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

Abstract

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 R2 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.

Keywords

• Siberian silkmoth
• damaged stands
• remote sensing
• spectral indices