Discrimination of Bark Beetle-Damaged Forest Stands Using Vegetation Indices Derived from Landsat 8

Bark beetle infestations present a considerable risk to coniferous forest ecosystems, resulting in substantial ecological and economic losses. Monitoring and mapping vegetation health using remote sensing techniques is an important step in identifying and controlling areas of susceptibility, especially for bark beetles that cause significant damage to forest ecosystems. This study assessed the statistical discriminative efficacy of vegetation indices obtained from Landsat 8 OLI data at the stand level in discriminating between forest stands impacted and unaffected by Ips sexdentatus damage. Eighty forest stands (40 forest stands, both with and without Ips sexdentatus damage) selected through fieldwork in the Araç Forest Directorate, Kastamonu, were studied. Five frequently utilized vegetation indices (NDVI, NDMI, MSI, TCW, and RGI) were applied, and the minimum, maximum, and average values were computed for each stand. Given the non-normal distribution of the data, the Mann–Whitney U test was utilized, revealing significant differences (p < 0.001) between stands with and without beetle damage across all indices except TCW(max.). NDVI and NDMI values decreased in damaged stands, whereas MSI and RGI values increased. MANOVA results indicated substantial distinction among groups (Pillai’s Trace = 0.870, p < 0.001), whereas PCA demonstrated significant differentiation, accounting for 75.4% of the total variance. The mean values of NDVI and NDMI showed the greatest discriminatory potential among the indices. In summary, the Landsat 8 vegetation indicators tested in this study showed substantial discriminating potential.