Mapping Tree Mortality Caused by Siberian Silkmoth Outbreak Using Sentinel-2 Remote Sensing Data

The Siberian silkmoth is one of the most dangerous coniferous forests pests. Siberian silkmoth outbreaks cause massive defoliation and subsequent forest fires over vast areas. Remote forest disturbance assessments performed after an outbreak make it possible to assess carbon emissions and the potent...

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Bibliographic Details
Published in:Forests
Main Authors: Olga A. Slinkina, Pavel V. Mikhaylov, Svetlana M. Sultson, Denis A. Demidko, Natalia P. Khizhniak, Andrey I. Tatarintsev
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2023
Subjects:
Siberian silkmoth
damaged stands
remote sensing
spectral indices
Plant ecology
QK900-989
The Sentinel
Krasnoyarsk Krai
Online Access:https://doi.org/10.3390/f14122436
https://doaj.org/article/7020f52d1af4434ca381d6ce11316671
Description
Summary:The Siberian silkmoth is one of the most dangerous coniferous forests pests. Siberian silkmoth outbreaks cause massive defoliation and subsequent forest fires over vast areas. Remote forest disturbance assessments performed after an outbreak make it possible to assess carbon emissions and the potential for natural regeneration, estimate forest fire danger, and reveal the need to implement forest management practices. The goal of the present research was to investigate the use of modern satellite imagery of medium spatial resolution to estimate the percentage of dead trees in a given area. The subject of this study is the Siberian silkmoth outbreak that occurred in 2018–2020 and covered 42 thousand ha in the Irbey region of the Krasnoyarsk Krai. Imagery from the Sentinel-2/MSI sensor was used to calculate a number of spectral indices for images received before and after the outbreak. Field study data were used to create regression models relating the index values to the percentage of dead trees. A number of spectral indices, such as NDVI, dNDVI, NBR, dNBR, NDMI, EVI, and TCG, were used. As a result, spectral indices based on the data from NIR/SWIR bands (NBR, NDMI, dNBR) demonstrated the best correlations with field-measured tree mortality. Therefore, these indices may be used to accurately estimate the percentage of dead trees by remote sensing data. The best was the NBR index with an R 2 equal to 0.87, and the lowest RMSE and MAE errors. Consequently, Sentinel-2 imagery can be successfully used for tree mortality assessment over large inaccessible areas disturbed by Siberian silkmoth outbreaks at a relatively low cost.

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