Differentiation of plant age in grasses using remote sensing

N. Knox, A.K. Skidmore, H.M.A. van der Werff, T.A. Groen, W.F. de Boer, H.H.T. Prins, E. Kohi, M. Peel

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

Phenological or plant age classification across a landscape allows for examination of micro-topographical effects on plant growth, improvement in the accuracy of species discrimination, and will improve our understanding of the spatial variation in plant growth. In this paper six vegetation indices used in phenological studies (including the newly proposed PhIX index) were analysed for their ability to statistically differentiate grasses of different ages in the sequence of their development. Spectra of grasses of different ages were collected from a greenhouse study. These were used to determine if NDVI, NDWI, CAI, EVI, EVI2 and the newly proposed PhIX index could sequentially discriminate grasses of different ages, and subsequently classify grasses into their respective age category. The PhIX index was defined as: (An VNIR+ log(An SWIR2))/(An VNIR- log(An SWIR2)), where An VNIRand An SWIR2are the respective nor- malised areas under the continuum removed reflectance curve within the VNIR (500-800 nm) and SWIR2 (2000-2210 nm) regions. The PhIX index was found to produce the highest phenological classification accuracy (Overall Accuracy: 79%, and Kappa Accuracy: 75%) and similar to the NDVI, EVI and EVI2 indices it statistically sequentially separates out the developmental age classes. Discrimination between seedling and dormant age classes and the adult and flowering classes was problematic for most of the tested indices. Combining information from the visible near infrared (VNIR) and shortwave infrared region (SWIR) region into a single phenological index captures the phenological changes associated with plant pigments and the ligno-cellulose absorption feature, providing a robust method to discriminate the age classes of grasses. This work provides a valuable contribution into mapping spatial variation and monitoring plant growth across savanna and grassland ecosystems.
Original languageEnglish
Pages (from-to)54-62
JournalInternational Journal of applied Earth Observation and Geoinformation
Volume24
Issue number10
DOIs
Publication statusPublished - 2013

Keywords

  • difference water index
  • monitoring vegetation
  • nitrogen concentration
  • imaging spectroscopy
  • hyperspectral data
  • boreal regions
  • time-series
  • green-up
  • phenology
  • reflectance

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