卫星遥感雪盖制图方法对比与分析

利用ＬａｎｄｓａｔＴＭ、ＮＯＡＡ／ＡＶＨＲＲ和中分辨率成像光谱仪（ＭＯＤＩＳ）三个平台传感器的遥感数据,分别使用训练样本监督分类、阈值数字信号统计、雪盖指数方法制作雪盖图和提取积雪面积。结果表明：不同传感器遥感图像因时相和时空分辨率的差异,提取积雪信息的有效方法有所不同。但基于反射特性的雪盖指数计算法具有普遍的实际操作性意义,即雪盖制图精度高,分类合理,是提取积雪信息的最佳技术手段;当使用监督积雪分类时,只有取得精确的信号文件,分类结果才是可信的;而阈值数字信号统计的雪的阈值确定具有很大的经验性和随机性,但对数据不完整或只有单波段时也不失为有效和简便的途径;山影补偿处理法基本可以消除地形阴影的影响;而去云后其覆盖下的积雪恢复技术值得进一步讨论。     

卫星遥感雪盖制图方法对比与分析

利用ＬａｎｄｓａｔＴＭ、ＮＯＡＡ／ＡＶＨＲＲ和中分辨率成像光谱仪（ＭＯＤＩＳ）三个平台传感器的遥感数据,分别使用训练样本监督分类、阈值数字信号统计、雪盖指数方法制作雪盖图和提取积雪面积。结果表明：不同传感器遥感图像因时相和时空分辨率的差异,提取积雪信息的有效方法有所不同。但基于反射特性的雪盖指数计算法具有普遍的实际操作性意义,即雪盖制图精度高,分类合理,是提取积雪信息的最佳技术手段|当使用监督积雪分类时,只有取得精确的信号文件,分类结果才是可信的|而阈值数字信号统计的雪的阈值确定具有很大的经验性和随机性,但对数据不完整或只有单波段时也不失为有效和简便的途径|山影补偿处理法基本可以消除地形阴影的影响|而去云后其覆盖下的积雪恢复技术值得进一步讨论。     

一种基于边缘轮廓信息的多源遥感图象子象素匹配定位方法

提出了一种基于图象边缘轮廓信息的多源图象匹配定位方法,其目的是利用定位精度较高的高分辩率遥感图象对低分辨率图象实现子象素级的匹配定位,该方法有效地利用了多源遥感图象中共有的区域结构信息,将特征匹配和最小二乘影象匹配相结合,具有较好的普适性,且运算快速、抗噪性能好,采用该方法进行ＮＯＡＡＡＶＨＲＲ图象和Ｌａｎｄｓａｔ ＴＭ图象、１：１００万数字地图的边缘图象匹配,并应用于ＮＯＡＡ ＡＶＨＲＲ图象的几     

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，计算了在卫星高度上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ＴＭＤＶＩ＿（４，３）提取冬小麦种植面积和ＮＯＡＡＮＤＶＩ＿（２，１）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。     

关于NOAA AVHRR图象重采样及投影方法的研究

本文论述了对极轨气象卫星ＮＯＡＡＡＶＨＲＲ图象进行重采样的必要性，研究了重采样与投影的方法，并利用ＥＲＭａｐｐｅｒ图象处理系统对重采样前后及经过投影处理的彩色图象进行了对比分析。     

应用遥感技术进行无资料区积雪动态监测方法的研究

积雪是重要的水资源，监测技术的改进对无资料区积雪的监测具有重要意义。采用遥感与地理信息系统（ＧＩＳ）相结合的方法对积雪监测具有极大的潜力。本文通过对西藏某一流域多年资料的研究，证明了这种方法的可行性和优越性。研究结果表明，应用ＮＯＡＡ／ＡＶＨＲＲ数据，结合ＧＩＳ方法，可以准确确定积雪范围，提供积雪面积，同时可以提供高质量的积雪分布图。     

ICARCV＇96 FOURTH INTERNATIONAL CONFERENCE ON CONTROL，AUTOMATION，ROBOTICS AND VISION

ＩＣＡＲＣＶ＇９６ＦＯＵＲＴＨＩＮＴＥＲＮＡＴＩＯＮＡＬＣＯＮＦＥＲＥＮＣＥＯＮＣＯＮＴＲＯＬ，ＡＵＴＯＭＡＴＩＯＮ，ＲＯＢＯＴＩＣＳＡＮＤＶＩＳＩＯＮ￥／／３－６Ｄｅｃｅｍｂｅｒ１９９６ＷｅｓｔｉｎＳｔａｍｆｏｒｄ，Ｓｉｎｇａｐｏｒｅ，ＣＡＬＬＦＯ...     

利用NOAA－AVHRR资料提取水体信息的初步研究

讨论了应用ＮＯＡＡ－ＡＶＨＲＲ资料来提取水体信，包的方法。采用一、二通道反射率数据（ＣＨ＿１、ＣＨ＿２）构成的归一化植被指数（ＮＤＶＩ）来识别水体，并初步提出了应用模糊数学的方法提取混合像元中的水体面积信息。     

全息粒子场再现图象自动分析方法与实现

全息粒子场再现图象自动分析方法与实现刘雷健，杨静宇，邬永革，刘克（南京理工大学计算机科学系南京２１００９４）ＡＵＴＯＭＡＴＩＣＡＮＡＬＹＳＩＳＳＹＳＴＥＭＯＦＲＥＴＲＩＥＶＥＤＰＡＲＴＩＣＬＥＨＯＬＯＧＲＡＭＳ￥ＬｉｕＬｅｉｊｉａｎ；ＹａｎｇＪｉｎｇ...     

COLLISION DETECTION AMONG MOVING OBJECTSIN MACHINING PROCESS SIMULATION

ＣＯＬＬＩＳＩＯＮＤＥＴＥＣＴＩＯＮＡＭＯＮＧＭＯＶＩＮＧＯＢＪＥＣＴＳＩＮＭＡＣＨＩＮＩＮＧＰＲＯＣＥＳＳＳＩＭＵＬＡＴＩＯＮＹａｎｇＨｅｍｉｎｇ；ＬｕＡｎｓｈｅｎｇ；ＺｈｏｕＪｉＣＯＬＬＩＳＩＯＮＤＥＴＥＣＴＩＯＮＡＭＯＮＧＭＯＶＩＮＧＯＢＪＥＣ...     

卫星雪盖监测与玛纳斯河融雪径流模拟

根据国家“九五”重点科技攻关项目“冰雪水资源和出山口径流量变化及趋势预测研究”专题(96-912-01-02)的部分研究内容,运用改进后的融雪径流模型(SRM),通过天山雪盖的卫星遥感监测和流域模型参数的研究,实现了对玛纳斯河肯斯瓦特水文站春季逐日流量的数值模拟,结果表明,模拟的确定系数可达0.89,模拟积差为5.1,满足应用精度,可进一步推广应用。     

Agreement assessment of NOAA/AVHRR NDVI with Landsat TM NDVI for mapping burned forested areas

Meteorological satellites are appropriate for operational applications related to early warning, monitoring and damage assessment of forest fires. Environmental or resources satellites, with better spatial resolution than meteorological satellites, enable the delineation of the affected areas with a higher degree of accuracy. In this study, the agreement of two datasets, coming from National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Landsat TM, for the assessment of the burned area, was investigated. The study area comprises a forested area, burned during the forest fire of 21-24 July 1995 in Penteli, Attiki, Greece. Based on a colour composite image of Landsat TM a reference map of the burned area was produced. The scatterplot of the multitemporal Normalized Difference Vegetation Index (NDVI) images, from both Landsat TM and NOAA/AVHRR sensors, was used to detect the spectral changes due to the removal of vegetation. The extracted burned area was compared to the digitized reference map. The synthesis of the maps was carried out using overlay techniques in a Geographic Information System (GIS). It is illustrated that the NOAA/AVHRR NDVI accuracy is comparable to that from Landsat TM data. As a result NOAA/AVHRR data can, operationally, be used for mapping the extent of the burned areas.     

Landsat TM热红外遥感数据定量反演地下水富集带的温度信息--以甘肃河西地区石羊河流域为例

利用ＬａｎｄｓａｔＴＭ６热红外遥感数据定量反演了干旱地区的地表温度,研究结果表明,区典型地表覆盖类型的地表亮温比地表起初温度低０．４－１Ｋ,遥感反演的地面真实温度与当地３月下旬的实测温度误差在０．８Ｋ以下,这说明用ＬａｎｄｓａｔＴＭ６定量反演干旱区的地表温度是可行的。研究结果表明,地下水富集带地表温度具有异常现象,其地表温度比地表水体高５Ｋ左右,而比其它地表类型低７Ｋ以上,据此,可以利用热红外遥感     

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.     

Satellite observations of snowcover in the Sierra Nevadas during the great California drought

Images from the NOAA polar orbiting satellites are used to assess the extent of snowcover in the Sierra Nevada mountain range during the 1977 drought year. A real snowcover measurements derived from late April satellite imagery reveal the entire mountain range to have less than one third the snowcover that was present at a comparable date in 1975. Ratios of 1977 to 1975 snowcover for individual basins of the Sierra Nevadas ranged from a low of 1:9 in low elevation watersheds to a high of 1:2 in high elevation watersheds. Reduction of the satellite data was accomplished through the use of a density slicer, a color additive viewer, and an optical rectification device.     

SPOT VEGETATION for characterizing boreal forest fires

The potential of the recent SPOT VEGETATION (VGT) sensor for characterizing boreal forest fires was investigated. Its capability for hotspot detection and burned area mapping was assessed by analysing a series of VGT, NOAA/AVHRR, and Landsat TM images over a 1541 km²     

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.     

Estimation of the vapour pressure deficit using NOAA-AVHRR data

In this study, the calculation of vapour pressure deficit (VPD) using the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data images were arranged and turned to account for both VPD and land surface temperature (LST), which was necessary to calculate the VPD. The most accurate LST values were obtained from the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of 2.7 K, whereas the VPD values were retrieved with an RMSE of 6 mb. Furthermore, the VPD value was calculated on an average monthly basis and its correlation coefficient was found to be 0.991, while the RMSE value was calculated to be 2.67 mb. As a result, VPD can be used in studies that examine plants (germination, growth, and harvest), controlling illness outbreak, drought determination, and evapotranspiration.     

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.