Estimation of sparse vegetation coverage in arid region based on hyperspectral mixed pixel decomposition
Li, X-Song.; Gao, Z-Hai.; Li, Z-Yuan.; Bai, L-Na.; Wang, B-Yu.
Ying Yong Sheng Tai Xue Bao 21(1): 152-158
2010
ISSN/ISBN: 1001-9332 PMID: 20387437 Document Number: 646342
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.