A unified framework for land cover monitoring based on a discrete global sampling grid (GSG)

  • Lutz FehrmannEmail author
  • Collins B. Kukunda
  • Nils Nölke
  • Sebastian Schnell
  • Dominik Seidel
  • Steen Magnussen
  • Christoph Kleinn


Environmental monitoring and assessment of the extent and change of land uses and their renewable natural resources over time is a key element in many international processes and one crucial basis for sustainable management. Remote sensing plays an increasingly important role in these monitoring systems, especially if the interest is in large areas. Integration of remote sensing requires comprehensive and careful preprocessing and a high level of expertise which is not always at hand in all applications. However, easy-to-implement sampling techniques based on visual interpretation are an alternative approach for utilizing remote sensing imagery, including the evolving archives of georeferenced and preprocessed data provided by virtual globes like Google Earth, Bing, and others. The goal of this paper is to propose a simple unified framework that may be used in the context of sampling studies and environmental monitoring from local to global scale. Besides the definition of a sampling design, the observation or plot design, i.e., defining how observations are to be made and recorded, has a strong influence on the precision of estimates as well as the overall efficiency of a sampling exercise. As an example, we present a simulation study focusing on the estimation of forest cover in artificial landscapes with different coverage and degree of fragmentation. The sampling units we compare are point clusters with different configuration and spatial extent.


Natural resources Probability based survey Visual interpretation Observational design 



We thank anonymous reviewers for helpful comments and suggestions.

Funding information

This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—project no. 273259202.

Supplementary material

10661_2018_7152_MOESM1_ESM.pdf (774 kb)
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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Forest Inventory and Remote SensingGeorg-August Universität GöttingenGöttingenGermany
  2. 2.Thünen Institute of Forest EcosystemsEberswaldeGermany
  3. 3.Silviculture and Forest Ecology of the Temperate ZonesGeorg-August Universität GöttingenGöttingenGermany
  4. 4.Pacific Forestry CentreNatural Resources CanadaOttawaCanada

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