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1.
地理空间数据的尺度转换 总被引:3,自引:0,他引:3
尺度一般是指空间范围的大小,地理空间数据的尺度转换是尺度研究的重要问题之一。针对地理信息系统(GIS)技术支持下的地理空间数据尺度转换问题,首先回顾了尺度转换的理论基础,即等级理论、分形理论、区域化随机变量理论、地理学第一定律等理论的基本内涵;然后总结了地理学不同研究领域内主要的尺度转换方法,重点分析了重采样法、变异函数法、分形分维法及小波分析法的基本原理、模型方法与典型应用案例;最后介绍了地理空间数据尺度转换效应研究的进展。基于上述总结和分析认为:构建一套无级变换的、系统的尺度转换方法,整合不同学科领域的数据与过程模型、形成数据模型同化的技术体系,这是地理空间数据尺度转换研究的重要课题。 相似文献
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3.
水系在不同尺度的遥感影像上表现出不同的光谱特征和形状、纹理特征,为了得到提取水系的适宜尺度,本文采用三种经典的尺度转换方法,将信息容量作为尺度转换的评价指标;最终选择三次卷积法为最优的尺度转换方法,在得到的一系列尺度图像上提取水系,分析提取水系的适宜尺度为10 m到45 m之间。 相似文献
4.
定量遥感升尺度转换研究综述 总被引:4,自引:0,他引:4
尺度转换问题是定量遥感领域基础而重要的科学问题之一.重点针对升尺度转换研究现状,从现象描述、尺度效应产生原因分析、尺度转换方法归纳及尺度转换效果评价4个方面进行细致论述.认为目前的研究主要存在3个问题:①基于离散的多传感器影像进行的反演量尺度转换研究,受到不同传感器间成像参数归一化精度的影响;②反演量物理模型发展有限,基于这些模型的反演量连续空间尺度转换研究仍不成熟;③基于分形理论等数学方法的反演量连续空间尺度转换研究取得了一定的进展,但仍受限于尺度上推理论与技术的发展水平.对于定量遥感升尺度转换研究的发展趋势,做出如下展望:①随着成像参数归一化技术的进步,问题①将得到更有效的处理,这将有助于实际应用问题的解决;②连续空间尺度转换模型构建是升尺度转换研究发展的重要趋势.随着多学科知识的融入,定量遥感反演理论的发展及尺度上推理论与技术的进步,问题②与③亦将得到较好的解决,这将有益于揭示遥感反演量真正意义上的尺度转换规律. 相似文献
5.
地学信息图谱所研究的地理空间规律和地理过程均具有一定的区域相似性, 故其在空间和时间上具有一定的区域特征,因此对地学信息图谱的研究均有空间和时间尺度方面的范畴。尺度是研究客体或过程的空间维和时间维。根据研究性质、研究对象的空间规模等方面的不同,地学信息图谱的空间尺度可分为大尺度、中尺度和小尺度;按研究的时间尺度不同,地学信息图谱的时间尺度可分为长时间尺度、中等尺度和短时间尺度。地学信息图谱的时空维是指地学信息图谱数据源的时空维或地学信息图谱可以表达的时空维,不同的时空维表达不同的特征和内容。 相似文献
6.
全球尺度水文模型: 机遇、 挑战与展望 总被引:2,自引:2,他引:2
水循环发生在全球尺度, 局地和流域水问题的解决也往往需要全球视角, 因此全球尺度水文模型应运而生。从科学意义和国家需求等多个视角, 论述了全球水文学研究的意义和巨大的发展潜力, 系统总结了现有全球尺度水文模型发展现状, 剖析了十余个主流全球尺度水文模型的结构和功能, 以及全球水文-气象-地理信息等全球模型所需数据集。进而分析了现有全球尺度水文模型存在的主要问题和挑战, 为进一步完善模型提供参考。最终分别从模型机理、 大数据、 新技术、 多部门模型耦合等几个方面, 探讨了全球尺度水文模型未来发展的重要方向。 相似文献
7.
基于块克里金的土壤水分点观测向像元尺度的尺度上推研究 总被引:3,自引:1,他引:2
土壤水分是陆面水文过程的一个重要分量,土壤水分的地面点观测与卫星观测的尺度是不匹配的,用点观测数据进行遥感反演结果的验证或者融合这两种观测都需要开展点观测数据向卫星像元尺度的尺度上推研究.土壤水分时空异质性的分析和描述是进行尺度上推的基础,地统计方法是描述连续随机变量空间结构的经典方法.应用块克里金法分别将黑河寒区遥感试验阿柔试验区2008年4月1日与L波段微波辐射计同步的地面液态含水量和含冰量点观测数据经过尺度转换,得到与遥感像元相匹配的像元平均估计值和标准差,可用于该天L波段微波辐射计土壤液态含水量和含冰量反演结果的真实性检验,估计结果充分利用了像元临近位置的观测,得到了比直接的采样平均更合理的块估计结果,对块克里金估计值与采样平均值进行比较,发现两种结果趋势是一致的,块克里金法提供了更为合理的块估计结果,土壤水分空间结构的时间变异、小尺度的土壤特性的变化和测量误差都会对估计结果带来一定的不确定性. 相似文献
8.
WEI Wen-xin PAN Jun XING Li-xin College of GeoExploration Science Technology Jilin University Changchun China 《吉林大学学报(地球科学版)》2006,(Z2)
各种尺度的地学空间数据已成为地质找矿和环境研究中的重要信息源.在遥感数据与地球化学数据的融合处理中存在着空间尺度问题.基于两种数据的性质和特点,利用立方卷积的方法进行两种数据处理中的尺度转换研究,并以内蒙古额仁陶勒盖地区为例,实现了尺度转换后的化探数据和遥感数据融合处理应用. 相似文献
9.
遥感信息与化探信息为地表地质体或地质现象物理特性和化学特性的表现,矿物特征光谱是对矿物化学组分的响应,二者存在密切的关联。2种信息融合既要达到空间位置的精确对应,又要根据物质成因的相关性,实现2种数据间数据层、决策(符号)层等不同层次的数据耦合,提高异常信息提取的准确度。通过对遥感与化探信息形成机理及特点的分析可知,二者在空间和成因上具有一定的相关性。采用反距离加权插值法,将化探散点数据转化为栅格数据结构并进行空间尺度的转换和配准,选择和研究区异常相关的4种元素(Au、As、Sb、Bi)与原始遥感数据进行融合,对融合后数据采用主成分分析的方法对内蒙古苏尼特左旗地区进行蚀变信息提取,较单独利用遥感数据提取结果剔除了无意义的异常信息点,异常区域更为具体。经野外检查,验证多处地表岩石蚀变的存在,证实了该方法的有效性。 相似文献
10.
降雨时空尺度的变化对水文模型影响较大。本文从降水时间和空间两个尺度,结合水文模型方法,从静态和动态两个参数角度出发,以辽宁西部为研究实例,定量分析降雨时空尺度变化对辽宁西部地区水文模型的影响。结果表明:降雨时间尺度的静态变化直接影响洪水模拟精度,降雨空间尺度动态变化对洪水模拟影响程度较低。 相似文献
11.
统计降尺度法对未来区域气候变化情景预估的研究进展 总被引:65,自引:5,他引:65
由于迄今为止大部分的海气耦合气候模式(AOGCM)的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测,降尺度法已广泛用于弥补AOGCM在这方面的不足。简要介绍了3种常用的降尺度法:动力降尺度法、统计降尺度法和统计与动力相结合的降尺度法;系统论述了统计降尺度方法的理论和应用的研究进展,其中包括:统计降尺度法的基本假设,统计降尺度法的优缺点,以及常用的3种统计降尺度法;还论述了用统计降尺度法预估未来气候情景的一般步骤,以及方差放大技术在统计降尺度中的应用;同时还强调了统计降尺度方法和动力降尺度方法比较研究在统计降尺度研究中的重要性;最后指出统计与动力相结合的降尺度方法将成为降尺度技术的重要发展方向。 相似文献
12.
Application of Bias Correction and Spatial Disaggregation in Removing Model Biases and Downscaling over China 总被引:1,自引:0,他引:1
Global Climate Models (GCM) are the primary tools for studying past climate change and evaluating the projected future response of climate system to changing atmospheric composition. However, the state of art GCMs contain large biases in regional or local scales and are often characterized by low resolution which is too coarse to provide the regional scale information required for regional climate change impact assessment. A popular technique, Bias Correction and Spatial Disaggregation (BCSD), are widespreadly employed to improve the quality of the raw model output and downscaling throughout the world. Unfortunately, this method has not been applied in China. Consequently, the detailed principle and procedure of BCSD are introduced systematically in this study. Furthermore, the applicability of BCSD over China is also examined based on an ensemble of climate models from phase five of the Coupled Model Intercomparison Project (CMIP5), though the excellent performance of it has been validated for other parts of the world in many works. The result shows that BCSD is an effective, model independent approach to removing biases of model and downscaling. Finally, application scope of BCSD is discussed, and a suite of fine resolution multimodel climate projections over China is developed based on 34 climate models and two emissions scenarios (RCP4.5 and RCP8.5) from CMIP5. 相似文献
13.
在生态水文学的发展进程中,流域生态水文模型是研究的重要分支和热点问题。它可以通过不同尺度的生态过程和水文过程的耦合描述,揭示其相互作用机制与关系。发展至今,对流域生态水文模型的研究已取得一些阶段成果,从最初描述单过程的经验模型到清晰刻画物理过程的分布式模型,主要经历了单向耦合模型、概念模型、半物理过程模型和物理过程模型。目前,流域生态水文模型在森林流域和干旱半干旱区流域生态水文过程对气候变化的响应及土地利用对作物产量影响等方面的研究中得到广泛应用。现有模型在对植被-水文相互作用机制的刻画、流域尺度转换、模型参数估计、输入数据、模拟精度与不确定性等方面存在一些问题,这些问题也是未来生态水文模型研究的重点。生态水文模型的研究依赖于生态水文学的学科发展和理论研究,要紧密结合生态水文学的最新研究进展,才能够实现模型在理论上的突破。 相似文献
14.
A method for multiscale parameter estimation with application to reservoir history matching is presented. Starting from a
given fine-scale model, coarser models are generated using a global upscaling technique where the coarse models are tuned
to match the solution of the fine model. Conditioning to dynamic data is done by history-matching the coarse model. Using
consistently the same resolution both for the forward and inverse problems, this model is successively refined using a combination
of downscaling and history matching until model-matching dynamic data are obtained at the finest scale. Large-scale corrections
are obtained using fast models, which, combined with a downscaling procedure, provide a better initial model for the final
adjustment on the fine scale. The result is thus a series of models with different resolution, all matching history as good
as possible with this grid. Numerical examples show that this method may significantly reduce the computational effort and/or
improve the quality of the solution when achieving a fine-scale match as compared to history-matching directly on the fine
scale. 相似文献
15.
统计降尺度方法广泛应用于弥补大气环流模式(GCM)模拟区域气候变化能力较弱的不足。利用1960~2009年的NCEP/NCAR再分析资料和江淮流域52个站点降水观测资料,通过敏感性分析,针对4个季节分别选择10个大尺度预测因子,采用主成分分析(PCA)和支持向量机(SVM)相结合的方法,建立了江淮流域降水统计降尺度模型。检验结果表明,该模型获取的江淮流域降水的偏差显著减小,能够描述降水在月、年尺度的变化,适用于HadCM3输出的大尺度气候场,具有预测未来降水变化的能力。将统计降尺度模型应用于HadCM3在A2情景下输出的2020~2099年大尺度预测因子,分3个时段:2020~2039年,2050~2069年和2080~2099年,从年和季节两个时间尺度分析江淮流域未来降水变化。结果表明,相对1960~1999年,未来3个时段的降水有小幅增加,其中2080~2099年增幅最大,为3.6 mm;在未来3个时段的不同季节,降水变化呈现出不同特征。 相似文献
16.
Martins Ana Rafael Sandra Monteiro Alexandra Scotto Manuel Gouveia Sónia 《Mathematical Geosciences》2019,51(8):1021-1035
Mathematical Geosciences - EURO-CORDEX is an international initiative which provides regional climate projections based on multiple dynamical and empirical–statistical downscaling models. The... 相似文献
17.
Geothermal modeling is an important part of large-scale basin studies. Based on a new 3D structural model of the Northeast German basin, the present day regional geothermal field is modeled. Range and regional trend of the modeled temperature values are in agreement with the published data. Due to the high spatial resolution, the calculated temperature distribution provides additional information with respect to areas where no measured data is available. The results are used as input and boundary parameters for small-scale models of geothermal energy production. In general, in many regions not enough data is available to define all necessary physical or chemical parameters for modeling. In this context, data obtained from the large-scale model help to constrain unknown parameters. Subsequently, the small-scale model is used to simulate various production schemes focusing on enhanced predictions with respect to the possible lifetime of such installations. The simulation results also show the need for elaborated models if reliable predictions of the temperature evolution are required. 相似文献
18.
Barbara A. Muhling Carlos F. Gaitán Charles A. Stock Vincent S. Saba Desiree Tommasi Keith W. Dixon 《Estuaries and Coasts》2018,41(2):349-372
Estuaries are productive and ecologically important ecosystems, incorporating environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has potential to modify the physical properties of estuaries, with impacts on resident organisms. However, projections from general circulation models (GCMs) are generally too coarse to resolve important estuarine processes. Here, we statistically downscaled near-surface air temperature and precipitation projections to the scale of the Chesapeake Bay watershed and estuary. These variables were linked to Susquehanna River streamflow using a water balance model and finally to spatially resolved Chesapeake Bay surface temperature and salinity using statistical model trees. The low computational cost of this approach allowed rapid assessment of projected changes from four GCMs spanning a range of potential futures under a high CO2 emission scenario, for four different downscaling methods. Choice of GCM contributed strongly to the spread in projections, but choice of downscaling method was also influential in the warmest models. Models projected a ~2–5.5 °C increase in surface water temperatures in the Chesapeake Bay by the end of the century. Projections of salinity were more uncertain and spatially complex. Models showing increases in winter-spring streamflow generated freshening in the Upper Bay and tributaries, while models with decreased streamflow produced salinity increases. Changes to the Chesapeake Bay environment have implications for fish and invertebrate habitats, as well as migration, spawning phenology, recruitment, and occurrence of pathogens. Our results underline a potentially expanded role of statistical downscaling to complement dynamical approaches in assessing climate change impacts in dynamically challenging estuaries. 相似文献
19.
A multi-resolution workflow to generate high-resolution models constrained to dynamic data 总被引:2,自引:0,他引:2
Céline Scheidt Jef Caers Yuguang Chen Louis J. Durlofsky 《Computational Geosciences》2011,15(3):545-563
Distance-based stochastic techniques have recently emerged in the context of ensemble modeling, in particular for history
matching, model selection and uncertainty quantification. Starting with an initial ensemble of realizations, a distance between
any two models is defined. This distance is defined such that the objective of the study is incorporated into the geological
modeling process, thereby potentially enhancing the efficacy of the overall workflow. If the intent is to create new models
that are constrained to dynamic data (history matching), the calculation of the distance requires flow simulation for each
model in the initial ensemble. This can be very time consuming, especially for high-resolution models. In this paper, we present
a multi-resolution framework for ensemble modeling. A distance-based procedure is employed, with emphasis on the rapid construction
of multiple models that have improved dynamic data conditioning. Our intent is to construct new high-resolution models constrained
to dynamic data, while performing most of the flow simulations only on upscaled models. An error modeling procedure is introduced
into the distance calculations to account for potential errors in the upscaling. Based on a few fine-scale flow simulations,
the upscaling error is estimated for each model using a clustering technique. We demonstrate the efficiency of the method
on two examples, one where the upscaling error is small, and another where the upscaling error is significant. Results show
that the error modeling procedure can accurately capture the error in upscaling, and can thus reproduce the fine-scale flow
behavior from coarse-scale simulations with sufficient accuracy (in terms of uncertainty predictions). As a consequence, an
ensemble of high-resolution models, which are constrained to dynamic data, can be obtained, but with a minimum of flow simulations
at the fine scale. 相似文献
20.
The Multi-scale Soil Information System (MEUSIS) can be a suitable framework for building a nested system of soil data that could facilitate interoperability through a common coordinate reference system, a unique grid coding database, a set of detailed and standardized metadata, and an open exchangeable format. In the context of INSPIRE Directive, MEUSIS may be implemented as a system facilitating the update of existing soil information and accelerating the harmonization of various soil information systems. In environmental data like the soil one, it is common to generalize accurate data obtained at the field to coarser scales using either the pedotransfer rules or knowledge of experts or even some statistical solutions which combine single values of spatially distributed data. The most common statistical process for generalization is averaging the values within the study area. In this paper, we do not present a simple averaging of numerical values without any further processed information. The upscaling process is accompanied with significant statistical analysis in order to demonstrate the method suitability. The coarser resolution nested grids cells (10??? 10?km) represent broad regions where the calculated soil property (e.g., organic carbon) can be accurately upscaled. Multi-scaled approaches are urgently required to integrate different disciplines (such as Statistics) and provide a meta-model platform to improve current mechanistic modeling frameworks, request new collected data, and identify critical research questions. Past papers have described in detail the upscaling methodology while our present approach is to demonstrate an important application of this methodology accompanied with statistical evidence. 相似文献