from Permanent Crops. Umweltbundesamt, Report No. (UBA-FB) 001740/E Available online:
  • Schad, T. 2006. Introduction of generic landscape characteristics in refined aquatic exposure and risk assessment for spray-drift in Germany. Industrieverband Agrar (IVA) project group ‘geoPERA’. Available online:
  • Schad, T. 2013. Xplicit – A modelling framework for ecological risk characterisation at landscape-scales in regulatory risk assessment and risk management of plant protection products. PhD thesis, Univ. of Koblenz-Landau, Germany.
  • Thomas, K., Resseler, H., Spatz, R., Hendley, P., Sweeney, P., Urban, M. and Kubiak, R. 2016. A simple approach for a spatial terrestrial exposure assessment of the insecticide fenoxycarb, based on a high-resolution landscape analysis. Pest Manag. Sci. 72: 2099-2109. Available online:
  • Wang, M. and Rautmann, D. 2008. A simple probabilistic estimation of spray drift – factors determining spray drift and development of a model. Environmental Toxicology and Chemistry, Vol. 27, 12: 2617-2626.