The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1827–1831, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1827-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1827–1831, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1827-2019

  05 Jun 2019

05 Jun 2019

FEATURE FILTERING AND SELECTION FOR DRY MATTER ESTIMATION ON PERENNIAL RYEGRASS: A CASE STUDY OF VEGETATION INDICES

G. T. Alckmin1,2, L. Kooistra2, A. Lucieer1, and R. Rawnsley1,3 G. T. Alckmin et al.
  • 1School of Technology, Environments and Design, University of Tasmania, Hobart, Australia
  • 2Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands
  • 3Tasmanian Institute of Agriculture, University of Tasmania, Private Bag 3523, Burnie 7320, Tasmania, Australia

Keywords: Feature Selection, Collinearity, Vegetation Indices, Biomass, Dry Matter, Pasture, Perennial Ryegrass, Machine Learning

Abstract. Vegetation indices (VIs) have been extensively employed as a feature for dry matter (DM) estimation. During the past five decades more than a hundred vegetation indices have been proposed. Inevitably, the selection of the optimal index or subset of indices is not trivial nor obvious. This study, performed on a year-round observation of perennial ryegrass (n = 900), indicates that for this response variable (i.e. kg.DM.ha−1), more than 80% of indices present a high degree of collinearity (correlation > |0.8|.) Additionally, the absence of an established workflow for feature selection and modelling is a handicap when trying to establish meaningful relations between spectral data and biophysical/biochemical features. Within this case study, an unsupervised and supervised filtering process is proposed to an initial dataset of 97 VIs. This research analyses the effects of the proposed filtering and feature selection process to the overall stability of final models. Consequently, this analysis provides a straightforward framework to filter and select VIs. This approach was able to provide a reduced feature set for a robust model and to quantify trade-offs between optimal models (i.e. lowest root mean square error – RMSE = 412.27 kg.DM.ha−1) and tolerable models (with a smaller number of features – 4 VIs and within 10% of the lowest RMSE.)