Land Surface Phenology

  • Jonathan M. Hanes
  • Liang Liang
  • Jeffrey T. Morisette
Chapter
Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)

Abstract

Certain vegetation types (e.g., deciduous shrubs, deciduous trees, grasslands) have distinct life cycles marked by the growth and senescence of leaves and periods of enhanced photosynthetic activity. Where these types exist, recurring changes in foliage alter the reflectance of electromagnetic radiation from the land surface, which can be measured using remote sensors. The timing of these recurring changes in reflectance is called land surface phenology (LSP). During recent decades, a variety of methods have been used to derive LSP metrics from time series of reflectance measurements acquired by satellite-borne sensors. In contrast to conventional phenology observations, LSP metrics represent the timing of reflectance changes that are driven by the aggregate activity of vegetation within the areal unit measured by the satellite sensor and do not directly provide information about the phenology of individual plants, species, or their phenophases. Despite the generalized nature of satellite sensor-derived measurements, they have proven useful for studying changes in LSP associated with various phenomena. This chapter provides a detailed overview of the use of satellite remote sensing to monitor LSP. First, the theoretical basis for the application of satellite remote sensing to the study of vegetation phenology is presented. After establishing a theoretical foundation for LSP, methods of deriving and validating LSP metrics are discussed. This chapter concludes with a discussion of major research findings and current and future research directions.

Keywords

Normalize Difference Vegetation Index Vegetation Index Leaf Area Index Advanced Very High Resolution Radiometer Advanced Very High Resolution Radiometer 
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-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jonathan M. Hanes
    • 1
  • Liang Liang
    • 2
  • Jeffrey T. Morisette
    • 3
  1. 1.Department of GeographyUniversity of Wisconsin-MilwaukeeMilwaukeeUSA
  2. 2.Department of GeographyUniversity of KentuckyLexingtonUSA
  3. 3.North Central Climate Science CenterU.S. Geological SurveyFort CollinsUSA

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