title: [Flower species identification and coverage estimation based on hyperspectral remote sensing data in Hulunbeier grassland].
abstract: Monitoring grassland species and area real-timely and accurately is of great significance in species diversity research, as well as in sustainable development of ecosystem. Flowers have their own unique spectral characteristics. Compared with the nutrient stage, species are more easily identified by florescence. So, florescence is a critical period for identification. In the present paper, spectral differences among such flowers as Galium verum Linn., Hemerocallis citrina Baroni, Serratula centauroides Linn., Clematis hexapetala Pall., Lilium concolor var. pulchellum, Lilium pumilum and Artemisia frigida Willd. Sp. Pl. were found, along with identification methods, by analyzing canopies spectra and parametrizing characteristics. Verification results showed that when the coverage of flowers was greater than 10%, the accuracy of identification methods would be higher than 90%. On this basis, linear unmixing model was adopted to calculate the area of flowers in quadrates. Results showed that linear unmixing model was an effective method for estimating the coverage of flowers in grassland because the accuracy was about 4%.
authors.affiliation: RS and GIS Institute of Peking University, Beijing 100871, China. gaiyingying@pku.edu.cn
authors.forename: Ying-Ying
authors.initials: YY
authors.lastname: Gai
authors.forename: Wen-Jie
authors.initials: WJ
authors.lastname: Fan
authors.forename: Xi-Ru
authors.initials: XR
authors.lastname: Xu
authors.forename: Bin-Yan
authors.initials: BY
authors.lastname: Yan
authors.forename: Huan-Jiong
authors.initials: HJ
authors.lastname: Wang
authors.forename: Yuan
authors.initials: Y
authors.lastname: Liu
mesh_terms: D003247:Conservation of Natural Resources; D035264:Flowers / Q000145:classification*; D065948:Grassland; D016014:Linear Models; D058998:Remote Sensing Technology; D013057:Spectrum Analysis