The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 203–208, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-203-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 203–208, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-203-2021

  28 Jun 2021

28 Jun 2021

MAPPING BARLEY LODGING WITH UAS MULTISPECTRAL IMAGERY AND MACHINE LEARNING

O. Vlachopoulos1, B. Leblon1, J. Wang2, A. Haddadi3, A. LaRocque1, and G. Patterson3 O. Vlachopoulos et al.
  • 1Faculty of Forestry and Environmental Management, University of New Brunswick, 2 Bailey Dr, Fredericton, NB E3B5A3 New Brunswick, Canada
  • 2Department of Geography and Environment, University of Western Ontario, 1151 Richmond Street, ON N6A 5C2, London, Canada
  • 3A&L Canada Laboratories, 2136 Jetstream Rd., London, ON N5V 3P5, London, Canada

Keywords: UAS, UAV, Machine learning, Image processing, Multispectral, Precision agriculture, Random Forests, Lodging

Abstract. Unmanned Aircraft Systems (UAS) are demonstrated cost- and time-effective remote sensing platforms for precision agriculture applications and crop damage monitoring. In this study, lodging damage on barley crops has been mapped from UAS imagery that was acquired over multiple barley fields with extensive lodging damages in two aerial surveys. A Random Forests classification model was trained and tested for the discrimination of lodged barley with an overall accuracy of 99.7% on the validation dataset. The crop areas with lodging were automatically delineated by vector analysis and compared to manually delineated areas using two spatial accuracy metrics, the Area Goodness of Fit (AGoF) and the Boundary Mean Positional Error (BMPE). The average AGoF was 97.95% and the average BMPE was 0.235 m.