基于MODIS数据和BFAST方法的植被变化监测

植被是联结土壤、大气和水分的自然"纽带",在全球气候变化研究中具有"指示器"的作用。对归一化植被指数(normalized difference vegetation index,NDVI)时间序列分析,可以为相关部门的工作和决策提供更好的支持。使用MODIS NDVI数据结合BFAST(breaks for additive seasonal and trend)方法实现对老哈河流域及周边地区的植被变化监测,并确定其NDVI时间序列出现突变点的时间节点。结合气象数据以及数据本身的质量作为影响因子,分析出现突变点的主要原因。研究结果表明,降水量、相对湿度、温度、日照时数、流域蒸发量与NDVI变化趋势呈正相关,风速与NDVI变化趋势相关性很小。降水量对NDVI变化的影响具有滞后性,滞后时间与降水量大小有关。     

NDVI与气候因子关系在不同时间尺度上的结果差异

植被指数是反映地表植被覆盖状况的重要参数,分析气候因子与植被指数间的相互关系有助于揭示气候变化对植被的影响,然而当前研究有两种分析植被指数与气候因子关系的方法,分别为分析植被指数与生长季内和生长季间气候因子的关系,然而这两种法差异如何,何种方法更为合适需要进一步分析。利用2000年—2009年生长季的MODIS的归一化植被指数NDVI(Normalized Difference Vegetation Index)数据集和藏北那曲地区3个气象站逐月气象资料(月平均气温、≥0℃活动积温和月降水量),分析比较了生长季内和生长季间气候因子对植被生长影响的差异,并分析了两种方法的优劣。结果表明:(1)生长季内植被NDVI与同期气温和降水量均呈高度正相关,生长季内时滞时间尺度为1个月时,植被NDVI对月平均气温及降水响应均最为强烈。(2)生长季间NDVI与同期降水量相关性并不明显,气候因子的滞后效应在生长季间也较弱。(3)生长季内和生长季间植被NDVI与气候因子的关系所得出的结论有一定差异性,可能是因为两方面的原因:生长季内植被NDVI与水热因子的高相关性与中国季风季候造成的高温多雨出现在夏季有关,而生长季内高水热条件与高植被指数对应的多年重复必然造伪的高相关系数,但这种相关性不一定能真实反映植被与水热条件的关系,而生长季间水热等气候因子与植被指数年际变化相关性分析不存在水热与高植被指数同期问题,更能真实反映气候因子年际变化对植被的影响。     

微波植被指数在干旱监测中的应用

在植被覆盖区域,归一化植被指数(NDVI)被广泛地应用于干旱遥感监测。和基于光学遥感的植被指数相比,Shi等提出的微波植被指数MVI(Microwave Vegetation Index)被证实能够反映更多的植被生长信息。本文以MVI为基础,利用MVI代替目前比较成熟的温度植被指数TVDI(Temperature Vegetation Index)中的NDVI,构建温度微波植被干旱指数TMVDI(Temperature Microwave Vegetation Index),发展了一种新的干旱监测方法。本文以2006年夏季四川省发生的百年难遇的干旱为研究对象,将基于TMVDI与TVDI的干旱监测结果进行了对比分析。最后,为评估监测结果的准确性,将遥感监测的结果与基于气象站点降雨观测数据构建的标准降雨指数SPI(Standardized Precipitation Index)的计算结果进行了对比分析。结果表明,利用低频降轨微波辐射计数据计算的T MVDI最适合于进行植被覆盖区域的干旱监测。     

利用MERIS数据植被指数分析福建省植被长势季节变化

监测植被长势动态变化可以提供生态系统状况有价值的信息,可以检测到人类或气候作用引起的变化。本研究利用2004—2005年间10期MERIS影像数据,以福建省为例,探讨MERIS数据在区域植被长势季节变化监测中的应用效果;分析了MERIS数据用于区域植被季节变化监测时的数据处理方法;比较了MERIS数据几种植被指数,提出了利用10和8波段组合改进MERISNDVI的建议;利用多时相合成的NDVI简单分析了2004年夏季—2005年夏季三个季节的植被长势状况。结果表明,MERIS植被指数的时空变化有效反映了气候变化对植被长势的影响。     

25年来秦岭NDVI指数的气候响应

利用1982-2006年的植被指数和研究区域内4个气象站的气温、降水数据，研究陕西秦岭地区植被指数、气温、降水的多年变化趋势，分析植被指数与气温和降水的相关关系。利用植被类型数据分析不同植被种类的NDVI与不同气候因子的相关程度。结果表明，1982-2006年，研究区域年均气温有明显的上升，升幅达2.1℃，而年总降水量每10年下降约72 mm，秦岭地区NDVI略有上升。整体而言，植被指数的变化与气温之间的相关性在中部最大，向东西两侧递减；与降水之间的相关性在中部最小，向东西两侧递增。气温对果树园、经济林的影响最大，降水对阔叶林的影响最大。气温是影响该地区植被指数变化的主要因素。     

时序重构的长时间序列植被变化监测研究

植被是生态系统组成的重要部分,植被指数更是植被生长变化的重要监测因子,目前已有的植被指数数据集不能很好地剔除噪声等影响,在植被生长变化的反映中存在一定的误差。基于此,本研究以惠州市作为研究区,采用极限梯度提升方法,利用2000年至2015年的GIMMS3g NDVI数据作为训练集,借助地表温度数据作为因子,进行基于极限梯度回升算法的植被指数长时间序列重构,结果表明：时序重构后的GIMMS NDV在年际变化上特征相似度较高,达到0.88,时序重构后的NDVI数据集有明显的降低。通过年、季、月、生长周期等多维度对比,时序重构后的NDVI变化周期整体较原始数据集更平滑,异常值较少,整体更符合植被生长变化的实际情况。     

MODIS NDVI和AVHRR NDVI 对草原植被变化监测差异

以草地作为研究载体,对比分析草原植被AVHRR NDVI和MODIS NDVI两种NDVI序列的年内、年际变化特征,讨论两种NDVI序列对降水量、平均气温和水汽压3种气候因子的响应差异,为合理选择NDVI序列对植被进行监测研究提供参考。结果表明：(1)两种NDVI序列所反映的草原植被年内变化趋势相似,但MODIS NDVI对各类草原的区分度优于AVHRR NDVI;(2)两种NDVI序列所反映的2000年—2003年草原植被年际变化差异明显。较之于MODIS NDVI,AVHRR NDVI变化趋势分类图表现出更强的植被改善趋势,植被改善面积在AVHRR NDVI变化趋势分类图中占94.25%,在MODIS NDVI中为83.33%;两种NDVI变化趋势分类图反映的植被变化趋势吻合度为52.88%。(3)两种NDVI序列与水汽压、降水量相关性差异显著。MODIS NDVI与各站点平均气温的相关系数均大于GIMMS NDVI;而MODIS NDVI与水汽压的相关系数83%(10个站点)小于GIMMS NDVI,与降水量的相关系数67%(8个站点)小于GIMMS NDVI。     

不同趋势法的宁夏长时序植被变化分析

宁夏地处黄土高原，植被变化趋势直接影响其生态保护。本文应用2005—2015年的MODIS NDVI月合成产品，并采用最大合成法得到年NDVI数据，分别利用一元线性回归法和Sen+Mann-Kendall法对宁夏11年间植被变化趋势和空间差异进行研究分析。结果表明，在月际变化上，月NDVI均值呈高斯分布，7—9月是植被长势最好的阶段；在年际变化上，2005—2007年NDVI值明显增长，2008—2012年NDVI值呈稳定性增长，2013—2015年NDVI值有下降趋势；在变化趋势上，一元线性回归法与Sen+Mann-Kendall法得到的植被变化趋势基本一致，均表现为北部植被整体改善，但局部城区植被退化较为严重；中部地区轻微改善，局部存在明显改善；南部植被有明显改善且植被改善面积较大；通过差异性分析二者差异性仅为22.95%，且Sen+Mann-Kendall法能更好地监测轻微变化区域，变化趋势更加准确。     

基于无人机数据的植被指数空间尺度效应研究

利用cm级空间分辨率的无人机高光谱图像研究了NDVI、PRI等14种植被指数随空间尺度升高的变化规律,并分析了不同植被覆盖度(FVC)下植被指数的空间尺度效应,比较了各植被指数空间尺度效应的强弱。得到的结论如下:(1)随着空间尺度升高,不同植被指数的变化趋势和幅度并不一致;(2)cm级的空间分辨率下,由于植被类内光谱差异和冠层阴影的影响,植被指数在植被覆盖度大于等于0.5的中高植被覆盖下受空间尺度效应的影响更大;(3)由相对简单的比值形式构成的植被指数如NDVI、SR等,具有抵抗空间尺度效应的能力,增加计算公式的复杂性会降低这种能力;(4)对色素和冠层水分敏感的窄波段容易受到空间尺度效应的影响,进而使得由这些窄波段构成的植被指数对空间尺度变化更为敏感。     

基于GF-1 WFV和Landsat-8 OLI提取植被信息方法比较研究

针对GF-1 WFV和Landsat-8 OLI两种传感器的参数特点,选取归一化植被指数(NDVI)、增强型植被指数(EVI)、大气阻抗植被指数(ARVI)、土壤调整植被指数(SAVI)和修正的土壤调整植被指数(MSAVI)5种植被指数,采用同一时期的两种传感器数据对四川省茂县进行植被信息提取,并结合像元二分模型估算植被覆盖度,计算分析两种数据源下不同植被指数的差异性。结果表明:GF-1数据提取的NDVI植被效果最好,其中2013年分类总精度为94.55%,Kappa系数为0.88;2015年分类总精度为90.47%,Kappa系数为0.85。对于Landsat-8数据提取的SAVI的结果最佳,其中2013年分类总精度为94.38%,Kappa系数为0.86;2015年分类总精度为95.83%,Kappa系数为0.88。根据统计指标分析表明:在高原山区地形环境下,利用植被指数估算植被覆盖度,GF-1卫星采用NDVI、Landsat-8卫星采用SAVI比较合适,且GF-1数据的估算精度要高于Landsat-8数据。     

Seasonal Variation of MODIS Vegetation Indexes and Their Statistical Relationship With Climate Over the Subtropic Evergreen Forest in Zhejiang, China

Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 1-km vegetation index products, daily temperature, photosynthetically active radiation (PAR), and precipitation from 2001 to 2004 were utilized to analyze the temporal variations of the MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), as well as their correlations with climate over the evergreen forested sites in Zhejiang-a humid subtropical region in the southeast of China. The results showed that both NDVI and EVI could discern the seasonal variation of the evergreen forests. Attributed to the sufficient precipitation in the study area, the growth of vegetation is mainly controlled by energy; as a result, NDVI, and especially EVI, is more correlated with temperature and PAR than precipitation. Compared with NDVI, EVI is more sensitive to climate condition and is a better indicator to study vegetation variations in the study region     

多尺度城市地表温度降尺度方法

针对目前星载热红外传感器的空间分辨率低,无法满足城市尺度的生态环境研究需求的现状,该文选择地表覆盖类型复杂的区域,根据研究区土地覆盖类型,选取归一化植被指数(NDVI)、城市不透水面指数(ISA)、改进的归一化差异水体指数(MNDWI)等因子加入DisTrad模型,采用移动窗口逐步回归统计地表温度和因子的线性关系,利用半方差曲线函数和均方根误差综合确定最优移动窗口的大小,以提高地表温度降尺度精度。研究结果表明:改进的DisTrad模型在地表覆盖类型复杂区域,具有良好的降尺度目视效果,且具有较高的降尺度精度,尤其在低植被覆盖的建筑区、水体区域具有更高的精度。     

Integrating terrain and vegetation indices for identifying potential soil erosion risk area

The present paper offers an innovative method to monitor the change in soil erosion potential by integrating terrain and vegetation indices derived from remote sensing data. Three terrain indices namely, topographic wetness index (TWI), stream power index (SPI) and slope length factor (LS), were derived from the digital elevation model. Normalized vegetation index (NDVI) was derived for the year 1988 and 2004 using remote sensing images. K-mean clustering was performed on staked indices to categorize the study area into four soil erosion potential classes. The validation of derived erosion potential map using USLE model showed a good agreement. Results indicated that there was a significant change in the erosion potential of the watershed and a gradual shifting of lower erosion potential class to next higher erosion potential class over the study period.     

Satellite-based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi,India

The knowledge of the surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change and human-environment interactions. The study analyses land surface temperature (LST) estimation using temporal ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) datasets (day time and night time) over National Capital Territory Delhi using the surface emissivity information at pixel level. The spatial variations of LST over different land use/land cover (LU/LC) at day time and night time were analysed and relationship between the spatial distribution of LU/LC and vegetation density with LST was developed. Minimum noise fraction (MNF) was used for LU/LC classification which gave better accuracy than classification with original bands. The satellite derived emissivity values were found to be in good agreement with literature and field measured values. It was observed that fallow land, waste land/bare soil, commercial/industrial and high dense built-up area have high surface temperature values during day time, compared to those over water bodies, agricultural cropland, and dense vegetation. During night time high surface temperature values are found over high dense built-up, water bodies, commercial/industrial and low dense built-up than over fallow land, dense vegetation and agricultural cropland. It was found that there is a strong negative correlation between surface temperature and NDVI over dense vegetation, sparse vegetation and low dense built-up area while with fraction vegetation cover, it indicates a moderate negative correlation. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban area. The analysis also indicates that the relationship between the spatial distribution of LU/LC and vegetation density is closely related to the development of urban heat islands (UHI).     

Formulation of Time Series Vegetation Index from Indian Geostationary Satellite and Comparison with Global Product

To study impact of climate change on vegetation time series vegetation index has a vital role to know the behaviour of vegetation dynamics over a time period. INSAT 3A CCD (Charged Couple Device) is the only geostationary sensor to acquire regular coverage of Asia continent at 1 km × 1 km spatial resolution with high temporal frequency (half-an-hour). A formulation of surface reflectances in red, near infrared (NIR), short wave infrared (SWIR) and NDVI from INSAT 3A CCD has been defined and integrated in the operational chain. The atmospheric correction of at-sensor reflectances using SMAC (Simple Model for Atmospheric Correction) model improved the NDVI by 5–40% and also increased its dynamic range. The temporal dynamics of 16-day NDVI composite at 0500 GMT for a growing year (June 2008–March 2009) showed matching profiles with reference to global products (MODIS TERRA) over known land targets. The root mean square deviation (RMSD) between the two was 0.14 with correlation coefficient (r) 0.84 from 200 paired datasets. This inter-sensor cross-correlation would help in NDVI calibration to add continuity in long term NDVI database for climate change studies.     

THE DEPARTMENT OF PHOTOGRAMMETRY AND REMOTE SENSING AT WUHAN TECHNICAL UNIVERSITY,PEOPLE'S REPUBLIC OF CHINA

Many remote sensing applications are predicated on the fact that there is a known relationship between climate and vegetation dynamics as monitored from space. However, few studies investigate vegetation index variation on individual homogeneous land cover units as they relate to specific climate and environmental influences at the local scale. This study focuses on the relationship between the Palmer Drought Severity Index (PDSI) and different vegetation types through the derivation of vegetation indices from Landsat 7 ETM+ data (NDVI, Tasseled Cap, and SAVI). A series of closely spaced through time images from 1999 to 2002 were selected, classified, and analyzed for an area in northeastern Ohio. Supervised classification of the images allowed us to monitor the response in individual land cover classes to changing climate conditions, and compare these individual changes to those over the entire larger areas. Specifically, the images were compared using linear regression techniques at various time lags to PDSI values for these areas collected by NOAA. Although NDVI is a robust indicator of vegetation greenness and vigor, it may not be the best index to use, depending on the type of vegetation studied and the scale of analysis used. A combination of NDVI and other prominent vegetation indices can be used to detect subtle drought conditions by specifically identifying various time lags between climate condition and vegetation response.     

山西省西山煤田长时间序列NDVI时空变化趋势及对气候响应

研究采矿扰动区内植被变化规律,能够为矿区生态修复提供理论依据。本文以山西省西山煤田为研究区,通过设立对比试验区,利用MODIS/NDVI（2001-2019年）结合同期的气温、降水气候因子,分别从植被指数的时空变化及与气象因子之间的关系等方面展开对比,用于探究采矿扰动区内植被变化情况。研究结果表明：①19年来西山煤田与间接影响区及校验区的植被均呈增加趋势,但西山煤田相比于校验区NDVI均值低11.42%。②西山煤田相较于自然生态条件下植被增长率为-5.53%。③西山煤田与校验区的NDVI值均受到气温、降水两种气象因子的影响,但是与降水的相关性更高,即受降水影响更大。     

河南省植被指数变化特征研究

为了研究河南省植被指数变化特征,采用最大值合成法(MVC)对MODIS-NDVI和MODIS-EVI两种指数产品进行处理,然后进行时空变化分析,得到归一化植被指数(NDVI)与增强型植被指数(EVI)两种指数产品的特点,实验结果表明:1)在时间分布特征上,两种植被指数均随季节呈现规律性变化,并且最大值均出现在7月或8月,但EVI相比NDVI更具季节性规律,能够更好地反映高植被覆盖区的植被季节性变化特征;2)在空间分布特征上,两种植被指数的区域性都非常明显,但在高植被覆盖区,NDVI出现饱和现象,而EVI未出现饱和现象。     

温度反演和植被供水指数的农田灌溉面积提取

针对传统的农田灌溉面积提取方法单一且费时费力的问题,提出基于遥感地表温度反演及植被供水指数(VSWI)的方法对石津灌区农作物的灌溉面积进行了提取。在利用Landsat 8影像提取灌溉面积的同时通过高分1号影像提取的小麦种植结构对灌溉区域进行了约束,从而减少了其它地物对灌溉面积提取的影响。结果显示,两种方法计算得到的灌溉区域重叠率达87%,同时将利用遥感方法提取的灌溉结果与实地调查的结果相比较,发现在灌溉时间和面积上具有较好的一致性,因此认为所得结果较为可靠。该方法不仅可以快速、高效的获取到较高精度的灌溉面积数据,同时大大减少了外业工作量,因此可以为农业灌溉区域的调查和监测提供有效的技术支撑。