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区域作物生长过程的遥感提取方法 总被引：16，自引：3，他引：16
提出利用时序NDVI数据提取作物生长过程方法。遥感数据在采集过程中受云、大气因子的影响 ,以及混合像元问题 ,造成时序植被指数值变得没有规律 ,对比性不强。采用基于最小二次方拟合的谐函数分析方法 ,依据作物轮作规律和生长周期性特征 ,用主要频率的正弦、余弦谐函数重建时序图像 ,去除了影像中云污染的影响。以中国的旱地为例 ,考虑到像元内旱地对NDVI值的贡献率 ,计算区域内旱地像元加权平均值来反映其作物生长过程。同时与区域所有像元的平均值、旱地平均值等统计方法的结果进行对比分析 ,表明区域内旱地的加权平均值能够削弱旱地比例和地域间的差异 ,突出耕地上作物的生长过程特征。通过与地面实测数据分析 ,平滑前后的作物生长过程与叶面积指数相关性增加 5 %— 11% ,采用区域加权平均的方法得到的作物生长过程 ,比旱地平均和NDVI平均的结果与叶面积指数的相关性增加 14 %— 17%。 相似文献
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遥感定量反演地表参数时间序列产品已被广泛应用于植被动态变化、全球气候变化、防灾减灾及环境保护等领域。由于卫星观测往往受到大气条件（如云、气溶胶、水汽等）以及传感器自身稳定性的影响等,许多由卫星观测反演得到的陆表产品,如归一化差值植被指数（NDVI）、叶面积指数（LAI）、地表温度（LST）、微波极化亮温（PDBT）等存在严重的时空不连续问题。为了获取时间序列上连续、空间上完整的地表参数遥感产品以满足长时序的陆面过程分析与建模的需求,目前已发展多种遥感时间序列重建模型。本文介绍了基于傅里叶变换的时间序列谐波分析（HANTS）方法,能够识别并去除受到云和大气影响的像元（噪声）,对原始时序数据进行时间插值来重建连续时间序列的数据,并针对其面向多种不同时空尺度的遥感反演地表参数以及在非洲、南美洲、欧洲、中国及印度等全球不同地区的应用研究进行了综述,包括植被动态变化对于气候变化及流域水循环过程的响应、干旱监测、基于土壤含水量饱和度时间序列分析的洪涝灾害易发区监测、遥感估算地表蒸散发时间尺度扩展等方面的研究,充分阐释了遥感时间序列产品在地气相互作用的各类研究领域的应用。 相似文献
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复种指数遥感监测方法 总被引：36，自引：6，他引：36
复种指数是反映水土光与自然资源利用程度的指标 ,其实质是沿时间序列 ,反映某一种植制度对耕地的利用程度。联系复种指数与时间序列NDVI曲线的纽带是农作物年内的循环规律。时间序列的NDVI值蕴涵着植被的生长和枯萎的年循环节律 ,经时间序列谐函数分析法 (HarmonicAnalysisofTimeSeries ,HANTS)重构的NDVI曲线 ,可以准确地反映农作物的出苗、拔节、抽穗、收获等物理过程。因此 ,根据时间序列的NDVI曲线的周期性 ,可以反向捕捉到耕地农作物动态的信息 ,进而得到耕地的复种指数。本文依据上述原理 ,提出复种指数遥感监测的方法 ,然后用 1999年至 2 0 0 2年 4年的VGT(SPOT4卫星vegetation数据 )旬合成NDVI时间序列数据集提取了复种指数 ,并利用地面样区观测结果和统计数据进行检验 ,取得很高的精度。 相似文献
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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。 相似文献
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基于获取的塔河流域2000~2014年历年4~10月间逐月MODIS植被指数产品,采用时间序列谐波分析法(HANTS)对最大值合成的逐月NDVI时间序列数据进行了重建,用趋势线分析法对塔河流域近15年生长季(4~10月)MODIS NDVI的时间变化进行计算,用一元线性回归趋势法计算得到了塔河流域近15年生长季(4~10月)NDVI变化趋势的空间分布。结合植被类型分布图对计算得到的实验结果进行了研究分析,总结了塔河流域多年植被覆盖的时空分布及其变化规律,成果可为塔河流域综合治理及生态环境评价提供依据。 相似文献
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红树林湿地植被生物量的雷达遥感估算 总被引：19，自引：0，他引：19
根据雷达后向散射系数建立了红树林湿地植被生物量的估算模型,并运用遗传算法确定其中非线性模型的最优参数.对比分析表明,雷达后向散射系数模型比NDVI模型在植被生物量估算中有更高的精度.使用NDVI指数有可能导致某些植被类型的生物量估算出现较大的误差.这是因为一些具有密集冠层的草本植被（例如互花米草等）有比红树林高得多的NDVI值.而雷达遥感所具有的侧视特点及一定的穿透能力能有效地获取植被的垂直信息,大大减低植被生物量估算的误差. 相似文献
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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400m以下的地区植被变化较大,400m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。 相似文献
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《地理信息系统科学与遥感》2013,50(3):183-190
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. 相似文献
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青藏高原小嵩草高寒草甸返青期遥感识别方法筛选 总被引：3，自引：1，他引：2
小嵩草高寒草甸是青藏高原的主要植被类型,研究其返青期识别方法对于模拟及预测青藏高原植被物候变化具有重要意义。常用的植被返青期遥感识别方法主要是先对遥感植被指数原始时序数据进行拟合去噪声再求取返青期,各种方法对研究区域、研究经验、参数设置、函数初值设置等有很强的依赖性。为避免返青期识别方法在曲线拟合时对参数初值的依赖性和陷入局部最优解,本文引入了模拟退火算法对双高斯和双逻辑斯蒂函数进行参数优化,并分别对基于以上两种函数及多项式拟合的植被指数时序曲线进行对比,从而选出最佳拟合方法,最后采用最大斜率阈值法、动态阈值法和曲率法识别返青期。利用青藏高原小嵩草高寒草甸34个样本点的返青期地面观测数据及相应的8 km分辨率的NOAA归一化差值植被指数(NDVI)时序数据对以上各种组合的返青期遥感识别方案进行了测试,并选取了153个遥感实验点求取了近30年(1982年—2011年)青藏高原小嵩草高寒草甸的返青期,结果表明:采用双高斯函数拟合的NDVI曲线与原始NDVI时序数据最为接近,在此基础上采用最大斜率阈值法识别的小嵩草高寒草甸返青期及其变化趋势与地面物候观测结果最为一致;同时发现近30年青藏高原小嵩草高寒草甸的平均返青期主要集中在每年的第120—140天,并且呈逐年提前趋势,30年来提前了7天。 相似文献
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Xia Zhang Rui Sun Bing Zhang Qingxi Tong 《ISPRS Journal of Photogrammetry and Remote Sensing》2008,63(4):476-484
Moderate Resolution Imaging Spectroradiometer (MODIS) data have played an important role in global environmental and resource research. However, its low spatial resolution has been an impediment to researchers pursuing more accurate classification results. In this research, the high temporal resolution of MODIS was employed to improve the accuracy of land cover classification of the North China Plain using MODIS_EVI time series from 2003. Harmonic Analysis of Time Series (HANTS) was performed on the MODIS_EVI image time series to reduce cloud and other noise effects. The improved MODIS_EVI time series was then classified into 100 clusters by the Iterative Self-Organizing Data Analysis Technique (ISODATA). To distinguish ambiguous land cover classes, a decision tree was built on five phenological features derived from EVI profiles, Land Surface Temperature (LST) and topographic slope. The overall accuracy of the final land cover map was 75.5%, indicating the promise of using MODIS EVI time series and decision trees for broad area land cover classification. 相似文献
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针对城市及周边区域建造区和自然地表交织分布的特点,探讨了利用归一化植被指数(NDVI)和归一化建造指数(NDBI)构造趋势面的地表温度(LST)降尺度方法,以北京市市区及周边较平坦区域为例实现了LST自960 m向120 m的降尺度转换。分析了LST空间分布特征及NDVI、NDBI对地物的指示性特征;以北京市四至六环为界分析NDVI、NDBI趋势面对地表温度的拟合程度及各自的适用区域;在120 m、240 m、480 m和960 m 4个尺度上评价了NDVI、NDBI和NDVI+NDBI趋势面对LST的拟合程度和趋势面转换函数的尺度效应;对NDVI、NDBI和NDVI NDBI等3种方法的降尺度结果分覆盖类型、分区域对比评价。实验结果表明结合两种光谱指数的NDVI NDBI方法降尺度转换精度有所改善,改善程度取决于地表覆盖类型组合。 相似文献
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基于NDVI序列影像精化结果的植被覆盖变化研究 总被引：5，自引：0，他引：5
植被归一化指数(NDVI)是地表植被覆盖特征的重要指标之一。本文以三峡库区2001-2003年MODIS遥感数据反演的NDVI时间序列影像为例,研究NDVI影像序列的精化问题,包括降云及去噪处理的有效方法。在改进的BISE技术降云处理的基础上,采用小波软阈值降噪方法提取有效变化趋势。然后进行库区2001-2003年植被变化的变化矢量分析,采用阈值分割的方法将库区变化强度影像分为未变化、小变化、中等变化与剧烈变化四个类型。研究成果可为三峡库区宏观生态环境变化的掌握提供一定的依据。 相似文献
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Image compositing is a multi-objective optimization process. Its goal is to produce a seamless cloud and artefact-free artificial image. This is achieved by aggregating image observations and by replacing poor and cloudy data with good observations from imagery acquired within the timeframe of interest. This compositing process aims to minimise the visual artefacts which could result from different radiometric properties, caused by atmospheric conditions, phenologic patterns and land cover changes. It has the following requirements: (1) image compositing must be cloud free, which requires the detection of clouds and shadows, and (2) the image composite must be seamless, minimizing artefacts and visible across inter image seams. This study proposes a new rule-based compositing technique (RBC) that combines the strengths of several existing methods. A quantitative and qualitative evaluation is made of the RBC technique by comparing it to the maximum NDVI (MaxNDVI), minimum red (MinRed) and maximum ratio (MaxRatio) compositing techniques. A total of 174 Landsat TM and ETM+ images, covering three study sites and three different timeframes for each site, are used in the evaluation. A new set of quantitative/qualitative evaluation techniques for compositing quality measurement was developed and showed that the RBC technique outperformed all other techniques, with MaxRatio, MaxNDVI, and MinRed techniques in order of performance from best to worst. 相似文献
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Anton Vrieling Steven M. de Jong Geert Sterk Silvio C. Rodrigues 《International Journal of Applied Earth Observation and Geoinformation》2008
Erosion reduces soil productivity and causes negative downstream impacts. Erosion processes occur on areas with erodible soils and sloping terrain when high-intensity rainfall coincides with limited vegetation cover. Timing of erosion events has implications on the selection of satellite imagery, used to describe spatial patterns of protective vegetation cover. This study proposes a method for erosion risk mapping with multi-temporal and multi-resolution satellite data. The specific objectives of the study are: (1) to determine when during the year erosion risk is highest using coarse-resolution data, and (2) to assess the optimal timing of available medium-resolution images to spatially represent vegetation cover during the high erosion risk period. Analyses were performed for a 100-km2 pasture area in the Brazilian Cerrados. The first objective was studied by qualitatively comparing three-hourly TRMM rainfall estimates with MODIS NDVI time series for one full year (August 2002–August 2003). November and December were identified as the months with highest erosion risk. The second objective was examined with a time series of six available ASTER images acquired in the same year. Persistent cloud cover limited image acquisition during high erosion risk periods. For each ASTER image the NDVI was calculated and classified into five equally sized classes. Low NDVI was related to high erosion risk and vice versa. A DEM was used to set approximately flat zones to very low erosion risk. The six resulting risk maps were compared with erosion features, visually interpreted from a fine-resolution QuickBird image. Results from the October ASTER image gave highest accuracy (84%), showing that erosion risk mapping in the Brazilian Cerrados can best be performed with images acquired shortly before the first erosion events. The presented approach that uses coarse-resolution temporal data for determining erosion periods and medium-resolution data for effective erosion risk mapping is fast and straightforward. It shows good potential for successful application in other areas with high spatial and temporal variability of vegetation cover. 相似文献
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Time series data on cropping pattern at disaggregated level were analysed and its implications on geospatial drought assessment were demonstrated. An index of Cropping Pattern Dissimilarity (CP-DI) between a pair of years, developed in this study, proved that the cropping pattern of a year has a higher degree of similarity with that of recent past years only and tends to be dissimilar with longer time difference. The temporal divergence in cropping pattern has direct implications on geospatial approach of drought assessment, in which, time series NDVI data are compared for drought interpretation. It was found that, seasonal NDVI profiles of drought year and normal year did not show any anomaly when the cropping patterns were dissimilar and two normal years having dissimilar cropping pattern showed different NDVI profiles. Therefore, it is suggested that such temporal comparisons of NDVI are better restricted to recent past years to achieve more objective interpretation. 相似文献
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P. G. Diwakar P. Prabhakar I. Venkataramana R. Devarajan 《Journal of the Indian Society of Remote Sensing》1989,17(1):1-6
Multidate NOAA-AVHRR Data has been used for monitoring dynamic changes of the vegetation and coastal processes. However, the interpretation of the data is significantly affected by presence of the clouds. An attempt has been made in this paper to evaluate the technical aspects of a processing methodology to generate a cloud masked imagery using multidate NOAA-AVHRR data to minimize cloud cover in the scene. A Maximum Value Composite Image is generated after cloud minimisation. The utility of the technique has been tested in a case study to generate Normalised Difference Vegetation Index (NDVI) over the Indian Subcontinent. The process over two days produced spatially continuous cloud-masked imagery to study green vegetation dynamics. The technique minimizes cloud contamination, reduces off-nadir viewing effects and generates maximum value composite image. 相似文献