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。     

雪盖卫星遥感信息的提取方法探讨

着重论述了从卫星遥感资料中提取雪盖信息的一些方法,结果表明,利用积雪阈值参数从ＮＯＡＡ／ＡＶＨＲＲ图象中提取雪盖信息方法和利用积雪指数（ＮＤＳＩ）从陆地卫星ＴＭ图象中提取雪盖面积的技术,以及利用ＮＯＡＡ／ＡＶＨＲＲ和ＴＭ信息复合的技术,可提高信息获取的精度,具有实用价值。     

分叉函数向量化计算的改进和开拓

分叉函数向量化计算的改进和开拓黄清南，张景琳，阳述林（北京应用物理与计算数字研究所，计算物理实验室）ＴＨＥＩＭＰＲＯＶＥＭＥＮＴＡＮＤＥＸＴＥＮＳＩＯＮＯＮＴＨＥＶＥＣＴＯＲＩＺＥＤＥＶＡＬＵＡＴＩＯＮＯＦＢＲＡＮＣＨＥＤＦＵＮＣＴＩＯＮＳ￥Ｈｕａｎ...     

基于选择竞争的自适应并行模拟退火方法

基于选择竞争的自适应并行模拟退火方法彭玉青，沈雪勤（河北工业大学计算机系）ＡＰＡＲＡＬＬＥＬＡＤＡＰＴＩＶＥＳＩＭＵＬＡＴＥＤＡＮＮＥＡＬＩＮＧＡＬＧＯＲＩＴＨＭＢＡＳＥＤＯＮＳＥＬＥＣＴＩＶＥＣＯＭＰＥＴＩＴＩＯＮ￥ＰｅｎｇＹｕｑｉｎｇ；ＳｈｅｎＸ...     

对流扩散方程的边界元解法及其在水质分析中的应用

对流扩散方程的边界元解法及其在水质分析中的应用谭宏，袁政强，祝家麟（重庆建筑工程学院）ＴＨＥＢＥＭＭＥＴＨＯＤＦＯＲＳＯＬＶＩＮＧＴＨＥＣＯＮＶＥＣＴＩＯＮＤＩＦＦＵＳＩＯＮＥＱＵＡＴＩＯＮＡＮＤＡＰＰＬＩＣＡＴＩＯＮＴＯＷＡＴＥＲＱＵＡＬＩＴＹＡＮ...     

COLLISION DETECTION AMONG MOVING OBJECTSIN MACHINING PROCESS SIMULATION

ＣＯＬＬＩＳＩＯＮＤＥＴＥＣＴＩＯＮＡＭＯＮＧＭＯＶＩＮＧＯＢＪＥＣＴＳＩＮＭＡＣＨＩＮＩＮＧＰＲＯＣＥＳＳＳＩＭＵＬＡＴＩＯＮＹａｎｇＨｅｍｉｎｇ；ＬｕＡｎｓｈｅｎｇ；ＺｈｏｕＪｉＣＯＬＬＩＳＩＯＮＤＥＴＥＣＴＩＯＮＡＭＯＮＧＭＯＶＩＮＧＯＢＪＥＣ...     

在专用网上实现的中西文兼容Videotex终端的设计

在专用网上实现的中西文兼容Ｖｉｄｅｏｔｅｘ终端的设计任伟利（西安交通大学信息与控制工程系西安７１００４９）ＤＥＳＩＧＮＯＦＣＯＭＰＡＴＩＢＬＥＣＨＩＮＥＳＥ－ＥＮＧＬＩＳＨＶＩＤＥＯＴＥＸＴＥＲＭＩＮＡＬＢＡＳＥＤＯＮＳＰＥＣＩＡＬ－ＰＵＲＰＯＳＥＮ...     

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，计算了在卫星高度土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TMDVI4.3提取冬小麦种植面积和NOAA NDVIZ2,1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。     

用VISUAL FOXPRO设计多媒体播放器

本文介绍了利用ＶＩＳＵＡＬＦＯＸＰＲＯ的应用程序接口ＦＯＸＴＯＯＬＳ调用ＷＩＮＤＯＷＳ的ＤＬＬ库开发多媒体播放器的原理及实现技术，利用该播放器可以对波形文件（．ＷＡＶ），数字音乐，（．ＭＩＤ），视频图象（．ＡＶＩ）动画（．ＦＬＡ）音频，压缩视频（．ＭＰＧ）Ｖ－ＣＤ视频，（．ＤＡＴ）等进行控制和播放。     

混合型符号几何规划的递归二次规划算法

混合型符号几何规划的递归二次规划算法张希，张可村（西安交通大学科学计算与应用软件系）ＡＳＵＣＣＥＳＳＩＶＥＱＵＡＤＲＡＴＩＣＰＲＯＧＲＡＭＭＩＮＧＡＬＧＯＲＩＴＨＭＦＯＲＭＩＸＥＤＳＩＧＮＯＭＩＡＬＧＥＯＭＥＴＲＩＣＰＲＯＧＲＡＭＭＩＮＧ￥Ｚｈａｎｇ...     

Simulation and Analysis on the Influence of Soil Background of Wheat Farmland on NDVI under Different Vegetation Coverage

It is quite confusing to effectively monitor and precisely evaluate growing conditions of wheat by using normalized differential vegetation index (NDVI)which is based on pixel scale as they are significantly different when acquired by the same growth status wheat with different background of soil types.This paper selects 9 typical soil types in our country as background with the wheat canopy spectrum is fixed which means the NDVIc is a constant value to study the influence of different soil background types on NDVI of wheat and analyze the sensitivity of NDVI of wheat to the vegetation coverage simulated by diverse liner mixed ratio of wheat canopy and soil background.The results show that：(1)wheat NDVI of farmland increases along with the increase of vegetation coverage under the same of soil background type,and vice versa;(2)wheat NDVI of farmland vary greatly with different soil background types,and the difference decrease while the vegetation coverage exceed 25%;(3)NDVI sensitivity also shows a quite difference to vegetation coverage under the diverse soil background types.With the increase of vegetation coverage,NDVI sensitivity decreases with the lower\|reflectance soil background while it increases monotonously with the higher reflectance soil background.It provides the foundation for the times of calculating the remote sensing’s NDVI information of all wheat growing periods under different types of soil background.     

利用航空成像光谱数据进行冬小麦产量预测

以国产成像光谱仪PHI(Pushbroom Hyperspectral Imaget)所获遥感影像数据为基础，根据田间冬小麦单产遥感研究试验数据建立了研究区不同时相冬小麦单产预测模型，实现了利用航空高光谱遥感数据对研究区小麦产量的整体预测；对试验区土壤氮素水平与不同时相冬小麦预测产量以及试验区实测产量进行了初步分析，分析结果显示：土壤氮素分布的差异性对小麦的产量有明显影响。     

土壤背景对冠层NDVI的影响分析

归一化差值植被指数NDVI是植被遥感中应用最为广泛的指数之一, 但它受土壤背景等因素的干扰比较强烈。结合实测的土壤数据以及公式推导、PROSAIL 模型模拟等方法分析了这种影响。首先, 假定与土壤线性混合且叶片呈水平分布的植被冠层, 根据土壤与植被分别在红光、近红外波段处的反射率值、植被覆盖度等参数, 利用公式推导了土壤背景对不同覆盖度下冠层NDVI的影响。其次, 利用PROSAIL冠层光谱模拟模型, 模拟分析了土壤背景对不同LAI下冠层NDVI的影响。分析的结果表明:LAI 越小, 土壤背景的影响越大; 暗土壤背景下的冠层NDVI值大于亮土壤背景下冠层的NDVI值; 并且,暗土壤条件下,NDVI值对土壤亮度的变化更敏感,而亮土壤下,NDVI值则对LAI或覆盖度的变化更敏感。最后利用实测的不同土壤背景下的冬小麦冠层光谱数据, 验证了公式推导和模型模拟的结果。     

Early Estimation of Winter Wheat Planting Area in Qingyang City by Decision Tree and Pixel UnmixingMethods based on GF-1 Satellite Data

In Northwest China,there are many mixed pixels in the winter wheat area,so the combination of decision tree and mixed pixel decomposition is of great significance to improve the interpretation accuracy.The data source of this result is GF-1 satellite data which excellent in the high temporal resolution and high spatial resolution.Based on the difference about variation characteristics and NDVI value for winter wheat and the other crops in different phase data,we build decision tree to extract winter wheat pixels preliminary.Then selected linear spectral mixture model,further analysis the previous data by mixed pixel decomposition,get the final planting area data more exactly.Compared with the winter wheat samples measurement data,calculate the extraction accuracy eventually.The result shows that the extraction accuracy of winter wheat planting area in the study area was more than 90%,Kappa coefficient is close to 0.8,can reflect the distribution of winter wheat in the region accurately.This study found that the method which combined with decision tree classification and pixel unmixing based on high resolution remote sensing image can extract the winter wheat planting area precisely,This is helpful for the development of crop area remote sensing monitoring.     

TVDI在冬小麦春季干旱监测中的应用

应用冬小麦春季生长期的NOAA/AVHRR资料,反演归一化植被指数（NDVI）、土壤调整植被指数（SAVI）和下垫面温度（T_s）,分析了植被指数和下垫面温度空间特征,采用温度植被旱情指数（TVDI）,研究了河北省2005年3~5月的冬小麦旱情状况。结果表明：基于SAVI的温度植被旱情指数与土壤表层相对湿度的相关性好于基于NDVI的温度植被旱情指数。通过与气象站土壤水分观测资料进行相关性分析,表明温度植被旱情指数与10 cm土壤相对湿度关系最好,20 cm次之,50 cm较差。因此,基于SAVI的温度植被旱情指数更适于监测冬小麦春季的旱情。     

基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段，对农业生产管理具有重要意义。以安徽龙亢农场为研究区，收集了中高分辨率多源卫星遥感数据并进行了定量化处理，构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型，生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律，分析了不同品种冬小麦的长势情况，发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上，构建了基于归一化植被指数（NDVI）的冬小麦估产模型，结果表明：利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算，据此绘制了龙亢农场2017年冬小麦产量遥感估算地图，产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测，为区域范围内农作物长势监测提供了一种有效途径。     

基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段,对农业生产管理具有重要意义。以安徽龙亢农场为研究区,收集了中高分辨率多源卫星遥感数据并进行了定量化处理,构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型,生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律,分析了不同品种冬小麦的长势情况,发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上,构建了基于归一化植被指数（NDVI）的冬小麦估产模型,结果表明：利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算,据此绘制了龙亢农场2017年冬小麦产量遥感估算地图,产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测,为区域范围内农作物长势监测提供了一种有效途径。     

Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction

The normalized difference vegetation index (NDVI) is the most widely used vegetation index for retrieval of vegetation canopy biophysical properties. Several studies have investigated the spatial scale dependencies of NDVI and the relationship between NDVI and fractional vegetation cover, but without any consensus on the two issues. The objectives of this paper are to analyze the spatial scale dependencies of NDVI and to analyze the relationship between NDVI and fractional vegetation cover at different resolutions based on linear spectral mixing models. Our results show strong spatial scale dependencies of NDVI over heterogeneous surfaces, indicating that NDVI values at different resolutions may not be comparable. The nonlinearity of NDVI over partially vegetated surfaces becomes prominent with darker soil backgrounds and with presence of shadow. Thus, the NDVI may not be suitable to infer vegetation fraction because of its nonlinearity and scale effects. We found that the scaled difference vegetation index (SDVI), a scale-invariant index based on linear spectral mixing of red and near-infrared reflectances, is a more suitable and robust approach for retrieval of vegetation fraction with remote sensing data, particularly over heterogeneous surfaces. The proposed method was validated with experimental field data, but further validation at the satellite level would be needed.     

Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using in situ measurements and the PROSAIL model

Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. The simplest and most widely used model for the estimation of FVC is the dimidiate pixel model. The normalized difference vegetation index (NDVI) is commonly used as a vegetation index (VI) in this model. A range of VIs is possible alternative to the use of NDVI in the dimidiate pixel model. In this article, six VI-based dimidiate pixel models were compared using in situ measurements and canopy reflectances simulated by the PROSAIL model over nine different soil backgrounds. A comparison with in situ measurements showed that the Gutman–Ignatov method overestimated FVC, with a mean root mean square error (RMSE) of 0.14. The mean RMSE had an intermediate value of 0.08 in the Carlson–Ripley method and was further reduced to 0.05 in the method proposed by Baret et al. The use of both modified soil-adjusted vegetation index (MSAVI) and a mixture of NDVI and the ratio vegetation index (RVI) to replace NDVI in the Gutman–Ignatov model reduced the RMSE to 0.06. The mean RMSE in the difference vegetation index (DVI)-based model was 0.08. The simulated results indicated that soil backgrounds have significant effects on these VI-based models. The sensitivity of the first three models and the NDVI plus RVI-based model to soil backgrounds decreased with an increase in soil reflectance. In contrast, the DVI-based model is sensitive to soil backgrounds with high reflectances. MSAVI, which is less sensitive to soil backgrounds, represents a feasible alternative to the use of NDVI in the Gutman–Ignatov model.     

Vegetation spatial variability and its effect on vegetation indices

Abstract

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification programme. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12·7 per cent in determining these areas. NDVI values less than 0·3 represented fractional vegetated areas of 5 per cent or less, while a value of 0·7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0·89 and 0·95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.