Development of a method for monitoring of insect induced forest defoliation - limitation of MODIS data in Fennoscandian forest landscapes

• Published in: Silva Fennica
• Main Authors: Olsson, Per-Ola, Kantola, Tuula, Lyytikäinen-Saarenmaa, Päivi, Jönsson, Anna Maria, Eklundh, Lars
• Other Authors: Department of Forest Sciences, Forest Health Group, Forest Ecology and Management
• Format: Article in Journal/Newspaper
• Language: English
• Published: FINNISH SOC FOREST SCIENCE-NATURAL RESOURCES INST FINLAND 2016
• Subjects: insect defoliation detection; remote sensing; coarse-resolution; EVI2; z-score; Sentinel-2; SIBERIAN SILKMOTH OUTBREAK; SATELLITE SENSOR DATA; TIME-SERIES; CLIMATE-CHANGE; EPIRRITA-AUTUMNATA; SCOTS PINE; OPEROPHTERA-BRUMATA; VEGETATION INDEXES; DETECTING TRENDS; BIRCH FOREST; 4112 Forestry; 1171 Geosciences; 1181 Ecology; evolutionary biology; Orlova; Fennoscandia; Fennoscandian; Northern Sweden
• Online Access: http://hdl.handle.net/10138/161259

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 (Pinus sylvestris L.) (pinaceae) and with defoliation of European pine sawfly (Neodiprion sertifer Geoffr.) (Hymenoptera: Diprionidae) and common pine sawfly (Diprion pini L.) (Hymenoptera: Diprionidae). The method was also applied to subalpine mountain birch (Betula pubescens ssp. Czerepanovii N. I. Orlova) forests in northern Sweden, infested by autumnal moth (Epirrita autumnata Borkhausen) and winter moth (Operophtera brumata 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 Sentinel-2. Peer reviewed