Extracting vegetation phenology metrics in Changbai Mountains using an improved logistic model

Extracting vegetation phenology metrics in Changbai Mountains using an improved logistic model

Chinese Geographical Science

Remotely sensing images are now available for monitoring vegetation dynamics over large areas. In this paper, an improved logistic model that combines double logistic model and global function was developed. Using this model with SPOT/NDVI data, three key vegetation phenology metrics, the start of growing season (SOS), the end of growing season (EOS) and the length of growing season (LOS), were extracted and mapped in the Changbai Mountains, and the relationship between the key phenology metrics and elevation were established. Results show that average SOS of forest, cropland and grassland in the Changbai Mountains are on the 119th, 145th, and 133rd day of year, respectively. The EOS of forest and grassland are similar, with the average on the 280th and 278th, respectively. In comparison, average EOS of the cropland is relatively earlier. The LOS of forest is mainly from the 160th to 180th, that of the grassland extends from the 140th to the 160th, and that of cropland stretches from the 110th to the 130th. As the latitude increases for the same land cover in the study area, the SOS significantly delays and the EOS becomes earlier. The SOS delays approximately three days as the elevation increases 100 m in the areas with elevation higher than 900 m above sea level (a. s. l.). The EOS is slightly earlier as the elevation increases especially in the areas with elevation below 1200 m a. s. l. The LOS shortens approximately four days as the elevation increases 100 m in the areas with elevation higher than 900 m a. s. l. The relationships between vegetation phenology metrics and elevation may be greatly influenced by the land covers. Validation by comparing with the field data and previous research results indicates that the improved logistic model is reliable and effective for extracting vegetation phenology metrics.