Spatio-temporal Analysis of Vegetation Phenology with Multiple Methods over the Tibetan Plateau based on MODIS NDVI Data

In the context of global climate change,vegetation phenology analysis based on remote sensing has become an critical method for studying the characteristics of physical and physiological changes of vegetation.This paper uses the MODIS NDVI time\|series data of 96 meteorological stations over the Tibetan Plateau during 2000\|2014 to explore the development trend of vegetation phenological and geographical environment factors of each meteorological station,typical vegetation coverage and the whole plateau region.Firstly,using three cubic spline function method (Spline),double logistic function method(D\|L)and singular spectrum analysis (SSA),NDVI time\|series data is reconstructed,then using the derivative method (Der)and threshold method (Trs),the key parameters of phenological information is extracted,after that differences and application conditions between the six methods are analyzed and compared.Secondly,using M\|K test trend analysis method,the phenological development trend of each site and area were calculated,the relationship between phenological development trend and altitude,precipitation,temperature is studied.Finally,by the Growing season length(GSL)obtained by temperature threshold method,LOS is compared and verified.in grassland and forest land cover types,SSA,Spline,D\|L combined with threshold method to get the Start of Season(SOS),end of season(EOS),Length of season (SOS)respectively is a good combination strategy.(2)The spatial differences of various phenological parameters extracted by different methods are large,and the trend is relatively consistent at small scales.Southeast humid and semi\|humid shrub steppe region and northwestern desert steppe in the Tibetan Plateau,SOS and EOS delayed,but LOS prolonged;southwestern humid region,SOS and EOS delayed,LOS shortened;widely distributed grassland,the phenological parameters did not show significant tendency.(3)Temperature is related to the development trend of phenological parameters.With temperature increasing,the phenomena of SOS advance,EOS lag are presented.Because of the complexity of the plateau landform and climate,there was no significant relationship between phenological development trend for most of the site with the altitude and precipitation,only a few sites have strong correlation,the correlation between GSL and LOS also showed similar characteristics.For remote sensing based phonological analyses,our study identify there is no method existing here that is a adaptive across all the Tibetan Plateau.in addition,at point scale the phenological parameters do not represent a significant earlier or later trend.     

基于遥感和气象数据的蒙古高原1982~2013年植被动态变化分析

蒙古高原生态系统及其变化对中国北方乃至整个东北亚的生态安全有着重要影响,了解蒙古高原干旱半干旱区植被生长的动态如何在不同时间和空间尺度上响应气候变化十分必要。利用NDVI数据构建长时间序列,分析植被生长动态变化的过程和时空特征,并与气象数据进行相关性分析。主要结论如下:①NDVI的分布具有地带性;②大约39.5%的区域NDVI呈显著的增加(P=0.1),7.3%的区域NDVI显著减少(P=0.1),说明植被条件在蒙古高原有所好转;③蒙古高原NDVI的变异系数均值16.99%,这表明过去32年里植被覆盖变化情况有较强的波动性;④蒙古高原植被的生长状况与降水量有极显著的正相关关系,与气温有极显著的负相关关系。     

基于被动微波与时空联合算法的云下像元LST重建

地表温度作为衡量地球表面水热平衡的关键参数,具有两大时空分布特征:第一,空间分布一致性,即属性相近的像元地表温度与其地表亮温间的相关关系相对稳定;第二,时间序列周期性,且同一地区时间越接近地表温度值越相似。基于这两大特征将空间统计模型与时间序列滤波相结合,提出了用于云下像元地表温度重建的时空联合算法。以2008年MODIS地表温度产品为研究对象,采用Landsat TM数据和AMSR_E地表亮温数据重建中国9个省份的地表温度值,并与基于MODIS地表分类产品的多通道统计模型重建结果进行对比。实验结果表明,所提算法实用性强,能有效实现大面积复杂下垫面区域的地表温度重建;平均重建误差约为1.2 K,相较于基于下垫面分类的多通道统计模型下降了76%,算法精度明显提高。
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Analysis of the Spatio-temporal Variation in FPAR of the Tibetan Plateau from 1982 to 2015

The absorbed and utilized Fraction of Photosynthetically Active Radiation(FPAR) reflects the capacity of carbon fixation and oxygen release by vegetation, which may vary over space and time in large scale. Analysis of spatial-temporal variation in FPAR is an important topic of plant ecology. Based on GIMMS NDVI3g (1982~2015) and MODIS NDVI (2001~2015) data in the Tibetan plateau, here we used the nonlinear, semi-theoretical and semi-empirical models to inverse and analyze the spatial and temporal variation in FPAR. The results showed that (1) The spatial distributions of FPAR derived from GIMMS NDVI3g and MODIS NDVI were highly consistent, with the correlation coefficient being 0.82 (P<0.01). The area in which the trends of inter-annual change in the two inversion data were consistent for at least 6 years made up 80% of the studying area. (2) FPAR in Tibetan Plateau was greatly affected by slope and altitude. Changes in FPAR were fastest at slopes of 15~35 degrees and highest at altitude of 700~2 100 m. The effect of slope direction on FPAR was limited. There was little difference in FPAR among different slope directions except for the south where the FPAR was relatively lower. (3)The FPAR data from 1982 to 2015 demonstrated seasonal variation. The inter-annual variation in FPAR was most significant in winter, in which FPAR in about 78.5% of the area increased. FPAR declined most significantly in the fall. (4) FPAR derived from both the MODIS NDVI and GIMMS NDVI data demonstrated a small, temporary decline in 2012. The trend of inter-annual variation in FPAR was largely different among different vegetation types. In conclusion, the FPAR data from 1982 to 2015 in the Tibetan plateau demonstrated both spatial and seasonal variation, which may have important implications for further studies concerning climate and environmental changes in the region.     

青藏高原1982~2015年FPAR时空变化分析

植被吸收利用太阳光合有效辐射比率反映了植被固碳释氧能力,根据青藏高原GIMMS NDVI3g（1982~2015年）和MODIS NDVI（2001~2015年）数据,采用非线性半理论半经验模型进行FPAR反演及时空变化分析。结果表明：①2001~2015年GIMMS NDVI3g和MODIS NDVI反演FPAR在空间分布上具有较高的一致性,相关系数为0.82（P<0.01）,年际变化趋势一致至少6年的区域占80%;②青藏高原FPAR受坡度和海拔影响较大,其中15~35坡度FPAR变化最快,700~2 100 m海拔区间FPAR值最大;不同坡向对应的FPAR除南坡方向偏低外其他方向差异不大。③1982~2015年青藏高原四季FPAR时空变化研究中,冬季FPAR年际变化最明显,约78.5%的区域表现为增长趋势;秋季FPAR下降区域最多,但超过71.5%区域变化不显著;④基于MODIS NDVI和GIMMS NDVI两数据反演的所有植被类型的FPAR都在2012年间出现小幅度下降趋势,且不同植被类型FPAR的年际变化趋势各不相同。     

Research Progress on the Application of Google Earth Engine in Geoscience and Environmental Sciences

With the rapid development and large integration of global informatization and industrialization since the 21st century,the Internet of things and cloud\|computing have emerged.The world has entered an era of big data.There are a huge amount geographical and remote sensing data generated every day in the field of geoscience,environmental science and related disciplines.However,the traditional approaches for storing,managing and analyzing massive data on the local platform,which take up lots of resources,time and energy,have been unable to meet the needs of the current researches.Google Earth Engine(GEE) cloud platform is powered by Google’s cloud infrastructure,and it combines a large number of geospatial datasets and satellite imagery,in which the datasets could be processing,analyzing as well as visualizing on a global scale.Meanwhile,it uses Google’s powerful computational capabilities to analyze and process a variety of environmental and social issues including climate change,vegetation degradation,food security and water resource shortages.Firstly,an introduction of GEE cloud platform has been given.Secondly,recent researches that using GEE cloud platform were reviewed.Thirdly,GEE cloud platform and MODIS land cover type data were used to analyze spatio\|temporal changes patterns of major land use and land cover type in Three Gorges Reservoir in the period of 2002~2013.The results indicate the largest changes occurring in forest lands,shrub grasslands and croplands.Finally,after a rough calculation,GEE cloud platform is superior to the traditional approaches in terms of both cost and economic efficiency,improving the overall efficiency by more than 90%.GEE cloud platform could not only provide powerful support to experts in the field of geosciences and remote sensing,but also offer valuable help to researchers in related disciplines.GEE cloud platform is an excellent tool for scientific research in geosciences,environment sciences and related disciplines.     

Dynamic Monitoring of Grassland Vegetation Phenology in Tibetan Plateau based on EVI2 and Three Trend Approaches

Based on grassland vegetation phenology extracting from EVI2 data sets,distribution and dynamic variation characteristic of SOG,EOG and LOG were analyzed in recent 30 years.The results showed that grassland phenology had shown an obvious regional difference from southeast to northwest in TP.The grassland vegetation in eastern and northwestern part of plateau turned green earlier and brown late,with a relatively longer growth season than other regions.The changes of grassland vegetation phenology from 1981 to 2010 in the east and west regions were also remarkable in TP.The Start of Growth Season(SOG) was in advance in eastern region,with the advanced rate of 0.49 d/a(R²=0.54).There were remarkable difference in phenology distribution and changes in different elevations and aspects.When the altitude had risen 1000 m,the SOG delayed 4 days,EOG advanced 5 days,and LOG shorten 9 days.With the increase of altitude,the SOG rate of grassland increased gradually,and LOG change rate showed a decreasing trend.In addition,SOG in south aspect was later than that in north aspect.LOG in south aspect was shorten than that in others.Average delay rate of SOG in north aspect was lower than that in south aspect.     

基于遥感的黑河流域植被物候空间格局提取分析

植被物候的检测对于认识自然季节现象的变化规律,服务农作物生产、全球变化、生态学应用方面具有重要价值。植被指数是描述植被数量、质量、植被长势和生物量指标的重要参数。利用SPOT VEGETATION NDVI时间序列数据,采用Savitzky-Golay滤波方法重建了NDVI的年内变化序列,并利用此数据提取了黑河流域植被物候空间分布格局。结果表明,采用此方法得到的植被物候信息和该区域的农事历信息符合较好。黑河流域植被物候具有明显的空间格局。上游的高寒草地区生长季长度较短。中游地区受人类活动影响严重,较为符合该区农作物生长信息。     

对象关系型GIS中改进基态修正时空数据模型的实现

通过对几种典型时空数据模型特性的分析 ,提出了一种改进的基态修正模型 .此模型以空间数据的现状作为基态 ,从而避免了系统频繁载入现状数据的开销 ;同时 ,在对象关系型 GIS的支持下 ,该模型利用关系运算来实现“非起始”状态的随机整合 ,因而提高了系统的执行效率 .最后还从“时空快照恢复”、“时态拓扑分析”和“空间对象的历史沿革”等 3个方面阐述了此模型在对象关系型 GIS中的实现方法 ,并以大兴县的村边界变更为例给出了所提出模型在 Geo Media3.0环境中的具体应用过程 .经验证 ,此模型在对象关系型 GIS中是一种较为实用的模型     

Depicting the Asymmetries of Vegetation Phenology over Northeast China Using Remote Sensing NDVI Dataset

Vegetation phenology is an important ecological indicator for global climate change.Plant greenup phenology in the spring time has been well studied,whereas autumn phenology and its asymmetry with spring phenology still remain unclear.Here,the GIMMS NDVI3g dataset for Northeast China was applied to extract the key phenological parameters during plant growth process,then three phenological asymmetry indices were defined according to the difference between greenup rate and senescence rate(AsyR),growth length in spring and autumn(AsyL),mean vegetation greenness index in spring and autumn(AsyV).First,plant growing curve was fitted with double logistic function and the phenological parameters was calculated.Second,the spatiotemporal pattern of asymmetry indices was explored.The results indicate that the three phenological asymmetry indices show a significant interannual variability and a time cycle of around ten years.The direction of amplitude for AsyV and AsyL was opposite with that of AsyR.Three indices could depict the phenological asymmetries from various perspectives and have a degree of uncertainty.The landscape pattern for AsyV and Asy R is similar.AsyV and AsyR show a capability of distinguishing cropland and natural vegetation cover.AsyL reflects a complex spatial distribution.Phenological asymmetries reveal that coniferous forest and broad-leaved forest present a dominant control of senescence vegetation activities.These natural vegetation commonly show a growth feature of rapid growth in spring and slow decrease in autumn.Cropland exhibits a slowly growing rate in spring and a rapid decrease in autumn.Phenological asymmetry is not significant in grassland area.Phenological asymmetry could enhance our knowledge on ecosystem carbon sink.In a practical way,phenological asymmetry could serve as a useful tools in vegetation type classification,agricultural investigation and plant ecosystem management.     

多云雾地区高时空分辨率植被覆盖度构建方法研究

针对多云雾地区高时空分辨率数据缺乏现状,提出了一套区域尺度高时空分辨率植被覆盖度数据构建方法.首先,通过时空适应反射率融合模型(STARFM)有效地将TM 的较高空间分辨率与MODIS的高时间分辨率融合在一起,构建了研究区植被生长峰值阶段的NDVI数据;然后,以植被生长峰值阶段的NDVI为输入,基于地表覆被类型,综合应用等密度和非密度亚像元模型对研究区的植被覆盖度进行估算.结果表明:①即使数据源存在大量的云雾,且存在一定的时相差异,研究区植被覆盖度的估算结果过渡自然,不存在明显的不接边效应;②以植被生长峰值阶段的NDVI数据为输入进行植被覆盖度估算,有效拉开了同一地表覆被类型不同覆盖度像元的NDVI梯度,提高了亚像元估算模型对输入数据的抗扰动性;③基于地表覆被类型,应用亚像元混合模型,能够提高植被覆盖度的估算精度.经野外实测数据验证,总体约８５％的估算精度表明,针对高时空分辨率遥感数据缺乏的多云雾区域,本研究提出的方法能够实现区域尺度植被覆盖度数据的构建.     

Temporal and Spatial Variation of Vegetation Phenology in Temperate China and Its Impact on Gross Primary Productivity

Land surface phenology is defined as the seasonal timing of life cycle events of vegetated land surface on local or global scale.Most studies of vegetation phenology in China’s temperate zone are focused on single vegetation type in certain area，the studies about long-time vegetation phenology on large scale is rare.The influence of vegetation phenology on GPP(gross primary productivity) remains to be determined.Using Moderate Resolution Imaging Spectroradiometer(MODIS) MCD12Q2 data from 2001 to 2014，start of growing season(SOS)，end of growing season(EOS) and length of growing season(LOS) in temperate China(>30°N) are obtained.GPP from MODIS MOD17A3 data for the same period is also obtained.Using regression analysis and correlation analysis methods，spatial and temporal patterns of SOS，EOS and LOS are analyzed.The impacts of SOS，EOS and LOS on interannual variability of GPP are also analyzed.Results show that the average and standard deviation of SOS，EOS and LOS from 2001 to 2014 are 121±10，270±12 and 153±12 days，respectively.The trend of earlier SOS，delayed EOS and increased LOS are not significant(p>0.05)，but LOS shows positively correlated to GPP.The spatial distribution of annual average LOS and GPP from 2001 to 2014 presents an increase trend from northwest to southeast.Regions with significant interannual variation(p<0.05) of SOS，EOS and LOS are 13%，21% and 13.2%，respectively.Regions of significant correlation(p<0.05) of SOS，EOS and LOS to GPP account for 8.31%，9.33% and 8.72% of the study area.GPP has mainly medium correlations(p<0.05，0.5<|r|<0.8) to SOS，EOS and LOS.     

21世纪初中国农作物与自然植被物候时空差异

利用离散小波方法对2001~2012年MODIS EVI时序数据进行平滑,基于动态阈值法提取我国植被物候信息,探讨农作物和自然植被物候的时空变化特征。结果表明:(1)我国第一季农作物开始、峰值和结束日期主要以华北平原为中心随海拔的上升而推迟,而自然植被物候更早20d左右,且随海拔的上升先推迟后提前;(2)物候在时序上有显著变化的第一季区域,43.98%开始日期、52.83%峰值日期呈现提前趋势,多在开始晚、结束早的西南区及东北与内蒙古交界处,其余区域开始、峰值日期及81.80%结束日期呈推迟趋势,发生在开始早、结束晚的黄土高原及双季农作区;农作物物候推迟幅度小于自然植被。     

基于K最近邻模型的青藏高原CMORPH日降水数据的订正研究

青藏高原的降水数据主要由遥感产品和多源观测数据融合产生,由于青藏高原的观测站点分布稀疏不均,遥感数据误差较大,因此常用的CMORPH(Climate Prediction Center Morphing Technique)等降水数据集精度有限。通过K最近邻(K-Nearest Neighbor,简称KNN)模型,可以建立环境(海拔、坡度、坡向、植被)、气象因子(气温、湿度、风速)和日降水量的关系,从而订正青藏高原的CMORPH日降水数据集,提高数据精度。对CMORPH日降水数据的误差分析表明,采用KNN模型订正后的CMORPH降水数据优于原始数据和采用PDF(Probability Density Function Matching Method)法订正的CMORPH数据,且空间分布较好地符合青藏高原的降水分布特征。     

蒙古语语音识别系统语言模型的设计

目前,蒙古语语音识别的研究尚处于空白阶段,因此蒙古语语音识别系统的研究与开发具有重要意义。而语言模型的确立是语音识别系统中最重要的环节之一。本文根据自己的实践,通过实验的方法最终确立了蒙古语、大量词汇语音识别系统中适宜的语言模型。     

CRF和词典相结合的蒙古文地名识别研究

蒙古语在命名实体识别方面开展过人名的识别,但在地名的识别方面还没有开展相应的研究。首次实现了基于条件随机场模型的蒙古文地名识别。首先从蒙古语黏着性特点分析入手,研究了蒙古语语料库中地名的存在形式以及各类地名的特点,针对蒙古语语料库中地名的特点,在词汇特征、指示词特征、特征词特征等特征基础上引入了词性特征。之后通过地名词典补召了未识别的地名。以内蒙古大学开发的100万词规模的标注语料库为训练数据,该模型的地名识别性能达到了94.68%的准确率、84.40%的召回率和89.24%的F值。     

蒙古语标准音辅音组合的协同发音研究

该文在以往实验研究的基础上,利用美国KAY公司6300型电子腭位仪(EPG)、3700Multi-Speech和南开大学“桌上语音工作室”(MiniSpeechLab)等生理和声学分析仪器和软件,通过观察辅音组合的声学语图、动态腭位图和LCV曲线图,比较系统地描述和归纳了蒙古语辅音组合中相邻青段之间的协同发音规律.     

2001~2011年中国农田最大光能利用率参数时空变化特征

基于遥感数据的光能利用率模型被广泛应用于计算陆地生态系统的生产力,其结果对最大光能利用率(ε max)参数非常敏感。利用农业产量统计数据、MODIS遥感数据、气象观测数据和植被光合模型(VPM)推算2001~2011年全国各省逐年的农田平均ε max,并分析其时空变化特征及其影响因素。研究结果表明:2001~2011年全国31个省的农田ε max的变化范围为0.57~2.20 g C·MJ-1,呈现出东部和中部较高、西北和西南较低的分布特征。大部分省份农田ε max呈现上升趋势,但在2001~2007年存在年际波动,2008年后ε max呈相对稳定增长趋势。各省农田ε max的年际波动幅度呈现北高南低、东高西低的分布特征。大部分省份农田ε max的年际变化与单位耕地面积农用化肥施用量存在显著的正相关性(P<0.05);C4作物面积比例变化也是导致农田ε max变化的原因之一。在利用光能利用率模型计算农田生产力时,需要发展考虑ε max 时空变化的参数化方案。
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甘肃黄土高原地区土壤水热特征分析研究

利用甘肃黄土高原代表站地温、土壤含水量及降水资料,运用统计模拟方法,分析了土壤温度的日变化及土中热交换特性,评述了水热耦合效应。地温的日变化特性用谐波分析方法描述,各季典型天气下24小时热通量值由热平衡台站规范方法计算。结果表明,各层次地温的日变化基本表现为一阶谐波,这种正弦的波形尤以晴天最为明显。不同季节典型天气下土中热通量的变化由正值转为负值的时间基本一致出现在16时,阴天提前1时,由负值转为正值的时间基本一致出现在7时,冬季阴天出现在9时。水热交互作用与土壤含水量的变化有显著相关关系。