Landsat8和MODIS融合构建高时空分辨率数据识别秋粮作物

本文利用Wu等人提出的遥感数据时空融合方法 STDFA(Spatial Temporal Data Fusion Approach)以Landsat 8和MODIS为数据源构建高时间、空间分辨率的遥感影像数据。以此为基础,构建15种30 m分辨率分类数据集,然后利用支持向量机SVM(Support Vector Machine)进行秋粮作物识别,验证不同维度分类数据集进行秋粮作物识别的适用性。实验结果显示,不同分类数据集的秋粮作物分类结果均达到了较高的识别精度。综合各项精度指标分析,Red+Phenology数据组合对秋粮识别效果最好,水稻识别的制图精度和用户精度分别达到91.76%和82.49%,玉米识别的制图精度和用户精度分别达到85.80%和74.97%,水稻和玉米识别的总体精度达到86.90%。     

遥感与化探数据融合处理技术方法及应用研究

遥感找矿信息与化探异常之间存在套合和藕合两种空间关系，通过数据融合处理，可以对其相关性作出正确的识别和判断，以达到综合找矿的目的。基于此，笔者以东天山地区作为试验区，进行了遥感与化探数据融合处理的技术方法研究及试验应用。     

中国大陆地壳形变GNSS站网简述及数据产品应用现状

自20世纪80年代中期以来,GNSS技术在高精度地壳运动观测与构造形变研究中取得了丰硕的成果,为大地测量、地球动力学研究和防震减灾等诸多领域的业务深化和应用拓展提供了强大的技术支撑。本文在回顾中国大陆地壳形变GNSS站网发展历程的基础上,阐述该网络产出的中国大陆长期构造运动速度场、中国大陆应变率场、位移时间序列、基线时间序列和多边形应变时间序列等几类基础产品,分析这些产品在中国大陆构造运动动态趋势和地震预测分析中的应用情况以及所面临的瓶颈问题,最后展望未来GNSS在高精度地壳运动监测应用中的发展方向。以中国大陆构造环境监测网络为基础,大力推进国内海量GNSS观测数据的共享,提升GNSS多系统融合定位精度,将产出更为精细的科学产品,更好地服务于中国大陆地壳运动和地震预测分析等研究。     

基于Brovey融合与HSV锐化的ASTER影像绿地信息提取分析

为了突出图像的边缘、线状目标或某些亮度变化率大的部分,生成边缘图像,运用Brovey融合和HSV锐化方法,对广州市部分地区的ASTER遥感影像进行处理,然后,对两种方法分别处理后的图像,采用平行六面体法监督分类,提取城市用地信息,并对分类后的图像进行对比试验,着重对城市绿地信息的提取进行分析,探究ASTER遥感数据的两种影像处理方法对城市绿地信息的提取效果,及其空间分布为城市生态评价和景观生态建设提供必要的基础数据。通过对比试验,结果显示:Brovey方法较好地显示城市绿地的分布状况,可作为城市绿地动态监测的判别方法之一;融合后的影像能够清晰地显示研究区城市绿地的分布结构及毗邻关系;通过采用区域放大、勾画不同地类边缘轮廓、配合人工解译等工作,能够增加城市绿地与其他用地类型的差异,突显城市绿地的分布、结构、生态学特征。     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

X射线荧光光谱仪同时测定红土型镍矿中主次量组份

以无水四硼酸锂作熔剂,硝酸铵为氧化剂,碘化钾为脱模剂,在高频熔样机上于1050℃熔融,制成玻璃片,使用标准曲线对谱线重叠干扰和基体效应进行校正,建立XRF测定红土型镍矿中TFe、SiO2、Al2O3、Cr2O3、MgO、Ni、Co的分析方法,经标准样品和检测样品外部比对结果验证,结果准确,可靠,方法快速、简便。     

熔融制样-波长色散X射线荧光光谱法测定铁矿石中主次量组分

建立了采用波长色散X射线荧光光谱法测定铁矿石中TFe、SiO2、AI2O3、MgO、CaO、K2O、Na2O、TiO2、MnO、P、S和Cu等12种主次量组分的分析方法。采用LiBO2-Li2B4O7混合熔剂熔融制样,以铁矿石一级国家标准物质为校准样品,通过加入Co2O3内标、使用理论α系数法和经验系数相结合的方法校正基体效应和谱线重叠干扰。方法的准确度、精密度达到了传统化学分析方法的要求,分析时间大大缩短,具有很好的使用价值。     

基于多源数据的活动断裂遥感图像处理技术

本文在综合分析活动断裂遥感影像特征的基础上, 主要研究了活动断裂与隐伏断裂调查中遥感图像处理。基于SAR影像、 ETM+影像、 SPOT-5影像及高精度DEM数据, 运用SAR图像与ETM+多光谱图像融合方法、 ETM+图像纹理特征和光谱特征组合增强方法等, 结合断裂周围高分辨率影像及其三维影像, 综合分析解译了济南市周边活动断裂, 为该地区地震地质调查和精确定位活动隐伏构造提供了依据。     

杜氏藻与大肠杆菌跨界融合初探

以链霉蛋白酶制得了杜氏藻(嗜盐单胞绿藻)的原生质体。采用聚乙二醇(PEG)-钙离子法将它们与大肠杆菌(含氯霉素乙酰转移酶基因质粒)的球1,20,1状体进行融合。“融合子”酷似杜氏藻。它们对氯霉素抗性的显著提高寓示了大肠杆菌的质粒已转移到“融合子”细胞中;而且,“融合子”的蛋白质组分与杜氏藻和大肠杆菌相比已发生了较明显的变化。     

Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data

Site productivity is essential information for sustainable forest management and site index (SI) is the most common quantitative measure of it. The SI is usually determined for individual tree species based on tree height and the age of the 100 largest trees per hectare according to stem diameter. The present study aimed to demonstrate and validate a methodology for the determination of SI using remotely sensed data, in particular fused airborne laser scanning (ALS) and airborne hyperspectral data in a forest site in Norway. The applied approach was based on individual tree crown (ITC) delineation: tree species, tree height, diameter at breast height (DBH), and age were modelled and predicted at ITC level using 10-fold cross validation. Four dominant ITCs per 400 m² plot were selected as input to predict SI at plot level for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). We applied an experimental setup with different subsets of dominant ITCs with different combinations of attributes (predicted or field-derived) for SI predictions. The results revealed that the selection of the dominant ITCs based on the largest DBH independent of tree species, predicted the SI with similar accuracy as ITCs matched with field-derived dominant trees (RMSE: 27.6% vs 23.3%). The SI accuracies were at the same level when dominant species were determined from the remotely sensed or field data (RMSE: 27.6% vs 27.8%). However, when the predicted tree age was used the SI accuracy decreased compared to field-derived age (RMSE: 27.6% vs 7.6%). In general, SI was overpredicted for both tree species in the mature forest, while there was an underprediction in the young forest. In conclusion, the proposed approach for SI determination based on ITC delineation and a combination of ALS and hyperspectral data is an efficient and stable procedure, which has the potential to predict SI in forest areas at various spatial scales and additionally to improve existing SI maps in Norway.