Monitoring and Change Trends of Phenological Phase in the Qilian Mountains Based on Remote Sensing
Received date: 2015-03-14
Revised date: 2015-06-21
Online published: 2015-09-20
Based on GIMMS NDVI data during 1982-2006, the spatial pattern of the start of growing season (SOS), the end of growing season (EOS) and the length of growing season (LOS) were extracted to analyze the average spatial distribution, change trends as well as its spatial distribution characteristics of phenological phase by using double logistic model in Qilian Mountains. The results show that: vegetation in the Qilian Mountains from southeast to northwest gradually turn green, and gradually turn yellow from the northwest to the southeast. The LOS of vegetation is longer in southeast area than that in northwest area, and LOS is longer in valley area than that in the alpine area. During 1982 to 2006, the inter annual variation trend of SOS dates shows earlier by the rate of 0.044 days per year, in which the variation trend in different decadal years of the SOS shows delay-advance-delay. The inter annual variation trend of EOS dates also shows earlier by the rate of 0.059 days per year, in which the variation trend in different decadal years of the EOS shows delay-advance. The LOS of Vegetation becomes slightly shorter by the rate of 0.015 days per year, in which the variation trend in different decadal years of the LOS shows shorter-longer-shorter. The regions without earlier SOS and EOS during 1982-2006 were mainly located in alpine area with 51.46% and 42.77%, respectively. But the regions without postponed SOS and EOS were located in valley area with 44.41% and 52.91%, respectively. The LOS of vegetation in alpine is shortened unobvious, while that of prolonged unobvious area is in valley area. Generally, there is no significant variation existed in the vegetation phenology in the study area.
Zhao Zhen , Jia Wenxiong , Zhang Yushun , Liu Yarong , Chen Jinghua . Monitoring and Change Trends of Phenological Phase in the Qilian Mountains Based on Remote Sensing[J]. Journal of Desert Research, 2015 , 35(5) : 1388 -1395 . DOI: 10.7522/j.issn.1000-694X.2015.00093
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