Sentinel-2 accurately maps green-attack stage of European spruce bark beetle (Ips typographus, L.) compared with Landsat-8

Haidi Abdullah*, Andrew K. Skidmore, Roshanak Darvishzadeh, Marco Heurich

*Corresponding author for this work

    Research output: Contribution to journalArticle

    17 Citations (Scopus)
    59 Downloads (Pure)

    Abstract

    Natural disturbances induced by insect outbreaks have increased in forest ecosystems over the past decades. To minimize economic loss and prevent a mass outbreak, early detection of bark beetle green attack – a period when trees have yet to show visual signs of infestation stress – is therefore crucial to effective and timely forest management. In this study, we evaluated the ability of spectral vegetation indices extracted from Landsat-8 and Sentinel-2 imagery to map bark beetle green attack using principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA). A recent infestation map produced through visual interpretation of high-resolution aerial photographs validated the final infestation output maps. Leaf spectral measurements alongside total chlorophyll and nitrogen concentration, leaf water content and leaf dry matter content were measured to assess the impact of bark beetle green attack on foliar properties. We observed that the majority of spectral vegetation indices (SVIs) calculated from Sentinel-2, particularly red-edge dependent indices (NDRE 2 and 3) and water-related indices (SR-SWIR, NDWI, DSWI and LWCI), were able to discriminate healthy from infested plots. In contrast, only the water-related indices (NDWI, DSWI and RDI) from Landsat-8 were able to discriminate between healthy and infested plots efficiently. The total number of pixels identified as harboring a green attack that matched with ground truth data (aerial photography) was higher for Sentinel-2 (67%) than for Landsat-8 (36%) SVIs, indicating the elevated sensitivity of Sentinel-2 imagery to changes induced by bark beetle green attack. We also determined that foliar chlorophyll and leaf water content were significantly higher (P < 0.05) in healthy trees than in green-attacked trees. Our study highlights the potential of Sentinel-2 data for the early detection of bark beetle infestations and the production of reliable infestation maps at the green-attack stage. This study aims to evaluate the ability of different spectral vegetation indices from Sentinel-2 and Landsat-8 imagery to map and detect bark beetle infestation at the green-attack stage. We observed that the majority of Spectral Vegetation Indices (SVIs) calculated from Sentinel-2 were able to discriminate healthy from infested plots. In contrast, only the water-related indices (NDWI, DSWI, and RDI) from Landsat-8 were able to discriminate between healthy and infested plots efficiently. The total number of pixels identified as harboring a green attack that matched with ground truth data (aerial photography) was higher for Sentinel-2 (67%) than for Landsat-8 (36%) SVIs, indicating the elevated sensitivity of Sentinel-2 imagery to changes induced by bark beetle green attack. We also determined that foliar chlorophyll and leaf water content were significantly higher (P < 0.05) in healthy trees than in green-attacked trees. Our study highlights the potential of Sentinel-2 data for the early detection of bark beetle infestations and the production of reliable infestation maps at the green-attack stage.

    Original languageEnglish
    Pages (from-to)87-106
    Number of pages20
    JournalRemote Sensing in Ecology and Conservation
    Volume5
    Issue number1
    Early online date25 Aug 2018
    DOIs
    Publication statusPublished - Mar 2019

    Bibliographical note

    Copyright the Author(s) 2018. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • bark beetle (Ips typographus L.)
    • green attack
    • Landsat-8
    • Norway spruce
    • Sentinel-2
    • spectral vegetation indices

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