中亚地区植被净初级生产力时空动态及其与气候因子关系

植被净初级生产力(Net Primary Productivity,NPP)及其对气候变化的响应是全球变化的核心研究内容之一,研究中亚地区NPP的时空格局变化对理解植被—环境的作用机理以及应对全球变化具有重要的意义。基于MOD17A3数据集、气象数据结合GIS分析方法研究中亚地区2000~2014年的植被NPP时空动态特征及其与气候因子的关系。结果表明：①中亚地区空间上NPP的变化范围在0~874 gC/m²·a之间,平均值为151.90 gC/m²·a,NPP年总量平均值为482.41TgC (1 Tg=10¹² g),NPP平均值与总量均呈现出下降趋势;②中亚地区NPP的高值区主要分布在高纬度地区和东南部高山地区,中部和南部荒漠区则为NPP的低值区;③中亚地区2000~2014年间NPP在空间上总体呈现下降趋势,达到显著下降的区域总体面积的39.89%。NPP呈下降趋势的区域主要集中在哈萨克斯坦的大部分区域,不同分区内以典型草原区最为显著;④中亚地区NPP受降水量的影响作用高于气温,荒漠草原区、典型草原区以及荒漠区主要受到降水量的控制,高山草甸区与高山林地区则受到降水和气温的共同作用。     

祁连山区植被净初级生产力的空间变化特征分析

基于MODIS数据分析了2000~2010年祁连山区植被净初级生产力(Net Primary Productivity,NPP)的空间变化特征。结果显示:祁连山区植被NPP并不高,多年平均植被NPP仅为121.95gC/(m2·a),自东向西植被NPP逐渐减少。不同植被类型其NPP具有明显差异,大体上为:常绿阔叶林平原草地常绿针叶林典型草地农田高寒草甸草地荒漠草地落叶针叶林。祁连山区植被NPP变化在区域间也存在差异。植被NPP呈增长趋势的地区主要分布在青海年南山、拉脊山、达坂山和青海湖及其西侧,约占47.30%;乌鞘岭东部及以东的地区(约占1.97%)植被NPP呈减少趋势。降水是祁连山区植被NPP变化的主要因素,气温对植被NPP的影响并不明显,不合理的人类活动可能是造成部分区域植被NPP减少的重要原因。     

基于MODIS数据的黄土高原草地净初级生产力的估算研究

在光能利用率模型的基础上,考虑了温度和地面水气压差的影响,建立了基于MODIS数据的草地净初级生产力遥感估算模型。利用2003年生长季（4~11月）1 km分辨率的MODIS数据和黄土高原73个站点的累年各月平均太阳总辐射值数据以及其它相关数据,估算了2003年黄土高原地区草地净初级生产力。结果表明:2003年黄土高原地区草地的净初级生产力最高值达516 gc/(m2•a),最低值为1 gc/(m2•a)。黄土高原NPP空间分布格局是西部和南部两区域高,然后以东南西北方向为轴心逐渐向内陆中心部分递减,这与区域的水热条件差异和草地地带性分异规律紧密相关。     

Changes of Major Lakes in Central Asia and Analysis of Key Influencing Factors

The five Central Asian countries are located in the inland areas with a serious shortage of water resources and the lakes are the main support for the ecosystem in the area.Due to the lack of long-term information support both in the year and in the intervening years，it is difficult to fully understand the characteristics of the major lakes in the five Central Asian countries.In this study，MODIS 8-day reflectivity products were used as data sources from 2001 to 2016，and the Normalized Difference Water Index (NDWI) method was used to extract the water body area.The annual and inter-annual water surface of 9 major lakes in Central Asia Change Characteristics and Key Influence Factors.The results show that:(1) Lake water surface changes in the five Central Asian countries are affected by anthropogenic disturbance and climate change.The annual and interannual time series of the lake tail and alpine enclosed lake show different trend and fluctuation characteristics;(2) the changes of the Aral sea in the south and north are most obvious.The surface area of the South Aral Sea has decreased obviously in recent 15 years，reducing the area by more than 70%，while the North Aral Sea fluctuates with the variation of precipitation.(3) the total area of the sub-main lakes has been declining.The area has shrunk by 23.51% over the past 15 years.The South Aral Sea is a contributor to the statistical decrease of all lakes.However，the surface area of other lakes shows an increasing trend.     

基于多时相遥感数据的徐州市植被净初级生产力估算及相关性分析

以遥感数据和气象数据为主要数据源,应用改进的光能利用率模型估算徐州市2006、2008和2010年3年间6月份的植被净初级生产力(Net Primary Productivity,NPP),研究了该区域6月份NPP的时空变化及其与气象因子的相关性。结果表明：时间上,受气候和环境等因素综合变化的影响,研究区域6月份NPP呈逐年下降趋势;空间上,NPP的分布表现为在林地、草地和农田相对集中的区域偏高,且不同植被类型6月份的NPP大小关系在不同年份可能不同,其中在2006和2008年为农田>草地>林地,而2010年为农田>林地>草地。通过分析与气象因子的相关性和偏相关性,限制NPP的主要气象因子不是固定不变的,其中2006和2008年,限制NPP的主要气象因子为太阳辐射,而2010年为降雨量和温度。不同植被类型下NPP与气象因子相关性和偏相关性差异反映了不同类型植被生长对光、热、水条件要求的差异。
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2007~2018年中国陆地植被总初级生产力与日光诱导叶绿素荧光的时空格局及其气候调控

日光诱导叶绿素荧光（SIF）作为总初级生产力（GPP）的替代指标,在区域GPP估算上表现出巨大的潜力。植被SIF和GPP具有较好的线性关系,但是不同的气候条件对SIF-GPP关系的影响仍不清晰。利用2007~2018年MODIS GPP和GOME-2 SIF以及环境条件（温度、降水、辐射等）研究中国陆地植被GPP和SIF的时空格局以及环境因子的调控。结果发现中国陆地植被GPP和SIF时空格局是一致的,但是作为表征光能分配的新指标GPP/SIF在空间分布上存在显著的差异。此外,SIF产量（SIF_Yield）受到约束GPP的环境因子（最小温度、饱和水汽压差、土壤水分和APAR）的共同调控,间接证实了SIF和GPP紧密的联系。因此,由于在时间和空间上SIF与GPP的关系受到环境条件的调控,使用基于卫星的SIF准确估算区域GPP应该考虑环境因子的约束。     

基于MODIS数据的河北省草地和林地的物候期及其与NPP相关分析

通过2006～2016年中等分辨率成像光谱仪MODIS(Moderate Resolution Imaging Spectroradiometer)的MCD12Q2数据集和NPP(Net Primary Productivity,净初级生产力)数据MOD17A3HGF为数据源,研究河北省的草地和林地的物候期：生长季起始期SOS(Start of Season)、生长季结束期EOS(End of Season)和生长季长度LOS(Length of Season)的多年均值空间分布和多年变化趋势,通过相关分析法研究草地和林地的物候期对NPP的相关关系,并进行显著性检验判断变化趋势和相关系数在α=0.05水平上的显著性。结果表明：河北省草地SOS大多出现在第108～153 d,EOS大多出现在第273～304 d,LOS大多为128～190 d。林地SOS大多出现在第107～128 d,EOS大多出现在第282～306 d,LOS大多为162～194 d。河北省草地和林地的SOS主要呈现出提前趋势,EOS呈现延后趋势,LOS主要呈现增长趋势;草地和林地的物候期与NPP主要呈现中度相关,其中...     

Evaluation of Collections 4 and 5 of the MODIS Gross Primary Productivity product and algorithm improvement at a tropical savanna site in northern Australia

In this study, we assessed the accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) GPP (gross primary productivity) Collections 4.5, 4.8 and 5 along with Leaf Area Index (LAI), fraction of absorbed Photosynthetically Active Radiation (fPAR), light use efficiency (LUE) and meteorological variables that are used to estimate GPP for a northern Australian savanna site. Results of this study indicated that the MODIS products captured the seasonal variation in GPP, LAI and fPAR well. Using the index of agreement (IOA), it was found that Collections 4.5 and 4.8 (IOA 0.89 respectively) agreed reasonably well with flux tower measurements between 2001 and 2006. It was also found that MODIS Collection 4.5 predicted the dry season GPP well (Relative Predictive Error (RPE) 4.17%, IOA 0.72 and Root Mean Square Error (RMSE) of 1.05 g C m^− 2 day^− 1), whilst Collection 4.8 performed better in capturing wet season dynamics (RPE 1.11%, IOA 0.80 and RMSE of 0.91 g C m^− 2 day^− 1). Although the wet season magnitude of GPP was predicted well by Collection 4.8, an examination of the inputs to the GPP algorithm revealed that MODIS fPAR was too high, but this was compensated by PAR and LUE that was too low. Although LAI and fPAR estimated by Collection 5 were more accurate, GPP for this Collection resulted in a much lower value (RPE 25%) due to errors in other factors. Recalculation of MODIS GPP using site specific input parameters indicated that MODIS fPAR was the main reason for the differences between MODIS and tower derived GPP followed by LUE and meteorological inputs. GPP calculated using all site specific values agreed very well with tower data on an annual basis (IOA 0.94, RPE 6.06% and RMSE 0.83 g C m^− 2 day^− 1) but the early initiation of the growing season calculated by the MODIS algorithm was improved when the vapor pressure deficit (VPD) function was replaced with a soil water deficit function. The results of this study however, reinforce previous findings in water limited regions, like Australia, and incorporation of soil moisture in a LUE model is needed to accurately estimate the productivity.     

黑河流域NDVI周期性分析及其与气候因子的关系

利用2007~2009年的SPOT VEGETION NDVI数据来延长1982~2006年GIMMS(Global Inventory Monitoring and Modeling Studies) NDVI数据,从而得到1982~2009年的NDVI数据,结合同时期的气象资料,利用经验模态分解方法(EMD),对黑河流域季节合成植被指数(SINDVI)、气温以及降水序列的周期性进行了分析,并进一步分析了黑河上中下游NDVI和气温、降水之间的关系。结果表明：上游SINDVI与气温、降水均存在准3 a和准6 a的相似周期,中游SINDVI与气温存在准3 a和准10 a的相似周期,而与降水存在准3 a、准6 a、准8 a和准15 a的相似周期,下游SINDVI与气温存在准3 a和准10 a的相似周期,与降水存在准3 a和准6 a的相似周期。表明在黑河流域,气温和降水均是影响植被变化的重要因子,而气候因子的周期性变化主要受大气运动的影响,进而影响植被的周期变化。因此,EMD方法在分析植被动态的周期性特征及其与气象条件周期性的关系方面具有较好的效果。