Nondestructive, stereological estimation of canopy surface area

Research output: Contribution to journalJournal articleResearchpeer-review

  • Dvora-Laio Wulfsohn
  • Marco Sciortino
  • Jesper M. Aaslyng
  • Marta García-Fiñana
We describe a stereological procedure to estimate the total leaf surface area of a plant canopy in vivo, and address the problem of how to predict the variance of the corresponding estimator. The procedure involves three nested systematic uniform random sampling stages: (i) selection of plants from a canopy using the smooth fractionator, (ii) sampling of leaves from the selected plants using the fractionator, and (iii) area estimation of the sampled leaves using point counting. We apply this procedure to estimate the total area of a chrysanthemum (Chrysanthemum morifolium L.) canopy and evaluate both the time required and the precision of the estimator. Furthermore, we compare the precision of point counting for three different grid intensities with that of several standard leaf area measurement techniques. Results showed that the precision of the plant leaf area estimator based on point counting is high. Using a grid intensity of 1.76 cm2/point we estimated plant and canopy surface areas with accuracies similar to or better than those obtained using image analysis and a commercial leaf area meter. For canopy surface areas of approximately 1 m2 (10 plants), the fractionator leaf approach with sampling fraction equal to 1/9 followed by point counting using a 4.3 cm2/point grid produced a coefficient of error of less than 7%. The smooth fractionator can be used to ensure that the additional contribution to the estimator variance due to between-plant variability is small.
Original languageEnglish
JournalInternational Journal of Biometrics
Issue number1
Pages (from-to)159-168
Number of pages10
Publication statusPublished - 2010

    Research areas

  • Chrysanthemum morifolium L, Coefficient of error, Fractionator, Nested cluster sampling, Point counting, Smooth fractionator, Stereology, Surface area, Systematic sampling, Variance prediction

ID: 12236230