全球历史森林数据中国区域的可靠性评估

全球历史土地利用数据集对于深入理解全球或区域环境变化具有重要意义。历史森林数据作为其重要组成部分,在区域尺度上的可靠性至今鲜有评估。以中国区域为研究对象,依据中国学者基于历史文献资料重建的中国历史森林数据(CHFD),采用趋势、数量和空间格局等对比法,对全球数据集(SAGE、PJ和KK10)中国森林数据的可靠性进行评估。结果表明:①虽然全球数据集中国森林数据与CHFD在近300年的变化趋势上均呈减少态势,但数量上差异较大。其中,SAGE数据集对中国1700年以来的森林面积估算较CHFD高出约20%～40%;KK10数据集重建的1700-1850年森林数量则高出约32%～46%;而PJ数据集由于吸纳了区域性研究成果,其总量与CHFD较为接近,多数时点的数量差异低于20%。②在省区尺度上,从总量与CHFD较为接近的PJ数据集来看,其与CHFD数据集森林变化趋势差异较大省区占到84%,而数量差异较大的省区占比高达92%。③在网格尺度上,PJ与CHFD数据集相对差异率> 70%的网格占比高达60%～80%,二者的时空动态格局差异明显。④全球数据集中国历史森林数据未能客观反映该区域森林变化的过程与格局特征,造成这一现象的原因在于全球与区域性数据集重建历史数据所依据的资料源不同,以及基于不同空间尺度构建的重建方法的差异等。     

Surface wave tomography for azimuthal anisotropy in a strongly reduced parameter space

Large scale seismic anisotropy in the Earth's mantle is likely dynamically supported by the mantle's deformation; therefore, tomographic imaging of 3-D anisotropic mantle seismic velocity structure is an important tool to understand the dynamics of the mantle. While many previous studies have focused on special cases of symmetry of the elastic properties, it would be desirable for evaluation of dynamic models to allow more general axis orientation. In this study, we derive 3-D finite-frequency surface wave sensitivity kernels based on the Born approximation using a general expression for a hexagonal medium with an arbitrarily oriented symmetry axis. This results in kernels for two isotropic elastic coefficients, three coefficients that define the strength of anisotropy, and two angles that define the symmetry axis. The particular parametrization is chosen to allow for a physically meaningful method for reducing the number of parameters considered in an inversion, while allowing for straightforward integration with existing approaches for modelling body wave splitting intensity measurements. Example kernels calculated with this method reveal physical interpretations of how surface waveforms are affected by 3-D velocity perturbations, while also demonstrating the non-linearity of the problem as a function of symmetry axis orientation. The expressions are numerically validated using the spectral element method. While challenges remain in determining the best inversion scheme to appropriately handle the non-linearity, the approach derived here has great promise in allowing large scale models with resolution of both the strength and orientation of anisotropy.     

基于卫星遥感和再分析数据的青藏高原土壤湿度数据评估

土壤水是地表与大气在水热交换方面的关键纽带,是关键水循环要素,更是地表产汇流过程的关键控制因子。青藏高原是地球第三极,也是亚洲水塔,探讨青藏高原土壤水变化对于探讨青藏高原热力学特征变化及其对东亚乃至全球气候变化的影响具有重要意义,而获取高精度长序列大尺度土壤水数据集则是其关键。本文利用青藏高原100个土壤水站点观测数据,从多空间尺度（0.25°×0.25°,0.5°×0.5°,1°×1°）、多时间段（冻结和融化期）等角度,采用多评价指标（R、RMSE、Bias）,对多套遥感反演和同化数据（ECV、ERA-Interim、MERRA、Noah）进行全面评估。结果表明：① 除ERA外,其他数据均能反映青藏高原土壤水变化,且与降水量变化一致。而在那曲地区,遥感反演和同化数据均明显低估实测土壤水含量。从空间分布来看,MERRA和Noah与植被指数最为一致,可很好地反映土壤水空间变化特征;② 青藏高原大部分地区土壤水变化主要受降水影响,其中青藏高原西部边缘与喜马拉雅地区土壤水变化则受冰雪融水和降水的共同影响;③ 除阿里地区外,大部分遥感反演和同化数据在融化期与实测土壤水相关性高于冻结期,其中在那曲地区,遥感反演和同化数据均高估冻结期土壤含水量,却低估融化期土壤含水量。另外,遥感反演和同化数据对中大空间尺度土壤水的估计要好于对小空间尺度土壤水的估计。本研究为青藏高原土壤水研究的数据集选择提供重要理论依据。     

Spatial optimization for regionalization problems with spatial interaction: a heuristic approach

Spatial optimization techniques are commonly used for regionalization problems, often represented as p-regions problems. Although various spatial optimization approaches have been proposed for finding exact solutions to p-regions problems, these approaches are not practical when applied to large-size problems. Alternatively, various heuristics provide effective ways to find near-optimal solutions for p-regions problem. However, most heuristic approaches are specifically designed for particular geographic settings. This paper proposes a new heuristic approach named Automated Zoning Procedure-Center Interchange (AZP-CI) to solve the p-functional regions problem (PFRP), which constructs regions by combining small areas that share common characteristics with predefined functional centers and have tight connections among themselves through spatial interaction. The AZP-CI consists of two subprocesses. First, the dissolving/splitting process enhances diversification and thereby produces an extensive exploration of the solution space. Second, the standard AZP locally improves the objective value. The AZP-CI was tested using randomly simulated datasets and two empirical datasets with different sizes. These evaluations indicate that AZP-CI outperforms two established heuristic algorithms: the AZP and simulated annealing, in terms of both solution quality and consistency of producing reliable solutions regardless of initial conditions. It is also noted that AZP-CI, as a general heuristic method, can be easily extended to other regionalization problems. Furthermore, the AZP-CI could be a more scalable algorithm to solve computational intensive spatial optimization problems when it is combined with cyberinfrastructure.     

Computational improvements to multi-scale geographically weighted regression

ABSTRACT

Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different bandwidths for each covariate. However, the multiscale property of MGWR brings additional computation costs. The calibration process of MGWR involves iterative back-fitting under the additive model (AM) framework. Currently, MGWR can only be applied on small datasets within a tolerable time and is prohibitively time-consuming to run with moderately large datasets (greater than 5,000 observations). In this paper, we propose a parallel implementation that has crucial computational improvements to the MGWR calibration. This improved computational method reduces both memory footprint and runtime to allow MGWR modelling to be applied to moderate-to-large datasets (up to 100,000 observations). These improvements are integrated into the mgwr python package and the MGWR 2.0 software, both of which are freely available to download.     

Methods for visualizing the explosive remnants of war

This study aimed to answer the question how cartography can help decision makers visualize the problem of contamination by explosive remnants of war (ERW). We thus explored a set of six cartographic visualization methods and systematically evaluated their usefulness with respect to four categories of stakeholders in the humanitarian demining process (i.e., database administrators, operations officers, directors of national mine action authorities, and donors) at four geographical scales, ranging from municipal to global. The main application of our work is for stakeholders involved in humanitarian demining. We provide them with a comprehensive framework for visualizing ERW hazards at the geographical scale at which they have to make decisions, as well as customized cartographic visualization tools and recommendations to help them make informed decisions. For example, we provide potential donors with a method for obtaining a global overview of ERW contamination while remaining aware of regional variation and hot spots. We also enhance cartographic visualization capabilities using traditional kernel density estimation by customizing key parameters. Specifically, we propose a method for adjusting kernel bandwidth for datasets with highly heterogeneous spatial distributions and a method for generating kernel surfaces from polygon data that consists of infilling the polygons with points before using them as inputs in the kernel density estimation.     

Modelling spatial patterns of urban growth in Africa

The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.     

基于多期数据集的中亚五国土地利用/覆盖变化分析

针对目前中亚地区土地利用变化和分布格局方面的信息相对匮乏,现有资料较为陈旧且零散,无法满足中亚生态与环境变化研究需求的现状,利用全球的UMD, DISCover,GLC2000,GlobCover2005和GlobCover2009的5期土地覆被遥感数据集,提取中亚地区长时间序列土地覆被信息。并针对上述4个土地覆被分类系统无法进行直接对比和变化分析的问题,分别将其综合为4类土地覆被类型：耕地、自然植被、水体和其他,以分析近30 a中亚土地利用/土地覆被变化趋势。中亚土地利用类型多样,草地、裸地、农田、灌丛占绝对优势。自前苏联解体以后,20世纪90年代初至2000年期间,耕地面积大幅度减少,至2010年尽管有所恢复,但仍无法达到20世纪90年代初水平。而自然植被表现出了相反的趋势,这说明在此时间段内,由于社会政治制度的变化和市场经济的建立,耕地发生了较大规模的弃耕,弃耕地通常转换为草地、灌丛等自然植被。近10 a由于社会经济条件的变化,前苏联解体后所弃耕的土地又被收复和重新开发为耕地。90年代初至2000年期间,水体呈现先减少后增加的趋势。利用全球基于多期不同信息源获得的中亚土地覆被数据,尽管分类体系不统一,但均可较好地表征当时地表覆被状况。这在一定程度上弥补了中亚地区土地覆被数据不足的现状。通过对耕地、自然植被、水体及其他土地覆被类型进行大类合并,可基本体现中亚土地覆被的宏观特征和变化趋势。     

A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution.     

Measuring inter-city connectivity in an urban agglomeration based on multi-source data

A comprehensive understanding of inter-city connectivity is important for regional planning. However, most studies adopted only one single data source for measurements, which is incomplete since each source has its own limitations. There are biases and uncertainties in the connectivity results when using different data sources. To address this problem, our study proposed a novel method that could combine the advantages of multi-source data. First, we measured inter-city connectivities using several datasets individually, and then analyzed each city’s node strength based on the connectivities. Next, the performance of each dataset was validated according to several correlation analyses between the node strength and various socio-economic metrics. Based on these validations, we used the genetic algorithm to search for the optimal weights for combination. Only those datasets with higher weights were retained for further calculation. The final connectivity result is more reasonable than any single result according to the validation. For the first time, this study compares different data sources related to inter-city connectivity, and combines their advantages based on selective weighted combination. The results are expected to provide strong support for large-scale regional planning. In addition, the proposed method could be further applied to other large areas for analyzing inter-city connectivities.     

西北地区土地资源类型TM影像解译标志的建立

通过对西北地区典型1:10万TM影像和地理情况的对比研究,采用两级分类系统建立了土地资源6类29个类型的解译标志,为在微机平台上全数字化解译全区土地资源类型提供了基础。     

在全数字方式下对宁夏草地覆盖的遥感宏观研究

以TM影象为信息源,在全数字方式下运用遥感技术与地理信息系统技术结合建立了宁夏草地覆盖的TM影象数据库、图形数据库和属性数据库。通过分析得出全区现有草地面积2 481 345 hm²,占全区面积的47.92%,其中高覆盖度草地占3.93%,中覆盖度草地占43.45%,低覆盖度草地占52.62%。     

Stable Iterative Methods for the Inversion of Geophysical Data

Summary. Interpretation of earth electrical measurements can often be assisted by inversion, which is a non-linear model-fitting problem in these cases. Iterative methods are normally used, and the solution is defined by' best fit'in the sense of generalized least-squares.
The inverse problems we describe are ill-posed. That is, small changes in the data can lead to large changes in both the solution and in the iterative process that finds the solution. Through an analysis of the problem, based on local linearization, we define a class of methods that stabilize the iteration, and provide a robust solution. These methods are seen as generalizations of the well-known Singular Value Truncation and Marquardt Methods of iterative inversion.
Here, and in a companion paper, we give examples illustrating the successful application of the method to ill-posed problems relating to the resistivity of the Earth.     

Geospatial data conflation: a formal approach based on optimization and relational databases

ABSTRACT

Geospatial data conflation is aimed at matching counterpart features from two or more data sources in order to combine and better utilize information in the data. Due to the importance of conflation in spatial analysis, different approaches to the conflation problem have been proposed ranging from simple buffer-based methods to probability and optimization based models. In this paper, I propose a formal framework for conflation that integrates two powerful tools of geospatial computation: optimization and relational databases. I discuss the connection between the relational database theory and conflation, and demonstrate how the conflation process can be formulated and carried out in standard relational databases. I also propose a set of new optimization models that can be used inside relational databases to solve the conflation problem. The optimization models are based on the minimum cost circulation problem in operations research (also known as the network flow problem), which generalizes existing optimal conflation models that are primarily based on the assignment problem. Using comparable datasets, computational experiments show that the proposed conflation method is effective and outperforms existing optimal conflation models by a large margin. Given its generality, the new method may be applicable to other data types and conflation problems.     

On the construction of geomagnetic timescales from non-prejudicial treatment of magnetic anomaly data from multiple ridges

When marine magnetic-anomaly data are used to construct geomagnetic polarity timescales, the usual assumption of a smooth spreading-rate function at one seafloor spreading ridge forces much more erratic rate functions at other ridges. To eliminate this problem, we propose a formalism for the timescale problem that penalizes non-smooth spreading behaviour equally for all ridges. Specifically, we establish a non-linear Lagrange multiplier optimization problem for finding the timescale that (1) agrees with known chron ages and with anomaly-interval distance data from multiple ridges and (2) allows the rate functions for each ridge to be as nearly constant as possible, according to a cumulative penalty function. The method is applied to a synthetic data set reconstructed from the timescale and rate functions for seven ridges, derived by Cande & Kent (1992) under the assumption of smooth spreading in the South Atlantic. We find that only modest changes in the timescale (less than 5 per cent for each reversal) are needed if no one ridge is singled out for the preferential assumption of smoothness. Future implementation of this non-prejudicial treatment of spreading-rate data from multiple ridges to large anomaly-distance data sets should lead to the next incremental improvement to the pre-Quaternary geomagnetic polarity timescale, as well as allow a more accurate assessment of global and local changes in seafloor spreading rates over time.     

Leveraging parallel spatio-temporal computing for crime analysis in large datasets: analyzing trends in near-repeat phenomenon of crime in cities

ABSTRACT

Crime often clusters in space and time. Near-repeat patterns improve understanding of crime communicability and their space–time interactions. Near-repeat analysis requires extensive computing resources for the assessment of statistical significance of space–time interactions. A computationally intensive Monte Carlo simulation-based approach is used to evaluate the statistical significance of the space-time patterns underlying near-repeat events. Currently available software for identifying near-repeat patterns is not scalable for large crime datasets. In this paper, we show how parallel spatial programming can help to leverage spatio-temporal simulation-based analysis in large datasets. A parallel near-repeat calculator was developed and a set of experiments were conducted to compare the newly developed software with an existing implementation, assess the performance gain due to parallel computation, test the scalability of the software to handle large crime datasets and assess the utility of the new software for real-world crime data analysis. Our experimental results suggest that, efficiently designed parallel algorithms that leverage high-performance computing along with performance optimization techniques could be used to develop software that are scalable with large datasets and could provide solutions for computationally intensive statistical simulation-based approaches in crime analysis.     

改进的案例推理CA模型及土地覆盖变化模拟

对基于案例推理的元胞自动机模型(CBR-CA)进行改进,将各类别的宏观转移概率添加到目标函数中,体现各类别的转变特征,并增加时间权重来确定转移概率,实现时间尺度上的模拟;由于土地覆盖变化的多样性和空间结构的复杂性,利用Monte Carlo(M-C)法确定土地覆盖的最终转换类别。选择黄河源区为试验区,利用1977年、1985年土地覆盖数据建立原始案例库,模拟了该区域1995年、2000年和2006年的土地覆盖变化,模拟的各类别转换的数量精度与实际相吻合,各年份的总体误差分别为0.002%、0.012%和0.005%,空间位置精度总体在70%以上,并进行未来土地覆盖情景预测。该模型可用于多类别、长时间序列区域土地覆盖变化的模拟与预测。     

基于分层分区法的中国历史耕地数据的网格化重建

网格化历史耕地数据集能为历史时期耕地变化研究提供更精确的支持,并且为全球环境气候变化研究模型模拟提供驱动数据。本文综合考虑了中国历代土地利用开发的特点及自然人文因子对耕地的影响,设计了一套对中国耕地先分区再分层分配的网格化方法。基于国内3个主流区域耕地数据研究成果,采用上述方法建立了1820年（清仁宗嘉庆二十五年）和1936年（民国二十五年）中国10 km×10 km分辨率的耕地数据集,并绘制了分布图。本文还利用国内具有代表性的区域数据集对重建结果进行对比验证。结果表明,该方法可以保证耕地数量的权威性,并且建立具有区域性的高精度历史耕地数据集。     

Assessment of land-use and land-cover changes from 1965 to 2014 in Tam Giang-Cau Hai Lagoon,central Vietnam

This study integrates the use of multi-source and multi-resolution remote sensing, topographic and field-based datasets to quantify land-use and land-cover (LULC) changes along a coastal stretch of Thua Thien Hue Province (central Vietnam). The LULC change analysis involves the Tam Giang-Cau Hai lagoon, the largest lagoon system in Southeast Asia, which is running nearly 70 km along the coast and having about 22,000 ha of water surface.The LULC change analysis was performed by computer-aided visual interpretation for 5 years (1965, 1989, 2000, 2006 and 2014) using satellite imagery from LANDSAT MSS, TM, ETM+ and 8, ASTER and SPOT5. National topographic maps were also used for the 1965 and 2000 years.To adequately represent the LULC features and peculiarities of central Vietnam coastal areas, an adapted CORINE Land Cover nomenclature was used where new 3rd and 4th level classes were adopted. Due to their intrinsic relative high spatial and radiometric resolution, SPOT5 images from 2006 were assumed as a reference for interpretation keys and first delineation. Changes were mapped by editing those vectors representing features which underwent LULC change prior or after 2006. Spatial and temporal changes were analyzed by post-classification approach and validated by ground truth information. High detail object-based classification was finally performed to infer the capability of medium spatial resolution imagery for extracting cadastral scale pond maps. The accuracy of classification was checked by a polygon by polygon comparison with an existing aquaculture facility inventory.Five LULC maps were obtained by applying a legend of 21 classes including two newly defined: “Aquaculture ponds” and “Mangrove forest”. The overall classification accuracy of the LULC map is 85% while the KHAT statistics 0.81 for the year 2006. Accuracy of the object-based aquaculture facilities classification is 84% or better for the SPOT5 imagery and 47.9% for the ASTER imagery. The study provides a synoptic LULC representation for the largest lagoon system of Southeast Asia and delivers quantitative estimates of main changes occurred during the last 50 years. Moreover, it reveals the adaptability of the CORINE Land Cover method outside European environment. Finally, SPOT5 provides good results to map aquaculture features at cadastral scale, even if in some circumstances (e.g. tidal areas), the integration with higher spatial resolution multispectral sensors should be envisaged.     

黄河源区土地利用/覆被变化及其生态环境效应

应用1989年TM影像和2005年ETM+影像提取黄河源区两期土地利用/覆被数据,并利用GIS的空间叠加分析对两期土地利用图进行土地利用动态信息的提取。在建立黄河源区土地利用数据库的基础上,采用生态环境质量指数模型建立各土地利用类型与生态环境质量之间的数量关系,定量的分析土地利用变化对生态环境质量的影响。结果表明:①黄河源区1989-2005年间,土地利用变化缓慢而匀速,存在着生态环境的恶化和改善两种相反的趋势。在大尺度范围内,生态环境质量相对稳定,有轻微的恶化,但是在研究区局部范围内,生态环境质量变化显著;②植被是影响黄河源区生态环境质量的主要因素,其中草地对环境质量的影响处于主导地位;③利用生态环境质量指数模型不仅可以从整体上了解研究区生态环境质量,也可以发现影响区域环境质量的主要驱动力。