Abstract
High temporal resolution remote sensing satellite data can be used to collect vegetation phenology observations over regional and global scales. Logistic and polynomial functions are the most widely used methods for fitting time series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, the maximum in the curvature of the fitted curve is usually considered as the spring green-up date. However, the existing green-up date calculation methods have low accuracy for sparse vegetation. This paper proposes an improved green-up date calculation method using a coupled model and anomalous point detection (CMAPD). This model is based on a combination of logistic and polynomial functions, which is used to fit time series vegetation index. Anomalous values were identified using the nearest neighbor algorithm, and these values were corrected by the combination of growing degree-days (GDD) and land use type. Then, the trends and spatial patterns of green-up date was analyzed in the Sanjiangyuan area. The results show that the coupled model fit the time series data better than a single logistic or polynomial function. Besides, the anomalous point detection method properly controlled the green-up date within the local threshold, and could reflect green-up date more accurately. In addition, a weak statistically significant advance trend for average vegetation green-up date was observed from 2000 to 2016. However, in 10.4% of the study area, the the green-up date has significant advanced. Regression analysis showed that the green-up date is correlated to elevation: the green-up date is clearly later at higher elevations.
Highlights
The scope of global warming has expanded to the ecological zone in China, and one of the most serious impacts is observed in the Sanjiangyuan Nature Reserve located in Qinghai Province
The green-up date in the east is obviously later than that in the west, which is related to the altitude situation, as the east of the Sanjiangyuan Nature Reserve is lower than the west
The vegetation is sparse in the central part of the Sanjiangyuan Nature Reserve, and the green-up date is later than other areas, which is related to the high altitude
Summary
The scope of global warming has expanded to the ecological zone in China, and one of the most serious impacts is observed in the Sanjiangyuan Nature Reserve located in Qinghai Province. Lin et al [5] studied the effects of grazing and other human activities on the ecological environment and spring vegetation phenology of the Sanjiangyuan Nature Reserve. Human activities were the main factors that affected vegetation growth in the area 10 years ago [6,7]. Climate change has become the main factor affecting vegetation degradation recent years [8,9]. Wang et al [12,13] studied the change of snow cover in high altitude areas and evaluated the effects on vegetation in the Sanjiangyuan Nature Reserve. Monitoring vegetation spring phenology provides a good reference for the better understanding and prediction of the effects of climate change in this area [14]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call