冬小麦播期的卫星遥感及应用

播种日期对冬小麦生长发育、产量和品质形成均有一定的影响。利用2003年拔节期的Landsat TM卫星的NDVI数据．成功地监测了冬小麦的播种日期。提出了基于NDVI和播种日期的冬小麦的遥感估产的优化模型，并在抽穗期至乳熟期的3次生育期的遥感估产中得到了成功验证与应用。利用出粉率与播种日期的显相关特性，采用拔节期的Landsat TM卫星的NDVI数据，成功预测了小麦籽粒的出粉率。     

A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry

The bi-directional reflectance distribution function (BRDF) alters the seasonal and inter-annual variations exhibited in Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and this hampers the detection and, consequently, the interpretation of temporal variations in land-surface vegetation. The magnitude and sign of bi-directional effects in commonly used AVHRR data sets depend on land-surface properties, atmospheric composition and the type of atmospheric correction that is applied to the data. We develop an approach to estimate BRDF effects in AVHRR NDVI time series using the Moderate Resolution Imaging Spectrometer (MODIS) BRDF kernels and subsequently adjust NDVI time series to a standard illumination and viewing geometry. The approach is tested on NDVI time series that are simulated for representative AVHRR viewing and illumination geometry. These time series are simulated with a canopy radiative transfer model coupled to an atmospheric radiative transfer model for four different land cover types—tropical forest, boreal forest, temperate forest and grassland - and five different atmospheric conditions - turbid and clear top-of-atmosphere, turbid and clear top-of-atmosphere with a correction for ozone absorption and Rayleigh scattering applied (Pathfinder AVHRR Land data) and ground-observations (fully corrected for atmospheric effects). The simulations indicate that the timing of key phenological stages, such as start and end of growing season and time of maximum greenness, is affected by BRDF effects. Moreover, BRDF effects vary with latitude and season and increase over the time of operation of subsequent NOAA satellites because of orbital drift. Application of the MODIS kernels on simulated NVDI data results in a 50% to 85% reduction of BRDF effects. When applied to the global 18-year global Normalized Difference Vegetation Index (NDVI) Pathfinder data we find BRDF effects similar in magnitude to those in the simulations. Our analysis of the global data shows that BRDF effects are especially large in high latitudes; here we find that in at least 20% of the data BRDF errors are too large for accurate detection of seasonal and interannual variability. These large BRDF errors tend to compensate, however, when averaged over latitude.     

Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999

We used land surface temperature (LST) algorithms and NDVI values to estimate changes in vegetation in the European continent between 1982 and 1999 from the Pathfinder AVHRR Land (PAL) dataset. These two parameters are monitored through HANTS (Harmonic ANalysis of Time Series) software, which allows the simultaneous observation of mean value, first harmonic amplitude and phase behaviors in the same image. These results for each complete year of data show the effect of volcanic aerosols and orbital drift on PAL data. Comparison of time series of HANTS cloud-free time series with the original time series for various land cover proves that this software is useful for LST analysis, although primarily designed for NDVI applications. Comparison of yearly averages of HANTS LST over the whole Europe with air temperature confirms the validity of the results. Maps of the evolution for both parameters between periods 1982/1986 and 1995/1999 have been elaborated: NDVI data show the well confirmed trend of increase over Europe (up to 0.1 in NDVI), Southern Europe seeing a decrease in NDVI (− 0.02). LST averages stay stable or slightly decrease (up to − 1.5 K) over the whole continent, except for southern areas for which the increase is up to 2.5 K. These results evidence that arid and semi-arid areas of Southern Europe have become more arid, the rest of Europe seeing an increase in its wood land proportion, while seasonal amplitude in Northern Europe has decreased.     

利用TM数据估算山丹军马场的植被生物量

山丹军马场是我国面积最大的马场,近年来由于过度放牧和不合理开发,植被正在逐渐退化。所以植被的生长状况的研究就显得特别重要。利用2003年9月的Landsat TM图像估算了山丹军马场植被的地上生物量。主要方法是在GPS的帮助下,通过野外实地采样,然后根据样点数据和NDVI、第一主成分、DVI、TM4通道的光谱反射率数据之间的线性相关性,并利用逐步回归方法建立了估算生物量的最优模型,用此模型计算了山丹军马场典型区的生物量。     

基于“北京一号”小卫星数据的密云水库流域植被覆盖度估算方法比较

“北京一号”小卫星（BJ-1）是一颗拥有高时频、覆盖宽度大等优势的对地观测小卫星。运用BJ-1遥感数据，以密云水库流域为研究区域，通过NDVI像元二分法、三波段梯度差法估算其植被覆盖度，并尝试利用重归一化植被指数（RDVI）法进行植被覆盖度估算。通过对3种方法估算结果的比较发现：RDVI法的估算结果与实测值的吻合度较高，而三波段梯度差法则出现较大误差，NDVI像元二分法的估算结果精度居中。结果表明：运用BJ-1数据，采用RDVI法可以有效地进行连续的、大范围的植被覆盖度估算。     

A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data

Annual, inter-annual and long-term trends in time series derived from remote sensing can be used to distinguish between natural land cover variability and land cover change. However, the utility of using NDVI-derived phenology to detect change is often limited by poor quality data resulting from atmospheric and other effects. Here, we present a curve fitting methodology useful for time series of remotely sensed data that is minimally affected by atmospheric and sensor effects and requires neither spatial nor temporal averaging. A two-step technique is employed: first, a harmonic approach models the average annual phenology; second, a spline-based approach models inter-annual phenology. The principal attributes of the time series (e.g., amplitude, timing of onset of greenness, intrinsic smoothness or roughness) are captured while the effects of data drop-outs and gaps are minimized. A recursive, least squares approach captures the upper envelope of NDVI values by upweighting data values above an average annual curve. We test this methodology on several land cover types in the western U.S., and find that onset of greenness in an average year varied by less than 8 days within land cover types, indicating that the curve fit is consistent within similar systems. Between 1990 and 2002, temporal variability in onset of greenness was between 17 and 35 days depending on the land cover type, indicating that the inter-annual curve fit captures substantial inter-annual variability. Employing this curve fitting procedure enhances our ability to measure inter-annual phenology and could lead to better understanding of local and regional land cover trends.     

基于OpenCL的NDVI算法

随着计算机技术的不断发展,软件的规模也越来越大。一张遥感图像可达到数G以上,处理起来有时候可能需要数个小时。因此,针对这些大数据量的系统来说,加速比提高一倍,就会使运行时间减少几个小时,这对于系统来说就是一种非常可观的现实,非常值得去实现。本文将以NDVI算法为例,主要介绍了NDVI算法、NDVI的应用和性质、OpenCL介绍。     

2001~2010年松木希错流域植被动态变化遥感研究

遥感在区域植被变化研究中具有十分重要的作用,能为大面积监测植被状况的演化过程提供技术支持。NDVI在高植被覆盖地区存在过饱和现象,对稀疏地区的植被变化尤其敏感。以古里雅冰帽南部的松木希错流域植被相对稀疏区域为研究区,基于MODIS NDVI数据和逐月气象观测数据,以及RS和GIS平台,对该区域2001~2010年主要植被变化趋势进行了初步研究,并对植被变化与气候驱动因子的关系进行了分析和探讨。结果表明:① 2001~2010年间该区域的植被活动有加强趋势;② NDVI表明研究区植被生长季较短(5~9月),NDVI浮动区间为0.11～0.13,低于全国水平(0.3～0.35),也低于全球稀疏灌丛的平均水平(0.2～0.4);③NDVI与年均气温整体上呈正相关,而与年降水量相关性不强。表明近年来持续升温是影响该区域植被活动加强的最主要原因。     

Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework

Long-term vegetation dynamics associated with climatic changes can be assessed using Advanced Very High Resolution Radiometer (AVHRR) red and near-infrared reflectance data provided that the data have been processed to remove the effects of non-target signal variability, such as atmospheric and sensor calibration effects. Here we present a new method that performs a relative calibration of reflectance data to produce consistent long-term vegetation information. It is based on a simple biological framework that assumes that the position of the vegetation cover triangle is invariant in reflectance space. This assumption is in fact an intrinsic assumption behind the commonly used Normalised Difference Vegetation Index (NDVI) and is violated when the NDVI is calculated from inadequately corrected reflectance data. In this new method, any temporal variability in the position of the cover triangle is removed by geometrically transforming the observed reflectance data such that two features of the triangle—the soil line and the dark point—are stationary in reflectance space. The fraction of Photosynthetically Active Radiation absorbed by vegetation (fPAR; 0.0-0.95) is then calculated, via the NDVI, from calibrated reflectances. This method was tested using two distinct, monthly AVHRR products for Australia: (i) the coarse-resolution, fully calibrated, partially atmospherically corrected PAL data (1981-1994); and (ii) the fine-resolution, fully calibrated, non-atmospherically corrected HRPT data (1992-2004). Results show that, in the 20-month period when the two datasets overlap (1992-1994), the Australia-wide, root mean square difference between the two datasets improved from 0.098 to 0.027 fPAR units. The calibrations have produced two approximately equivalent datasets that can be combined as a single input into time-series analyses. The application of this method is limited to areas that have a wide-enough variety of land-cover types so that the soil line and dark point are evident in the cover triangle in every image of the time-series. Another limitation is that the methodology performs only bulk, relative calibrations and does not remove the absolute effects of observation uncertainties. The simplicity of the method means that the calibration procedure can be easily incorporated into near-real-time operational remote-sensing environments. Vegetation information produced using this invariant-cover-triangle method is expected to be well suited to the analysis of long-term vegetation dynamics and change.     

Hierarchical image segmentation based on similarity of NDVI time series

Although a variety of hierarchical image segmentation procedures for remote sensing imagery have been published, none of them specifically integrates remote sensing time series in spatial or hierarchical segmentation concepts. However, this integration is important for the analysis of ecosystems which are hierarchical in nature, with different ecological processes occurring at different spatial and temporal scales. Therefore, the objective of this paper is to introduce a multi-temporal hierarchical image segmentation (MTHIS) methodology to generate a hierarchical set of segments based on spatial similarity of remote sensing time series. MTHIS employs the similarity of the fast Fourier transform (FFT) components of multi-seasonal time series to group pixels with similar temporal behavior into hierarchical segments at different scales. Use of the FFT allows the distinction between noise and vegetation related signals and increases the computational efficiency. The MTHIS methodology is demonstrated on the area of South Africa in an MTHIS protocol for Normalized Difference Vegetation Index (NDVI) time series. Firstly, the FFT components that express the major spatio-temporal variation in the NDVI time series, the average and annual term, are selected and the segmentation is performed based on these components. Secondly, the results are visualized by means of a boundary stability image that confirms the accuracy of the algorithm to spatially group pixels at different scale levels. Finally, the segmentation optimum is determined based on discrepancy measures which illustrate the correspondence of the applied MTHIS output with landcover-landuse maps describing the actual vegetation. In future research, MTHIS can be used to analyze the spatial and hierarchical structure of any type of remote sensing time series and their relation to ecosystem processes.