Jeong Seok Won, Lee Il Hwan, Kim Yang-Gil, Kang Kyu-Suk, Shim Donghwan, Hurry Vaughan, Ivanov Alexander G, Park Youn-Il
Department of Biological Sciences, Chungnam National University, Korea.
Department of Forest Bio-Resources, National Institute of Forest Science, Suwon, Korea.
Physiol Plant. 2025 Jan-Feb;177(1):e70090. doi: 10.1111/ppl.70090.
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.
在叶片物候事件的整个周期中，叶片颜色会发生变化，这些变化受非生物胁迫或生物感染的影响。这些颜色变化与光合色素的数量和质量密切相关，而光合色素直接影响植物的初级生产力。因此，监测和量化叶片颜色变化对于区分松材线虫造成的损害与自然树木衰老至关重要。在本研究中，使用高光谱相机传感器对田间试验中生长的针叶树针叶颜色变化进行非侵入性和非破坏性评估。使用高光谱传感器选择并监测了六种针叶树种的三种不同的针叶颜色变化：呈现季节性秋季颜色的、经历线虫感染坏死过程的以及自然死亡的。为了减轻高光谱数据固有的混合光谱特性，在端元线性混合假设下，使用纯度像素指数算法从单个图像中提取端元。从六种松树的378幅高光谱图像中提取的总共1321个端元中，最终选择了八个端元来重建高光谱图像并生成丰度图。在这八个端元中，有四个代表了不同水平的光合色素含量——从非常低到高。因此，这些针叶树端元有望用于评估季节性叶片物候以及感染松材线虫的松树的损害程度。这种综合方法强调了通过细致的针叶特征分析，高光谱图像光谱解混在推进精准林业方面的有效性。
