基于图像融合与混合像元分解的城市植被盖度提取

城市植被盖度提取对于开展城市绿色空间保护和城市规划具有重要意义。随着遥感技术的发展,混合像元分解模型被广泛用于从中等分辨率的多光谱影像提取城市植被盖度,但较低的影像空间分辨率限制了该模型的应用领域。为此,以杭州市为例,首先引入Gram-Schmidt(GS)方法对Landsat ETM+的多光谱波段和全色波段进行融合,再通过混合像元分解模型从ETM+融合影像上提取城市植被盖度,最后利用SPOT影像进行精度检验。结果发现,采用GS方法对影像进行融合后,标准差、信息熵、平均梯度提高,相对偏差小于0.07,说明在保留多光谱信息的基础上提高了其空间分辨率。与SPOT影像相比,在融合影像上75%以上样本的植被盖度值相似,误差较大的区域是市区植被特别稀疏或茂盛的像元。与源影像相比,从融合影像上提取的植被盖度的均方根误差和系统误差降低了0.01。该方法在降低城市植被监测成本、提高监测精度方面具有潜力。

Estimation of urban vegetation fraction by image fusion and spectral unmixing

Estimation of urban vegetation fraction is helpful for urban green space protection and urban land use planning. With the development of remote sensing technologies, the spectral unmixing method has been widely used in estimating urban vegetation fraction based on middle-resolution multispectral imagery. However, the spectral unmixing method largely depends on the spatial resolution of the images used, limiting its extensive applications in practice. Taking Hangzhou as a case study, we proposed the Gram-Schmidt (GS) algorithm to fuse the Landsat Enhanced Thematic Mapper plus (ETM+) PAN band with the ETM+ multispectral bands. A linear model of spectral unmixing was then applied in the estimation of vegetation fraction based on the fused ETM+ image. Finally, the accuracy of vegetation fraction derived from the fused ETM+ image was assessed using high-resolution SPOT imagery. The results show that the fused image had a higher standard deviation, information entropy and average gradient than the original image. The relative deviation between the images was less than 0.07, indicating advantages of increasing spatial resolution while maintaining spectral consistency with the original image by GS method. Based on random sampling, we found the estimated results of vegetation fraction from the fused ETM+ and SPOT images were comparable, which was reflected by more than 75% of samples having similar values of vegetation fraction from the two data sources, except for a few unmatched pixels with very high or low vegetation fraction. Furthermore, the root-mean-square error and systematic error of the fused image decreased by 0.01 compared with those of the original image. These results suggest that the new method holds potential for improving the estimation accuracy of urban vegetation fraction without the substantial cost of acquiring high spatial resolution images.