ANALYSIS OF TEMPORAL AND SPATIAL CHARACTERISTICS OF MANGROVE PHYSIOLOGICAL STRUCTURE PARAMETERS QUANTITATIVE INVERSION BASED ON TIME SERIES SENTINEL-2 – TAKING SHANKOU MANGROVE NATURE RESERVE AS AN EXAMPLE
- College of Geomatics and Geoinformation, Guilin University of Technology, No. 12 Jian’gan Road, Guilin, Guangxi, China
Keywords: Physiological Structural Parameters, Mangroves, Sequential Sentinel-2,Remote Sensing Inversion, Neural Network Algorithm
Abstract. The physiological structure parameter of vegetation is an important index to measure the ecological health status of mangroves, and it is of great value to measure the ecological health status. Taking Shankou Mangrove Nature Reserve of Guangxi as the study area, The Chlorophyll-A/B(CAB),Leaf Area Index (LAI),Fractional Vegetation Cover (FVC) and Fraction of absorbed photo synthetically active radiation (FAPAR) were calculated by using Sentinel-2 image data to calculate the chlorophyll content of mangrove vegetation in the study area. The BP neural network algorithm is used to verify the accuracy difference between the inversion results and the corresponding products of MODIS, and the dynamic changes of physiological structure parameters of vegetation in mangroves are further studied. Results showed that: (1)The correlation coefficients between LAI, CAB, FAPAR, FVC and MODIS products were higher than 0.71 in 95% confidence interval in mangrove years, it is proved that Sentinel-2A/B multispectral image inversion of mangrove physiological structure parameters has high accuracy and quality. (2)The physiological structure parameters of mangroves fluctuated during the year. In February, the lowest values of LAI, CAB, FAPAR and FVC were 0.30, 0.08, 0.08 and 0.13, respectively. The highest values were 0.69 in October, 0.29 in December, 0.27 in August, 0.40 in April, 0.24 and 0.24 in September, and 0.27 and 0.33 in LAI in November, respectively, with the highest LAI in October, 0.29 in December, 0.27 in August, 0.40 in April, 0.24 and 0.24 in September, and 0.27 and 0.33 in November. The results provide the basis for the monitoring of mangrove vegetation change and provide a reference for ecological assessment and protection.