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。     

利用MERIS和AATSR资料估算黄土高原塬区植被含水量时空变化

通过利用2005年黄土高原塬区夏季地表过程野外观测试验期间收集的地面观测的植被含水量、中分辨率影像光谱仪(Medium Resolution Imaging Spectrometer,MERIS)和高级沿轨迹扫描辐射计(Advanced Along-Track Scanning Radiometer,AATSR)卫星遥感资料,分别对归一化差值植被指数(Normalized Different Vegetation Index)和归一化差值水分指数(NormalizedDifferent Water Index)与植被含水量(Vegetation water content)变化关系进行了分析比较,得到了两种不同的植被指数在作物生长背景影响下的异同。并分别利用MERIS的观测资料计算了NDVI,利用AATSR观测资料计算了NDWI,通过分析得出:随着作物的生长或生物量的增加,归一化差值植被指数变化趋于饱和,而归一化差值水分指数仍然继续增加。进一步通过同步地面野外观测植被含水量与卫星遥感观测资料的对比,建立了归一化差值植被指数、归一化差值水分指数和实际野外测量植被含水量的关系,并且得到由两种植被指数反演植被含水量的方法和地面观测之间的误差分别为1.030 64 kg·m-2和0.940 45 kg·m-2。最后通过分析后总结出,利用归一化差值水分指数来反演黄土高原塬区夏季玉米冠层的含水量优于利用归一化差值植被指数。     

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。     

锡林格勒典型植被类型光谱特征

内蒙古草原是全球变化研究的热点区域。遥感是进行大尺度草地动态监测最为有效的工具。为基于遥感数据的草地分类识别和动态变化监测提供依据,该文以锡林格勒盟的典型植被类型为研究对象,采集冠层反射率光谱数据,分析其波形和植被指数光谱特征。研究结果表明:红边面积、红边斜率以及680nm附近的叶绿素吸收谷特征参量,能够有效区分不同密度的草地和农业植被。归一化植被指数NDVI、绿度归一化植被指数GNDVI和优化调节植被指数OSAVI的变化趋势一致,能够反映植被绿度信息,适宜于监测植被长势。     

基于GF-1影像的耕地地块破碎区水稻遥感提取

耕地地块破碎区水稻遥感提取是作物监测研究的热点问题之一。以苏州市高新区为例,通过挖掘关键物候期水稻与下垫面水体光谱特征组合差异,基于分蘖期与齐穗期两景16 m分辨率的GF-1 WFV数据,构建归一化差值植被指数（NDVI）差值法、归一化水体指数和比值植被指数（NDWI-RVI）差值法提取水稻分布,并深入探究了水稻面积提取精度及空间重合度影响因素。结果显示：与非监督分类和监督分类方法相比,植被指数差值法水稻识别精度贡献率可提升30%以上,NDVI差值法提取水稻种植面积的精度、空间重合度、制图总体精度和Kappa系数分别为86.2%、66.1%、92.2%和0.72;NDWI-RVI差值法上述指标分别高达95.5%、78.4%、93.5%和0.846,实现了利用少量中高分辨率遥感影像精确提取耕地地块破碎区水稻分布的目的,可实际服务于太湖地区农业生产及相关决策支持。     

基于GF-1影像的耕地地块破碎区水稻遥感提取

耕地地块破碎区水稻遥感提取是作物监测研究的热点问题之一。以苏州市高新区为例，通过挖掘关键物候期水稻与下垫面水体光谱特征组合差异，基于分蘖期与齐穗期两景16 m分辨率的GF-1 WFV数据，构建归一化差值植被指数（NDVI）差值法、归一化水体指数和比值植被指数（NDWI-RVI）差值法提取水稻分布，并深入探究了水稻面积提取精度及空间重合度影响因素。结果显示：与非监督分类和监督分类方法相比，植被指数差值法水稻识别精度贡献率可提升30%以上，NDVI差值法提取水稻种植面积的精度、空间重合度、制图总体精度和Kappa系数分别为86.2%、66.1%、92.2%和0.72；NDWI-RVI差值法上述指标分别高达95.5%、78.4%、93.5%和0.846，实现了利用少量中高分辨率遥感影像精确提取耕地地块破碎区水稻分布的目的，可实际服务于太湖地区农业生产及相关决策支持。     

地面反射波谱与卫星磁带记录数据间关系模拟的初步探讨 -以冬小麦和土壤为例

根据冬小麦和土壤地面反射波谱测试数据，计算了在卫星高度土与卫星磁带数据相对应波段的辐亮度值，对NOAAAvHRR和TM某些通道的差值绿度植被指数Dvi、归一化绿度植被指数NDVI和比值绿度植被指数Rvi的分析，从理论上证明了目前采用TMDVI4.3提取冬小麦种植面积和NOAA NDVIZ2,1区分植被和土壤背景的有效性。同时在冬小麦种植面积和长势监测方面提出了一些新建议。     

利用AMSR-E遥感数据反演地表温度——以2008年广东省春季寒害为例

基于2008年1月25日至2008年2月5日期间的AMSR-E/Aqua L2A微波亮度温度数据,以广东省为研究对象,依据微波极化差异指数（MPDI）、归一化植被指数（NDVI）和比率植被指数（RVI）等3种植被指数,将广东省地表植被覆盖情况分为裸地、草地、灌木林、针叶林和阔叶林等5种类型,利用逐步回归分析方法,建立了基于不同植被覆盖类型的微波亮度温度与地面气象温度多元回归模型。同步地面气象温度数据验证表明,本文建立的基于地表植被覆盖分类的多波段地表温度反演模型,地表温度反演精度基本可达到3.0℃,其中有大约86%的地区地表温度反演精度可以控制在2.5℃以内,为广东省作物寒害预测提供了微波遥感技术支持。     

NDWI与NDVI指数在区域干旱监测中的比较分析——以2003年江西夏季干旱为例

植被水分指数NDWI是基于短波红外（SWIR）与近红外（NIR）的归一化比值指数。与NDVI相比，它能有效地提取植被冠层的水分含量；在植被冠层受水分胁迫时，NDWI指数能及时地响应，这对于旱情监测具有重要意义。结合2003年夏季MODIS影像数据和地面气象数据，以江西省内一片农田和一片林地为试验区域，分析比较了NDWI与NDVI距平值在短期旱情监测中的有效性。监测结果表明， NDWI对植被冠层水分信息比NDVI更为敏感；在短期干旱监测中，NDWI指数能准确地反映旱情的时空变化。     

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。     

利用中巴-2号卫星CCD估测土壤全氮的研究

遥感监测土壤对于及时快速掌握农田肥力状况,合理施肥意义重大。NDVI在监测地表植被覆盖中发挥着重要作用,水田植被覆盖种类单一,其他影响因素少,这使得通过NDVI监测水稻长势,间接监测土壤肥力状况变得可行。本文就是利用中巴-2号卫星的CCD的植被指数对南京溧水县水田的土壤质量进行监测,回归方程的决定系数R2=0.741,相对误差为0.1663。结论发现利用中巴卫星的植被指数对研究区的的水田氮素的监测是可行的,并在此基础上做了研究区的氮素等级分布图,实现了遥感监测溧水水田土壤全氮的估测。     

The relation between Normalized Difference Vegetation Index and vegetation moisture content at three grassland locations in Victoria,Australia

The supposition that, for most practical purposes, a single, generic, widely applicable relation exists between Normalized Difference Vegetation Index (NDVI) and grassland vegetation moisture content is tested. An experiment is described in which the vegetation moisture content at three Victorian grassland sites of varying composition is measured over the course of a complete curing episode. For each site, corresponding satellite radiation measurements are used to extract surface reflectances corrected for atmospheric and view-angle effects, and NDVI values based on these. On relating NDVI so obtained to the field measurements of vegetation moisture expressed in terms of a parameter commonly employed in assessing grassland fire risk, namely Fuel Moisture Content (FMC), separate relations for each site are clearly identified. When the relation appropriate to each site is used to derive FMC for that site, accurate estimates are obtained. Accuracy decreases markedly if the relation appropriate to one site is used to derive estimates of FMC at the other sites. When FMC values are transformed to another commonly employed parameter of grassland vegetation moisture content, namely Grassland Curing Index (GCI), the loss of accuracy becomes much greater. More accurate estimates of GCI are obtained using a direct relation between NDVI and GCI.     

Estimation of vegetation fraction using RGB and multispectral images from UAV

The vegetation fraction (VF) monitoring in a specific area is a very important parameter for precision agriculture. Until a few years ago, high-cost flights on aeroplanes and satellite imagery were the only option to acquire data to estimate VF remotely. Recently, Unmanned Aerial Vehicles (UAVs) have emerged as a novel and economic tool to supply high-resolution images useful to estimate VF. VF is usually estimated by spectral indices using red-green-blue (RGB) and near-infrared (NIR) bands data. For this study, a UAV equipped with both kinds of sensors (RGB and NIR) was used to obtain high-resolution imagery over a maize field in progressive dates along the mid-season and the senescence development stages. The early-season stage was also monitored using only RGB spectral indices. Flights were performed at 52 m over the terrain, obtaining RGB images of 1.25 cm pixel^?1 and multispectral images of 2.10 cm pixel^?1. Three spectral indices in the visible region, Excess Green (ExG), Colour Index of Vegetation (CIVE), and Vegetation Index Green (VIg), and three NIR-based vegetation indices, Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Normalized Green (NG), were evaluated for VF estimation. Otsu’s method was applied to automatically determine the threshold value to classify the vegetation coverage. Results show that ExG presents the higher mean accuracy (85.66%) among all the visible indices, with values ranging from 72.54% to 99.53%, having its best performance in the earlier development stage. Nevertheless, GNDVI mean accuracy (97.09%) overcomes all the indices (visible and multispectral), ranging in value from 92.71% to 99.36%. This allowed comparing the accuracy difference gained by using a NIR sensor, with a higher economic cost than required using a simple RGB sensor. The results suggest that ExG can be a very suitable option to monitor VF in the early-season growth stage of the crop, while later stages could require NIR-based indices. Thus, the selection of the index will depend on the objectives of the study and the equipment capacity.     

基于NDVI和纹理变化的城镇扩展检测--以浙江省绍兴市为例

以绍兴市作为研究区，选取两景不同时相的TM影像，进行了城镇扩展的研究。该研究区位于中国东部沿海地区，其地理位置和投资环境决定了其城镇的快速发展，因此对其进行城镇扩展的研究具有非常重要的意义。本假定以植被覆盖为明显标志的景观生态和基于NDVI的城镇土地利用之间的相互关系，给出了一种新的城镇扩展检测方法，即使用NDVI公式生成植被指数，然后提取1984年和2000年归一化差分纹理指数的纹理影像，这些指数显示了这些纹理影像变化的比率，该方法成功地显示了两景影像间的变化区域。当然为了获取正确的结论往往需要相关的地面数据。研究结果显示，在城镇外围地区具有明显的植被覆盖变化。     

Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India

The Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites has been used for mapping vegetation cover and classification employing the Normalized Difference Vegetation Index (NDVI). Recently, this technique has been improved by converting NDVI with radiation measured in one of the thermal channels and converting brightness temperature into the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). These indices are being used for estimation of vegetation health and monitoring drought. The present study shows the application of vegetation and temperature condition indices for drought monitoring in India.     

Monitoring East African vegetation using AVHRR data

NOAA Advanced Very High Resolution Radiometer satellite data are applied to regional vegetation monitoring in East Africa. Normalized Difference Vegetation Index (NDVI) data for a one-year period from May 1983 are used to examine the phenology of a range of vegetation types. The integrated NDVI data for the same period are compared with an ecoclimatic zone map of the region and show marked similarities. Particular emphasis is placed on quantifying the phenology of the Acacia Commiphora bushlands. Considerable variation was found in the phenology of the bushlands as determined by the satellite NDVI, and is explained through the high spatial variability in the distribution of rainfall and the resulting green-up of the vegetation. The relationship between rainfall and NDVI is further examined for selected meteorological stations existing within the bushland. A preliminary estimate is made of the length of growing season using an NDVI thresholding technique     

遥感干旱指数在洛川苹果干旱监测中的适用性分析

目前对苹果干旱研究较少且主要运用站点数据,对空间信息表征有限,遥感干旱指数可用于大范围干旱时空动态监测,但在苹果干旱监测中的适用性还有待研究.基于2014～2018年MODIS反射率、地表温度以及地表覆被数据,结合土壤湿度数据和野外调查资料,分析洛川苹果区温度植被干旱指数(TVDI)、归一化植被水分指数(NDWI)、植...     

Interannual variations in vegetation activities and climate variability caused by ENSO in tropical rainforests

Interannual variations in terrestrial carbon cycle over tropical rainforests affect the global carbon cycle. Terrestrial ecosystem models show the interannual relationship between climate changes due to El Niño‐Southern Oscillation (ENSO) and net primary production over tropical rainforests. However, we need an independent analysis using satellite‐based vegetation index and climate parameters. In the present study, we extracted the ENSO‐related interannual variations from time‐series in Normalized Difference Vegetation Index (NDVI) and climate data from 1981 to 2000, and analysed their relevance. We detected relationships among NDVI, ENSO, and climate parameters from long‐term data with negative NDVI–ENSO, NDVI–temperature, and positive NDVI–precipitation relations. These correlations suggest that interannual variability in vegetation activities over tropical rainforests could be extracted from NDVI time‐series despite noise components in NDVI data, and that interannual changes in precipitation and temperature caused by ENSO play a more important role in vegetation activities over tropical rainforests than in incoming surface solar radiation.