Approximation of Gaussian spatial autoregressive models for massive regular square tessellation data

Most of the literature to date proposes approximations to the determinant of a positive definite n × n spatial covariance matrix (the Jacobian term) for Gaussian spatial autoregressive models that fail to support the analysis of massive georeferenced data sets. This paper briefly surveys this literature, recalls and refines much simpler Jacobian approximations, presents selected eigenvalue estimation techniques, summarizes validation results (for estimated eigenvalues, Jacobian approximations, and estimation of a spatial autocorrelation parameter), and illustrates the estimation of the spatial autocorrelation parameter in a spatial autoregressive model specification for cases as large as n = 37,214,101. The principal contribution of this paper is to the implementation of spatial autoregressive model specifications for any size of georeferenced data set. Its specific additions to the literature include (1) new, more efficient estimation algorithms; (2) an approximation of the Jacobian term for remotely sensed data forming incomplete rectangular regions; (3) issues of inference; and (4) timing results.     

应用“北京一号”遥感数据计算官厅水库库滨带植被覆盖度

“北京一号”卫星是拥有多方面技术优势的一颗对地观测小卫星。本文在运用“北京一号”、SPOT5、QuickBird遥感图像对官厅水库库滨带植被覆盖度进行综合监测的基础上,对三种不同分辨率的遥感图像进行基于统计的尺度转换,并应用尺度转换的结果修正了“北京一号”图像提取的植被覆盖度。经检验,运用SPOT5和QuickBird图像对“北京一号”图像进行像元分解,将统计结果与“北京一号”图像的提取信息建立统计模型,应用该统计模型可以有效地提高“北京一号”图像提取植被覆盖度的精度。对湿生植被进行样方调查,结果证明运用像元分解和统计模型的方法使“北京一号”提取植被覆盖度的精度较运用植被指数转换模型的计算精度提高了22.7%。应用该方法可以更有效地运用“北京一号”遥感数据进行连续、大面积的植被监测。     

塔里木河干流植被遥感监测时空多尺度协同分析方法

利用遥感植被指数、典型植被样方和地面观测信息进行塔里木河干流植被监测是目前的主要方法。由于塔里木河干流具有流域下垫面均匀性差,自然植被随机分布的特点,使得现有研究方法局限在特定的时间和空间尺度,很难使用地面的观测数据和不同尺度的遥感数据进行植被生长状态的协同分析。针对这些问题,本文提出了利用不同分辨率遥感数据和地面观测数据进行多尺度协同分析的方法MSSA(Multiple Scale Synergy Analysis)。该方法包括以下几个步骤:①通过低空间分辨率的遥感数据构建时间序列的塔里木河干流植被指数分布图像,在分析图像特征的基础上划分塔里木河遥感监测单元;②对监测单元内部不同组分的时间和空间状态参数进行量化与率定;③根据几何光学模型原理和植被随机分布特性,采用线性混合模型模拟单元植被指数;④根据模拟结果和遥感数据的对比分析,获得地面植被参量的可靠估计。该方法将地面组分的状态参量和遥感数据通过模拟模型相关联,实现了不同时空尺度遥感数据以及地面样方或者点观测数据的协同分析,为塔里木河干流植被监测进行长期、细致的研究建立了海量数据综合分析的方法体系。     

Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.     

Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.     

区域尺度蒸散发遥感估算——反演与数据同化研究进展

遥感技术近年来在估算区域尺度蒸散发中应用广泛。不同方法在驱动数据、模型机理和适用范围往往存在很大差别。鉴于此,阐述了基于传统方法空间尺度扩展的遥感模型,经验统计公式,特征空间法,单源、双源垂向能量平衡余项法等几类的遥感蒸散发反演方法,简要介绍了三温模型、非参数化模型、半经验模型、集成模型等常用模型。同时,分析了遥感数据同化实现连续估算区域蒸散发的主要思路,综述了基于能量平衡和基于复杂过程模型的数据同化的原理、方法演进及常用同化算法等。最后,探讨了各类区域蒸散发遥感方法的优劣、展望了模型机理完善、不确定性研究、结果验证等与蒸散发直接反演和数据同化相关的研究方向。     

A rough set approach to the discovery of classification rules in spatial data

This paper proposes a novel rough set approach to discover classification rules in real‐valued spatial data in general and remotely sensed data in particular. A knowledge induction process is formulated to select optimal decision rules with a minimal set of features necessary and sufficient for a remote sensing classification task. The approach first converts a real‐valued or integer‐valued decision system into an interval‐valued information system. A knowledge induction procedure is then formulated to discover all classification rules hidden in the information system. Two real‐life applications are made to verify and substantiate the conceptual arguments. It demonstrates that the proposed approach can effectively discover in remotely sensed data the optimal spectral bands and optimal rule set for a classification task. It is also capable of unraveling critical spectral band(s) discerning certain classes. The framework paves the road for data mining in mixed spatial databases consisting of qualitative and quantitative data.     

城市植被尺度鉴别与分类研究

城市地物具有多尺度分布特点,尺度鉴别与确定是分类的前提。提出改进的面积相对差指标,根据城市植被的分布状态确定最优分割尺度。采用面向对象方法,利用对象的光谱和空间信息对高空间分辨率影像进行植被分类。与基于像元的传统光谱分类方法和单尺度分类结果比较,最优分割尺度的鉴别和面向对象的分类方法分类精度较高,6种城市植被的分类总精度达85.5%,Kappa系数为0.83;同时有效抑制了光谱数据分类中存在的地物破碎问题。     

Indicators of vegetation productivity under a changing climate in British Columbia,Canada

Understanding the relationship between vegetation and climate is essential for predicting the impact of climate change on broad-scale landscape processes. Utilizing vegetation indicators derived from remotely sensed imagery, we present an approach to forecast shifts in the future distribution of vegetation. Remotely sensed metrics representing cumulative greenness, seasonality, and minimum cover have successfully been linked to species distributions over broad spatial scales. In this paper we developed models between a historical time series of Advanced Very High Resolution Radiometer (AVHRR) satellite imagery from 1987 to 2007 at 1 km spatial resolution with corresponding climate data using regression tree modeling approaches. We then applied these models to three climate change scenarios produced by the Canadian Centre for Climate Modeling and Analysis (CCCma) to predict and map productivity indices in 2065. Our results indicated that warming may lead to increased cumulative greenness in northern British Columbia and seasonality in vegetation is expected to decrease for higher elevations, while levels of minimum cover increase. The Coast Mountains of the Pacific Maritime region and high elevation edge habitats across British Columbia were forecasted to experience the greatest amount of change. Our approach provides resource managers with information to mitigate and adapt to future habitat dynamics. Forecasting vegetation productivity levels presents a novel approach for understanding the future implications of climate change on broad scale spatial patterns of vegetation.     

Automated terrain feature identification from remote sensing imagery: a deep learning approach

ABSTRACT

Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science.     

土地利用变化的多尺度空间自相关分析——以内蒙古翁牛特旗为例

用传统统计学方法模拟和解释土地利用变化的前提条件是研究分析的数据在统计上必须独立且均匀分布。但是空间数据相互之间通常具有依赖性 (即空间自相关),某一变量的值随着测定距离的缩小而变得更相似或更为不同。由于经典线性回归方法未能抓住数据的空间自相关特征,而空间自相关包含一些有用的信息,为了克服这一缺点,利用Moran的I系数自相关图来描述研究区土地利用变化的空间自相关,并且建立了不仅考虑回归而且又考虑空间自相关的混合回归-空间自相关回归模型 (即空间滞后模型)。研究得到：① 研究区土地利用变化模型中不但自变量之间而且因变量之间存在空间正自相关,这表明土地利用变化数据的空间自相关很强;② Moran的I系数随着尺度的变粗而减小,这是由于数据平均时的滤波特性和Moran的I系数对距离的非线性特征造成的;③ 经典线性回归模型的残差也表现出正相关,这表明标准的多元线性回归模型未能考虑土地利用数据所存在的空间依赖性;④ 混合回归-空间自相关回归模型 (即空间滞后模型) 的残差未存在空间自相关,并且有更好的拟合度;⑤ 相对于经典线性回归模型,混合回归-空间自相关回归模型 (即空间滞后模型) 对于存在空间自相关性的数据来说有着统计上的合理性,而经典线性回归模型未能考虑这些因素。     

Predicting socioeconomic conditions from satellite sensor data in rural developing countries: A case study using female literacy in Assam,India

Social data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GAR_err) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GAR_err model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities.     

道路网络中心性对餐饮业空间布局的影响——以青岛市主城区为例

基于道路网络数据和兴趣点（POI）数据,以青岛市主城区为研究区域,分析道路网络中心性及餐饮业空间分布特征,采用全局和局部回归模型探究道路网络中心性对餐饮业分布的影响机制。结果表明：① 中介中心性高值区集中于城市主干道,邻近中心性呈现由中心向四周递减的环状分布特征,特征向量中心性的空间分布表现为多核心结构;② 青岛市餐饮业呈现出块状聚集、区域差异明显的空间分布格局,中餐厅、西餐厅和休闲餐饮具备多中心结构特征;③ 道路网络中心性指标与餐饮业的核密度值具有较高的空间相似性,高-高与低-低聚集区构成了主要的空间关联模式;④ 青岛市餐饮业分布受到道路网络中心性的显著影响,其中,中介中心性对餐饮业分布的影响程度较低,邻近中心性和特征向量中心性的影响程度较高。从局部来看,道路网络中心性对餐饮业分布的影响具有空间异质性,对不同类型餐饮业的影响具有明显的差异。     

Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing

The transformation of land cover, in particular coniferous forest, constitutes one of the most notable agents of regional-to-global-scale environmental change. Remote sensing provides an excellent opportunity for providing forest cover information at appropriate spatial and temporal scales. The optimal exploitation of remote sensing relies on the link between known forest cover and the remotely sensed dataset. This paper explores the accuracy of three methods – vegetation indices, regression analysis and neural networks – for estimating coniferous forest cover across the United States Pacific Northwest. All methods achieved a similar accuracy of forest cover estimation. However, in view of the benefits and limitations of each, the neural network approach is recommended for future consideration.     

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale.     

Additive models in mining and exploration

In this paper we present the use of additive models (AMs) for geostatistical applications. AMs are generalizations of linear regression models which hold the central place in the toolbox of applied statisticians. Generally speaking, the linear relationship between response and predictors is replaced with a general functional form. Recently such models were introduced in geostatistics. Especially, we give an approach for binary data. In this case we get generalized additive models (GAMs). Logistic regression is quite popular in medical and biological research. Using logit links also in GAMs we get so called additive logistic models. An application for geostatistical data is introduced. In a second approach we use AMs for spatial prediction and surface modelling. In both cases an advantage of multivariate data can be taken. The proposed applications can be used in the development of exploration strategies, especially in the early stage of exploration     

基于遥感方法的长白山地区植被物候期变化趋势研究

目前,越来越多的遥感数据被用来监测大面积植物物候的动态变化。利用长时间序列的SPOT/NDVI旬合成数据,通过double logistic模型获取了1999~2008年长白山地区植被的3个关键物候参数:生长季始期、生长季末期和生长季长度的多年平均值,并绘制了它们的变化趋势空间格局图。结果表明,林地的生长季开始日期为第100~120天,草地和耕地相对较晚,分别为第130~140天和第140~150天;林地和草地生长季的结束日期为第275~285天,耕地的相对较早,为第265~275天;林地、草地和耕地的生长季长度范围分别为160~180 d、140~160 d和110~130 d。植被物候期的变化趋势表现为一定的空间差异性,生长季长度延长区域主要分布在长白山地区的中东部,平均每年延长约0.7 d;缩短的区域在西北地区,平均每年缩短1.1 d。最后通过部分物候观测数据及前人在相同研究区的结果验证了利用double logistic模型提取预测长白山植被物候期的可行性。     

冰川冰储量计算方法及发展趋势

冰川冰储量不仅是冰川的重要属性,而且是核算冰川水资源及预测冰川变化的基础数据,因此准确计算冰川冰储量及其变化具有重要的理论与现实意义。目前冰川储量估算的主要方法有经验公式法、冰厚模型估算法、探地雷达法;冰川储量相对变化计算方法有实地测量法和遥感监测法。通过系统分析和讨论各计算方法的原理、现状及存在的问题,以期为冰川储量估算提供方法参考。研究表明：对于冰川冰储量计算而言,经验公式法适用于区域性或全球性的冰川储量估算;模型估算法适用于个体或小范围冰川储量估算;探地雷达法适用于人类易到达区域冰川储量的估算。对于冰川冰储量相对变化计算,实地测量法适用于对精度要求高且满足实地测量条件的单条或中小型冰川,遥感监测法适用于全球性冰储量变化估算,但需改进算法和提高数据空间分辨率。目前,随着无人机技术的逐步应用,以及冰川流速等理论模型的提出,为冰川冰储量估算方法的发展提供了新契机。     

基于动态视角的生态空间识别及其生态系统服务功能时空变化模拟：以四川省邛崃市为例（英文）

科学识别生态空间、合理预测主导生态系统服务功能时空变化趋势,是构建国土空间生态保护格局的基础,具有重要的理论意义和应用价值。目前,大多数生态空间识别、功能分区和格局重构以当前生态系统服务功能及其结构信息为参照,忽略了生态系统服务综合功能和主导生态系统服务功能的时空动态性,对未来生态空间主导生态系统服务功能变化模拟不重视,一定程度上影响了生态空间保护格局构建的合理性。本研究提出了一种基于生态系统服务功能动态变化特征的生态空间划定方法,实现了邛崃市生态空间范围识别,解决了当前研究忽略生态系统服务功能时空动态性的问题。在此基础上,研究还应用Markov-CA模型,集成主导生态系统服务功能时空变化特征,实现了2025年邛崃市生态空间主导生态系统服务功能时空变化模拟,为生态空间变化模拟寻找到了合适的方法,也为合理构建生态空间保护格局提供了基础支撑。研究发现邛崃市生态系统服务综合功能量及其年际变化率呈现出明显的动态性,这一发现证实我们在识别生态空间时考虑生态系统服务功能动态特性的必要性。应用本文提出的生态空间识别方法确定邛崃市生态空间面积为98307 ha,与地方生态文明建设规划中确定的相应生态空...     

The changing economic spatial structure of Europe

Many theoretical and practical works aim at describing the spatial structure of Europe, where spatial relations have undergone continuous change. The article gives an overview of models describing the spatial structure of Europe. The models' diversity is highlighted, without any claim to the completeness of the list of models discussed. The authors describe the economic spatial structure of Europe through bidimensional regression analysis based on a gravity model. With the help of the gravity model, they generate a spatial image of the economic spatial structure of Europe. With the images, the appropriateness of the models based on different methodological backgrounds can be justified through comparison with the authors' results. The authors aim to contribute to understanding the European economic spatial structure through a new methodological approach, rather than to create and show a new model that overwrites existing ones.