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Articles containing the keyword 'insect defoliation detection'

Category : Research article

article id 1495, category Research article
Per-Ola Olsson, Tuula Kantola, Päivi Lyytikäinen-Saarenmaa, Anna Maria Jönsson, Lars Eklundh. (2016). Development of a method for monitoring of insect induced forest defoliation – limitation of MODIS data in Fennoscandian forest landscapes. Silva Fennica vol. 50 no. 2 article id 1495. https://doi.org/10.14214/sf.1495
Keywords: remote sensing; insect defoliation detection; coarse-resolution; EVI2; z-score; Sentinel-2
Highlights: We developed and tested a method to monitor insect induced defoliation in forests based on coarse-resolution satellite data (MODIS); MODIS data may fail to detect defoliation in fragmented landscapes, especially if defoliation history is long. More homogenous forests results in higher detection accuracies; The method may be applied to future coarse and medium-resolution satellite data with high temporal resolution.
Abstract | Full text in HTML | Full text in PDF | Author Info

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.

  • Olsson, Lund University, Department of Physical Geography and Ecosystem Science, Sölvegatan 12, S-223 62 Lund, Sweden E-mail: per-ola.olsson@nateko.lu.se (email)
  • Kantola, Texas A & M University, Knowledge Engineering Laboratory, Department of Entomology, College Station, TX, USA E-mail: tuula.kantola@helsinki.fi
  • Lyytikäinen-Saarenmaa, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: paivi.lyytikainen-saarenmaa@helsinki.fi
  • Jönsson, Lund University, Department of Physical Geography and Ecosystem Science, Sölvegatan 12, S-223 62 Lund, Sweden E-mail: anna_maria.jonsson@nateko.lu.se
  • Eklundh, Lund University, Department of Physical Geography and Ecosystem Science, Sölvegatan 12, S-223 62 Lund, Sweden E-mail: lars.eklundh@nateko.lu.se

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