Satellite-based estimates of groundwater storage variations in large drainage basins with extensive floodplains

This study presents monthly estimates of groundwater anomalies in a large river basin dominated by extensive floodplains, the Negro River Basin, based on the synergistic analysis using multisatellite observations and hydrological models. For the period 2003-2004, changes in water stored in the aquifer is isolated from the total water storage measured by GRACE by removing contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the root zone reservoir simulated by WGHM and LaD hydrological models. The groundwater anomalies show a realistic spatial pattern compared with the hydrogeological map of the basin, and similar temporal variations to local in situ groundwater observations and altimetry-derived level height measurements. Results highlight the potential of combining multiple satellite techniques with hydrological modeling to estimate the evolution of groundwater storage.     

电炉法CS_2废气的治理

简介了电炉法CS2 废气吸附的原理及工艺流程 ,开辟了H2 S利用的新途径 ,具有较好的环境效益和经济效益     

应用MODIS资料对城乡地表热通量的估算

为了揭示城市热岛形成机制,基于MODIS资料,结合自动气象站实测的气象资料,利用地表能量参数化方法估算了地表热通量,分析了城乡地表热通量的空间分布及变化特征.结果表明城乡地气热交换差异明显,与相关文献对比证明该方法是可行、有效的.     

Compressive sensing with sparse domain division using probability

With the development of Compressive Sensing theory in recent years, many new algorithms have been introduced to this field. But still, these algorithms tend to judge the probability of the nonzero signal in each position of the sparse domain as the same, which is in fact not true. In this topic we discuss orthogonal coefficient distribution and divide the whole sparse domain into different parts using probability. With the method called Sparse domain Division using Probability (SDP), the reconstructed speed would increase 20～60 times without producing any negative effect on image quality at the same sampling rate.     

基于Kohonen神经网络聚类方法在遥感分类中的比较

设计完成和比较了基于Kohonen自组织网络的Kohonen聚类网络（Kohonen Clustering Network, KCN）、模糊Kohonen聚类网络（Fluzzy KCN, FKCN）和基于进化规划的Kohonen聚类网络（Evalutionary Programming based KCN, EPKCN）三种聚类算法在遥感土地利用/覆盖分类中的应用。结果表明三种非监督学习方法在进行遥感土地利用/覆盖分类过程中，在分类性能上有显著差异。EPKCN分类目视效果最好，单次迭代的速度最快；FKCN总的收敛速度最快；而按遥感土地利用/覆盖分类要求而言，EPKCN方法在三种分类方法中效果最好，因此可采用该算法进行遥感土地利用/覆盖的非参数分类。     

闽江口湿地遥感影像最佳景观观察尺度的选择

应用遥感和地理信息系统技术,对景观最佳观察尺度选择的方法进行了探讨;构建了“定量化景观指数粒度敏感区间,定性化尺度大小,定性与定量相结合决定最佳观察尺度值“的最佳景观观察尺度选择模式,并以ASTER遥感影像为数据源,应用该模式对闽江口湿地景观最佳观察尺度进行了研究,指出闽江口湿地的景观观察尺度应以50m和100m为主,最大尺度不应超过200m.     

遥测遥控技术及其在钢铁工业中的应用

对我国工业遥测、遥控技术及其在钢铁工业中的应用现状进行了述评。二遥技术作为信息技术的一个分支,在信息时代必将有一个大的发展,本文针对我国的现状指出了其发展方向。     

从TM影像中提取城镇建筑覆盖区专题信息的改进方法

选用TM影像作为城镇建筑覆盖区专题信息提取的数据源，在Bayes的最大似然法半自动提取城镇专题信息和建筑指数法自动提取城镇专题信息分类方法的基础上，通过二值逻辑演算，综合两种方法的分类结果，去掉不属于城镇的信息，得到真正的城镇建筑覆盖区专题信息。实验结果表明，改进后的分类方法，兼有自动与半自动的优点，其分类精度大大提高。特别是对于短期变化(如防洪区)较大、面积较大而复杂的研究区来说，这一方法的应用弥补了单一分类方法的不足。     

Optimal classification methods for mapping agricultural tillage practices

The classification of agricultural tillage systems has proven challenging in the past using traditional classification methods due to the similarity of spectral reflectance signatures of soils and senescent crop residues. In this study, five classification methods were examined to determine the most suitable classification algorithm for the identification of no-till (NT) and traditional tillage (TT) cropping methods: minimum distance (MD), Mahalanobis distance, Maximum Likelihood (ML), spectral angle mapping (SAM), and the cosine of the angle concept (CAC). A Landsat ETM+ image acquired over southern Michigan and northern Indiana was used to test these classification methods. Each classification method was validated with 293 ground truth sampling locations collected commensurate with the satellite overpass. Classification accuracy was then assessed using error matrix analysis, Kappa statistics, and tests for statistical significance. The results indicate that of the classification routines examined, the two spectral angle methods were superior to the others. The cosine of the angle concept algorithm outperformed all the other classification routines for tillage practice identification and mapping, yielding an overall accuracy of 97.2% (Kappa=0.959).