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。     

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...     

基于缨帽变换分析地表温度变化

利用缨帽变换提取土壤亮度指数、绿度植被指数、湿度指数等地表参数,利用模型提取归一化植被指数NDVI、比值植被指数RVI、修改型土壤调整指数MSAVI等植被指数和水体指数MNDWI,利用Artis单窗算法估算热红外波段像元尺度地表温度,将地表温度的影响因素作为BP神经网络输入估算30m空间分辨率的亚像元地表温度,分析1989~2006年桂林城区土地利用变化、缨帽变换特征分量变化、植被参数变化、水体指数变化对地表温度的影响机理。     

植被指数与退耕还林( 草) 初期的遥感监测应用

探讨了植被指数的几种主要形式( IDV 、NDVI、Tasseled Cap Greenness) 及其在退耕还林( 草) 初期( 2000～2002 年) 效能监测中的应用。运用遥感数据处理、GIS( ARC/ INFO) AML 编程统计出青藏-黄土高原结合部复杂地形条件下退耕还林( 草) 各类型地块的3 期平均植被指数, 及两年间相应的植被指数变化, 对比分析了各类型植被指数与其它属性数据间的关系, 发现7～9 月份积温和湿润度条件对植被指数的影响主要表现为累积效应。研究认为, 通过更详实的地表植被状态的适时调查, 建立并应用遥感成像前期地表水热因子与各类型的植被指数向量之间的映照关系, 上述方法将有更实际的意义。     

一种基于植被指数的遥感影像决策树分类方法

以江苏省徐州市为研究区,采用2000年ETM+多光谱影像作为遥感信息源,选择影像的光谱特征和归一化植被指数（NDVI）、绿度植被指数（GVI）、比值植被指数（RVI）等10种植被指数作为分类特征,基于See5决策树学习软件构建分类决策树,实现了研究区景观格局的遥感分类。研究结果表明,决策树分类法易于综合多种特征进行遥感影像的分类,植被指数参与到决策树分类中能够提高分类的总体精度。     

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NDVI与LAI的水稻生长状况研究

在水稻反射光谱特性与水稻生物参数关系的支持下,以吉林省德惠市夏家店镇为研究区,探讨了一条基于TM遥感影像反演得到的归一化植被指数(NDVI)与地面观测数据叶面积指数(LAI)的水稻生长状况的研究途径,并利用NDVI和LAI对该区2000年和2001年的水稻生长状况进行了分析研究。     

基于RSEI的苏锡常城市群生态环境遥感评价

快速准确掌握地区生态环境质量及其变化分布对于区域生态环境监测与治理、城市建设规划等问题具有重要的参考价值。基于此,以苏锡常城市群为研究区,选取多时相Landsat影像,分别提取湿度、绿度、热度和干度4项指标并通过主成分分析计算遥感生态指数RSEI(Remote Sensing Ecological Index),定量评价2001～2018年间区域生态质量变化情况。结果表明:①2001～2018年间苏锡常地区生态质量呈先下降后回升的趋势。2008年前后先下降17.69%后回升7.69%,其中苏州、无锡回升幅度大于常州;②2008年之后10 a区域生态恶化趋势得到明显遏制,生态恶化区从年均增长5.85%减至2.06%,其中老城区生态质量得到明显改善。③建筑类地物占比上升是生态质量下降的重要原因之一,而逐步回归分析中绿度在4个指标中所占权重最大且与干度权重呈负相关,表明恢复植被覆盖是改善区域生态质量的关键。     

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。     

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。     

Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology

Remote sensing provides spatially and temporally continuous measures of forest reflectance, and vegetation indices calculated from satellite data can be useful for monitoring climate change impacts on forest tree phenology. Monitoring of evergreen coniferous forest is more difficult than monitoring of deciduous forest, as the new buds only account for a small proportion of the green biomass, and the shoot elongation process is relatively slow. In this study, we have analyzed data from 186 coniferous monitoring sites in Sweden covering boreal, southern-boreal, and boreo-nemoral conditions. Our objective was to examine the possibility to track seasonal changes in coniferous forests by time-series of MODIS eight-day vegetation indices, testing the coherence between satellite monitored vegetation indices (VI) and temperature dependent phenology. The relationships between two vegetation indices (NDVI and WDRVI) and four phenological indicators (length of snow season, modeled onset of vegetation period, tree cold hardiness level and timing of budburst) were analyzed.The annual curves produced by two curve fitting methods for smoothening of seasonal changes in NDVI and WDRVI were to a large extent characterized by the occurrence of snow, producing stable seasonal oscillations in the northern part and irregular curves with less pronounced annual amplitude in the southern part of the country. Measures based on threshold values of the VI-curves, commonly used for determining the timing of different phenological phases, were not applicable for Swedish coniferous forests. Evergreen vegetation does not have a sharp increase in greenness during spring, and the melting of snow can influence the vegetation indices at the timing of budburst in boreal forests. However, the interannual variation in VI-values for specific eight-day periods was correlated with the phenological indicators. This relation can be used for satellite monitoring of potential climate change impacts on northern coniferous spring phenology.     

基于SPOT4数据的黄土高原植被动态变化研究

以SPOT4/VEGETATION数据为基础,以NDVI变化率和年均NDVI值作为植被覆盖动态变化的指标,分析了1998~2005年黄土高原植被覆盖的时空动态变化特征。结果表明黄土高原地区植被动态变化显著增强,1998~2001年黄土高原的植被覆盖有所减少,幅度约为10.5%,2001年后,植被活动显著增强,植被覆盖面积呈增加趋势,2004年后稍有回落。植被生长季的延长和生长加速是该区域NDVI值增加的主要原因,黄土高原地区植被增加和减少的区域相互交错,这一特性是由农业生产活动、城市建设、政府决策及植被对气候变化的响应等综合因素作用的结果。     

Land-use and vegetation change detection in Plastira artificial lake catchment (Greece) by using remote-sensing and GIS techniques

Detecting changes in the land-use and vegetation conditions by using remote-sensing techniques is a common approach nowadays to assessing human-induced impacts in a specific area. For this purpose, a series of vegetation indices and change detection algorithms such as the normalized difference vegetation index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), and k-means have been efficiently developed and used in many studies worldwide. However, identifying the driving forces for the estimated changes in land use and vegetation has always been a difficult and challenging task. In this study, Landsat and Système Pour l'Observation de la Terre (SPOT) images have been used to estimate the NDVI and land-use changes at the Plastira artificial lake catchment in the period 1984–2009. The recorded vegetation changes were correlated with a series of environmental and human-related parameters such as precipitation, temperature, specific land uses, and topography to identify the dominant factors of the aforementioned changes. This was done using both linear and geographically weighted regression methods. The results indicate that the precipitation and temperature fluctuations are strongly correlated with the vegetation conditions, whereas, as far as the topographic parameters are concerned, the aspect and slope affect mostly the particular vegetation index (the NDVI) of the study area.     

Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest

It is challenging to detect burn severity and vegetation recovery because of the relatively long time period required to capture the ecosystem characteristics. Multitemporal remote sensing data can provide multitemporal observations before, during and after a wildfire, and can improve the change detection accuracy. The goal of this study is to examine the correlations between multitemporal spectral indices and field-observed burn severity, and to provide a practical method to estimate burn severity and vegetation recovery. The study site is the Jasper Fire area in the Black Hills National Forest, South Dakota, that burned during August and September 2000. Six multitemporal Landsat images acquired from 2000 (pre-fire), 2001 (post-fire), 2002, 2003, 2005 and 2007 were used to assess burn severity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized burn ratio (NBR), integrated forest index (IFI) and the differences of these indices between the pre-fire and post-fire years were computed and analysed with 66 field-based composite burn index (CBI) plots collected in 2002. Results showed that differences of NDVI and differences of EVI between the pre-fire year and the first two years post-fire were highly correlated with the CBI scores. The correlations were low beyond the second year post-fire. Differences of NBR had good correlation with CBI scores in all study years. Differences of IFI had low correlation with CBI in the first year post-fire and had good correlation in later years. A CBI map of the burnt area was produced using regression tree models and the multitemporal images. The dynamics of four spectral indices from 2000 to 2007 indicated that both NBR and IFI are valuable for monitoring long-term vegetation recovery. The high burn severity areas had a much slower recovery than the moderate and low burn areas.     

不同等级石漠化区MODIS-NDVI与MODIS-EVI对比分析

利用2001～2010年10 a的MODIS资料,比较分析广西喀斯特不同等级石漠化区MODIS\|NDVI和MODIS\|EVI的时间变化特征差异,利用全时间序列及16 d10 a均值序列分析NDVI和EVI之间的相关关系,比较线性及对数相关模型对两种植被指数相关关系的拟合效果,结果表明:石漠化等级由重度到潜在,两者之间的差值也随着植被覆盖度的增加而增大,植被覆盖度越低,NDVI和EVI所表征的植被变化特征越相似。NDVI的峰值出现时间多晚于EVI且其反映的植被变化趋势与实况更吻合,但其NDVI偏高;各等级石漠化的两种时间序列NDVI与EVI的对数相关关系优于线性相关,两种植被指数的相关性随着植被覆盖度的降低而增大,但全时间序列中轻度、中度石漠化相关性变化规律与16 d 10 a均值序列相反。     

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

We have analysed monthly composites of normalized difference vegetation index (NDVI) calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) for the Amazonian region of northern Brazil across a decade (August 1981 to June 1991) to ascertain if the dominant vegetation types could be differentiated,and to seek inter-annual climatic variation due to changing environmental conditions. The vegetation types observed included dense forest ( submontana and terras baixas ), open forest ( submontana and terras baixas ), transitional forest, seasonal forest ( caatinga ), and two types of savanna ( cerrado ). We found that monthly NDVI composites revealed seasonality in cerrado and especially in caatinga cover types, which can be used in their identification, whilst the phenology of other forest cover types varies little throughout the year. Additionally, yearly composite NDVI values showed a clear and significant reduction ( p 0.95) in dry years, such as those with El Nino Southern Oscillation events. These results indicate the potential use of multi-temporal NDVI data for the environmental characterization and identification of forest ecosystems. Our research found NDVI images from NOAA AVHRR offer a long-term data set that is unequalled for monitoring terrestrial land cover. However, these data have to be used with a degree of caution, especially in regards to atmospheric interference, such as cloud contamination and volcanic eruptions, and post-launch changes in calibration.     

In situ examination of the relationship between various vegetation indices and canopy phenology in an evergreen coniferous forest,Japan

We examined the relationship between four vegetation indices and tree canopy phenology in an evergreen coniferous forest in Japan based on observations made using a spectral radiometer and a digital camera at a daily time step during a 4 year period. The colour of the canopy surface of Japanese cedar (Cryptomeria japonica) changed from yellowish-green to whitish-green from late May to July and turned reddish-green in winter. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and plant area index (PAI) showed no seasonality. In contrast, the green–red ratio vegetation index (GRVI) increased from March to June and then decreased gradually from July to December, resulting in a bell-shaped curve. GRVI revealed seasonal changes in the colour of the canopy surface. GRVI correlated more positively with the evaluated maximum photosynthetic rate for the whole forest canopy, A _max, than did NDVI or EVI. These results suggest the possibility that GRVI is more useful than NDVI and EVI for capturing seasonal changes in photosynthetic capacity, as the green and red reflectances are strongly influenced by changes in leaf pigments in this type of forest.     

利用遥感监测长江三角洲森林植被物候对气候变化的响应

植物的物候与气候等环境因素息息相关,是指示气候与自然环境变化对生态影响的重要指标。目前,气候变暖日益为人所关注,使用遥感技术研究植物物候与气候变化之间的关系具有重要的意义。监测人口密度高和城市经济发达地区的植物物候对气候变暖的响应,可以揭示区域热环境变化及其产生的生态效应。本研究选取长江三角洲地区为研究区域,使用SPOT卫星VGT传感器的长时间NDVI数据序列,对经济发达区域森林植被的NDVI序列进行非对称性高斯函数拟合法平滑处理,并提取与研究其物候特征,发现①NDVI与气温具有较强相关性,随气候变暖,森林植被NDVI年均值有增加趋势;②森林植被生长活跃期起始日期提前,终止日期延后,时长有明显的延长趋势,生长活跃期内NDVI有所增加;③森林植被NDVI极大值与极小值出现日期均明显提前,NDVI极大值有增大趋势,而极小值呈下降趋势,年内极差增加,NDVI增长期缩短,衰落期延长;④森林植被在春、夏两季NDVI均值有所增长,秋季无明显变化,冬季略有降低。     

Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia

Satellite observations play an important role in characterization of the interannual variation of vegetation. Here, we report anomalies of two vegetation indices for Northern Asia (40°N-75°N, and 45°E-179°E), using images from the SPOT-4 VEGETATION (VGT) sensor over the period of April 1, 1998 to November 20, 2001. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), which are correlated to a number of vegetation properties (e.g., net primary production, leaf area index), were compared. The results show that there is a large disagreement between NDVI and EVI anomalies in 1998 and 1999 for Northern Asia. The NDVI anomaly in 1998 was largely affected by atmospheric contamination, predominantly aerosols from extensive forest fires in that year. The EVI anomaly in 1998 was less sensitive to residual atmospheric contamination, as it is designed to be, and thus EVI is a useful alternative vegetation index for the large-scale study of vegetation. The EVI anomaly also suggests that potential vegetation productivity in Northern Asia was highest in 1998 but declined substantially in 2001, consistent with precipitation data from 1998-2001.