利用NDVI与EVI再合成的植被指数算法

针对单独使用NDVI、EVI对植被覆盖变化分析出现的饱和问题,提出了植被指数再合成法。本研究基于EVI与NDVI中国合成产品在时间上通过植被指数归一化再合成、空间上通过植被指数向量分析法对云南省月植被覆盖变化进行趋势分析并进一步利用方差、标准差、变异系数、回归分析等方法对单一植被指数与合成植被指数进行定量评价。结果表明:NDVI与EVI月趋势变化呈显著负相关;基于EVI mean与NDVI max再合成指数VI的方差、标准差均最大,相对其他单一植被指数(NDVI、EVI)而言,其变异系数VC最大为0.551,回归分析结果k值为0.039,对描述植被覆盖在时间维度与空间维度上的变化情况效果最佳。     

TVDI在冬小麦春季干旱监测中的应用

应用冬小麦春季生长期的NOAA/AVHRR资料,反演归一化植被指数（NDVI）、土壤调整植被指数（SAVI）和下垫面温度（T_s）,分析了植被指数和下垫面温度空间特征,采用温度植被旱情指数（TVDI）,研究了河北省2005年3~5月的冬小麦旱情状况。结果表明：基于SAVI的温度植被旱情指数与土壤表层相对湿度的相关性好于基于NDVI的温度植被旱情指数。通过与气象站土壤水分观测资料进行相关性分析,表明温度植被旱情指数与10 cm土壤相对湿度关系最好,20 cm次之,50 cm较差。因此,基于SAVI的温度植被旱情指数更适于监测冬小麦春季的旱情。     

利用MODIS产品分析长江上游川江段植被变化

使用2000年到2005年每年8月的中分辨率成像光谱仪(MODIS)的植被指数产品研究了长江上游川江流域加快长江防护林建设以来的植被变化。在选定的特定区域对广泛使用的植被指数NDVI和新开发的增强型植被指数EVI进行了对比分析,两种植被指数在湿润环境下对高密度植被的描述有明显差别:NDVI的季节性不明显,表现为全年高平的曲线;而EVI仍然有季节性,表现为钟形曲线,与月平均温度关系更密切。通过对EVI产品的分析,近6年来长江上游川江流域的植被得到了较好的恢复,尤其是在湖北和嘉陵江附近植被得到了明显的增加,整体上最明显的变化是较高植被值域区(0.6～0.8),所占比例由3.26%增加到13.96%。     

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。     

基于ETM＋的北京西部山区乔木群落结构研究

利用遥感技术结合地面调查的方法对2008年春季北京西部山区乔木群落的多样性进行了研究。提取归一化植被指数（NDVI）,并计算NDVI随时间的变化率（△NDVI）;利用地面调查数据计算研究区的健康指数、Mar-galef丰富度指数、Shannon-Wiener多样性指数和Simpson多样性指数,并分析△NDVI、健康指数和多样性指数之间的关系。结果表明：（1）健康指数和多样性指数之间存在正相关关系,群落多样性指数越高,群落的整体健康状况越好。（2）△NDVI与健康指数之间呈正相关关系,健康指数越高的群落,单位时间内NDVI的增加值越大,植被群落的生长变化越明显。（3）△NDVI的高低可以代表区域植被的健康程度,反映区域植物群落的丰富度、多样性。△NDVI的值越高,植被群落的健康程度越好,植被群落的丰富度、多样性指数越高。本文的研究较好的将遥感技术和地面调查相结合,对△NDVI与群落植被健康程度、多样性指数进行了研究和验证,为以后研究植被群落多样性提供了一定的借鉴。     

基于不同植被指数的重庆地区物候参数提取对比研究

基于不同植被指数提取物候参数是分析长时间物候变化的重要基础。以多云雾的重庆地区为例，使用2010～2019年MODIS NDVI/EVI/EVI2共3种长时序的植被指数数据，通过D-L滤波方法分析了不同植被指数特征；并使用动态阈值法和趋势分析法，对比研究了基于3种植被指数提取的物候参数结果及其随不同地形因子的分异规律，结果如下：(1)EVI和EVI2的时间序列拟合曲线比NDVI的拟合曲线更加平滑，3种植被指数原始值与拟合值的差值主要分布为NDVI(0.05～0.18)、EVI(0.03～0.11)、EVI2(0.03～0.1)。(2)基于3种植被指数提取的物候参数在空间分布和变化趋势上呈现一致性，其中EVI和EVI2提取的植被指数参数皆相近，相差5d之内占比79%以上，SOSEVI2变化显著性区域所占比面积最高（16.36%）,SOSNDVI最低为12.37%。(3)SOS随海拔升高而推迟，EOS随海拔升高先延后再提前，LOS随海拔升高先延长后缩短，且EOSNDVI、LOSNDVI随着海拔增加分别与EOSEVI/EOSEVI2、LOSEVI/LOSEVI2差异增大，不同植被类型上，EV...     

利用NOAA数据分析中国地区植被覆盖变化周期

对中国地区连续数年的改进的高解析度传感器（AVHRR）均一化植被指数（NDVI）时间序列数据进行研究，利用时间序列谐波分析（HANTS）算法对NDVI数据进行去云处理，同时计算NDVI时间序列的特征分量，用以分析地表植被覆盖的生长周期性变化特性，从而了解地表植被覆盖情况。研究HANTS之后提取出的NDVI傅里叶特征分量。得知0频率表明了全年植被覆盖率均值，1、2频率谐波分量简明地描述了观测点的全年与半年幅值与相位值；1频率谐波的幅值表明了全年中植被生产率的变化，1频率谐波的相位值表明了植被绿度的时间变化快慢；2频率谐波表示的是半年的植被覆盖强度以及时间变化率。     

植被指数遥感定量研究--以民勤绿洲为例

研究以我国西北干旱区的代表性绿洲一民勤绿洲为例，使用法国CE313光谱仪，对典型样区植被反射率进行了野外测定，计算常用的6种植被指数，通过对降低土壤背景影响的效果和不同植被指数提取植被信息的能力进行分析，遴选出适宜于干旱区民勤绿洲的植被指数估算模型。定量研究了民勤绿洲近20年来植被覆盖空间变化过程，对预测生态环境的变化和防治绿洲沙漠化具有重要意义。     

利用色调—亮度彩色分量的可见光植被指数

目的 无人机遥感具有高时效、高分辨率、低成本、操作简单等优势。但由于无人机通常只携带可见光传感器,无法计算由可见光-近红外波段组合所构造的植被指数。为解决这一问题,提出一种归一化色调亮度植被指数NHLVI （normalized hue and lightness vegetation index）。方法 通过分析HSL （hue-saturation-lightness）彩色空间模型,构建一种基于色调亮度的植被指数,将该植被指数以及其他常用的可见光植被指数,如归一化绿红差值指数NGRDI （normalized green-red difference index）、过绿指数ExG （excess green）、超绿超红差分指数ExGR （excess green minus excess red）等,分别与野外实测光谱数据和无人机多光谱数据的NDVI （normalized difference vegetation index）进行相关性比较;利用受试者工作特征曲线ROC （receiver operating characteristic curve）的特点确定阈值,并进行植被信息提取与分析。结果 NHLVI与NDVI相关性高（R²=0.776 8）,而其他可见光植被指数中,NGRDI与NDVI相关性较高（R²=0.687 4）;ROC曲线下面积大小作为评价不同植被指数区分植被与非植被的指标,NHLVI指数在ROC曲线下面积为0.777,小于NDVI （0.815）,但大于NGRDI （0.681）,区分植被与非植被能力较强。为进一步验证其精度,利用阈值法提取植被,NHLVI提取植被信息的总体精度为82.25%,高于NGRDI （79.75%）,尤其在植被稀疏区,NHLVI的提取结果优于NGRDI。结论 提出的归一化色调亮度植被指数,提取植被精度较高,适用于无人机可见光影像植被信息提取,为无人机可见光影像的应用提供了新方法。     

基于大疆精灵4无人机多光谱影像的西沙植被监测

西沙群岛位于热带,常年多云,在光学卫星数据获取时易受天气影响导致缺失,使得地表动态监测困难。为解决这一问题,探讨无人机低空平台对西沙群岛植被的监测能力,选取大疆精灵4多光谱无人机,通过5个多光谱波段提取4项植被指数,包括归一化差值植被指数（NDVI）、叶绿素指数（GCI）、绿色归一化植被指数（GNDVI）以及归一化绿红差值指数（NGRDI）,评估了2020年5月西沙群岛北岛的植被生长状况,并结合关键气象参数以及Worldview2卫星光学影像对比分析了2020年5月和2018年5月北岛植被生长变化及其潜在归因。研究结果表明：2020年5月北岛平均NDVI、GCI、GNDVI和NGRDI别为0.30、0.84、0.26和0.05,反映出植被覆盖度较低,可能存在枯黄现象,与地面监测结果一致;2020年人工管理植被区和自然生长植被区各项指数差异由2018年的-23%—15%增加到15%—40%,表明2020年自然生长植被长势显著差于人工管理植被,反映出较强的环境胁迫;气象数据显示2020年4月—5月该地区日平均温度较常年同期升高、累计降水量减少、平均风速增大同时增加了土壤水分亏缺,可能是引起植被生长状况变差的主要原因。综上所述,大疆精灵4无人机可定量反映热带岛屿植被生长状况,可为其生态环境监测提供有效途径。     

Integrating fuzzy logic with piecewise linear regression for detecting vegetation greenness change in the Yukon River Basin,Alaska

Effective methods are required to detect vegetation changes and attribute these changes to different driving forces. This article presents a new hybrid change detection method by integrating fuzzy logic with piecewise linear regression (PLR) as applied to the Yukon River Basin (YRB) in Alaska, USA, to detect the changes in spatial and temporal patterns of vegetation greenness based on Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data from 1995 to 2004 (2000 data missing). Abrupt changes (or breakpoints), and speed and tendency of gradual changes in short-, mid-, or long-term segments, were simultaneously detected during the study period. Fire scars, precipitation, and temperature data were applied to explain the spatial and temporal patterns of breakpoints and temporal trends (i.e. slopes or change rates). Results showed no significant systematic temporal trend (p?=?0.1) in more than 90% of the study area. Abrupt changes in NDVI were partly attributed to wildfire disturbances. The NDVI decline partly overlaid the fire scars provided by the Alaskan Fire Science Center in time and space. The temporal change rates of NDVI were closely related to the temporal trends of temperature/precipitation. The approach presented in this article can be effectively adapted to other study areas.     

利用MODIS植被指数评估分水对黑河中游的影响

为缓解黑河下游额济纳旗生态恶化的现状,国家对黑河流域实施了分水政策。在下游环境得到了充分改善的同时,中游绿洲地区的发展却受到了不同程度的限制。利用2000~2004年的MODIS归一化植被指数数据,对中游两个绿洲地区连续5 a生长季的植被覆盖面积进行监测。分析并对比其植被覆盖面积的增长趋势,对黑河流域中游地区受分水影响的程度做出评估。     

基于ETM和图像融合的优势植被冠层叶面积指数和消光系数的遥感反演

叶面积指数和消光系数是表征植被群体冠层结构及光能利用的地球表层下垫面参量,国内外对叶面积指数的遥感反演有较多的研究与应用,但对消光系数的遥感反演尚不多见。我国南方少见单一大面积的均匀植被分布。为更好地匹配叶面积指数和光合有效辐射(用于估算消光系数)的实测数据,反映植被混交和疏密不均的状态,以Landsat ETM作为遥感信息源,通过HSV、Brovey和Gram-Schmidt(GS)3种图像融合方法的比较,选取效果最佳的图像融合方法,将ETM融合成空间分辨率为15 m的多光谱数据。以鄱阳湖源头梅江流域为研究区,在实测优势植被叶面积指数和光合有效辐射的基础上,利用植被指数法经验公式法反演流域的叶面积指数,并根据Beer-Lambert定律,建立了流域优势植被冠层消光系数的反演模型。在此基础上,反演了流域植被冠层叶面积指数和消光系数的空间分布,为SWAT植物生长模式的修正提供输入数据基础。     

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.     

近10a新疆地区植被动态与R/S分析

利用SPOT VGT传感器的NDVI时序数据,采用时间序列谐波分析算法(HANTS)对NDVI数据进行去云预处理,基于趋势分析、Hurst指数分析等方法,研究了1999~2008年新疆植被覆盖的时空变化,分析了Hurst指数的空间分布规律及其在不同土地覆盖类型下的差异。结果表明：近10 a来,新疆植被总体呈现不显著的增加趋势且具有明显的空间差异性。R/S分析表明,新疆大部分地区植被变化将保持现在的趋势,局部地区具有反持续性。各土地覆盖类型Hurst指数均在0.63以上,沙漠、戈壁、裸地的Hurst指数明显高于其它土地覆盖类型,受人类活动影响较大的土地覆盖类型其Hurst指数较低,可持续性低于自然或半自然状态下的植被覆盖类型。     

Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction

The normalized difference vegetation index (NDVI) is the most widely used vegetation index for retrieval of vegetation canopy biophysical properties. Several studies have investigated the spatial scale dependencies of NDVI and the relationship between NDVI and fractional vegetation cover, but without any consensus on the two issues. The objectives of this paper are to analyze the spatial scale dependencies of NDVI and to analyze the relationship between NDVI and fractional vegetation cover at different resolutions based on linear spectral mixing models. Our results show strong spatial scale dependencies of NDVI over heterogeneous surfaces, indicating that NDVI values at different resolutions may not be comparable. The nonlinearity of NDVI over partially vegetated surfaces becomes prominent with darker soil backgrounds and with presence of shadow. Thus, the NDVI may not be suitable to infer vegetation fraction because of its nonlinearity and scale effects. We found that the scaled difference vegetation index (SDVI), a scale-invariant index based on linear spectral mixing of red and near-infrared reflectances, is a more suitable and robust approach for retrieval of vegetation fraction with remote sensing data, particularly over heterogeneous surfaces. The proposed method was validated with experimental field data, but further validation at the satellite level would be needed.     

Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: a case study for South and Southeast Asian countries

A geospatial database on the spatial distribution of rice areas and rice cultural types of major rice-producing countries of South and Southeast Asia has been developed in this study using remote-sensing and ancillary data sets. Multitemporal SPOT VGT normalized difference vegetation index (NDVI) data for the period 2009–2010 were used for the analysis. The classification was performed adopting ISODATA clustering to build a non-agricultural area mask followed by rice area mapping. The derived rice area was stratified by logical modelling of ancillary data sets into five rice cultural types: irrigated wet, upland, flood-prone, drought-prone, and deep-water. The uniqueness of this study is a synergistic approach based solely on single-source, high-temporal remote-sensing data coupled with ancillary data, which demonstrate the application of SPOT VGT NDVI data in building a geospatial database for rice crops over a wide spatial extent. This approach was adopted for cost effectivity as the study extent was vast and thus lacking ground truth information. Comparison of the derived rice area against the reported literature values for validation yielded a good correlation (linear coefficient of determination, R² = 0.95–0.99). The high-temporal resolution NDVI data enabled effective characterization of vegetation phenology. The derived spatial outputs can be used in various studies associated with the assessment of greenhouse gas emissions from paddy fields, change detection, and inputs to crop simulation models, which are significantly related to different rice cultural types.     

基于Geodatabase数据模型三峡库区面源污染空间数据库的设计方法研究

从整体上描述了Geodatabase空间数据模型 ,并基于Geodatabase模型运用面向对象的方法和UML设计了三峡库区面源污染空间数据库。结果表明利用Geodatabase模型可以将面向对象方法比较好地应用于空间数据库的设计中     

Climatic and demographic determinants of vegetation cover in northern Cameroon

The objective of the study was to evaluate the spatio-temporal impacts of seasonal rainfall and urban population growth on the variations in normalized difference vegetation index (NDVI) in north Cameroon, which includes climates from south to north, the Sudanese and Sahelian climates. To this end, 48 points of measured rainfall were interpolated based on the kriging method at a spatial resolution of 8 km in accordance with the NOAA-AVHRR NDVI data set. Relationships between rainfall and NDVI, on the one hand, and urban population growth and NDVI, on the other, were analysed considering the 79 administrative units (AUs) in Cameroon. Seasonal (rainy season) variations of the vegetation cover were studied for the period 1987–2002 using the NDVI product at 8 km (NOAA-AVHRR) and 1 km (SPOT-VEGETATION) of spatial resolution. This article emphasizes the importance of the urban signal for the NDVI studies at finer scales, specifically in tropical areas.     

Quantification of spatial distribution of vegetation in the Qilian Mountain area with MODIS NDVI

The spatial distribution of vegetation in the Qilian Mountain area was quantified with remote sensing data. The MODIS NDVI values for June, July, August and September are the best indicators for the vegetation growth during a year in this area and thus were used in this study. The results obtained by analysing NDVI data for seven years from 2000 to 2006 clearly indicated that elevation and aspect, as a proxy for precipitation and temperature, are two very important factors for the vertical distribution of vegetation in Qilian Mountain area. In the Qilian Mountain area the vegetation growth is optimal between the elevations of 3200 m and 3600 m, with NDVI values larger than 0.50 and a peak value of?>0.56 around 3400 m. It is the combination of plentiful precipitation and suitable land surface temperature that provides less soil moisture stress and thus suitable conditions for vegetation growth in this range of elevations. The optimal vegetation growth is found in the shady slope between NW340° to NE70° with the largest NDVI value (>0.56) within the elevation range of 3200 m and 3600 m. The methodology developed in this study should be useful for similar ecological studies on vegetation distribution.