Vegetation indices(VIs) from satellite remote sensing have been extensively applied to analyze the trends of vegetation phenology. In this paper, the NDVI(normalized difference vegetation index) and SR(simple ration),...Vegetation indices(VIs) from satellite remote sensing have been extensively applied to analyze the trends of vegetation phenology. In this paper, the NDVI(normalized difference vegetation index) and SR(simple ration), which are calculated from the same spectral bands of MODIS data with different mathematical expressions, were used to extract the start date(SOS) and end date(EOS) of the growing season in northern China and Mongolia from 2000 to 2015. The results show that different vegetation indices would lead to differences in vegetation phenology especially in their trends. The mean SOS from NDVI is 15.5 d earlier than that from SR, and the mean EOS from NDVI is 13.4 d later than that from SR. It should be noted that 16.3% of SOS and 17.2% of EOS derived from NDVI and SR exhibit opposite trends. The phenology dates and trends from NDVI are also inconsistent with those of SR among various vegetation types. These differences based on different mathematical expressions in NDVI and SR result from different resistances to noise and sensitivities to spectral signal at different stage of growing season. NDVI is prone to be effected more by low noise and is less sensitive to dense vegetation. While SR is affected more by high noise and is less sensitive to sparse vegetation. Therefore, vegetation indices are one of the uncertainty sources of remote sensing-based phenology, and appropriate indices should be used to detect vegetation phenology for different growth stages and estimate phenology trends.展开更多
Daily precipitation rates observed at 576 stations in China from 1961 to 2000 were classified into six grades of intensity, including trace （no amount）, slight （≤ 1 mm d^-1）, small, large, heavy, and very heavy. ...Daily precipitation rates observed at 576 stations in China from 1961 to 2000 were classified into six grades of intensity, including trace （no amount）, slight （≤ 1 mm d^-1）, small, large, heavy, and very heavy. The last four grades together constitute the so called effective precipitation （〉 1 mm d^-1）. The spatial distribution and temporal trend of the graded precipitation days are examined. A decreasing trend in trace precipitation days is observed for the whole of China, except at several sites in the south of the middle section of the Yangtze River, while a decreasing trend in slight precipitation days only appears in eastern China. The decreasing trend and interannual variability of trace precipitation days is consistent with the warming trend and corresponding temperature variability in China for the same period, indicating a possible role played by increased surface air temperature in cloud formation processes. For the effective precipitation days, a decreasing trend is observed along the Yellow River valley and for the middle reaches of the Yangtze River and Southwest China, while an increasing trend is found for Xinjiang, the eastern Tibetan Plateau, Northeast China and Southeast China. The decreasing trend of effective precipitation days for the middle- lower Yellow River valley and the increasing trend for the lower Yangtze River valley are most likely linked to anomalous monsoon circulation in East China. The most important contributor to the trend in effective precipitation depends upon the region concerned.展开更多
This paper explores urban temperature in Hong Kong using long-term time series. In particular, the characterization of the urban temperature trend was investigated using the seasonal unit root analysis of monthly mean...This paper explores urban temperature in Hong Kong using long-term time series. In particular, the characterization of the urban temperature trend was investigated using the seasonal unit root analysis of monthly mean air temperature data over the period January 1970 to December 2013. The seasonal unit root test makes it possible to determine the stochastic trend of monthly temperatures using an autoregressive model. The test results showed that mean air temperature has increased by 0.169~ C （10 yr） - 1 over the past four decades. The model of monthly temperature obtained from the seasonal unit root analysis was able to explain 95.9% of the variance in the measured monthly data -- much higher than the variance explained by the ordinary least-squares model using annual mean air temperature data and other studies alike. The model accurately predicted monthly mean air temperatures between January 2014 and December 2015 with a root-mean-square percentage error of 4.2%. The correlation between the predicted and the measured monthly mean air temperatures was 0.989. By analyzing the monthly air temperatures recorded at an urban site and a rural site, it was found that the urban heat island effect led to the urban site being on average 0.865~C warmer than the rural site over the past two decades. Besides, the results of correlation analysis showed that the increase in annual mean air temperature was significantly associated with the increase in population, gross domestic product, urban land use, and energy use, with the R2 values ranging from 0.37 to 0.43.展开更多
植被净初级生产力(Net Primary Productivity,NPP)是反映陆地生态系统碳封存能力和环境变化的直接指标,受气候变化与人类活动的共同影响,且在不同地形上有分异性。然而,人类活动及地形对秦巴山区植被NPP变化的影响研究尚且不足。采用CAS...植被净初级生产力(Net Primary Productivity,NPP)是反映陆地生态系统碳封存能力和环境变化的直接指标,受气候变化与人类活动的共同影响,且在不同地形上有分异性。然而,人类活动及地形对秦巴山区植被NPP变化的影响研究尚且不足。采用CASA模型,综合利用线性趋势分析、转移矩阵和残差分析等方法研究了秦巴山区2001—2022年长时序NPP时空动态和地形效应,并进一步探讨了气候变化和人类活动对NPP变化的相对贡献率,主要结论如下:①秦巴山区2001—2022年的NPP空间分布表现为中间高,四周低,均值为585.11g C/m^(2),并以4.30g C m^(-2)a^(-1)的速度增加。②林地有最高的年NPP均值,而退耕还林区域具有最高的NPP增长速率(8.17g C m^(-2)a^(-1)),表明退耕还林是秦巴山区NPP增长的有效措施;③NPP随海拔和坡度变化具有明显的分异性。在海拔3400m以下,植被NPP随着高程的增加而增加,而当高程超过3400m时,植被NPP显著减少,坡度在10°—40°范围内植被NPP的多年均值和变化趋势较高;④秦巴山区NPP变化是气候变化和人类活动共同作用的结果,二者对NPP变化的相对贡献率分别为37.81%和62.19%,其中人类活动导致陇南等生态脆弱区NPP显著提高。展开更多
基金Under the auspices of the Strategic Priority Research Program of the Chinese Academy Sciences(No.XDA19080303)the National Key Research and Development Program for Global Change and Adaptation(No.2016YFA0600201)+1 种基金the Distinctive Institutes Development Program,Chinese Academy of Sciences(No.TSYJS04)the National Natural Sciences Foudation of China(No.41171285)
文摘Vegetation indices(VIs) from satellite remote sensing have been extensively applied to analyze the trends of vegetation phenology. In this paper, the NDVI(normalized difference vegetation index) and SR(simple ration), which are calculated from the same spectral bands of MODIS data with different mathematical expressions, were used to extract the start date(SOS) and end date(EOS) of the growing season in northern China and Mongolia from 2000 to 2015. The results show that different vegetation indices would lead to differences in vegetation phenology especially in their trends. The mean SOS from NDVI is 15.5 d earlier than that from SR, and the mean EOS from NDVI is 13.4 d later than that from SR. It should be noted that 16.3% of SOS and 17.2% of EOS derived from NDVI and SR exhibit opposite trends. The phenology dates and trends from NDVI are also inconsistent with those of SR among various vegetation types. These differences based on different mathematical expressions in NDVI and SR result from different resistances to noise and sensitivities to spectral signal at different stage of growing season. NDVI is prone to be effected more by low noise and is less sensitive to dense vegetation. While SR is affected more by high noise and is less sensitive to sparse vegetation. Therefore, vegetation indices are one of the uncertainty sources of remote sensing-based phenology, and appropriate indices should be used to detect vegetation phenology for different growth stages and estimate phenology trends.
基金This research was supported by the National Natural Science Foundation of China （Grant Nos. 90502001 and 40475032）the National Key Program for Developing Basic Sciences in China （Grant No. 2006CB403602）.
文摘Daily precipitation rates observed at 576 stations in China from 1961 to 2000 were classified into six grades of intensity, including trace （no amount）, slight （≤ 1 mm d^-1）, small, large, heavy, and very heavy. The last four grades together constitute the so called effective precipitation （〉 1 mm d^-1）. The spatial distribution and temporal trend of the graded precipitation days are examined. A decreasing trend in trace precipitation days is observed for the whole of China, except at several sites in the south of the middle section of the Yangtze River, while a decreasing trend in slight precipitation days only appears in eastern China. The decreasing trend and interannual variability of trace precipitation days is consistent with the warming trend and corresponding temperature variability in China for the same period, indicating a possible role played by increased surface air temperature in cloud formation processes. For the effective precipitation days, a decreasing trend is observed along the Yellow River valley and for the middle reaches of the Yangtze River and Southwest China, while an increasing trend is found for Xinjiang, the eastern Tibetan Plateau, Northeast China and Southeast China. The decreasing trend of effective precipitation days for the middle- lower Yellow River valley and the increasing trend for the lower Yangtze River valley are most likely linked to anomalous monsoon circulation in East China. The most important contributor to the trend in effective precipitation depends upon the region concerned.
文摘This paper explores urban temperature in Hong Kong using long-term time series. In particular, the characterization of the urban temperature trend was investigated using the seasonal unit root analysis of monthly mean air temperature data over the period January 1970 to December 2013. The seasonal unit root test makes it possible to determine the stochastic trend of monthly temperatures using an autoregressive model. The test results showed that mean air temperature has increased by 0.169~ C （10 yr） - 1 over the past four decades. The model of monthly temperature obtained from the seasonal unit root analysis was able to explain 95.9% of the variance in the measured monthly data -- much higher than the variance explained by the ordinary least-squares model using annual mean air temperature data and other studies alike. The model accurately predicted monthly mean air temperatures between January 2014 and December 2015 with a root-mean-square percentage error of 4.2%. The correlation between the predicted and the measured monthly mean air temperatures was 0.989. By analyzing the monthly air temperatures recorded at an urban site and a rural site, it was found that the urban heat island effect led to the urban site being on average 0.865~C warmer than the rural site over the past two decades. Besides, the results of correlation analysis showed that the increase in annual mean air temperature was significantly associated with the increase in population, gross domestic product, urban land use, and energy use, with the R2 values ranging from 0.37 to 0.43.
文摘植被净初级生产力(Net Primary Productivity,NPP)是反映陆地生态系统碳封存能力和环境变化的直接指标,受气候变化与人类活动的共同影响,且在不同地形上有分异性。然而,人类活动及地形对秦巴山区植被NPP变化的影响研究尚且不足。采用CASA模型,综合利用线性趋势分析、转移矩阵和残差分析等方法研究了秦巴山区2001—2022年长时序NPP时空动态和地形效应,并进一步探讨了气候变化和人类活动对NPP变化的相对贡献率,主要结论如下:①秦巴山区2001—2022年的NPP空间分布表现为中间高,四周低,均值为585.11g C/m^(2),并以4.30g C m^(-2)a^(-1)的速度增加。②林地有最高的年NPP均值,而退耕还林区域具有最高的NPP增长速率(8.17g C m^(-2)a^(-1)),表明退耕还林是秦巴山区NPP增长的有效措施;③NPP随海拔和坡度变化具有明显的分异性。在海拔3400m以下,植被NPP随着高程的增加而增加,而当高程超过3400m时,植被NPP显著减少,坡度在10°—40°范围内植被NPP的多年均值和变化趋势较高;④秦巴山区NPP变化是气候变化和人类活动共同作用的结果,二者对NPP变化的相对贡献率分别为37.81%和62.19%,其中人类活动导致陇南等生态脆弱区NPP显著提高。
