基于GIS的水稻遥感估产模型研究

以NOAA/AVHRR资料为主,利用GIS技术提取水稻可能种植区域,在此基础上计算各区和各县的比值植被指数和规一化植被指数,提出的水稻遥感估产比值模型和回归模型,预报浙江省的水稻总产,1998年的拟合精度和1999年的预报精度都达到95%以上.     

南方稻区遥感水稻长势监测与估产研究

经过“七五”攻关研究，基本上找出我国南方稻区遥感水稻估产的方法、途径和问题，如用工材资料调查水稻面积、用综合方法进行水稻估产，并获得稻田光谱与水稻长势及产量结构的关系及模式等，为“八五”大区域水稻遥感估产莫定了基础。     

运用NOAA AVHRR和Landsat TM数据估算多年水稻种植面积

介绍了综合运用NOAA AVHRR和Landsat TM数据进行多年水稻种植面积监测的一种方法,以湖北省为例,首先运用Landsat TM数据计算了该省1992年的水稻种植面积;接着运用1992年和1994年的NOAA AVHRR数据分别计算这两年的水稻像元数,以这两年水稻像元数的变化来反映水稻种植面积的变化;最后运用线性模型,估算1994年的水稻种植面积。所得的1994年水稻种植面积与湖北省农调队资料相比精度为84.5%。运用同样的方法估算1995年该省的水稻种植面积,精度达90%以上。     

利用气象卫星AVHRR资料监测闽东南双季晚稻产量

一、前言遥感估产是卫星遥感科学的重要内容之一,70年代末到80年代初,国外开始将改进的气象卫星甚高分辨率辐射计(AVHRR)资料用于植被的遥感研究。1985年开始,我国北方11个省相继开展利用气象卫星AVHRR资料对冬小麦长势进行监测、产量预测的研究,至今已取得不少成果,但是对南方水稻生产的监测和估产的研究开展较少。近几年来我省也开展利     

天津市应用气象卫星资料进行冬小麦估产

NOAA卫星又称TIROS—N系列卫星,是美国国家海洋大气局控制和管理的第三代极轨道业务气象卫星,其主要传感器是高分辨率辐射仪(AVHRR)。AVHRR的通道1和2与陆地卫星MSS5,7吻合,能较好地表达绿色植被的吸收波段和对近红外的特征反射峰。NOAA卫星视场角大,覆盖度宽,又是双星运行,每天可两次过境,获得资料及时。而陆地卫星每16天覆盖一次,若遇阴雨天气,获得资料的周期更长。AVHRR像元面积大,可节省计算机处理时间和费用。所以,近几年来NOAA/AVHRR资料越来越广泛地被应用于植被动态监测和农作物估产。但是AVHRR空间分辨率为1.1公里,远低于MSS,限制了NOAA卫星的应用领域。天津市土地面积只有1731.5万亩,种植混杂,混合像元所占比例较大,致使低分辨率的图像难以反映作物长势。因此,NOAA/AVHRR信息     

气象卫星数据冬小麦估产的区域订正

一、引言气象卫星遥感资料用于农作物估产,近年来世界各地已普遍展开。气象卫星遥感信息由于分辩率较低,混合像元所占比例较大,往往使图像失去提取细节的价值,因此需做必要订正。本文介绍天津市冬小麦估产中应用平均方程残差对NOAA卫星AVHRR传感器通道1和2的CCT资料进行的区域订正。结果表明此种方法有效,可使估产的准确率提高2％以上。二、资料来源和基本方法 NOAA卫星CCT资料由国家气象局卫星     

南方丘陵地区水稻种植面积遥感信息提取的可行性分析*

以浙江省为试验区,在地理信息系统支持下综合利用多种地理信息,探讨丘陵地区大面积提取水稻种植面积信息的可行性。开展了分类识别方法的比较试验及训练样点相对稳定性试验。针对丘陵地区的复杂地形,在数字化地形图的基础上,建立数字地形模型(DTM),并衍生出地面坡度等地貌因子的数字化图像,结合NOAA/AVHRR数据,进行分类。试验结果表明,传统的分类识别方法中,最大似然法的分类精度可满足业务化运行的要求;建立在混合像元分解基础上的模糊监督分类,有较高的分类精度和较好的稳定性,具有较强的适应性;地貌因子参与遥感影像的分类,不仅可以有效地提高丘陵地区水稻种植面积信息的提取精度,而且还可以使面积信息提取精度保持一定的稳定性,提高空间精度;为探讨丘陵地区水稻种植面积信息遥感提取的可靠性和客观性,在训练样点保持相对稳定的前提下,对1996年和1997年浙江省水稻种植面积进行测算,两年的数量精度均在92%以上。     

用模糊数学处理气象卫星资料预报大白菜产势

气象卫星具有覆盖范围大、成像周期短、每日可获取资料等优点。但是它的空间分辨率低,尤其大白菜种植面积较小,又是包心作物,其光谱特性与一般植被不同。因此,气象卫星资料(NOAA/AVHRR)是否能用于大白菜估产,是值得研究的问题。根据L·A·Zadeh提出的模糊集(Fuzzsets)概念,用模糊数学对处理对象不必要求那么严谨,不限于“是”与“非”     

NOAA影像在宏观植被季相动态遥感中的应用

本文通过对国内接收的NOAA气象卫星AVHRR数字影像资料的处理和分析,进行了植被季相动态遥感应用研究。针对植被目标中AVHRR存在的数据误差,并参考国外经验对原始数据进行了改正。用可见光和近红外通道信息生成的绿色植被指数(GVI)作为植被生长状况的判读标准。对1983年至1984年接收的多时相影像系列的应用分析表明,NOAA/AVHRR影像可清楚反映宏观植被季相变化中的绿波与褐波效应及垂直地带的物质差异,体现了NOAA/AVHRR在宏观环境动态遥感中应用的前景。     

NOAA气象卫星植被遥感研究动态

NOAA气象卫星已成为世界上最重要的遥感信息源之一。本文根据近年来国内外应用NOAA气象卫星资料,进行植被遥感的研究,着重介绍了NOAA气象卫星在景观稳定性监测、天然草场牧草长势监测与估产,玉米、水稻、小麦长势监测、面积估测及产量预测中的应用,并且根据NOAA气象卫星的特点,与Landsat及Spot卫星相比,无论是其研究思路、方法,还是提供的成果,都具有本质区别。     

Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR.

Abstract

It is possible to assess crop yields at the end of the growing season in a semi-arid environment using data from meteorological satellites. This is the result of a work carried out in northern Burkina Faso. The technique used is based on linear correlation between millet yield and the time integral of the Normalized Difference Vegetation Index (iNDVI) derived from NOAA AVHRR data. In contrast to earlier related studies, the correlation has been established using satellite data extracted exclusively within the agricultural domain. The integration period for the iNDVI correponds to the reproductive phase only of the growing period of millet. Furthermore, iNDVI can also be used to estimate the acreage or the agricultural domain, by the application of a suitable threshold to classify areas into agricultural and non-agricultural domains.

It is therefore possible to assess the yield and the acreage of the agricultural domain and to derive an estimate of the millet production of the area by the end of the season, on the basis of NOAA AVHRR data alone.     

A simplified method of accurate geometric correction for NOAA AVHRR 1B data

There are different methods for geometric correction of NOAA AVHRR data, but these methods either pay less attention to the accuracy, or are technically complex, almost unsuitable for most users. To combine AVHRR data with other high spatial resolution satellite data, or with ancillary data in GIS, it is necessary to develop an accurate geometric correction method, which should be easy to use even for non-professional users. After analysing the pixel shape and size of AVHRR 1B data along scan line and evaluating the quality of geographical data of NOAA AVHRR 1B data set, we found that the geographical data was adequately accurate for identifying the pixel size and shape and the method was developed accordingly. The proposed method has two steps. The first step is to correct pixel distortion. The separate program performs the distortion correction, applying the geographical data of AVHRR 1B data set to assigning the value of each pixel of the desired output geographical area with given pixel size, and making logical judgment for unassigned pixel. The second step is to perform conventional polynomial transformation on the results of the first step. An application of this method is presented in the paper. To examine the precision, SAVI images derived from the geometrically corrected NOAA AVHRR band image were used to perform overlay with each other and also with a 1 :50000 river system map. A half-pixel accuracy was achieved.     

NOAA/AVHRR数据的雪盖信息提取与复合

在对NOAA/AVHRR数据特征与雪冰波谱特性分析的基础上，对各种提取雪盖信息的方法进行了比较，指出了各种方法的优劣，认为在实时的雪灾监浏与评估系统中，直方图分割的方法快速有效。另一方面，通过雪盖影像与GIS中各种矢量图形的复合配准实验，指出宜先对AVHRR影像进行点位计算，然后利用控制点、进行精校正，所产生的图像才能达到与矢量图形的准确配准。     

An automatic system for AVHRR land surface product generation

Making products from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar‐orbiting satellites can be time consuming and an automated technique for image processing is required to generate long time series of AVHRR imagery. This paper aims to describe the development of a system for fully‐automated AVHRR image processing, including radiometric calibration, precise geo‐registration and generating land‐surface products, such as vegetation indices, maximum value composites and cloud masks. Tests for crop monitoring purposes were carried out using High Resolution Picture Transmission (HRPT) images between October 2003 and April 2004. The region used to evaluate the system was the State of Paraná, one of the primary soybean producers in Brazil. Results have shown that for severely cloud‐filtered images, the system was effective in generating geometrically precise image products, with geolocation errors less than a pixel. The developed system can be operated with no human intervention and can be used as an important tool for NOAA‐AVHRR image users especially those who need to use long time series.     

Estimating bidirectional angles in NOAA AVHRR images

In studies concerning the surface bidirectional reflectance distribution function (BRDF) and thermal-infrared multiangular emissions, Sun-sensor geometry must be known. This Letter provides a potential and simple method for NOAA Advanced Very High Resolution Radiometer (AVHRR) users to estimate the imaging configuration of each pixel in a geometrically corrected image. Our formulas were tested with example AVHRR data and their precision was shown to be comparatively high with a maximum error of either the satellite zenith or azimuth angle less than 4°. The standard deviation for the zenith is 2.07° and azimuth is 2.47°.     

多源遥感影像融合在太湖蓝藻监测中的应用

基于空间分辨率分别为1 100 m和500 m的NOAA/AVHRR和EOS/MODIS遥感数据,考虑遥感影像区域内各像素之间的区域特征,设计了基于小波分析的区域能量融合方法(REFS_wt),低频小波系数采用平均值而高频系数采用区域能量法,并与基于像素灰度值的区域能量法(REFS_pl)进行融合性能比较,结果表明REFS_wt法的融合性能明显优于REFS_pl。将此方法应用于太湖蓝藻监测,将空间分辨率较低的AVHRR影像蓝藻水华信息与较高分辨率的MODIS影像融合,得到较高分辨率的太湖蓝藻水华遥感监测图,融合图像信息量和清晰度都有所提高。     

Accuracy of land cover area estimated from coarse spatial resolution images using an unmixing method

Many techniques intended to estimate land coverage of multiple categories occupied within each pixel from such coarse resolution data have been proposed. However, in traditional unmixing studies with coarse resolution imagery such as Advanced Very High Resolution Radiometer (AVHRR) data, it is assumed that only a few endmembers exist throughout an entire image. Therefore, it is essential to evaluate how well an unmixing method would work for various categories within pixels of coarse resolution images. In this study, the land coverage of eight classes in National Oceanic and Atmospheric Administration (NOAA) AVHRR imagery by using finer resolution Landsat Thematic Mapper (TM) imagery was estimated, and the accuracy of these estimated classes was evaluated. The results suggest that this method may be generally useful for comparing multi-spectral images in space and time.     

Mapping sub-pixel proportional land cover with AVHRR imagery

A problem with NOAA AVHRR imagery is that the intrinsic scale of spatial variation in land cover in the U.K. is usually finer than the scale of sampling imposed by the image pixels. The result is that most NOAA AVHRR pixels contain a mixture of land cover types (sub-pixel mixing). Three techniques for mapping the sub-pixel proportions of land cover classes in the New Forest, U.K. were compared: (i) artificial neural networks (ANN); (ii) mixture modelling; and (iii) fuzzy c -means classification. NOAA AVHRR imagery and SPOT HRV imagery, both for 28 June 1994, were obtained. The SPOT HRV images were classified using the maximum likelihood method, and used to derive the 'known' sub-pixel proportions of each land cover class for each NOAA AVHRR pixel. These data were then used to evaluate the predictions made (using the three techniques and the NOAA AVHRR imagery) in terms of the amount of information provided, the accuracy with which that information is provided, and the ease of implementation. The ANN was the most accurate technique, but its successful implementation depended on accurate co-registration and the availability of a training data set. Supervised fuzzy c -means classification was slightly more accurate than mixture modelling.