Psedudomonas sp．A03对Rhiaoctonia solani的抗生现象

在ＰＤＡ、ＫＢ和含Ｆｅ（＋＋＋）的ＫＢ琼脂平板上，Ｐｓｅｕｄｏｍｏｎａｓｓｐ．Ａ０３和Ｒｈｉｚｏｃｔｏｎｉａｓｏｌａｌｎｉ之间出现抑菌带。ＰｓｅｕｄｏｍｏｎａｓｓｐＡ０３培养物的无菌滤液及其粗提物以及在ＫＢ平板上产生的挥发性抗生物质都能抑制Ｒｈｉｚｏｃｈｔｏｎｉａｓｌｏａｎｉ菌丝生长。电镜扫描发现Ａ０３在杉木和云杉幼苗根面定殖。     

基于SPOT/VGT NDVI的大区域农作物空间分布

随着遥感技术在农业上的广泛应用,及时准确掌握小区域农作物长势和分布的方法相对成熟,但对于省级以上的大区域而言,进行作物遥感测量涉及数据、耗费时间和经费等问题,而中国的农业普查制度又存在时效性的问题。以江苏省为研究区,利用SPOT/VGT NDVI长时间序列数据作为农作物分布研究的底图,分别与研究区农业普查数据和野外调查样方数据进行农作物空间分布的统计回归分析和对比,结果表明：对于大区域而言,SPOT/VGT NDVI长时间序列数据对农业作物生长期特征监测有较好地反应,其逐旬的时间分辨率可以弥补空间分辨率的不足,可有效地对农作物长势进行动态监测;农业普查数据、野外调查样方与SPOT/VGT NDVI时间序列分类结果结合进行回归分析,基本可以满足空间分布情况的调查研究,相对于遥感分类法和传统抽样调查等方式,该方法可以高效率低成本地掌握大区域农作物空间分布情况。     

土传小麦黄色花叶病对小麦植株的影响及产量损失估计

在田间系统观察了土传小麦黄色花叶病对小麦植株影响的基础上,测定了不同病级单株产量损失,并提出了以株高、花叶程度及推迟生育期为主的病情分级标准。通过小区试验,导出了病情指数对产量的回归方程((?)(%)=99.458-0.836x)及相关系数(r=-0.997~(**))。     

杀青叶水分及品质与蒸汽杀青机优化组合工艺参数的关系

本文采用正交设计Ｌ８（２７）研究了滚筒连续蒸汽杀青机轴转速、筒转速和筒倾角的优化组合与杀青叶水分及品质的关系。结果表明：轴、筒转速大,杀青叶水分含量低,茶叶品质好。     

决策树分类在红树林自然保护区SPOT影像解译中的应用

利用决策树分类方法对漳江口国家级红树林自然保护区SPOT遥感影像进行解译,解决了传统的调查分类方法难以区分滩涂红树林与山上果树的弊病,取得了较好的效果。     

基于SPOT5影像的郁闭度反演方法

为探讨区域植物措施因子在林地的赋值问题,以重庆市云阳县为研究区,以58个单元实地林地地块郁闭度调查数据为基础,计算了对应地块同期SPOT5影像各波段DN(灰度)均值和DN值标准差,分析了SPOT5影像在林地不同郁闭度下的稳定性。随机选取38个单元调查郁闭度数据,与对应地块同期SPOT5影像各波段DN均值进行回归、相关性分析,建立了不同波段组合反演郁闭度的方法。利用剩余20个单元的调查郁闭度数据进行精度检验。结果表明:SPOT5影像红外、红、绿波段DN均值在不同郁闭度林地地块表现稳定,最大偏离分别为13.8%,15.65%,24.52%,偏离平均值分别为10.11%,10.94%,17.78%;红、绿波段是反演郁闭度的最佳波段组合,拟合方程判定系数R2为0.66。精度检验结果表明,反演结果与实地调查数据正、负向最大偏差为+16.92%,-16.06%,偏差均值为-2.08%。按10%的分级统计,偏离均小于15%,总体精度较高。在反演结果误差为±20%时,植物措施因子赋值的最大误差<10%,可作为区域土壤侵蚀监测时植物措施因子计算的参考。     

杉木人工林平均树高遥感反演模型研究

以湖南攸县黄丰桥国有林场杉木人工林为研究对象,以高分辨率SPOT5影像及1∶10 000地形图为数据源,提取海拔、坡度、坡向、郁闭度,B1(反射率)、B2(反射率)、B3(反射率)、B4(反射率),B1/B4、B2/B4、B3/B1,EVI、NDVI、RVI等14个因子,运用主成分分析法以及岭迹估计法剔除与平均树高相关性小的变量因子,确定影响平均树高估测的主要因子为:B2(反射率)、B4(反射率)、坡向、郁闭度、NDVI。基于最小二乘法建立遥感反演关系模型,用实地调查数据进行模型检验,平均树高估测回归模型的相关系数、决定系数、调整相关系数以及标准估计的误差分别为0.891 0、0.793 0、0.774 0、0.842 2,树高估测模型达到较好的拟合效果,得到杉木人工林的平均树高模型。     

雅安十三种软体动物体内吸虫幼虫期的调查研究

1989年3-9月作者检查雅安9种淡水软体动物11838只,4种陆生软体动物790只,从中查出21种吸虫的胞蚴(或雷蚴)和尾蚴期。通过本次调查发现11种吸虫的中间宿主(或第一中间宿主)新记录共15个,文中对赤豆螺与人和动物间的相互关系作了讨论。     

Fire models and methods to map fuel types: The role of remote sensing

Understanding fire is essential to improving forest management strategies. More specifically, an accurate knowledge of the spatial distribution of fuels is critical when analyzing, modelling and predicting fire behaviour. First, we review the main concepts and terminology associated with forest fuels and a number of fuel type classifications. Second, we summarize the main techniques employed to map fuel types starting with the most traditional approaches, such as field work, aerial photo interpretation or ecological modelling. We pay special attention to more contemporary techniques, which involve the use of remote sensing systems. In general, remote sensing systems are low-priced, can be regularly updated and are less time-consuming than traditional methods, but they are still facing important limitations. Recent work has shown that the integration of different sources of information and methods in a complementary way helps to overcome most of these limitations. Further research is encouraged to develop novel and enhanced remote sensing techniques.     

Evaluating high resolution SPOT 5 satellite imagery for crop identification

High resolution satellite imagery offers new opportunities for crop monitoring and assessment. A SPOT 5 image acquired in May 2006 with four spectral bands (green, red, near-infrared, and short-wave infrared) and 10-m pixel size covering intensively cropped areas in south Texas was evaluated for crop identification. Two images with pixel sizes of 20 m and 30 m were also generated from the original image to simulate coarser resolution satellite imagery. Two subset images covering a variety of crops with different growth stages were extracted from the satellite image and five supervised classification techniques, including minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper (SAM), and support vector machine (SVM), were applied to the 10-m subset images and the two coarser resolution images to identify crop types. The effects of the short-wave infrared band and pixel size on classification results were also examined. Kappa analysis showed that maximum likelihood and SVM performed better than the other three classifiers, though there were no statistical differences between the two best classifiers. Accuracy assessment showed that the 10-m, four-band images based on maximum likelihood resulted in the best overall accuracy values of 91% and 87% for the two respective sites. The inclusion of the short-wave infrared band statistically significantly increased the overall accuracy from 82% to 91% for site 1 and from 75% to 87% for site 2. The increase in pixel size from 10 m to 20 m or 30 m did not significantly affect the classification accuracy for crop identification. These results indicate that SPOT 5 multispectral imagery in conjunction with maximum likelihood and SVM classification techniques can be used for identifying crop types and estimating crop areas.