Qinling Phenophase And Remote Sensing Vegetation Growing Season Change Research

Aims to reveal the Qinling mountains vegetation growth periods changes in the recent50years, this paper studied the Phonological phenomenon in Qinling Mountains of shaanxi from1963to2008based on Xi’an phenophase observation station and from1998to2009SPOT/NDVI data sets by using Smoothed moving average and NDVI average value. correlation analysis and linear regression of one variable techni-ques. Main conclusions and progress are as follows:(1)46a phenological variation of the growing season.Average advance of1.8days in1960-1989,26years, eight kinds of plants the beginning of growing season. Ahead of trend in which the willow is the most signi-ficant of2.4d/26a. The growing season in late26years postponed for1.0days. Earli-er beginning of growing season in in the1990-2009time of1.1d/20a (r=0.44), is still ahead of trends of the willow is the most significant, as2.9d/20a (r=0.70). di-fferent stages of growth period trend of the ending of season more stable, the average in20years postponed0.6days (r=0.31).(2) Temperature on the phenological growing season affect the relationship and time-delay.The growing season in the temperature of the beginning of warming in1960-1989before the temperature of mutations (1989),30a within risen by0.75℃(r=0.98). Three10a1960-1989growing season in the beginning of the average tempe-rature, respectively:9.98℃,10.81℃.11.38℃. the temperature of the growing season in late in a30, increased by0.38℃(r=0.99) in three10a average tempe-rature:13.51℃,13.52℃,13.89℃. After the point of sudden change in temper-ature of1990-2009growing season in the beginning of the temperature increase of0.30℃/20a (r=0.92), the growing season in the end of the temperature dropped by0.18℃/20a.Effective accumulated temperature of the growing season beginning day showed a consistent trend through the Pearson correlation analysis, correlation coefficients of the two sequences:0.782,0.812, and all passed the test on the0.01level was signify cantly correlated.(3) Trend-based remote sensing of the growing season NDVI data simulation.Smoothed moving average analog growing season trend:The early start of the90th day, the end of the280th day, medium-term period of the beginning of the80th day, the290th day, late beginning of a period of the70th day, the290th day.NDVI mean Simulation results show that: The early growing season in beginning a period of the120th day, the280th day, the mid-growing season in the beginning of the100th day, the300th day, late growing season in beginning a period of the100th day,the300th day.The results of these two kinds of methods reveals the growing season of the stu-dy area began to show ahead of trends, the end of showing the trend postponed, alth-ough the range was different earlier or later, but the overall trend is very consistent.(4) The impact of temperature on remote sensing growing season and lag presc-ription.Impact on NDVI in temperature there is a the20-30th day time lag effect.in-dicating that the study area, plants started to recover, the date of NDVI in the rapid increase in the previous t2-3ten days temperatures have a significant relationship. It is can be explained by NDVI in the rapid increase in the time depends on before this20-30day temperatures.(5) NDVI timing verification phenological growing seasonQualitatively elevated NDVI timing, the the beginning of the growing season, was ahead of the trend, the end of growing season delay, if the NDVI timing decline represents the delay trend in the growing season, beginning of the end of growth seas-on ahead of trends.When the NDVI value of0.43, indicating the arrival of the beginning of the Remote Sensing growing season, NDVI series curve to achieve the highest inter-annual value, followed by gradual decline to NDVI in=0.41, most of the vegetation into the leaf color of surface green area is drastically reduced, a substantial decline in NDVI in value, the phenophase enter late.