Remote Sensing for Precision Crop Protection – A Matter of Scale

  • Kerstin Voss
  • Jonas Franke
  • Thorsten Mewes
  • Gunter Menz
  • Walter Kühbauch
Chapter

Abstract

Management strategies for precision crop protection necessitate spatially and temporally explicit knowledge about crop growth heterogeneity within fields. Remote sensing techniques are appropriate tools for the derivation of relevant crop parameters. However, even for a first discrimination between stressed and productive crop stands, several aspects related to phenomenon and sensor characteristics need to be considered. The question of which prerequisites a sensor must fulfil at specific scales for an effective identification of within-field heterogeneities arises. Besides scale -related issues of the observed phenomenon, the scale of remote sensing data needs to be differentiated into the sensor-defining dimensions: spatial, temporal and spectral . This chapter examines each dimension in detail. For the spatial dimension , different landscape metrics were calculated and a threshold of the minimal spatial resolution of remote sensing data for crop stress detection could thus be defined. The temporal scale of remote observations is rather phenomenon-dependent, as various factors such as the crop stress type produce different temporal dynamics, which determine the sensor-technical prerequisites. With respect to the spectral scale, its characteristics strongly depend on the given spatial and temporal dimensions. Different spectral wavebands need to be considered at different spatial scales (e.g., near-range sensing vs. remote sensing) as well as temporal variances (e.g., different phonological stages). The chapter demonstrates the importance of scale-related issues for precision crop protection and highlights that various perspectives have to be taken into account by using remote sensing.

Keywords

Land Cover Class Landscape Metrics Patch Type Disease Progress Curve Rust Wheat 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V.  2010

Authors and Affiliations

  • Kerstin Voss
    • 1
  • Jonas Franke
    • 2
  • Thorsten Mewes
    • 3
  • Gunter Menz
    • 1
  • Walter Kühbauch
    • 4
  1. 1.Remote Sensing Research Group, Department of GeographyUniversity of BonnBonnGermany
  2. 2.RSS – Remote Sensing Solutions GmbHOffice MunichMünchenGermany
  3. 3.Center for Remote Sensing of Land Surfaces (ZFL)BonnGermany
  4. 4.Crop Science Research Group, Institute of Crop Science and Resource ConservationBonnGermany

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