Seasonal Inversion of Living Vegetation Volume in Tangdao Bay Based on Optical and Sar Satellite Data

The ecological service functions of urban green vegetation are closely related to its three-dimensional spatial structure. Regression method is widely used in extraction of vegetation three-dimensional information, such inversion of living vegetation volume (LVV), owing to its simplicity and efficiency. This kind of inversion models commonly built based on characteristics of vegetations, which always varies with the seasons. Thus, establish inversion models for different seasons separately is desirable. In this study, optical and SAR satellite data are combined to develop a seasonal LVV inversion method in Tangdao Bay coastline, Qingdao, China. In this method, the Sentinel-2 and Sentinel-1 satellite images are firstly used to classify the vegetation in summer and winter. Then, a variety of spectral, polarized and structural features are extracted from Sentinel-2 and Sentinel-1 data to build a LVV candidate inversion feature set. Lastly, two linear regression models based on different inversion features are developed. Accordingly, the seasonal LVV of urban area in Tangdao Bay coastline are obtained. The results show that the seasonal model can effectively improve the accuracy of LVV inversion.