Spectral unmixing of hyperspectral images revealed pine wilt disease sensitive endmembers

Citations Over TimeTop 10% of 2025 papers

Abstract

Throughout the entire cycle of leaf phenological events, leaf colour undergoes changes that are influenced by either abiotic stress or biotic infection. These changes in colouration are closely linked to the quantity and quality of photosynthetic pigments, which directly impact the primary productivity of plants. Therefore, monitoring and quantifying leaf colouration changes are crucial for distinguishing damage caused by pine wilt nematodes from natural tree senescence. In this study, a hyperspectral camera sensor was employed for the non-invasive and non-destructive evaluation of needle colour changes in coniferous trees grown in field tests. Three distinct needle colour variations of six coniferous tree species were selected and monitored using a hyperspectral sensor: those displaying seasonal autumn colours, undergoing nematode-infected necrosis processes, and experiencing natural death. To mitigate the inherently mixed spectral properties of hyperspectral data, endmembers were extracted from individual images using the Purity Pixel Index algorithm under the assumption of linear mixing of endmembers. From a total of 1,321 endmembers extracted from 378 hyperspectral images of six pine species, eight endmembers were ultimately chosen to reconstruct hyperspectral images and generate abundance maps. Among these eight endmembers, four represent varying levels of photosynthetic pigment contents-ranging from very low to high. Consequently, these coniferous endmembers hold promise for assessing seasonal leaf phenology and the extent of damage in pine trees infected by pine wilt nematodes. This comprehensive approach underscores the effectiveness of spectral unmixing of hyperspectral images in advancing precision forestry through meticulous coniferous needle trait analysis.

Related Papers

• → Hyperspectral imaging and analysis of mixed algae species in liquid media(2012)6 cited
• → A comparative study of hyperspectral unmixing using different algorithm approaches(2020)2 cited
• → Current Advances in Hyperspectral Face Recognition(2020)1 cited
• → FT-IR/ATR hyperspectral imaging - applications and data analysis(2007)2 cited
• → A novel reduced-order method for analysis of hyperspectral images(2021)