## 3 Application of Hyperspectral RS into Plant RS and Military Actions
### Research Advances of Plant Hyperspectral RS Technology
[PERSON] and [PERSON] used different classifiers to identify tree species in hyperspectral images and found nonlinear classifiers were more suitable for hyperspectral data classification [54]. [PERSON] et al. used AVIRIS (Airborne Visible InfraRed Imaging Spectrometer) RS images to identify tree species using characteristic factors related to spectral information and crown structure and found these factors can improve the accuracy of tree species classification [55]. [PERSON] et al. used comparative classification methods based on hyperspectral images to study support vector machine and random forest classifiers in complex heterogeneous forests in Muir Woods National Monument and Saklikent Canyon in Marin County, California and showed the overall accuracy of all classifications was above 90% [56]. [PERSON] et al. used new hyperspectral satellite images for identifying forest tree species and found hyperspectral sensors can better distinguish all forest types and improve performance when the complexity of the naming system also increased [57]. [PERSON] et al. studied the performance of multi-temporal hyperspectral datasets for classifying some forest tree species and found the classification results in early summer had the highest accuracy. Compared with a single collection of images, the implementation of multi-temporal hyperspectral data can improve the classification results [58]. Currently, hyperspectral RS has been widely used in multi-level plant diversity research, such as large-scale habitat mapping, plant function, and phylogenetic diversity inversion [59]. According to different diversity measurement requirements, hyperspectral RS-based diversity inversion methods can be divided into direct estimation based on spectral characteristics and indirect estimation based on quantitative inversion. The former mainly achieves diversity estimation by establishing a direct relationship between spectral heterogeneity and biological diversity and has been applied to diversity estimation of species, functional traits, and phylogeny [60]. The latter mainly quantitatively extracts plant traits or vegetation parameters through fine spectral features of hyperspectral data and further correlates them with biodiversity indicators. For example, quantitative inversion of specific canopy traits combined with corresponding diversity indicators can be used to estimate functional diversity [61], and species distribution patterns can be indirectly expressed through habitat mapping [62]. The continuous and rich spectral information of hyperspectral images not only more accurately reflects the subtle differences in the reflection properties of surface materials, but it also exhibits a strong correlation between bands and contains abundant redundant information, which can characterize the plants that are sensitive to disease and insect pest stress. [PERSON] et al. used hyperspectral imaging data and found that modeling combining the sequential projection algorithm (SPA) and random forest (RF) can improve the accuracy in estimating the severity of mangrove pests and diseases [63]. [PERSON] et al. proposed a pine caterpillar pest classification framework based on the spectral space of UAV hyperspectral images, which can effectively assess the losses caused by pine caterpillar pests [64]. [PERSON] et al. studied Yunnan pine forests infected with insect pests; extracted the vegetation index and continuous wavelet characteristics; and combined discriminant analysis, support vector machine, and backpropagation neural network modeling to monitor the pests in the Yunnan pine forests [65]. [PERSON] et al. implemented automatic detection of winter wheat yellow rust in hyperspectral images based on high spatial resolution UAV hyperspectral images [66]. [PERSON] et al. used hyperspectral data to monitor stress on deciduousiferous forests and effectively detected larch looper insect damage in the early stages of coniferous forests [67]. [PERSON] et al.