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The Change of Growing Season of the Vegetation in Huanghe-Huaihe-Haihe Region and Its Responses to Climate Changes
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摘要: 利用1982—2000年的NOAA/AVHRR逐旬数据，采用最大变化斜率法、曲线拟合法等方法分析黄淮海地区植被生长季开始及结束时间，选取典型样带，制作基于逐像元的INDV(归一化植被指数) 变化图，研究1982—2000年植被生长季的变化规律及其对气候变化的响应。结果表明：黄淮海地区生长季开始的平均时间为3月下旬，结束时间为11月上旬。20年来研究区植被四季平均INDV呈上升趋势，春季增长尤为显著，且随着年代的推移，植被生长季有延长的趋势，生长季提前是黄淮海地区植被活动对气候变化响应的主要方式。Abstract: Using the each ten-day data of NOAA/AVHRR from 1981 to 2000 and adopting the Maximum-Slope method, Curve-Fitting method and stepwise regression, the beginning and ending of the growth season of the vegetations in the Huanghe-Huaihe-Haihe (HHH) region are analyzed. Meanwhile, the pixel by pixel INDV map is worked out with typical bands as the sample. Through analyzing the change of green wave and brown wave, the changing rules of vegetation activities and its responses to climate changes in the past 20 years are revealed. The growth season in HHH region starts at the last 10 days of March with INDV 0.19604 and ends at the first 10 days of November with INDV 0.22899 on average. The tendency is that the growing season has prolonged obviously by starting earlier and earlier, while ending later and later from 1982 to 2000. The average INDV for the vegetation in the researched area in the past 20 years is generally increasing, especially in spring. According to the INDV map of typical bands as the sample worked out with the pixel by pixel, the green wave changes from south to north during January—July on the band of 116°E, the maximum of INDV occurs in August and decreases gradually after September. On the band of 36°N, to the east and nearby 116°E, the INDV has two peaks because of two crops a year, which is obviously different from other regions. The two peaks occur in April and August and they become clearer and clearer from 1982 to 2000, especially in agriculture area. However, the two peaks are not obvious to the north of 38.5°N, in the area of Beijing, Tianjin and the mountain areas, and the peak value of early summer is lower than that of autumn. In agriculture area, the peak interval of green wave is broadening with the time passing by, and it's more obviously from south to north. With the time passing by, the green wave of the critical value of the growth season in the area changes from south to north, while the emergence of brown wave prolongs from north to south. According to the stepwise regression result of the every ten-day meteorological data and the INDV of the typical sampling areas in the HHH region, the INDV of vegetation is closely related to the temperature and precipitation, especially it's more sensitive to the temperature, and the significance of the correlation is 0.01. The prolonging of vegetation growth season is a main response to climate changes of vegetation activities in target areas.
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Key words:
- INDV;
- climate change;
- growing season;
- Huanghe-Huaihe-Haihe region
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图 1 多年旬平均INDV变化曲线 (a) 及变化斜率曲线 (b)
Fig. 1 Multi-year average NDVI ten-day changing curve (a) and changing slope curve (b)
图 2 多年旬平均INDV变化曲线多项式拟合
Fig. 2 Multi fitting of multi-year average NDVI ten-day changing curve
图 3 1982—2000年生长季开始时间 (a) 和结束时间 (b) 变化曲线
Fig. 3 Changing curve on the beginning time (a) and ending time (b) of growth season from 1982 to 2000
图 4 1982—2000年116°E经向带 (a) 和36°N纬向带 (b) 上常年月平均植被指数变化剖面图
Fig. 4 Cross section of vegetation index changing in 116°E meridional band (a) and 36°N zonal band (b) during 1982—2000
图 5 1982—2000年116°E经向带 (a) 和36°N纬向带 (b) 上植被指数变化剖面图
Fig. 5 Cross section of vegetation index changing in 116°E meridional band (a) and 36°N zonal band (b) during 1982—2000
图 6 1982—2000年不同季节植被平均INDV变化趋势
Fig. 6 Average INDV changes of vegetations in different seasons from 1982 to 2000
图 7 典型样区植被旬变化曲线 (a) 及3月下旬植被INDV年际变化曲线 (b)
Fig. 7 Annual INDV changing curve of vegetations (a) and vegetations in the last 10 days of March (b) in target areas
表 1 样区植被旬平均INDV与旬气候因子相关系数
Table 1 Relativity index of each ten-day average INDV of vegetations in target areas and the ten-day climate factors
植被类型 温度 降水 农田 0.538* 0.304* 落叶阔叶林 0.894* 0.435* 注：*表示通过α＝0.01的显著性检验。 
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