DEM空间插值方法对土壤侵蚀模拟的影响研究——以USPED分析干热河谷典型冲沟为例

为探索不同空间插值方法得到的DEM如何影响土壤侵蚀模拟效果,本文选择金沙江干热河谷区典型冲沟为研究对象,利用野外测量高程数据,采用反距离加权(IDW)、析取克里格(DK)、局部多项式(LPI)和张力样条函数(ST)4种方法构建高精度DEM。基于USPED模型模拟冲沟的土壤侵蚀,对比不同空间插值方法的精度、土壤侵蚀的空间分布,采用相对差系数对比不同插值在土壤侵蚀研究中的相似性。结果表明：DEM空间插值的精度排序为ST相似文献   

The simulation of surface flow dynamics using a flow-path network model

This paper proposes a flow-path network (FPN) model to simulate complex surface flow based on a drainage-constrained triangulated irregular network (TIN). The TIN was constructed using critical points and drainage lines extracted from a digital terrain surface. Runoff generated on the surface was simplified as ‘water volumes’ at constrained random points that were then used as the starting points of flow paths (i.e. flow source points). The flow-path for each ‘water volume’ was constructed by tracing the direction of flow from the flow source point over the TIN surface to the stream system and then to the outlet of the watershed. The FPN was represented by a set of topologically defined one-dimensional line segments and nodes. Hydrologic variables, such as flow velocity and volume, were computed and integrated into the FPN to support dynamic surface flow simulation. A hypothetical rainfall event simulation on a hilly landscape showed that the FPN model was able to simulate the dynamics of surface flow over time. A real-world catchment test demonstrated that flow rates predicted by the FPN model agreed well with field observations. Overall, the FPN model proposed in this study provides a vector-based modeling framework for simulating surface flow dynamics. Further studies are required to enhance the simulations of individual hydrologic processes such as flow generation and overland and channel flows, which were much simplified in this study.     

Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation

Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.     

土壤厚度的空间插值方法比较——以青海三江源地区为例

利用青海三江源地区533个土壤剖面中的厚度数据, 在GIS技术的支持下, 采用确定性内插(反距离加权、全局多项式、局部多项式和径向基函数)和地统计内插(普通克里格、简单克里格、泛克里格和协同克里格)两类共八种插值方法对研究区土壤厚度的空间分布进行了预测, 并综合比较了各种插值方法的预测误差、统计特征值和插值结果分布图。结果表明:(1)在地统计内插方法中, 普通克里格方法(一阶)插值效果比普通克里格方法(二阶)要好;在普通克里格方法(一阶)的半方差函数模型中, 球状模型的插值效果优于指数模型和高斯模型;普通克里格方法在四种地统计内插方法中预测误差最小、预测结果准确性最好。(2)确定性内插方法中, 反距离加权(指数为1)法的误差较小, 并且对区域与局部趋势的反映效果最好。(3)从预测误差大小和对区域总体及局部趋势的综合反映效果来看, 有异向性的球状模型普通克里格(一阶)插值方法预测结果最能准确反映青海三江源地区土壤厚度的空间分布。     

The efficacy of Kriging spatial interpolation for filling temporal gaps in daily air temperature data: A case study in northeast China

Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps (i.e., missing data) are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging (UK) and Kriging with an external drift (KED), as appropriate, as if all the observations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients (R²) between the observed and estimated daily series were >0.98, and root mean square errors (RMSEs) of the estimated daily mean (T_mean), maximum (T_max), and minimum (T_min) of air temperature were <3℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.     

Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods: A case of northwestern China

The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual precipitation interpolation based on spatial sandwich sampling, including ME(0.1513), RMSE(95.91), ASE(101.84), MSE(-0.0036), and RMSSE(1.0397), were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application.     

A unified approach for location-allocation analysis: integrating GIS,distributed computing and spatial optimization

Location-allocation modeling is an important area of research in spatial optimization and GIScience. A large number of analytical models for location-allocation analysis have been developed in the past 50 years to meet the requirements of different planning and spatial-analytic applications, ranging from the location of emergency response units (EMS) to warehouses and transportation hubs. Despite their great number, many location-allocation models are intrinsically linked to one another. A well-known example is the theoretical link between the classic p-median problem and coverage location problems. Recently, Lei and Church showed that a large number of classic and new location models can be posed as special case problems of a new modeling construct called the vector assignment ordered median problem (VAOMP). Lei and Church also reported extremely high computational complexity in optimally solving the best integer linear programming (ILP) formulation developed for the VAOMP even for medium-sized problems in certain cases.

In this article, we develop an efficient unified solver for location-allocation analysis based on the VAOMP model without using ILP solvers. Our aim is to develop a fast heuristic algorithm based on the Tabu Search (TS) meta-heuristic, and message passing interface (MPI) suitable for obtaining optimal or near-optimal solutions for the VAOMP in a real-time environment. The unified approach is particularly interesting from the perspective of GIScience and spatial decision support systems (DSS) as it makes it possible to solve a wide variety of location models in a unified manner in a GIS environment. Computational results show that the TS method can often obtain in seconds, solutions that are better than those obtained using the ILP-based approach in hours or a day.     

基于地理探测器下的川西高原地表温度空间格局及影响因子分析——以西昌市为例

以西昌市为例,选取2010年和2015年 Landsat系列遥感影像,在“3S”技术的支持下,采用监督分类中的最大似然法,并结合Google Earth高分辨率影像和GPS野外实地验证数据获取西昌市2010年和2015年土地利用信息;再利用大气校正法反演获得西昌市2010年和2015年地表温度,并对反演结果中的高温异常区进行实地调研验证;最后,采用地理探测器定量分析了坡度、总辐射、坡向、海拔、年均降水量、年均气温、植被类型、土壤类型以及土地利用类型9个影响因子对地表温度影响的解释力。结果表明：（1）地表温度空间分布存在显著差异。（2）影响因子对地表温度的影响存在差异,其中海拔和年均气温对地表温度的影响较大,而总辐射影响最小。（3）不存在单一因素或是单一性质的因素影响地表温度。不同影响因子之间存在交互作用,对地表温度的影响是相互增强或非线性增强的。（4）部分影响因子对地表温度具有显著性差异影响,并且地表温度均值最大时,对应着影响因子不同的范围或类型。     

景观空间异质性对地表水质服务的影响研究——以河南省伊河流域为例

人类干扰使景观空间异质性发生了剧烈变化,进而对生态系统服务产生了严重影响。目前,针对景观空间异质性与生态系统服务的关联关系研究还较为薄弱。以河南省伊河流域为研究区域,以地表水质服务为研究对象,分析了地表水质服务的空间变化;通过以水质采样点为中心建立缓冲区,分析了不同尺度下的景观空间异质性特征,并与地表水质指标进行相关性研究,以探讨景观空间异质性与地表水质服务间的影响规律及响应机制。结果表明：① 研究区地表水质状况整体上为西南、东北部较差,中部较好;② 从景观组成来看,林地面积占比或斑块个数的增加有利于地表水质的提高;③ 类型水平上,林地的景观构型与化学需氧量的质量浓度在200 m半径缓冲区内显著相关（P<0.05）,城镇用地的景观构型对地表水质的影响则在2000 m半径缓冲区内更为明显;④ 景观水平上,总磷的质量浓度受到景观构型变化的强烈影响（P<0.05）,景观构型异质性越低,总磷的质量浓度就越高;⑤ 适当增加景观空间异质性可有效降低地表水质污染物的质量浓度,提高地表水质服务。研究结论可为伊河流域的空间开发及水环境保护政策的制定提供科学依据。     

Visualization and quantification of significant anthropogenic drivers influencing rangeland degradation trends using Landsat imagery and GIS spatial dependence models: A case study in Northeast Iran

Developing countries must consider the influence of anthropogenic dynamics on changes in rangeland habitats. This study explores happened degradation in 178 rangeland management plans for Northeast Iran in three main steps: (1) conducting a trend analysis of rangeland degradation and anthropogenic dynamics in 1986-2000 and 2000-2015, (2) visualizing the effects of anthropogenic drivers on rangeland degradation using bivariate local spatial autocorrelation (BiLISA), and (3) quantifying spatial dependence between anthropogenic driving forces and rangeland degradation using spatial regression approaches. The results show that 0.77% and 0.56% of rangelands are degraded annually during the first and second periods. The BiLISA results indicate that dry-farming, irrigated farming and construction areas were significant drivers in both periods and grazing intensity was a significant driver in the second period. The spatial lag (SL) model (wi=0.3943, Ei=1.4139) with two drivers of dry-farming and irrigated farming in the first period and the spatial error (SE) model (wi=0.4853, Ei=1.515) with livestock density, dry-farming and irrigated farming in the second period showed robust performance in quantifying the driving forces of rangeland degradation. To conclude, the BiLISA maps and spatial models indicate a serious intensification of the anthropogenic impacts of ongoing conditions on the rangelands of northeast Iran in the future.     

Time series forecasting of river flow using an integrated approach of wavelet multi-resolution analysis and evolutionary data-driven models. A case study: Sebaou River (Algeria)

The complexity of hydrological processes and lack of data for modeling require the use of specific tools for non-linear natural phenomenon. In this paper, an effort has been made to develop a conjunction model – wavelet transformation, data-driven models, and genetic algorithm (GA) – for forecasting the daily flow of a river in northern Algeria using the time series of runoff. This catchment has a semi-arid climate and strong variability in runoff. The original time series was decomposed into multi-frequency time series by wavelet transform algorithm and used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Several factors must be optimized to determine the best model structures. Wavelet-based data-driven models using a GA are designed to optimize model structure. The performances of wavelet-based data-driven models (i.e. WANFIS and WANN) were superior to those of conventional models. WANFIS (RMSE = 12.15 m³/s, EC = 87.32%, R = .934) and WANN (RMSE = 15.73 m³/s, EC = 78.83%, R = .888) models improved the performances of ANFIS (RMSE = 23.13 m³/s, EC = 54.11%, R = .748) and ANN (RMSE = 22.43 m³/s, EC = 56.90%, R = .755) during the test period.