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

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Olsson , P-O , Kantola , T , Lyytikäinen-Saarenmaa , P , Jönsson , A M & Eklundh , L 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 , 1495 . https://doi.org/10.14214/sf.1495

Title: Development of a method for monitoring of insect induced forest defoliation - limitation of MODIS data in Fennoscandian forest landscapes
Author: Olsson, Per-Ola; Kantola, Tuula; Lyytikäinen-Saarenmaa, Päivi; Jönsson, Anna Maria; Eklundh, Lars
Contributor: University of Helsinki, Texas A&M Univ, Texas A&M University College Station, Texas A&M University System, Dept Entomol, Knowledge Engn Lab
University of Helsinki, Department of Forest Sciences
Date: 2016
Language: eng
Number of pages: 22
Belongs to series: Silva Fennica
ISSN: 0037-5330
URI: http://hdl.handle.net/10138/161259
Abstract: 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.
Subject: 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
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