Uncertainty in ecosystem mapping by remote sensing

  • Authors:

  • Duccio Rocchini;Giles M. Foody;Harini Nagendra;Carlo Ricotta;Madhur Anand;Kate S. He;Valerio Amici;Birgit Kleinschmit;Michael FöRster;Sebastian Schmidtlein;Hannes Feilhauer;Anne Ghisla;Markus Metz;Markus Neteler

  • Affiliations:

  • Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy;School of Geography, University of Nottingham, University Park, Nottingham NG7 2RD, UK;Ashoka Trust for Research in Ecology and the Environment, Royal Enclave, Srirampura, Jakkur PO, Bangalore 560064, India and Center for the Study of Institutions, Population, and Environmental Chan ...;Department of Environmental Biology, University of Rome "La Sapienza", Piazzale Aldo Moro 5, 00185 Rome, Italy;School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada N1G 2W1;Department of Biological Sciences, Murray State University, Murray, KY 42071, USA;BIOCONNET, Biodiversity and Conservation Network, Department of Environmental Science "G. Sarfatti", University of Siena, Via P.A. Mattioli 4, 53100 Siena, Italy;Department of Geoinformation for Environmental Planning, Technical University of Berlin, Straíe des 17. Juni 145, 10623 Berlin, Germany;Department of Geoinformation for Environmental Planning, Technical University of Berlin, Straíe des 17. Juni 145, 10623 Berlin, Germany;Center for Remote Sensing of Land Surfaces, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany and Vegetation Geography, University of Bonn, Meckenheimer Allee 166, D-53115 Bonn, Germany;Department of Geography, University of Erlangen-Nuremberg, Kochstr. 4/4, 91054 Erlangen, Germany;Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy;Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy;Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Via E. Mach 1, 38010 S. Michele all'Adige (TN), Italy

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2013

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Abstract

The classification of remotely sensed images such as aerial photographs or satellite sensor images for deriving ecosystem-related maps (e.g., land cover, land use, vegetation, soil) is generally based on clustering of spatial entities within a spectral space. In most cases, Boolean logic is applied in order to map landscape patterns. One major concern is that this implies an ability to divide the gradual variability of the Earth's surface into a finite number of discrete non-overlapping classes, which are considered to be exhaustively defined and mutually exclusive. This type of approach is often inappropriate given the continuous nature of many ecosystem properties. Moreover, the standard data processing and image classification methods used will involve the loss of information as the continuous quantitative spectral information is degraded into a set of discrete classes. This leads to uncertainty in the products resulting from the use of remote sensing tools. It follows that any estimated ecosystem property has an associated error and/or uncertainty of unknown magnitude, and that the statistical quantification of uncertainty should be a core part of scientific research using remote sensing. In this paper we will review recent attempts to take explicitly into account uncertainty when mapping ecosystems.