基于BJ -1小卫星遥感数据的矿区土地覆盖变化检测

为了评价利用北京一号小卫星(BJ-I)遥感数据监测煤矿区土地利用/地表覆盖变化的效果,针对其数据特点,选择基于图像信息运算和图像信息变换的直接变化检洲法以及分类后比较法,对徐州东矿区2007～2008年土地利用/地表覆盖变化情况进行检测,以对比、评价各种方法在土地利用/地表覆盖变化检测中的应用效果和BJ -1数据的适用...     

海量矿业权核查成果数据存储与访问的优化技术研究

针对矿业权核查单矿权成果数据具有文件小、数量多、异构、多源等特点,为了有效地存储这些数据到Oracle数据库中,实现快速提取及显示,并能够将一个矿业权的提取时长控制在1秒以内,本文给出了从建立数据库实体到查询、显示过程所涉及诸多问题的一个完整的优化方案。首先建立了一个完整的、科学的数据存储模型,并采用范围-列表分区模式对大型表进行分区,同时采用通过调整Oracle系统高速缓冲区、减少表连接、优化SQL查询语句、在客户端分页显示查询结果等优化方案,有效提升表中数据的查询速度,以达到优化目的。     

Surface Ice Flow Velocity and Tide Retrieval of the Amery Ice Shelf using Precise Point Positioning

Five days of continuous GPS observation data were collected in the frontal zone of the Amery ice shelf and subsequently post-processed using precise point position (PPP) technology based on precise orbit and clock products from the International GNSS service. The surface ice flow velocity of the observed point was derived from PPP to be 2.25 m/day toward the northeast with an azimuth of 41°. Major semi-diurnal and diurnal oceanic tide constituents could be recovered from the 5 days of PPP-derived height variations and compared well with a hydrodynamic ocean tide model. The PPP technique can replace double-difference GPS positioning in remote or hostile environments, and be used to retrieve the surface ice flow velocity without any reference station. Furthermore, the solution can be derived epoch-by-epoch with accuracy in the centimeters to decimeter range.     

AIEM模型在积雪散射模拟中的应用

 介绍一种基于一阶辐射传输的积雪散射模型。该模型考虑了积雪覆盖地表微波散射的3种回波分量： 雪层表面散射、下垫面散射以及雪层体散射。对于其中2个面散射分量，文章中应用一种新的面散射模型——AIEM取代原有的IEM模型进行处理。最后，使用Michigan大学的实测数据对改进后模型的模拟结果进行验证，并与改进前的模拟结果进行了对比。     

Evaluation of directional normalization methods for Landsat TM/ETM+ over primary Amazonian lowland forests

Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross–Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration.     

Classification of forest land attributes using multi-source remotely sensed data

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.     

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.     

Crustal structure of Iraq from receiver functions and surface wave dispersion: implications for understanding the deformation history of the Arabian–Eurasian collision

We report the crustal structure for two locations in Iraq estimated by joint inversion of P -wave receiver functions (RFs) and surface (Rayleigh) wave group velocity dispersion. RFs were computed from teleseismic recordings at two temporary broad-band seismic stations located in Mosul (MSL) in the Zagros Fold Belt and Baghdad (BHD) in the Mesopotamian Foredeep. Group velocity dispersion curves at the sites were derived from continental-scale tomography. The inversion results show that the crustal thicknesses are 39 km at MSL and 43 km at BHD. We observe a strong Ps _Moho at BHD consistent with a sharp Moho discontinuity. However, at MSL we observe a weak Ps _Moho suggesting a transitional Moho where crustal thickening is likely to be occurring in the deep crust. Both sites reveal low velocity surface layers consistent with sedimentary thickness of about 3 km at station MSL and 7 km at BHD and agreeing well with the previous reports. Ignoring the sediments, the crystalline crustal velocities and thicknesses are remarkably similar at both stations. The similarity of crustal structure suggests that the crust of the northeastern proto-Arabian Platform was uniform before subsidence and deposition of the sediments in the Cenozoic. If crystalline crustal structure is uniform across the northern Arabian Platform then crustal thickness variations in the Zagros Fold Belt and Thrust Zone should reveal the history of deformation and crustal shortening in the Arabian–Eurasian collision zone and not reflect pre-existing crustal thickness variations in the Arabian Plate.     

向斜构造煤层开采地表移动规律研究

现行的地表移动预计方法，都是建立在开采工作面主断面为线性分布的基础上，因此，对于非线性分布煤层的预计将会产生较大的误差。本文以随机介质理论为基础，利用曲线积分的方法建立了地表移动与变形预计公式，并且利用数值积分法和叠加原理解算地表移动与变形值，扩大了地表移动预计理论的应用范围，同时也大大提高了预计值的精度     

Integration of hyperspectral and LiDAR data for mapping small water bodies

Inland water bodies are globally threatened by environmental degradation and climate change. On the other hand, new water bodies can be designed during landscape restoration (e.g. after coal mining). Effective management of new water resources requires continuous monitoring; in situ surveys are, however, extremely time-demanding. Remote sensing has been widely used for identifying water bodies. However, the use of optical imagery is constrained by accuracy problems related to the difficulty in distinguishing water features from other surfaces with low albedo, such as tree shadows. This is especially true when mapping water bodies of different sizes. To address these problems, we evaluated the potential of integrating hyperspectral data with LiDAR (hereinafter “integrative approach”). The study area consisted of several spoil heaps containing heterogeneous water bodies with a high variability of shape and size. We utilized object-based classification (Support Vector Machine) based on: (i) hyperspectral data; (ii) LiDAR variables; (iii) integration of both datasets. Besides, we classified hyperspectral data using pixel-based approaches (K-mean, spectral angle mapper). Individual approaches (hyperspectral data, LiDAR data and integrative approach) resulted in 2–22.4 % underestimation of the water surface area (i.e, omission error) and 0.4–1.5 % overestimation (i.e., commission error).The integrative approach yielded an improved discrimination of open water surface compared to other approaches (omission error of 2 % and commission error of 0.4 %). We also evaluated the success of detecting individual ponds; the integrative approach was the only one capable of detecting the water bodies with both omission and commission errors below 10 %. Finally, the assessment of misclassification reasons showed a successful elimination of shadows in the integrative approach. Our findings demonstrate that the integration of hyperspectral and LiDAR data can greatly improve the identification of small water bodies and can be applied in practice to support mapping of restoration process.