应用多传感器目标跟踪融合技术实现滑坡多点监测数据综合信息的提取

目前的滑坡预报模型只能利用一个关键监测点的监测数据，而不能同时利用多个监测点的监测信息，即只能处理一维时间序列问题.为了解决这个技术难题，本文将滑坡视为一个机动目标，将对滑坡的监测视为对机动目标的跟踪，提出了利用多传感器目标跟踪融合技术来处理滑坡多个点的监测数据的方法，分析了其可行性，并用实例说明了本文提出的方法在实际应用中是有效的.     

Visualization issues in the development of electronic atlas in China

In this paper, we discuss the development of electronic atlas in China, with focus on the issues of visualization. We particularly categorise this development into four periods, and then analyse the characters in each period and discuss the visualization issues. The four periods are highlighted: 1) Infant period (<1990) characterized as computer assisted mapping with products of screen maps; 2) Starting period (1991-1995) characterized as object-oriented mapping with products of interactive maps/atlases; 3) Advancing period (1996-2000) characterized as integrated mapping with products of multimedia cartographic maps; 4) New era (> 2001) characterized as web mapping and adaptive map design with products of Internet maps and atlas as well as adaptive maps. It is obvious that the development follows the logical way from static to dynamic, and even real time visualization, from single user to multiple users, from presentation to exploration for effective communication and knowledge construction. Current research and development projects are focused on customisation of atlas information systems for real-time tasks, Internet operability, small displays and mobile environments. The major challenges involved in each of such customisation processes are identified and commented in relation to the further development of visualization.     

基于OSTU算法利用新型水体指数进行Landsat数据自适应阈值水体自动提取研究

利用Landsat数据,结合NDWI与MNDWI提取水体信息的特点提出了一种新的水体指数NMWI,并结合OSTU算法自适应地确定最佳分割阈值,完成了对海洋、河流、湖泊和水库4种主要的典型水体信息的自动提取,均取得了较好的效果。     

高分二号卫星影像融合方法探析

图像融合技术是卫星影像处理过程中的一个重要步骤,它可以将全色影像的高空间分辨率特点与多光谱影像的高光谱分辨率特点有机地结合起来,使得融合后的影像更利于后续解译。由于融合处理方法的原理以及获取影像的传感器特性不同,不同的卫星影像适合于不同的融合方法。针对高分二号卫星影像的特点,本文采用Gram-Schmidt融合法、FIHS变换方法、HPF方法、Pansharp方法进行试验。实验结果表明:HPF方法所得融合结果在光谱特性保持方法性能最优,而Pansharp方法融合结果在信息量的保持以及清晰度方面效果更优,因此,在实际应用中,应根据具体需求进行方法选择。     

Integrating ASTER and GLASS broadband emissivity products using a multi-resolution Kalman filter

In this study, the multi-resolution Kalman filter (MKF) algorithm, which can handle multi-resolution problems with high computational efficiency, was used to blend two emissivity products: the Global LAnd Surface Satellite (GLASS) (BBE) product and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) narrowband emissivity (NBE) product. The ASTER NBE product was first converted into a BBE product. A new detrending method was used to transfer the BBEs into a process suitable for the MKF. The new detrending method was superior to the two existing methods. Finally, both the de-trended GLASS and ASTER BBE products were incorporated into the MKF framework to obtain the optimal estimation at each scale. Field measurements collected in North America were used to validate the integrated BBEs. Visually, the fusion map showed good continuity, with the exception of the border areas, and the quality of the fusion map was better than that of the original maps. The validation results indicate that the MKF improved the BBE product accuracy at the coarse scale. In addition, the MKF was capable of recovering missing pixels at a finer scale.     

TRMM星载测雨雷达和地基雷达反射率因子数据的三维融合

TRMM卫星上的测雨雷达(TRMMPR)探测资料分布均匀且具有很高的垂直分辨率,但灵敏度较低;地基雷达(GR)水平分辨率较高且具有较高的灵敏度,但其垂直分辨率低。通过将TRMM PR与GR反射率因子数据的三维数据融合,得到了更优的反射率因子图像。测雨雷达与地基雷达三维数据融合主要分为以下几步:测雨雷达与地基雷达数据预处理——如去杂波、衰减校正;测雨雷达与地基雷达时空匹配;选取和应用合适的三维图像融合算法;对融合后的图像进行效果评估。试验结果表明:融合后的图像不仅增大了信息量,更好地检测弱降水,还提高了空间三维(3D)分辨率,能更好地反映降水区域细节,且使得数据总体上具有更高的完整性和可靠性。此外,还将基于雷达资料估测的降水数据与地面雨量计数据进行对比,估计反射率因子数据融合在降水测量上的有效性。      

国家级格点化定量降水预报系统

利用主客观融合降水反演、降水统计降尺度、降水时间拆分等技术构建了国家级格点化定量降水预报系统。该系统结构合理,模块功能明确,于2014年6月在国家气象中心投入业务使用,生成0~168 h时效,10 km分辨率,逐3 h的格点化定量降水预报产品。通过对2015年第13号热带气旋苏迪罗的格点化降水预报个例检验,结果显示,相比欧洲中期数值预报中心的确定性模式预报和预报员主观预报,该产品能更好地体现台风降水的时空精细化分布特点,对福建东北部和浙江东南部的特大暴雨中心位置表现更准确细致。通过对2015年4—9月的格点化产品整体效果检验,结果显示,相比欧洲中期数值预报中心的确定性模式预报和由反距离客观分析后的预报员主观预报,该产品既能保持和预报员主观预报相同的准确率,同时也能较明显地提高降水预报的时空精细化程度。     

Heterogeneous surface displacement pattern at the Hatchobaru geothermal field inferred from SAR interferometry time-series

We estimated surface displacements using persistent scatterer SAR interferometry (PS-InSAR) around the Hatchobaru geothermal field, Japan, from 18 ALOS/PALSAR images acquired from July 2007 to December 2010. Generally, geothermal fields, covered with natural targets such as rocky terrain and vegetation, have been one of the difficult targets for PS-InSAR analysis. However, we applied space adaptive filtering to increase the number of pixels for measuring surface displacement. The results of our analysis demonstrate ground subsidence with decaying velocity over the observation period around the geothermal field. The spatial pattern of ground subsidence includes sharp boundaries of subsidence that can be interpreted as fault traces. We demonstrated the usefulness of PS-InSAR analysis with the space adaptive filtering to estimate surface displacements with high spatial resolution and high spatial density around a geothermal field.     

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.