The structural stability and electronic and magnetic properties of stoichiometric (Gd 2 O 3 ) n clusters with n = 1−10 were investigated using spin-polarized density functional theory through the broken-symmetry approach. Size-induced changes in the point symmetry of these clusters were observed. A large coordination number led to elongation of Gd−O bonding. Either adding an electron to or removing an electron from the ground state of a neutral cluster brought significant changes in the van der Waals volume for clusters n = 1−3. Binding energy increased with cluster size. However, the highest occupied molecular orbital−lowest unoccupied molecular orbital gap, ionization, and electron affinity fluctuated when n increased from 1 to 10. Natural population analysis and partial density of states revealed that the Gd-4f orbital hardly participated in bonding and that the Gd-5d/6s/6p orbitals were hybrid with the O-2p orbitals. The competition between Ruderman−Kittel−Kasuya−Yosida-type and superexchange-type interactions resulted in the magnetic oscillation of clusters between antiferromagnetic and ferromagnetic states. We also found similar behavior in the various structures of Gd 12 O 18 . The ground state was easily activated to the excited state because the antiferromagnetic and ferromagnetic coupling states were nearly identical in energy.

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Pine wilt disease (PWD) is caused by the pine wilt nematode and is a tremendous threat to coniferous forests. Remote sensing, particularly hyperspectral remote sensing, has been utilized to identify PWD. However, most studies have focused on distinguishing the spectra between infected and healthy pine trees and ignored further visualization of spectral symptoms, which could greatly improve the pre-visual diagnosis of PWD. This research used the false color feature maps (FCFMs) synthesized using the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) calculated from selected feature bands to analyze the changes in the spectral and image dimensions of the hyperspectral data. Our main findings were (1) the confirmed feature bands were 440, 550, 672, 752, 810, and 958 nm; and (2) NDVI (810, 440), NDVI (810, 672), NDVI (550, 672), RVI (810, 550), RVI (810, 672), and RVI (550, 672) were suitable to synthesize the FCFMs. As PWD developed, the color of the infected needles changed from blue and white to red on the NDVI-based feature maps and from blue to red on the RVI-based feature maps. Importantly, the color changes were captured by the FCFMs when the symptoms were not visible on the true color images, indicating the ability to identify PWD during the early infection stage. Study Implications: Many studies on PWD detection using remote sensing only focus on spectral information but ignore image information. In this article, a method was proposed to comprehensively utilize the spectral and image information of hyperspectral data. In addition, the ground-level imaging spectrometer was used to collected hyperspectral data of lateral branches of infected pine trees, which has rarely been the focus of other remote sensing platforms. This research helps to identify PWD as early as possible and thereby reduces the damage of PWD to pine forest resources.