Urban green space classification and water consumption analysis with remote-sensing technology: a case study in Beijing, China

摘要

The water consumption of green space in a large region is difficult to attain through traditional methods. In this article, a practical method is developed using different sources of remote-sensing data. The green space was first derived from a high spatial resolution RapidEye image using the stratified classification method. Then the primary vegetation types of green space were identified using the object-oriented classification method. Afterwards regional green space evapotranspiration was inversed based on multi-temporal Landsat 8 images using the Surface Energy Balance Algorithm for Land model. Finally, water consumption patterns for different types of vegetation were analysed, and regional water consumption was estimated. The method was applied to the northwest region of Beijing City with an area of 147.5km(2) where the green space area was 56.87km(2), and the deciduous broadleaf forest area was the largest among six vegetation types. The total quantity of water consumption for green space in the growing period in the study region was 41.52x10(6)m(3) (Mm(3)). The quantity of water consumed by different types of vegetation in an order from high to low were deciduous broadleaf forest, mixed green space, grassland, evergreen needleleaf forest, golf course, and aquatic vegetation, ranging from 17.43 to 0.79Mm(3). The results are helpful for identifying vegetation types, monitoring vegetation growth status, managing green space, and optimizing green space ecological functions in the Beijing region. The method presented in this article, having higher accuracy and more convenience, has great potential to be applied to other areas across the world.