采用6S模型的多时相MODIS植被指数NDVI归一化方法

目的 基于6S辐射传输模型,对连续6个时相的 MODIS影像进行逐像元大气校正获取地表反射率,以此实现 MODIS NDVI的时相归一化。归一化后,地物 NDVI的时相趋势与校正前相比差异显著,能更加准确地描述地物随时间变化的规律,在浓密植被区域表现更为明显。开展对 NDVI时相归一化的研究,对全球变化、作物物候监测等遥感时序分析相关应用具有着重要的意义和实用价值。     

基于MODIS NDVI的科尔沁沙地荒漠化动态监测

以MODIS植被指数产品为数据源,利用归一化植被指数(NDVI)与植被盖度的高相关性,以NDVI为荒漠化评价的定量依据进行荒漠化程度划分,得出科尔沁沙地2000年与2007年的NDVI分级分布图,并分别统计出相应的不同程度荒漠化土地面积所占比例; 最后,利用不同等级荒漠化土地面积所占比例和NDVI分级分布图所提供的空间分配状况来评价研究区荒漠化程度及动态变化状况.研究结果表明,2000年至2007年科尔沁沙地荒漠化程度总体上呈减少趋势.     

基于MODIS数据的植被指数分析

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并进行了分类试验。     

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。     

雷达相干性与NDVI经验模型

植被的覆盖程度是造成雷达影像失相干的重要因素.通常,在森林等植被覆盖严重的地区,相干性相对较低,而在城市等植被覆盖率较低的地区,相干性较高.本文基于2017年珠江三角洲地区的MODIS归一化植被指数(NDVI)与Sentinel-1雷达卫星影像相干性,建立线性回归和幂函数回归模型,并利用两种模型预测该地区2016年的相...     

基于归一化植被指数的城市热岛效应分析

植被指数模型的应用领域已经愈加广泛,利用陆地地表温度反演与归一化植被指数在空间变化上具有相反趋势的特点,将归一化植被指数引入到城市热岛效应现象评价中。基于CBERS CCD遥感影像数据源,通过阈值划分及水体提取建立人工决策树,对城市热岛效应进行定量分析与总体评价,建立一定的评价指标,为城市的环境监测、管理及规划提供依据。     

基于MODIS影像的山西省植被指数分析

以MODIS遥感影像为数据源,利用ENVI遥感影像处理软件,笔者对山西省2005年9月和2006年9月两期MODIS影像的植被指数进行计算,建立了两期植被指数密度分割模型,以获得全省植被覆盖的整体状况,通过对两期植被指数影像图进行动态链接、叠加以及结合相减比较,可以看出两年间全省的植被覆盖空间变化情况。结果表明：全省较低植被覆盖区不多,高植被地区主要分布在中部以南地区,中低植被覆盖集中在中部以北地区。两年的植被指数比较结果表明,全省中等植被覆盖区有所增加,但高植被覆盖区增加不明显。     

基于MODIS影像的山西省植被指数分析

以MODIS遥感影像为数据源,利用ENVI遥感影像处理软件,笔者对山西省2005年9月和2006年9月两期MODIS影像的植被指数进行计算,建立了两期植被指数密度分割模型,以获得全省植被覆盖的整体状况,通过对两期植被指数影像图进行动态链接、叠加以及结合相减比较,可以看出两年间全省的植被覆盖空间变化情况。结果表明:全省较低植被覆盖区不多,高植被地区主要分布在中部以南地区,中低植被覆盖集中在中部以北地区。两年的植被指数比较结果表明,全省中等植被覆盖区有所增加,但高植被覆盖区增加不明显。     

利用MODIS数据计算陆地植被指数VIUPD

介绍了利用MODIS卫星数据计算VIUPD的步骤,将计算结果与NDVI和EVI进行了比较,验证了本文方法的正确性。     

GPS反射信号信噪比与NDVI相关性研究

归一化植被指数（normalized difference vegetation index,NDVI）作为重要的植被生长状况植被指数,对其进行有效实时监测具有重要科学意义。选择4个大陆板块边界观测网（plate boundary observatory,PBO）观测站的GPS信噪比观测值,提取反射信号信噪比并计算归一化振幅,通过与MODIS（moderate resolution imaging spectroradiometer）NDVI产品时序频谱特征的相关性分析,建立GPS反射信号植被指数线性反演模型和BP神经网络反演模型。分析发现:GPS反射信号信噪比归一化振幅与NDVI指数存在显著年周期性和季候特性,NDVI线性反演模型相关系数均约为0.7,均方根误差处于0.05~0.09之间,BP神经网络反演模型相关系数提高了约5%。利用GPS反射信号反演NDVI变化趋势具有可行性,为获取高时间分辨率、低成本的NDVI指数提供了一种新思路。     

MODIS和AVHRR植被指数关系的研究

对同一区域不同时次的MODIS和AVHRR的归一化植被指数 (NDVI),从直方图和特征空间分布方面进行了分析比较。     

基于MODIS-NDVI时序数据的重庆市植被变化研究

采用最大值合成(MVC)与最佳指数斜率提取(BISE)组合法和时间序列谐波分析法(HANTS)对重庆地区2006～2010年的共五年MODIS-NDVI时序数据进行降噪处理与数据重构,对重构结果进行对比与评价,并选其优者来评估重庆市的植被生长动态变化。结果表明,HANTS方法的除云去噪效果优于MVC与BISE组合法;2006～2010年,重庆市400 m以下的地区植被变化较大,400 m以上的区域植被呈连续变好趋势,研究结果可为重庆市生态保护与生态建设提供依据。     

Comparison and assessment of NDVI time series for seasonal wetland classification

Satellite-based wetland mapping faces challenges due to the high spatial heterogeneity and dynamic characteristics of seasonal wetlands. Although normalized difference vegetation index (NDVI) time series (NTS) shows great potential in land cover mapping and crop classification, the effectiveness of various NTS with different spatial and temporal resolution has not been evaluated for seasonal wetland classification. To address this issue, we conducted comparisons of those NTS, including the moderate-resolution imaging spectroradiometer (MODIS) NTS with 500?m resolution, NTS fused with MODIS and Landsat data (MOD_LC8-NTS), and HJ-1 NDVI compositions (HJ-1-NTS) with finer resolution, for wetland classification of Poyang Lake. Results showed the following: (1) the NTS with finer resolution was more effective in the classification of seasonal wetlands than that of the MODIS-NTS with 500-m resolution and (2) generally, the HJ-1-NTS performed better than that of the fused NTS, with an overall accuracy of 88.12% for HJ-1-NTS and 83.09% for the MOD_LC8-NTS. Future work should focus on the construction of satellite image time series oriented to highly dynamic characteristics of seasonal wetlands. This study will provide useful guidance for seasonal wetland classification, and benefit the improvements of spatiotemporal fusion models.     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.     

应用卫星与气象数据及其关系研究黄河流域的荒漠化现状

本文应用20年（1981—2000年）的卫星数据反演归一化差值植被指数（NDVI）,同时获取地面格网的温度与降雨数据,并分析这些数据之间的关系。基于地面的温度和降雨格网数据将研究区划分为8个气候区域,再利用NDVI数据把降雨量最少的3个气候区——区1,2,3各划分为10个等级。此外,分析这3个气候区在1983—1998年15年间的NDVI变化状况,结果显示出研究区荒漠化状况的加剧。     

Vegetation mapping with multitemporal NDVI in North Eastern China Transect (NECT)

Temporal changes in the normalized difference vegetation index (NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI data in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. Research has also shown that NDVI provides potential to derive meaningful metrics that describe ecosystem functions. In this paper, we have applied both unsupervised “k-means” classification and supervised minimum distance classification as derived from temporal changes in NDVI measured in 1997 along the North Eastern China Transect (NECT), and we have also utilized the same two classification methods together with NDVI-derived metrics, namely maximum NDVI, mean NDVI, NDVI amplitude, NDVI threshold, total length of growing season, fraction of growing season during greenup, rate of greenup, rate of senescence, integrated NDVI during the growing season, and integrated NDVI during greenup/integrated NDVI during senescence to map vegetation. The main objectives of this study are: (1) to test the relative performance of NDVI temporal profile metrics and NDVI-derived metrics for vegetation cover discrimination in NECT; (2) to test the relative performance of unsupervised (k-means) and supervised (minimum distance) methods for vegetation mapping; (3) to test the accuracy of the IGBP-DIS released land cover map for NECT; (4) to provide an up-to-date vegetation map for NECT. The results suggest that the classifications based on NDVI temporal profile metrics have higher accuracies than those based on any other metrics, such as NDVI-derived metrics, or all (NDVI temporal profile metrics + NDVI-derived metrics), or 15 metrics (NDVI temporal profile + Rate of greenup, Rate of senescence, and Integrated NDVI in greenup/integrated NDVI in senescence) for both methods. And among them, unsupervised k-means classification had the highest overall accuracy of 52% and Kappa coefficient of 0.2057. Both unsupervised (k-means) and supervised (minimum distance) methods achieved similar accuracies for the same metrics. The accuracy of IGBP-DIS released land cover map had an overall accuracy of 37% and a Kappa coefficient is 0.1441, and can improve to 46% by decomposing the crop/natural vegetation mosaic to cropland and other natural vegetation types. The results support using unsupervised k-means classification based on NDVI temporal profile metrics to provide an up-to-date vegetation cover classification. However, new effort is necessary in the future in order to improve the overall performance on this issue.     

资料误差对大尺度NDVI-气温关系的影响

大量研究利用PathfinderAVHRR NDVI资料分析植被状况与气温、降水等气候要素之间的关系。许多分析指出Pathfinder资料包含误差 ,并分析这些资料误差对大尺度NDVI 气温耦合关系检测结果的影响。利用奇异值分解方法 (SVD) ,通过比较不同NDVI资料误差情况下北半球春季NDVI对气温变化响应的时空特征的差异 ,对资料误差造成的分析结果的可靠性进行判断。考虑了 4种误差形式 ,分别是不同强度的连续误差、不连续误差、强火山喷发造成的误差及趋势误差。分析结果表明 ,利用SVD分析大尺度的NDVI 气温耦合特征时 ,允许的NDVI资料误差的最大上限阈值大致在 0 5σ左右。PathfinderAVHRR NDVI原始资料包含的误差很可能低于此阈值 ,得到的分析结果有较高的可信度。此外 ,在不知道NDVI原始资料误差的情况下进行植被对气候变化响应的检测时 ,可以借鉴此方法对结果的可靠性进行检查和验证。     

基于定量遥感的武汉城市热岛强度时空格局演变分析

应用定量遥感揭示武汉市热岛强度在不同季节和不同年份的时空格局演变.首先,选取2002年(1月,3月,7月,10月)及2000年,2004年,2010年7景ETM数据反演地表温度;其次,经热岛强度指数归一化地温数据,使数据间更具可对比性.对比2002年不同季节地温反演数据,分析武汉市热岛强度在不同季节的变化情况及形成原因.比较2000年,2004年,2010年三个不同年份地温数据,探讨热岛强度年际时空格局演变,建立LST与ndvi、城市热岛强度与水域面积间的相关关系,分析武汉市城区多年ndvi均值以及水域面积的时间变化趋势.结果显示:武汉市城市热岛强度存在明显的季节差异;年际热岛强度空间演变格局表现为以沿江地带为中心,以西北和东南两个方向为主向外延展;LST同ndvi呈负相关关系,其中ndvi值下降0.1,则地表温度上升约1.4℃;热岛强度同城市内水域面积大小亦呈负相关;对比分析了城区多年植被覆盖及水体面积变化趋势,总结出绿地等植被覆盖面积逐渐减小及湖泊面积逐步萎缩是造成武汉城市热岛强度增强的主要因素.     

中国西北地区NDVI变化及其与温度和降水的关系

稀疏的植被覆盖是干旱和半干旱地区最主要的环境特征,因此长期定量的植被分布和变化观测能够分析干旱和半干旱地区的环境变化。在以干旱和半干旱地区为主要的中国西北地区存在着森林减少、土地侵蚀、盐碱化和沙漠扩张等严重的环境问题,生态环境十分脆弱。通过NOAA/AVHRR建立近20年来中国西北地区NDVI变化序列,利用差分法、斜率变化和主成分分析3种方法分析植被变化。3种方法显示出基本一致的结果,即大部分地区植被状况恶化,局部地区有所好转。通过分析植被变化与温度、降水变化的关系,发现NDVI与降水存在明显的正相关关系,而与温度变化的关系并不明显,表明降水是影响西北地区植被变化最主要的自然因素。     

山东半岛丘陵区NDVI的空间分布及与降水量的关系研究

本文应用3S技术生成数字高程模型(Digital Elevation Model,DEM),以山东半岛丘陵区为研究对象,解译出27条河流的流域范围。采用空间内插法计算流域内降水量,通过应用3S技术提取了相应年份的NDVI,最后对27个流域的降水量和NDVI进行了相关分析。结果表明:研究区内的NDVI较小,平均NDVI为0.0962;ND-VI与降水量在空间变化上存在一致性,关联程度极其显著(在0.01水平上),关联系数R=0.498>0.478=α0.01(26),呈线性回归关系。