The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1069–1076, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1069-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1069–1076, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1069-2020

  21 Aug 2020

21 Aug 2020

FIRST INVESTIGATION OF MEDITERRANEAN OAK TREE VITALITY WITH HIGH-RESOLUTION WORLDVIEW-3 SATELLITE DATA: COMPARING TEN VEGETATION INDICES AND THREE MACHINE LEARNING CLASSIFIERS

N. Tilly, F. Reddig, U. Lussem, and G. Bareth N. Tilly et al.
  • GIS & Remote Sensing Group, Institute of Geography, University of Cologne, 50923 Cologne, Germany

Keywords: Agroforestry, Tree vitality, Dehesa, Oak decline, Satellite remote sensing, Vegetation index, Machine learning

Abstract. Oak trees are the primary component in Mediterranean agro-silvopastoral systems. Since the second half of the 20th century, however, a severe oak decline has been observed. Climate change reinforces this problem, which is consistent with worldwide observable tree dieback. As the trees have significant ecological and socio-economic functions, their observation and assessment of vitality are increasingly researched. Satellite remote sensing is very well suitable for large-scale surveys of the extensive and sometimes hardly accessible areas. This study investigates the usability of high-resolution WorldView-3 data for the classification of tree vitality. The ground truth was collected on an Andalusian dehesa at the end of September 2019, timely corresponding with the satellite data acquisition. After customary post-processing of the WorldView-3 data, 10 vegetation indices (ARVI, CIgreen, CSI, DPI, EVI, GNDVI, NDVI, PSRI, RENDVI, and RGI) were calculated from the multispectral image. Three machine learning classifiers (Maximum Likelihood, Random Forest, and Support Vector Machine) were then used for a supervised image classification with three vitality classes (healthy, sick, and dead). Independent ground truth data were used for the validation. The best results were achieved with the red edge normalized difference vegetation index (RENDVI) and the Support Vector Machine classifier (F1 scores between 0.27 and 0.72). A maximal overall accuracy of around 0.6 is, however, improvable. Further studies should focus on other classification methods, more reliable ground truth, and combined analyses of spectral and structural data.