利用气象卫星资料估算全球作物总产研究

以美国为例,利用1996～2000年每旬的全球植被指数卫星遥感资料,计算出每年耕地上作物生长季内的总NPP（Net Primary Production）。农作物总产是耕地上总NPP的一部分,根据NPP与作物总产的关系,确定作物的产量转换系数,然后利用当年耕地的总NPP值来估算当年作物的总产。通过研究表明,该方法的预测精度较高,可操作性强,能够投入业务应用。     

Simulating Crop Net Primary Production in China from 2000 to 2050 by Linking the Crop-C model with a FGOALS's Model Climate Change Scenario

Net primary production(NPP)of crop represents the capacity of sequestrating atmospheric CO_2 in agro-ecosystem,and it plays an important role in terrestrial carbon cycling.By linking the Crop-C model with climate change scenario projected by a coupled GCM FGOALS via geographical information system (GIS)techniques,crop NPP in China was simulated from 2000 to 2050.The national averaged surface air temperature from FGOALS is projected to increase by 1.0℃over this period and the corresponding atmospheric CO_2 concentration is 535 ppm by 2050 under the IPCC AIB scenario.With a spatial resolution of 10×10 km~2,model simulation indicated that an annual average increase of 0.6 Tg C yr~(-1)(Tg=10~(12)g) would be possible under the AIB scenario.The NPP in the late 2040s would increase by 5%(30 Tg C) within the 98×10~6 hm~2 cropland area in contrast with that in the early 2000s.A further investigation suggested that changes in the NPP would not be evenly distributed in China.A higher increase would occur in a majority of regions located in eastern and northwestern China,while a slight reduction would appear in Hebei and Tianjin in northern China.The spatial characteristics of the crop NPP change are attributed primarily to the uneven distribution of temperature change.     

Application of Multi-Temporal MODIS NDVI Data to Assess Practiced Maize Calendars in Rwanda

Crop calendar is an important tool providing relevant information on crops cycles in a specific area for effective agricultural management. Crop calendars vary in different areas given dissimilarities in agro-ecosystems’ characteristics. This research used multi-temporal MODIS NDVI stratification to assess differences in practiced maize crop calendars in various areas of Rwanda. Four (4) sample NDVI strata dominated by agriculture were purposively chosen, and 433 local farmers were randomly selected from the strata for interviews. The collected information helped to know about their maize planting as well as harvesting dates in order to generate maize calendars per NDVI strata. The generated crop calendars were later classified using k-means unsupervised classification, and produced 4 groupings of practiced maize calendars irrespective of NDVI strata. ANOVA results revealed significant differences between both the generated maize crop calendars by NDVI strata and the practiced crop calendars irrespective of NDVI strata, at p = 0.05. Moreover, chi-square tests and t-tests revealed not only a significant relationship between maize calendars and number of crop growing seasons, but also a significant relationship between maize calendars and NDVI strata, at p = 0.05. Finally, findings of this research contrasted the present conviction that there exist a single general maize calendar all over the country. Instead, the results were in accordance with the fact that Rwanda agro-ecosystems differ from East to West in terms of, mainly, altitude and rainfall patterns variations.     

三维激光扫描仪变形监测识别度测试方法

在变形监测领域,三维激光扫描测量技术有着良好的应用前景。针对三维激光扫描仪能够识别被监测物体的最小变形量,即变形识别度,对变形监测方案设计的指导意义,该文提出了一种检测三维激光扫描仪变形监测识别度的方法。通过构建变形监测的理论误差模型,用实验对误差模型进行验证。结果显示,扫描仪扫描距离、角度对变形识别度的影响与实验结果较一致,验证了模型的正确性,说明该测试方法具有可行性。     

有限状态自动机辅助的行人导航状态匹配算法

行人导航状态的自动识别是行人导航研究的一个难点问题,对提升行人导航软件服务的精准反馈与改善导航性能至关重要,此方面已有的研究工作很少。本文提出了一种基于有限状态自动机的行人导航状态匹配算法,其核心思想是在识别行人动作基础上匹配行人当前导航状态。利用谷歌眼镜及智能手机采集的多种传感器数据对行人动作进行识别,得到其动作特征参数;然后将行人导航状态分为熟悉、陌生及迷路3类,根据有限状态自动机理论建立状态转移模型,设计基于该模型的行人导航状态匹配算法;最后,实现状态匹配算法,通过试验对该算法的有效性进行验证。试验结果表明,该算法能够较好地识别行人导航过程中的状态转移,其中对熟悉向陌生状态转移识别准确度较高,对迷路状态识别准确度达到90%。     

用动态规划的二维物体识别的方法

二维物体的识别是计算机视觉与航空摄影测量领域里重要的研究目标。本文提出在图像预处理阶段运用边缘提取的算法和数学形态学先得到轮廓线，形成对特征的描述。基于这些特征描述建立了用动态规划法进行特征匹配的数学模型和算法。实验结果表明它不仅具有可靠性和稳健性，而且在计算机上易于编程实现。     

土壤水分的遥感监测方法

本文讨论了用雷达图像散射系数法、NOAA-AVHRR数字图像热惯量法和作物缺水指数法监测土壤水分的结果,并将这些方法与常规气象方法、绿度指数法和温差法监测土壤水分的效果进行了比较和评价。结果表明,微波遥感监测土壤水分有广阔的应用前景,但必须深入开展基础研究。在我国目前情况下,采用NOAA-AVHRR数字图像及有关气象数据计算热惯量、作物蒸散和缺水指数,从而估算土壤水分的方法是一种比较切实可行的方法。     

一种二维编码标尺的数字水准仪系统原理设计与试验进展

数字水准仪是一种集光学、电子技术、编码技术、图像处理及计算机技术于一体的水准测量系统。它凭借快速度、高精度、易于操作等特点,已广泛应用于高精度水准测量、工业测量等多种领域。当前的各类数字水准仪产品是基于一维条码技术设计的编码标尺,水准仪的设计是围绕相应标尺编码方法的读数系统展开。经过十多年的开发,国外几大测绘仪器生产厂商已将条码标尺的编码方法实行专利保护;国内起步较晚,已难以找到合适的编码方法。文中提出了一种新的基于二维平面的编码设计方案。该方案将标尺面分为数字标注、二维码粗测和圆心精测3个区域;而水准仪部分采用面阵CCD采集标尺图像,运用快速数字图像处理算法进行读数定位。理论分析和实验样机测试表明,这种设计可以生产精度高、性能优异的数字水准仪。     

黑龙江省单一及混作农作物种植结构提取研究

以2012年MODIS数据产品250 m精度的MOD13Q1数据为数据源,采用Savitsky-Golay滤波法提取并平滑NDVI时间序列曲线,提取11种物候关键值,利用AMP,Start,Length三种物候关键值,采用面向对象分类方法的隶属度函数法提取出黑龙江省农用地范围,然后选取AMP,Start,Length,Left,Base,Right,Small七种物候关键值,采用面向对象分类方法的标准最邻近法,提取出黑龙江省单一及混作农作物的种植结构共十种。     

重力异常对地下异常体边界的识别算法

针对常见的地下异常体边界处理算法存在着识别出的边界发散、不准确的问题,该文通过两组理论模型进行了多种边界识别方法的试算,以比较分析其各自的效果。结果表明:常见方法中,水平梯度、HTD法和Theta图法均能一定程度上反映出边界信息,但效果一般;而与其相比,该文在此基础上构建的3种基于更高一阶导数的边界识别滤波器能更有效、准确地提取边界信息;并且在不同深度异常体的组合模型中,改进二阶倾斜角ITA_2算子依旧能在有效均衡不同强度异常的同时,非常清晰地识别出不同深度异常体的边界,有利于实际工作中重力异常的解释。