GPS滑坡监测专用的Gquicks软件

介绍了Guicks软件的数学模型,Gquicks软件的结构和功能.经实测检测检验,当基准点与监测点之间距离在3km内,用20分钟GPS观测值,Gquicks软件解算的水平精度可达±2-3mm,垂直精度可达±4-6mm.Gquicks软件不仅适用于GPS滑坡变形监测,也可应用于其他变形监测领域.     

高精度推求正高的两种方法

论述了高精度推求正高的两种方法,并对正高精度的推估及其在模型上的试算也作了讨论,对于海拔为5000m的高山,正高的误差一般不超过±10cm,这与距青岛水准原点达数千公里的西部高山(原)处正常高的精度也比较接近。     

地形RSG模型的动态构网算法

论述了一种基于规则格网地形模型的动态构网算法。通过定义模型中顶点间的约束关系以及顶点误差的继承方法，利用四叉树构建出顶点的层次结构，有效解决了不同分辨率网络间接边的裂缝问题，实现在模型误差控制下的多分辨网络的实时正确构网。实验证明，该方法对于实时控制地形模型的细节层次，增强地形模型的绘制效率是非常有效的。     

正交实验法在测绘中的应用

以检测数字水准仪及标尺综合精度为例，介绍了正交实验法在测绘领域中的应用。     

采用Creator生成三维地形

介绍了Creator地形转换的四种算法，讨论了在Creator中生成地形的过程，并以福建省闽清地区水口店为研究区域，建立直观性的、真实性的三维地形。     

NAL200系列水准仪补偿器的调校方法

介绍了NAL200系列水准仪的特点、水准仪工作原理以及补偿器检验和校正的方法,并详细给出了水准仪基本指标的校正方法,如圆水泡的调整、i角误差的校正、补偿精度的校正、补偿器交叉误差的校正等,为用户在进行测量过程中保证仪器的精度提供参考。     

Near real-time high-resolution airborne camera,AEROCam, for precision agriculture

Precision agriculture often relies on high-resolution imagery to delineate the variability within a field. Airborne Environmental Research Observational Camera (AEROCam) was designed to meet the needs of agriculture producers, ranchers, and researchers, who require high-resolution imagery in a near real-time environment for rapid decision support. AEROCam was developed and operated through a unique collaboration between several departments at the University of North Dakota, including the Upper Midwest Aerospace Consortium (UMAC), the School of Engineering and Mines, and flight operations at the John D. Odegard School of Aerospace Sciences. AEROCam consists of a Redlake MS4100 area-scan multi-spectral digital camera that features a 1920 × 1080 CCD array (7.4-μm detector) with 8-bit quantization. When operated at ～2 km above ground level, multispectral images with four bands in the visible and near infrared have a ground sample distance of 1 m with a horizontal extent of just over 1.6 km. Depending on the applications, flying at different altitudes can adjust the spatial resolution from 0.25 to 2 m. Rigorous spectral and radiometric calibrations allow AEROCam to be used in a variety of applications, qualitative and quantitative. Equipped with an inertial measurement unit (IMU) system, the images acquired can be geo-referenced automatically and delivered to end users near real time through our Digital Northern Great Plains system (DNGP). The images are also available to zone mapping application for precision farming (ZoneMAP), an online decision support tool for creating management zones from remote sensing imagery and data from other sources. Operational since 2004, AEROCam has flown over 250 sorties and delivered over 150,000 images to the users in the Northern Great Plains region, resulting in numerous applications in precision agriculture and resource management.     

精密水准测量中重力改正的归算

传统水准测量是高精度水准测量的重要手段之一。文章基于DiNi电子水准仪测量数据,针对长距离、高精度水准测量高程的多值性,对水准面不平行改正和重力异常改正的原理进行详述。将其应用于某城市二等水准网平差计算中,证明了精密水准改正的有效性和必要性。     

一等水准点之记标准化与规范化研究

为保证一等水准点之记精度与质量,提高测绘人员野外工作效率,对目前的一等水准点之记进行了探讨与分析,指出了其详细位置图与点位,详细说明表达、标石断面图绘制和经纬度书写及交通路线描述中存在的缺点,并基于此提出了若干改进意见,这在一定程度上促进了一等水准点之记信息的标准化与规范化。     

Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation

Abstract

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R ² of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.