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We investigated if coarse-resolution satellite data from the MODIS sensor can be used for regional monitoring of insect disturbances in Fennoscandia. A damage detection method based on z-scores of seasonal maximums of the 2-band Enhanced Vegetation Index (EVI2) was developed. Time-series smoothing was applied and Receiver Operating Characteristics graphs were used for optimisation. The method was developed in fragmented and heavily managed forests in eastern Finland dominated by Scots pine ( L.) (pinaceae) and with defoliation of European pine sawfly ( Geoffr.) (Hymenoptera: Diprionidae) and common pine sawfly ( L.) (Hymenoptera: Diprionidae). The method was also applied to subalpine mountain birch ( N.I. Orlova) forests in northern Sweden, infested by autumnal moth (Borkhausen) and winter moth ( L.). In Finland, detection accuracies were fairly low with 50% of the damaged stands detected, and a misclassification of healthy stands of 22%. In areas with long outbreak histories the method resulted in extensive misclassification. In northern Sweden accuracies were higher, with 75% of the damage detected and a misclassification of healthy samples of 19%. Our results indicate that MODIS data may fail to detect damage in fragmented forests, particularly when the damage history is long. Therefore, regional studies based on these data may underestimate defoliation. However, the method yielded accurate results in homogeneous forest ecosystems and when long-enough periods without damage could be identified. Furthermore, the method is likely to be useful for insect disturbance detection using future medium-resolution data, e.g. from Sentinelâ2.Pinus sylvestrisNeodiprion sertiferDiprion piniBetula pubescensssp. Czerepanovii Epirrita autumnata Operophtera brumata