title: [Estimation of sparse vegetation coverage in arid region based on hyperspectral mixed pixel decomposition].
abstract: Based on Hyperion hyperspectral image data, the image-derived shifting sand, false-Gobi spectra, and field-measured sparse vegetation spectra were taken as endmembers, and the sparse vegetation coverage (< 40%) in Minqin oasis-desert transitional zone of Gansu Province was estimated by using fully constrained linear spectral mixture model (LSMM) and non-constrained LSMM, respectively. The results showed that the sparse vegetation fraction based on fully constrained LSMM described the actual sparse vegetation distribution. The differences between sparse vegetation fraction and field-measured vegetation coverage were less than 5% for all samples, and the RMSE was 3.0681. However, the sparse vegetation fraction based on non-constrained LSMM was lower than the field-measured vegetation coverage obviously, and the correlation between them was poor, with a low R2 of 0.5855. Compared with McGwire's corresponding research, the sparse vegetation coverage estimation in this study was more accurate and reliable, having expansive prospect for application in the future.
authors.affiliation: Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China. lixs@irsa.ac.cn
authors.forename: Xiao-Song
authors.initials: XS
authors.lastname: Li
authors.forename: Zhi-Hai
authors.initials: ZH
authors.lastname: Gao
authors.forename: Zeng-Yuan
authors.initials: ZY
authors.lastname: Li
authors.forename: Li-Na
authors.initials: LN
authors.lastname: Bai
authors.forename: Beng-Yu
authors.initials: BY
authors.lastname: Wang
mesh_terms: D002681:China; D003247:Conservation of Natural Resources; D003889:Desert Climate; D017753:Ecosystem; D004784:Environmental Monitoring; D008962:Models, Theoretical; D006109:Poaceae / Q000254:growth & development*; D013057:Spectrum Analysis / Q000379:methods; D014197:Trees / Q000254:growth & development*