临汾地震台摆式倾斜仪的监测能力

我国境内几次中强震及山西地区6次有感地震前,临汾地震台两种摆式倾斜仪存在有不同反映,探讨不同摆式倾斜仪对同一地震监测能力的差异性和同一仪器对不同地震的异常反映能力,结果表明：①摆式倾斜仪监测震兆信息的距离范围由仪器自身的监测能力（综合噪声水平及观测精度）和未来震级的大小所决定;②对于不同地震,两摆式倾斜仪所表现出的异常形态不同,即便在同一观测仪器上表现出来的异常形态也存在差异;同一地震两摆式倾斜仪所反映的异常并非呈相关一致性,而且同一仪器的不同方向对异常信息的捕捉能力也存在明显差异。     

基于匹配追踪算法的叠后烃检技术在渤海QHD33/34构造的应用

渤海中北部海域的QHD33/34构造位于石臼坨凸起中段及中段东南部斜坡带上。受新构造运动影响,石臼坨凸起浅层发育一系列NE向、近EW向晚期活动断层,在斜坡带浅层明化镇组形成了多个断鼻、断块型圈闭,但是构造圈闭面积总体较小、幅度低,并且受断层对油气疏导作用影响,油气成藏有一定的风险。为揭示QHD33/34构造浅层储层的含油气性,应用基于匹配追踪算法的低频增加梯度异常对该区进行烃类检测,取得了一定效果。最终烃检结果与已钻井揭示的构造浅层储层含油气性较匹配,吻合率达到79%,应用烃检结果可进一步指导该构造井位部署,尽最大可能规避风险。通过研究表明,在研究区浅层,基于匹配追踪算法的低频增加梯度异常能够最大程度上识别油层与水层。该方法能够识别大于3 m的油层,水层则没有明显响应特征,应用该方法进行烃类检测,效果明显。     

Community forest management in Indonesia: Avoided deforestation in the context of anthropogenic and climate complexities

Community forest management has been identified as a win-win option for reducing deforestation while improving the welfare of rural communities in developing countries. Despite considerable investment in community forestry globally, systematic evaluations of the impact of these policies at appropriate scales are lacking. We assessed the extent to which deforestation has been avoided as a result of the Indonesian government’s community forestry scheme, Hutan Desa (Village Forest). We used annual data on deforestation rates between 2012 and 2016 from two rapidly developing islands: Sumatra and Kalimantan. The total area of Hutan Desa increased from 750 km² in 2012 to 2500 km² in 2016. We applied a spatial matching approach to account for biophysical variables affecting deforestation and Hutan Desa selection criteria. Performance was assessed relative to a counterfactual likelihood of deforestation in the absence of Hutan Desa tenure. We found that Hutan Desa management has successfully achieved avoided deforestation overall, but performance has been increasingly variable through time. Hutan Desa performance was influenced by anthropogenic and climatic factors, as well as land use history. Hutan Desa allocated on watershed protection forest or limited production forest typically led to a less avoided deforestation regardless of location. Conversely, Hutan Desa granted on permanent or convertible production forest had variable performance across different years and locations. The amount of rainfall during the dry season in any given year was an important climatic factor influencing performance. Extremely dry conditions during drought years pose additional challenges to Hutan Desa management, particularly on peatland, due to increased vulnerability to fire outbreaks. This study demonstrates how the performance of Hutan Desa in avoiding deforestation is fundamentally affected by biophysical and anthropogenic circumstances over time and space. Our study improves understanding on where and when the policy is most effective with respect to deforestation, and helps identify opportunities to improve policy implementation. This provides an important first step towards evaluating the overall effectiveness of this policy in achieving both social and environmental goals.     

多尺度地图面/面目标匹配模式及变化探测方法研究

多尺度地图数据更新的一个重要途径就是通过目标匹配技术获得已更新与待更新的不同比例尺地图之间表达同一地物的候选空间目标,并对候选匹配目标对进行比较,以便获得匹配目标间的差异以及发现未匹配的目标;进而,分析判断差异是否由该目标所表达的地理要素的实际变化所导致。该文以面目标匹配为例,对多尺度地图面目标匹配对的差异驱动因素和变化类型进行分析,针对不同模式的目标匹配对提出了利用长度差异、面积差异和重叠度指标进行差异驱动因素判别、变化探测和变化类型判定的方法,并验证了该方法的有效性和实用性。     

基于资源环境承载力的合肥市增长边界划定

在快速城市化和主体功能区战略背景下,滨湖城市作为城湖共生体,具有湖泊生态系统和城市社会经济系统双重属性,既是中国新型城市化格局中最具活力和潜力的核心单元之一,又是湖泊生态环境问题集中激化的高度敏感区,保护与开发并重,发展与风险共存。以行政区划调整后将巢湖纳入内湖的新合肥市为例,借助GIS平台,运用单因子评价法和综合敏感性评价法构成城市生态适宜性评价的方法体系,以水域因子、坡度因子、湿地因子、覆被因子、灾害风险因子组建资源环境约束指标体系,将合肥市域划分为极高敏感区、高敏感区、中敏感区、低敏感区、非敏感区5 种资源环境承载类型区;基于反规划理念,构建滨湖城市空间增长模型,并划定作为城市扩展生态底线的刚性和弹性空间增长边界。在此基础上,统筹考虑城市空间资源环境保障能力、人口与用地规模,结合城市总体规划方案,明确城市空间扩展方向,科学划定城市近期、中期和远期的增长边界。该方法在中国滨湖城市的空间增长规模与方向、湖泊生态系统安保与开发、城市用地调控与优化组合以及城市规划方案的合理性评价方面可提供科学决策依据。     

A data‐mining approach for assessing consistency between multiple representations in spatial databases

When different spatial databases are combined, an important issue is the identification of inconsistencies between data. Quite often, representations of the same geographical entities in databases are different and reflect different points of view. In order to fully take advantage of these differences when object instances are associated, a key issue is to determine whether the differences are normal, i.e. explained by the database specifications, or if they are due to erroneous or outdated data in one database. In this paper, we propose a knowledge‐based approach to partially automate the consistency assessment between multiple representations of data. The inconsistency detection is viewed as a knowledge‐acquisition problem, the source of knowledge being the data. The consistency assessment is carried out by applying a proposed method called MECO. This method is itself parameterized by some domain knowledge obtained from a second method called MACO. MACO supports two approaches (direct or indirect) to perform the knowledge acquisition using data‐mining techniques. In particular, a supervised learning approach is defined to automate the knowledge acquisition so as to drastically reduce the human‐domain expert's work. Thanks to this approach, the knowledge‐acquisition process is sped up and less expert‐dependent. Training examples are obtained automatically upon completion of the spatial data matching. Knowledge extraction from data following this bottom‐up approach is particularly useful, since the database specifications are generally complex, difficult to analyse, and manually encoded. Such a data‐driven process also sheds some light on the gap between textual specifications and those actually used to produce the data. The methodology is illustrated and experimentally validated by comparing geometrical representations and attribute values of different vector spatial databases. The advantages and limits of such partially automatic approaches are discussed, and some future works are suggested.     

海岛(礁)、滩涂和水下地形矢量数据融合方法研究与实现

海岛(礁)、滩涂和水下地形DLG数据库是重要的测绘成果,为开展海洋综合调查工作提供了数据基础。然而,现有的三库测绘成果是分阶段采集的,数据精度和数据模型不一致,且存在大量重复采集的情况,最终成果也是分库存储的,这使其无法进行统一管理和更新。为此,本文对多源海洋基础空间要素数据的融合方法进行了研究,首先采用矢量数据匹配技术获取对应实体,然后通过本文提出的属性融合技术和基于中误差与对应点的几何信息融合技术对三库DLG数据进行融合,以实现三库数据的属性共享、几何精度的改进和数据冗余的减少。最后,我们还基于.NET和Arc GIS Engine组件开发了海洋基础空间要素数据融合系统,用于浙江省海岛(礁)、滩涂和水下地形基础空间要素数据的融合。     

大重叠度面阵航空数码影像的自动转点

2009-03-01。     

AUV地形匹配导航快速收敛滤波

地形匹配定位（terrain aided position,TAP）的似然函数反映了AUV（autonomous underwater vehicle）的位置在空间中的分布概率,由于地形的强非线性、随机性以及测量误差的非高斯分布使得似然函数也表现出非高斯分布的特点。TAP的误差与局部地形特征和地形测量误差密切相关,由于现有的方法未考虑局部地形特征,仅考虑了测量误差的统计置信区间,使得TAP置信区间的估计结果明显偏小。为解决TAP置信区间的估计问题,建立了TAP定位点的跳变模型。设TAP定位点X^p可以向搜索区间内任一点跳变,且向某一点的跳变概率与该点的似然函数值正相关,X^p向某一点跳变的置信度小于α时,认为x^α不会向该点跳变,该点设为置信区间的边界点。另外,设地形匹配定位点的置信区间内匹配残差平方和函数为二次曲面,而X^p视为该曲面的待估计参数,则可以通过曲面参数的置信区间估计方法获得1-α置信度下的置信区间。新方法得到的置信区间范围大于现有的估计方法,试验结果表明,测量波束较少时,置信区间估计会出现异常,增加测量波束可以提高潮差和测量误差的估计精度,从而提高置信区间的估计精度,但测量误差非高斯分布条件下的补偿方法仍然需要进一步研究。     

Towards 3D map generation from digital aerial images

This paper describes the fusion of information extracted from multispectral digital aerial images for highly automatic 3D map generation. The proposed approach integrates spectral classification and 3D reconstruction techniques. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and near infrared (NIR) channels and form the basis for information extraction.Our land use classification is a 2-step approach that uses RGB and NIR images for an initial classification and the panchromatic images as well as a digital surface model (DSM) for a refined classification. The DSM is generated from the high resolution panchromatic images of a specific photo mission. Based on the aerial triangulation using area and feature-based points of interest the algorithms are able to generate a dense DSM by a dense image matching procedure. Afterwards a true ortho image for classification, panchromatic or color input images can be computed.In a last step specific layers for buildings and vegetation are generated and the classification is updated.