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This study investigated the statistical relationship between defoliation in pine forests infested by nun moths (Lymantria monacha) and the spectral bands of the RapidEye sensor, including the derived normalized difference vegetation index (NDVI) and the normalized difference red-edge index (NDRE). The strength of the relationship between the spectral variables and the ground reference samples of percent remaining foliage (PRF) was assessed over three test years by the Spearman’s ρ correlation coefficient, revealing the following ranking order (from high to low ρ): NDRE, NDVI, red, NIR, green, blue, and red-edge. A special focus was directed at the vegetation indices. In both discriminant analyses and decision tree classification, the NDRE yielded higher classification accuracy in the defoliation classes containing none to moderate levels of defoliation, whereas the NDVI yielded higher classification accuracy in the defoliation classes representing severe or complete defoliation. We concluded that the NDRE and the NDVI respond very similarly to changes in the amount of foliage, but exhibit particular strengths at different defoliation levels. Combining the NDRE and the NDVI in one discriminant function, the average gain of overall accuracy amounted to 7.8 percentage points compared to the NDRE only, and 7.4 percentage points compared to the NDVI only. Using both vegetation indices in a machine-learning-based decision tree classifier, the overall accuracy further improved and reached 81% for the test year 2012, 71% for 2013, and 79% for the test year 2014.

Sensitivity analysis of RapidEye spectral bands and derived vegetation indices for insect defoliation detection in pure Scots pine stands