青藏高原小嵩草高寒草甸返青期遥感识别方法筛选

小嵩草高寒草甸是青藏高原的主要植被类型,研究其返青期识别方法对于模拟及预测青藏高原植被物候变化具有重要意义。常用的植被返青期遥感识别方法主要是先对遥感植被指数原始时序数据进行拟合去噪声再求取返青期,各种方法对研究区域、研究经验、参数设置、函数初值设置等有很强的依赖性。为避免返青期识别方法在曲线拟合时对参数初值的依赖性和陷入局部最优解,本文引入了模拟退火算法对双高斯和双逻辑斯蒂函数进行参数优化,并分别对基于以上两种函数及多项式拟合的植被指数时序曲线进行对比,从而选出最佳拟合方法,最后采用最大斜率阈值法、动态阈值法和曲率法识别返青期。利用青藏高原小嵩草高寒草甸34个样本点的返青期地面观测数据及相应的8 km分辨率的NOAA归一化差值植被指数(NDVI)时序数据对以上各种组合的返青期遥感识别方案进行了测试,并选取了153个遥感实验点求取了近30年(1982年—2011年)青藏高原小嵩草高寒草甸的返青期,结果表明:采用双高斯函数拟合的NDVI曲线与原始NDVI时序数据最为接近,在此基础上采用最大斜率阈值法识别的小嵩草高寒草甸返青期及其变化趋势与地面物候观测结果最为一致;同时发现近30年青藏高原小嵩草高寒草甸的平均返青期主要集中在每年的第120—140天,并且呈逐年提前趋势,30年来提前了7天。     

SSM/I监测地表冻融状态的决策树算法

基于样本统计分析及冻结和融化地表的辐射/散射特性建立了判别地表冻融状态的决策树,首次联合使用散射指数、37GHz垂直极化亮温及19GHz极化差3个关键指标识别出地表或植被冠层的冻融状态,同时剔除了沙漠和降水的影响.利用国际协同加强观测期(CEOP)在青藏高原地区的土壤温度和湿度观测系统获取的4cm地温数据代表浅层土壤真实冻融状态验证分类结果,其准确性达87%.经分析,约40%和73%的误分分别发生在浅层土壤温度为-0.5-0.5℃和-2.0-2.0℃之间,即冻结点附近;且多发生在冷暖季节过渡时期,即4-5月和9-10月,分别占误分的33%和38%.基于该决策树获得的2002年1O月-2003年9月中国全境地表冻结日数图,以中国冻土区划及类型图为参考进行精度评价,其总体分类精度为91.66%,Kappa系数为80.5%,且冻融界线与季节冻土分布南界具有较好的一致性.     

根河地区冻融监测和降尺度算法的验证分析

高分辨率地表冻融监测对研究根河地区碳氮循环、水土流失和土壤冻融侵蚀非常重要。本文采用Kou等(2017)提出的被动微波亮温降尺度方法和1 km空间分辨率的温度数据,将0.25°空间分辨率的被动微波亮温降尺度至0.01°空间分辨率。利用通过模型模拟与实验数据发展得到的冻融判别式算法DFA_Zhao(Discriminant Function Algorithm)和改进的冻融判别式算法DFA_Kou(Improved Discriminant Function Algorithm),基于降尺度前后的被动微波亮温监测根河地区的地表冻融。以根河地区2013年7月—2015年12月的地下0—5 cm深度的实测土壤温度检验这两种冻融判识算法的分类精度。结果显示,降尺度前后两种冻融判识算法整体判对率差异在6.72%内;DFA_Zhao算法融化判对率的均值比DFA_Kou算法高10%,DFA_Kou算法冻结判对率均值比DFA_Zhao算法高1%。两种冻融判别式算法的冻结判对率均在90%以上,升轨期的融化判对率均在80%以上,但两算法降轨期的融化判对率较低,在40%—82%之间。同时,还进一步讨论并分析了两种冻融判别式算法和被动微波亮温降尺度方法可能存在的问题,指出了可能的改进方向。     

利用卫星重力探测南极冰盖质量变化

针对不同GIA模型在南极地区的改正差异较大,分析了采用不同GIA模型分析得到的南极冰盖融化误差值,研究了南极冰盖质量变化加速度。研究结果表明,扣除IJ05模型影响后,整个南极冰盖质量以(-81.5±4.2)Gt/a的速度和(-12.3±6.5)Gt/a2的加速度融化,对海平面上升贡献分别为(0.23±0.01)mm/a和(0.03±0.02)mm/a2。西南极处于加速融化状态,速度由2003—2008年间的-37.7Gt/a增加至2009—2013年的-156.0Gt/a。东南极冰盖在2009年前处于稳定,2009年后逐渐积累,且积累速度不断增加。2003-2013期间内东南极,西南极冰盖质量变化速度及加速度分别为(28±13.4)Gt/a、(9.8±2.8)Gt/a2、-(108.1±3.5)Gt/a和-(21.1±2.9)Gt/a2,南极冰盖融化主要来自西南极。南极冰盖周年变化影响较强,每年10月左右其质量变化振幅达到最大,半周年变化影响最大在西南极,S2潮波振幅在Ronne冰架附近较大,振幅最大值达到25mm。     

基于MODIS时序的陕西省植被物候时空变化特征分析

遥感技术作为对大尺度陆表监测研究的有效手段,被广泛应用于自然地理环境各要素的研究中。其中,植被物候作为自然界规律性、周期性的事件,对自然环境尤其是气候变化有着重要的指示作用。以陕西省为研究区,采用Savitzky-Golay(S-G)滤波方法对MODIS归一化植被指数(normalized difference vegetation index,NDVI)数据进行时间序列重构,并在此基础上,提取陕西省2001—2016年间的植被物候期信息进行其时空变化特征分析。研究结果表明:(1)陕西省的植被物候空间分布特征与其不同地形地貌的空间分布具有较好的一致性;(2)陕西省生长季开始的平均时间在每年的第120天,生长季结束的平均时间在第280天,生长季长度平均为160 d;(3)2001—2016年间陕西省植被生长季开始时间变化趋势为波动提前,变化率约为-0. 79 d/a(R2=0. 40,P 0. 01),生长季结束时间变化趋势表现为波动推迟,变化率约为0. 50 d/a(R2=0. 25,P 0. 05),生长季长度变化呈波动延长趋势,变化率约为1. 29 d/a(R2=0. 37,P 0. 05);(4)在不同的物候期,陕西省植被的物候变化趋势空间分布差异较大。     

红河断裂带闭锁程度和滑动亏损分布特征研究

利用1999~2013年青藏高原东南缘的GPS速度场观测数据,采用DEFNODE程序反演红河断裂带走滑速率、三维闭锁程度和滑动亏损分布。反演结果表明:红河断裂带北、中、南段右旋走滑速率分别约为(5.9±1.2)mm/a,(4.8±0.6)mm/a和(4.3±0.4)mm/a,在地表以下6km的闭锁程度分别为0.43,0.22和0.25,其滑动亏损速率分别为3.3mm/a,2.6mm/a和2.3mm/a,红河断裂带闭锁程度和应变积累程度都比较低,与近年来红河断裂带整体活动微弱的现状相吻合。     

1979年—2019年兴凯湖湖冰物候变化的被动微波遥感监测

季节性冻结与消融的湖冰是气候变化的重要指示器。本文以兴凯湖为例,基于1979年—2019年的被动微波遥感数据获取了兴凯湖的冻融日期,用2000年—2019年的中等分辨率成像光谱仪MODIS(Moderate-resolution Imaging Spectroradiometer)数据进行了验证,并用气候数据分析了湖冰物候变化的原因。结果表明被动微波与MODIS遥感数据在湖冰物候提取方面具有较好的一致性,也即MODIS的验证结果表明用低频被动微波亮度温度数据获取湖冰物候的方法是可行的,结果也是可靠的。平均而言,兴凯湖湖冰每年11-13左右开始冻结,11-23左右完全冻结,湖冰冻结持续时间9.80 d;次年04-23左右湖冰开始消融,04-30左右湖冰完全消融,消融持续时间8.03 d;湖冰完全封冻时间150.50 d,湖冰覆盖时间168.03 d。过去41 a,兴凯湖开始冻结日期没有明显变化,完全冻结日期平均推后了0.19 d/a,开始消融日期和完全消融日期分别提前了0.16 d/a和0.13 d/a,湖泊完全封冻时间和湖冰覆盖时间分别缩短了12.71 d和2.87 d。湖冰冻结日期推后与风速增大密切相关,消融日期提前和湖冰持续时间缩短与气温升高显著相关。     

陕北黄土高原植被覆盖时空变化及其归因分析北大核心CSCD

本文借助Google Earth Engine(GEE)云平台,以Landsat影像、气温降水和土地利用类型为基础,利用Theil-Sen Median趋势分析、Mann-Kendall检验、偏相关性和多元回归残差分析法,分析了1999—2018年陕北黄土高原植被覆盖时空特征、变化趋势及气候变化与人类活动对于不同土地利用类型的影响,得出以下结论:(1)1999—2018年陕北黄土高原年际FVC呈改善趋势,其平均增速为0.004 9/a(P<0.01),植被覆盖度呈增加趋势的面积占总面积的74.43%;(2)植被覆盖度与降水和气温的偏相关系数具有明显的空间差异,植被生长对降水变化较敏感;(3)气候变化和人类活动的共同作用是植被生长的主要原因,其中气候变化对植被FVC的影响范围为-0.001 0/a~0.003 6/a,而人类活动对植被FVC的影响范围为-0.046 1/a~0.049 0/a;(4)在不同土地利用类型中,气候变化对水体增幅影响最大,对针叶林和阔叶林增幅影响最小,而人类活动变化对人类占用地增幅影响最大,对阔叶林增幅影响最小。     

20世纪末华北地区冬小麦物候期监测及时空格局分析

基于1983—1999年7 d时间分辨率5 km空间分辨率的AVHRR传感器数据,利用曲线特征点的物候监测方法,反演获得华北地区冬小麦关键物候期并分析其时空演变规律。结果表明:1)冬小麦的拔节期、抽穗期和成熟期主要集中在60—100、105—125和120—155 d。冬小麦物候期空间格局特征和纬度相关,纬度每升高一度冬小麦的拔节期、抽穗期和成熟期分别推迟了5.2、3.5和3.1 d。2)1983年以来,整个研究区的冬小麦物候期呈现提前趋势,每十年冬小麦的拔节期、抽穗期和成熟期分别提前了0.7、3.1、1.9 d。     

1982-2013年青藏高原植被动态变化时序分析

针对不同的数据源及时间和空间尺度会使植被覆盖度及其与气象因子影响的结果有所差别这一情况,该文基于青藏高原1982-2012年GIMMS NDVI和2001-2013年MODIS NDVI遥感数据集,结合研究区内12个典型的气象站点数据,进行了青藏高原地区植被覆盖时空动态变化规律及其与气象因子响应的时序分析,并利用重合时间段的数据对比分析了两种传感器在青藏高原地区对植被动态变化监测方面的差异.结果表明:近30年来,青藏高原地区植被呈整体改善趋势,尤其是高海拔地区;不同阶段植被的变化趋势有所不同;两种传感器在反映植被动态变化趋势上差异显著,但两者与气候因子的响应规律相同.     

Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p < 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact.     

Permafrost environment monitoring on the Qinghai-Tibet Plateau using time series ASAR images

The permafrost in Qinghai-Tibet Plateau (QTP) has long been the focus of many researchers. In this study, we first use the method that integrates synthetic aperture radar (SAR) intensity and phase information to monitor permafrost environment in the Beiluhe Region, using time series advanced SAR images. The backscattering coefficients (σ⁰) and deformation were extracted for the main features, and the influences of meteorological conditions to them were also quantified. The results show that both the change in σ⁰ and surface deformation are closely related to the active layer, and the deformation is also affected by the permafrost table. First, over meadow and sparse vegetation regions, σ⁰ rose about 6.9 and 4 dB from the freezing to thawing period, respectively, which can be mainly attributed to the thaw of the active layer and increased precipitation. Second, seasonal deformation, derived from the freeze-thaw cycle of the active layer, was characteristic of frost heave and thaw settlement and exhibited a negative correlation with air temperature. Its magnitude was larger than 1 cm in a seasonal cycle. Last, significant secular settlement was observed, with rates ranging from –16 to 2 mm/a, and it was primarily due to the thaw of the permafrost table caused by climate warming.     

基于PALSAR数据的青藏高原冻土形变检测方法研究

季节性冻胀和融沉导致的地面形变是青藏高原冻土区建设施工与维护的主要问题。对冻融造成的形变进行有效监测是青藏铁 路建设与维护的前提。差分干涉测量技术是地表形变监测的重要手段之一,PALSAR（L波段的合成孔径雷达）数据在非城市区域具 有较高的相关性,适合青藏高原冻土区的地表形变监测。本文选用4景覆盖研究区域的PALSAR数据,研究利用该数据进行冻土形变 检测的方法,并对其检测结果进行了分析。结果表明,该方法与水准测量方法有较好的一致性。     

High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the Three-River Source Region on the Qinghai-Tibetan Plateau

Using satellite-observed Normalized Difference Vegetation Index (NDVI) data and Rotated Empirical Orthogonal Function (REOF) method, we analyzed the spatio-temporal variation of vegetation during growing seasons from May to September in the Three-River Source Region, alpine meadow in the Qinghai-Tibetan Plateau from 1982 to 2006. We found that NDVI in the centre and east of the region, where the vegetation cover is low, showed a consistent but slight increase before 2003 and remarkable increase in 2004 and 2005. Impact factors analysis indicted that among air temperature, precipitation, humid index, soil surface temperature, and soil temperature at 10 cm and 20 cm depth, annual variation of NDVI was highly positive correlated with the soil surface temperature of the period from March to July. Further analysis revealed that the correlation between the vegetation and temperature was insignificant before 1995, but statistically significant from 1995. The study indicates that temperature is the major controlling factor of vegetation change in the Three-River Source Region, and the currently increase of temperature may increase vegetation coverage and/or density in the area. In addition, ecological restoration project started from 2005 in Three-River Source Region has a certain role in promoting the recovery of vegetation.     

利用NOAA-AVHRR数据分析1982-2001年间中国植被的动态变化(英文)

The authors derived the normalized difference vegetation index (NDVI) from the NOAA/AVHRR Land dataset, at a spatial resolution of 8km and 15-day intervals, to investigate the vegetation variations in China during the period from 1982 to 2001. Then, GIS is used to examine the relationship between precipitation and the Normalized Difference Vegetation Index (NDVI) in China, and the value of NDVI is taken as a tool for drought monitoring. The results showed that in the study period, China’s vegetation cover h...     

Understanding long-term (1982–2013) patterns and trends in winter wheat spring green-up date over the North China Plain

Monitoring the spring green-up date (GUD) has grown in importance for crop management and food security. However, most satellite-based GUD models are associated with a high degree of uncertainty when applied to croplands. In this study, we introduced an improved GUD algorithm to extract GUD data for 32 years (1982–2013) for the winter wheat croplands on the North China Plain (NCP), using the third-generation normalized difference vegetation index form Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI). The spatial and temporal variations in GUD with the effects of the pre-season climate and soil moisture conditions on GUD were comprehensively investigated. Our results showed that a higher correlation coefficient (r = 0.44, p < 0.01) and lower root mean square error (22 days) and bias (16 days) were observed in GUD from the improved algorithm relative to GUD from the MCD12Q2 phenology product. In spatial terms, GUD increased from the southwest (less than day of year (DOY) 60) to the northeast (more than DOY 90) of the NCP, which corresponded to spatial reductions in temperature and precipitation. GUD advanced in most (78%) of the winter wheat area on the NCP, with significant advances in 37.8% of the area (p < 0.05). GUD occurred later at high altitudes and in coastal areas than in inland areas. At the interannual scale, the average GUD advanced from DOY 76.9 in the 1980s (average 1982–1989) to DOY 73.2 in the 1990s (average 1991–1999), and to DOY 70.3 after 2000 (average 2000–2013), indicating an average advance of 1.8 days/decade (r = 0.35, p < 0.05). Although GUD is mainly controlled by the pre-season temperature, our findings underline that the effect of the pre-season soil moisture on GUD should also be considered. The improved GUD algorithm and satellite-based long-term GUD data are helpful for improving the representation of GUD in terrestrial ecosystem models and enhancing crop management efficiency.     

Analysis of Seasonal Variability of Vegetation and Methane Concentration over India using SPOT-VEGETATION and ENVISAT-SCIAMACHY Data

This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH₄) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to analyze the atmospheric CH₄ concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index (NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH₄ concentration. Analysis showed mean monthly CH₄ concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH₄ concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation. This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH₄ concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different satellite based data in understanding the variability of atmospheric CH₄ concentration in relation to vegetation.