Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach

• Published in: Forests Vol. 15; no. 4; p. 644
• Main Authors: Goldbergs, Grigorijs, Upenieks, Emīls Mārtiņš
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Accuracy; Algorithms; Bark; Beetles; Chlorophyll; Coniferous forests; Dead wood; deadwood mapping; Deep learning; Drone aircraft; Efficiency; Identification and classification; Image acquisition; Mapping; Mathematical analysis; Measurement; Mixed forests; NDVI; Normalized difference vegetative index; Pest outbreaks; Plant communities; Residuals; Satellite imagery; Satellite imaging; Sensors; Sentinel-2 imagery; spruce bark beetle; Support vector machines; Timber; UAS reference data; Vegetation; vegetation index thresholding; Weather; Wood; Latvia
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f15040644

This study aimed to examine the efficiency of the vegetation index (VI) thresholding approach for mapping deadwood caused by spruce bark beetle outbreak. For this, the study used upscaling from individual dead spruce detection by unmanned aerial (UAS) imagery as reference data for continuous spruce deadwood mapping at a stand/landscape level by VI thresholding binary masks calculated from satellite Sentinel-2 imagery. The study found that the Normalized Difference Vegetation Index (NDVI) was most effective for distinguishing dead spruce from healthy trees, with an accuracy of 97% using UAS imagery. The study results showed that the NDVI minimises cloud and dominant tree shadows and illumination differences during UAS imagery acquisition, keeping the NDVI relatively stable over sunny and cloudy weather conditions. Like the UAS case, the NDVI calculated from Sentinel-2 (S2) imagery was the most reliable index for spruce deadwood cover mapping using a binary threshold mask at a landscape scale. Based on accuracy assessment, the summer leaf-on period (June–July) was found to be the most appropriate for spruce deadwood mapping by S2 imagery with an accuracy of 85% and a deadwood detection rate of 83% in dense, close-canopy mixed conifer forests. The study found that the spruce deadwood was successfully classified by S2 imagery when the spatial extent of the isolated dead tree cluster allocated at least 5–7 Sentinel-2 pixels.