基于Oracle数据库的专题地理数据模型

对Oracle Spatial组件中空间数据存储结构及对象关系数据模型进行了分析,然后结合专题地理数据库课题,提出了一种运用Oracle数据库的组件Oracle Spatial来管理专题地理数据的数据模型.并对这种数据模型的完整性和正确性进行了分析验证,取得了良好的效果.     

Evaluating Local Non-Stationarity when Considering the Spatial Variation of Large-scale Autocorrelation

Multi‐scale effects of spatial autocorrelation may be present in datasets. Given the importance of detecting local non‐stationarity in many theoretical as well as applied studies, it is necessary to “remove” the impact of large‐scale autocorrelation before common techniques for local pattern analysis are applied. It is proposed in this paper to employ the regionalized range to define spatially varying sub‐regions within which the impact of large‐scale autocorrelation is minimized and the local patterns can be investigated. A case study is conducted on crime data to detect crime hot spots and cold spots in San Antonio, Texas. The results confirm the necessity of treating the non‐stationarity of large‐scale spatial autocorrelation prior to any action aiming at detecting local autocorrelation.     

高分辨率遥感影像提取道路的方法综述与思考

 介绍了高分辨率遥感影像上道路的特征、模型和道路提取的基本思想，对已有的道路提取方法进行了分析，并对道路提取问题进行思考，提出下一步的研究方向。     

导航应用的按需下载区域网络模型研究

提出了一种导航应用的按需下载区域数据模型。对按需下载模式的技术要求进行了分析,提出了区域导航数据模型的定义及其算法,用UML定义了区域网络概念模型,并与ISO的网络模型进行了一致性检验。通过实际算例证明了方法的可用性。     

基于2.5维面的气象数据可视化技术——以风场数据显示为例

根据2.5维面的设计思想及曲面可视化的方法,结合部分气象数据信息具有相同的经纬度范围和海拔高度的特点,构造了基于温度和可降水量的2.5维面,并重点提出在2.5维面上进行风场流线可视化的方法。实践证明,应用2.5维面分析气象数据在一定程度上平衡了运算量和可视化结果之间的矛盾,对地理环境气象数据的分析和比较有帮助作用。     

三层BP网隐层节点数确定方法的研究

对确定三层BP网络隐层节点数的理论依据和现有做法进行了研究,提出了一种三层BP网络隐层节点数的双向确定法     

非匀一冠层热辐射方向性3维模型构建——以玉米冠层为例

建立适用于多类型植被场景的热辐射方向性模型是进行地表热辐射方向性研究的一种手段。利用真实植株几何及生理参数的统计平均值来刻画理念植株,并给定其空间分布特征,进行不同生长期植被冠层的描述。基于冠层双向孔隙率思想构建了冠层热辐射方向性3维模型,模型继承了孔隙率模型在计算冠层热辐射方向性上的简洁优势同时以几何光学的思想考虑了冠层空间异质性对冠层热辐射方向性的影响。以玉米冠层为例,进行了不同生长期玉米冠层热辐射方向亮温的模拟,通过与实地测量数据的比对表明,本文发展的模型能够较准确地模拟不同生长期玉米植被场景的方向亮温变化规律,模拟误差主要来自理念株的刻画误差、玉米叶片形状的近似以及忽略了多次散射贡献等3个方面。模型的构建方法对稀疏植被场景、浓密植被场景、多类型植被的混合场景均可适用,不同观测几何下的植被场景4组份面积比计算结果有望应用于复杂地表条件下地表返照率的研究。     

Assessing Performance of Tomo-SAR and Backscattering Coefficient for Hemi-Boreal Forest Aboveground Biomass Estimation

Tomo-SAR technique has been used for hemi-boreal forest height and further forest biomass estimation through allometric equation. Backscattering coefficient especially in longer wavelength (L- or P-band) is thought as a useful parameter for hemi-boreal forest biomass retrieval. The aim of this paper is to assess the performance of vertical backscattering power and backscattering coefficient for hemi-boreal forest aboveground biomass (AGB) estimation with airborne P-band data. The test site locates in southern Sweden called Remningstorp test site, and the in-situ forest AGB ranges from 14 t/ha to 245 t/ha at stand level. Multi-baseline P-band Pol-InSAR data in repeat-path mode collected during March and May in 2007 at Remningstorp test site was used. We found that the correlation coefficient (R) between backscattering coefficient of P-band HH polarization and the in-situ forest biomass reached 0.87. The R for P-band VV backscattering power at 5 m is 0.71 and 10 m is 0.72. Backscattering coefficient in HH polarization and vertical backscattering power at 5 m and 10 m were applied to construct a model for hemi-boreal forest AGB estimation by backward step-wise regression and cross-validation approach. The results showed that the estimated forest AGB ranges from 19 to 240 t/ha, and the constructed model obtained a higher R and smaller RMSE, the value of R is 0.91, RMSE is 30.43 t/ha at Remningstorp test site.     

The use of WorldView-2 satellite imagery to model urban drainage system with low impact development (LID) Techniques

Within a wide range of best management practices for stormwater management in urban areas, there has been an increasing interest in source control measures. Source controls such as low-impact development (LID) techniques are potentially attractive as retrofit options for older developed areas that lack available land to implement conventional measures such as stormwater management ponds. Hence, distributed urban drainage models requiring detailed representation of developed drainage areas should be developed to accurately estimate the benefits that LIDs may provide. This study (1) presents a two-stage classification process on a high-resolution WorldView-2 image, and (2) demonstrates how to use the extracted land cover information in the subsequent hydrologic modelling and assessment of different LIDs’ performance. The proposed two-stage classification method achieved an overall accuracy of 80.6%, whereas a traditional pixel-based achieved 68.4% in classifying the same urban area into six land cover classes. From the classification results, the hydrologic properties of micro-subcatchments were imported in the United States Environmental Protection Agency Storm Water Management Model to assess the performance of LIDs. A reduction of run-off volume 18.2% and 37.1% was found with the implementation of porous pavement and bioretention, respectively, in a typical low-rise residential area located in the city of San Clemente, California, US. The study demonstrates the use of high-resolution remote sensing image to aid in evaluating LID retrofit options, and thus benefits in situations where detailed drainage area information is not available.     

Classifying Human Activity Patterns from Smartphone Collected GPS data: A Fuzzy Classification and Aggregation Approach

Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone‐measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone‐collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g. up to 96% in activity identification) using of this method. Two examples are provided in the Appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.