Quantification of One-Year Gypsy Moth Defoliation Extent in Wonju, Korea, Using Landsat Satellite Images

Abstract

We quantified the extent and severity of Asian gypsy moth (Lymantria dispar) defoliation in Wonju, Korea, from May to early June in 2020. Landsat images were collected covering Wonju and the surrounding area in June from 2017 to 2020. Forest damage was evaluated based on differences between the Normalized Difference Moisture Index (NDMI) from images acquired in 8 June 2020 and the prior mean NDMI estimated from images in June from 2017 to 2019. The values of NDMI ranged from −1 to 1, where values closer to 1 meant higher canopy cover. The NDMI values for 7825 ha of forests were reduced by more than 0.05 compared to the mean NDMI values for the prior 3 years (2017 to 2019). The NDMI values of 1350 ha of forests were reduced by >0.125 to 0.2, and the NDMI values for another 656 ha were reduced by more than 0.2. A field survey showed that these forests were defoliated by gypsy moth and that forests with NDMI reductions of more than 0.2 were heavily defoliated by gypsy moth. A 311 ha area of Japanese larch (Larix kaempferi) was severely damaged by gypsy moth and the proportion of larch damaged was higher than that of other tree species. This intense damage to larch suggests that gypsy moths preferentially attack Japanese larch in Wonju. Our study shows that the use of NDMI values to detect areas defoliated by gypsy moth from satellite images is effective and can be used to measure other characteristics of gypsy moth defoliation events, such as host preferences under field conditions.

Detecting Pest-Infested Forest Damage through Multispectral Satellite Imagery and Improved UNet++

“…Bárta, et al [ 30 ] employed a random forest algorithm to analyze seasonal changes in vegetation indices of Norway spruce forests in Sentinel-2 imagery in order to monitor bark beetle infestations, achieving an overall accuracy of 78% in separating healthy and green attack categories. Choi, et al [ 31 ] used Landsat image-based computation of NDMI to detect defoliation caused by the Asian gypsy moth. Kern, et al [ 32 ] used NDVI calculated from MODIS data to detect oak lace bug hazard in oak forests and compared it to field data with greater than 61.1% agreement.…”

Effects of Lymantria dispar multiple nucleopolyhedrovirus and Bacillus thuringiensis var. kurstaki on different larval instars of Lymantria dispar asiatica

“…Lymantria dispar asiatica (the Asian spongy moth; Lepidoptera: Erebidae) is widely distributed in central and eastern Asia (Kang et al, 2017; Pogue & Schaefer, 2007; Zhao et al, 2019). This species is characterized by sporadic outbreaks, which can have considerable economic impacts on forests and fruit orchards in Korea (Choi et al, 2021; Song et al, 2022). The Asian strain L. dispar asiatica differs from the European strain L. dispar dispar with respect to certain genetic characteristics and host range (DeWaard et al, 2010).…”

“…Furthermore, unlike adult females of the European strain, those of the Asian strain have higher flight activity and are attracted to light (Keena et al, 2008). This behavior of the Asian spongy moth poses an inconvenience to human facilities near towns, such as parks, as well as mountain forests (Choi et al, 2021).…”

“…Furthermore, unlike adult females of the European strain, those of the Asian strain have higher flight activity and are attracted to light (Keena et al, 2008). This behavior of the Asian spongy moth poses an inconvenience to human facilities near towns, such as parks, as well as mountain forests (Choi et al, 2021). Entomopathogenic micro-organisms have been widely investigated as promising biopesticides as alternatives to synthetic chemical pesticides, the excess use of which can induce pesticide resistance as well as cause serious environmental contamination (Lacey et al, 2015;Sabbahi et al, 2022).…”

MA-SARNet: A one-shot nowcasting framework for SAR image prediction with physical driving forces

“…Remote-sensed images serve as an essential data source for earth observation and are widely used for flood extent mapping (Cherif et al, 2021;Dong et al, 2021;Moharrami et al, 2021;Dasgupta et al, 2022) and flood impact analysis (Xu et al, 2019). Among common types of remote sensing images, satellite images can provide large-scale observations with consistent quality and frequent revisit schedules (Tiwari et al, 2020;Choi et al, 2021), whereas aircraft imagery can provide very high resolution (VHR) images (Wang, Sun, et al, 2022;Wilson et al, 2022) with more flexibility and are thus ideal for local observing tasks. However, so far, most applications using remote sensing images are for retrospective rather than predictive tasks.…”