Estimating the Urban Fractional Vegetation Cover Using an Object-Based Mixture Analysis Method and Sentinel-2 MSI Imagery

Abstract

Accurate and efficient identification of the urban vegetation abundance is of great importance for urban planning and management. A lot of efforts have been made to estimate the urban fractional vegetation cover (FVC) using multispectral images by the pixel-based mixture analysis method. However, urban FVC maps comprising various meaningful landscapes have wider applications. Compared with other moderate spatial resolution multispectral imagery (e.g., SPOT, Landsat 8), the Sentinel-2 multispectral instrument (MSI) imagery has higher resolution, larger coverage, and shorter revisit time. So it may provide higher accuracy for urban FVC mapping. This article derives an accurate object-based urban FVC map for Changsha city, China, from the 10-m resolution Sentinel-2 data acquired in 2017. For producing the urban FVC maps, the mixture analysis methods were applied on segmental image objects instead of pixels. The results demonstrate that the object-based mixture analysis method achieved a higher FVC estimation accuracy than the pixel-based mixture analysis did, and it effectively removed the 'salt and pepper' phenomena. The object-based linear model fully constrained least squares and achieved the best estimation accuracy (R2 = 0.92, RMSE = 0.0956). The red-edge band reflectance information of the MSI imagery can improve the accuracy of the FVC maps, but not significantly. The object-based urban FVC maps would be a good alternative to the traditional pixel-based maps.