基于高光谱混合像元分解的干旱地区稀疏植被覆盖度估测

以Hyperion高光谱影像为数据源,选取流沙、假戈壁(影像端元)及荒漠植被(实测光谱端元)3种端元,利用非受限及全受限的混合像元分解对甘肃省民勤绿洲-荒漠过渡带的稀疏植被覆盖度进行了估测.结果表明:全受限混合像元分解得到的荒漠植被分量准确地代表了地表真实稀疏植被覆盖情况,两者之间的偏差不超过5%、均方根误差RMSE为3.0681;而非受限的混合像元分解结果则明显小于地面实测植被覆盖度,两者之间虽具有一定相关性,但相关性不高(R2=0.5855);与McGwire等的相关研究相比,全受限混合像元分解对稀疏植被覆盖度的估测具有更高的精度及可靠性,具有广阔的应用前景.

Estimation of sparse vegetation coverage in arid region based on hyperspectral mixed pixel decomposition.

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 constrai
ned linear spectral mixture model (LSMM) and non constrained LSMM, respectively. The results showed that the sparse vegetation fraction based on fully constrai
ned LSMM described the actual sparse vegetation distribution. The differences between sparse vegetation fraction and field measured vegetation coverage were l
ess 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.