Landform classification from a digital elevation model and satellite imagery

The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date, aerial photographs and topographic maps have been used for landform classification studies. The principal objective of this research is to propose a quantitative approach for landform classification based on a 10-m resolution digital elevation model (DEM) and some use of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. In order to extract and identify the various landforms, slope, elevation range, and stream network pattern were used as basic identifying parameters. These are extractable from a DEM. Further, ASTER images were required to identify the general outline shape of a landform type and the presence or absence of gravel. This study encompassed a relatively large watershed of 451 183 ha with a total elevation difference of 2445 m and a variety of landforms from flat River Alluvial Plains to steep mountains. Classification accuracy ranged from 91.8 to 99.6% with an average of 96.7% based upon extensive ground-truthing. Since similar digital and ASTER image information is available for Iran, an accurate landform map can now be produced for the whole country. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.     

Monitoring land changes in an urban area using satellite imagery,GIS and landscape metrics

Monitoring land changes is an important activity in landscape planning and resource management. In this study, we analyze urban land changes in Atlanta metropolitan area through the combined use of satellite imagery, geographic information systems (GIS), and landscape metrics. The study site is a fast-growing large metropolis in the United States, which contains a mosaic of complex landscape types. Our method consisted of two major components: remote sensing-based land classification and GIS-based land change analysis. Specifically, we adopted a stratified image classification strategy combined with a GIS-based spatial reclassification procedure to map land classes from Landsat Thematic Mapper (TM) scenes acquired in two different years. Then, we analyzed the spatial variation and expansion of urban land changes across the entire metropolitan area through post classification change detection and a variety of GIS-based operations. We further examined the size, pattern, and nature of land changes using landscape metrics to examine the size, pattern, and nature of land changes. This study has demonstrated the usefulness of integrating remote sensing with GIS and landscape metrics in land change analysis that allows the characterization of spatial patterns and helps reveal the underlying processes of urban land changes. Our results indicate a transition of urbanization patterns in the study site with a limited outward expansion despite the dominant suburbanization process.     

A comparison of geometric approaches to assessing spatial similarity for GIR

This research compares the geographic information retrieval (GIR) performance of a set of logistic regression models with those of five non‐probabilistic methods that compute a spatial similarity score for a query–document pair. All methods are applied to a test collection of queries and documents indexed spatially by two convex conservative geometric approximations: the minimum bounding box (MBB) and the convex hull. In the comparison, the tested logistic regression models outperform, in terms of standard information retrieval recall and precision measures, all of the non‐probabilistic methods. The retrieval performance achieved by the logistic regression models on MBB approximations is similar to that achieved by the use of the non‐probabilistic methods on convex hulls. Although these results are valid only for the test collection used in this study, they suggest that a logistic regression approach to GIR provides an alternative to the use of higher‐quality geometric representations that are more difficult to obtain, implement, and process. Additionally, this research demonstrates the ability of a probabilistic approach to effectively incorporate information about geographic context in the spatial ranking process.     

Land suitability evaluation for changing spatial organization in Urmia County towards conservation of Urmia Lake

One of the most effective ways to reverse the decline of Iran's iconic Urmia Lake is to directly confront the development patterns that have contributed to the current crisis. This study's objective is to create a model for conservation of Urmia Lake that identifies suitable lands for agricultural and residential development in Urmia County that are distant from Urmia Lake. This was accomplished through a Geographic Information System-based multi-stage process. The first step involved production of maps based on an initial assessment of the region's geography, geomorphology, landforms, and hazards potential. In the next step, all of the parameters were overlaid and land suitability maps were generated by using determinant maps. In developing the final map, the lands were divided into four classes for future development potential: highly suitable, suitable, marginally suitable and not suitable. The results showed that well away from highly populated regions adjacent to Urmia Lake, there are highly suitable and suitable lands that presently contain 14 and 8 percent of total settlements, respectively. The highly suitable and suitable lands, which cover 5.6 and 6.7% of the total area, may serve as appropriate axes for changing the traditional spatial organization of the region, redirecting future development and consequently decreasing ecological pressure on lands close to Urmia Lake as well as preventing further excessive usage of water resources in the region.     

Critical distances: Comparing measures of spatial accessibility in the riverine landscapes of Peruvian Amazonia

Quantitative measures of accessibility are increasingly used in land cover change modeling and in assessing human pressure on the environment. In riverine Amazonia the significance of physical accessibility for biodiversity, land use patterns and economic livelihoods is widely acknowledged, but attempts to quantify accessibility in practice have been few in number. In this study we compare different distance- and frequency-based measures of spatial accessibility and develop a quantitative model of accessibility patterns for the north-eastern Peruvian Amazonia where rivers form the core of the transportation network. We model accessibility between the rural areas of the Loreto region and the capital city of Iquitos, using different distance algorithms in a geographic information system, and complement the distance model with information on river boat frequencies and transport capacities. Patterns of accessibility are visualized in terms of potential production zones for different types of agricultural and non-timber forest products.This study demonstrates how results from different accessibility measures vary considerably. The mean Euclidean distance to Iquitos is almost 270 km, the mean network distance nearly 760 km and the mean travel time 70 h. Observed network distances from validation points to Iquitos are on average 1.6 times longer than Euclidean distances, and for the whole study area, the average ratio between modeled network distances and Euclidean distances is 3.1. The correlation between network distances and time distances is very strong, but time distances are relatively shorter along the major channels where boat traffic is considerably faster than along narrow, tightly meandering rivers. Measures of boat frequency and transport capacity show that availability of transport possibilities is highly varying across the region. These measures provide insights into the ’thickness’ of trade, indicating the level of market integration for riverine settlements. We conclude that quantifying accessibility in an environment like Peruvian Amazonia requires measures that take into account the spatial structure and dynamic nature of the riverine transportation network. Time as a unit of distance provides the most relevant measure of accessibility in the Amazonian context, where many human actions and traditional livelihoods are controlled by travel times between the regional core and the hinterland.     

国土空间管控的方法与途径

国土空间管控是对国土空间各类开发保护活动的科学谋划与管理安排,是进行国土空间宏观调控和微观管理的重要政策工具。探讨以地域功能和资源环境承载力为科学基点的国土空间管控体系框架,并以福建省和贵州省六盘水市为例,解析“三区三线”协同划定和国土开发强度并测算两个国土空间管控中技术难点的解决方法,在此基础上,提出以功能管控和参数管控为核心的多尺度、多时序国土空间管控途径。研究发现：① 功能管控以主体功能区划和“三区三线”划定方案为基础,按照四类主体功能定位、三类空间（生态、农业和城镇空间）、六类分区（生态保护红线及一般生态区、永久基本农田及一般农业区、城镇开发边界及城镇预留区）以及N类建设和非建设类用途分层级实施;② 参数管控则以国土开发强度为关键目标参数,实施基于总量-目标和过程-增幅双控的阈值管控,以及基于上限和下限双控的分类型管控途径,旨在实现各类空间内和区域整体的生产、生活、生态“三生”结构均衡协调,促进形成高质量、可持续的国土空间开发保护格局。     

卫星图像中不同水体类型识别研究

在具有高空间分辨率特性的图像上,城市中大型建筑物、道路、河流、湖泊和其他人工地物形状特征和纹理特征清晰可辨。针对高分辨率卫星图像的特点,文章以水体类型识别为例,从卫星数字图像目标地物波谱特征抽取入手,通过图像分类,将水体从背景中分离并予以识别,同时实现像素重组。在区域分割与边界跟踪基础上,对卫星图像进行水体形状特征的抽取与描述,实现不同水体类型的识别。     

信息时代空间结构研究新进展

以信息技术为主线 ,从新的研究课题、研究范式的转变及研究重点三个方面将西方学术界对信息时代区域与城市空间结构及其相关研究进行了总结。文章指出 ,信息技术的进步及其应用使得地理学处于不断的变化之中 ,对信息活动的关注使得新的研究课题和分支学科不断涌现。同时 ,信息技术与社会的互动作用也改变着传统的思维范式。主要的研究内容总结如下 :1)赛伯空间———新的空间逻辑 ;2 )区位研究 ,尤其是生产性服务业研究 ;3)空间的分散与集中、均衡与非均衡发展 ;4 )创新网络与区域空间结构 ;5 )信息发展模式的空间结果 ;6 )新空间极化现象。     

The scale effects of the spatial autocorrelation measurement: aggregation level and spatial resolution

The scale effects of the spatial autocorrelation (SA) measurement has been explored for decades. However, the effects of the data aggregation levels and spatial resolution on the SA measurement are often confused. Whether the two types of scale effects are the same is still unclear and requires further investigation. We retrieved the land surface temperature (LST) from Landsat 8 images in 30 capital cities of China. By aggregating the LST images, we observed a decrease in the SA of the LST as the data aggregation level increased; this relationship can be fitted well with a negative logarithmic function. We derived an indicator to measure the scale effects intensity of SA, which was negatively correlated with the spatial complexity of LST. Both aggregating images and the increasing spatial resolution induce weaker SA, but the effect of the former was stronger. The aggregating images negatively affected the SA degree regardless of the spatial resolutions of the original images. The SA degrees of the aggregated images were far below those of the real-life images. This study suggests that the scale effects caused by aggregation levels and spatial resolutions are different, and cautions should be taken when applying relevant conclusions derived from aggregating images.     

Scale-dependent analysis of satellite imagery for characterization of glacier surfaces in the Karakoram Himalaya

Information regarding process-structure relationships and change in the Karakoram Himalaya is of great importance in studying glacier hydrollogy, mass balance, and dynamic environmental change. Such information is not readily available. Detailed spatio-temporal assessment requires field investigation coupled with quantitative remote sensing studies. We conducted an investigation of the large Batura Glacier in Pakistan to determine if spectral variability can be quantified and used to characterize glacier surfaces. Specifically, SPOT Panchromatic satellite data were evaluated for differentiating features of glacier structure resulting from ice movement, ablation, and supraglacial fluvial action. Image semivariogram analysis was conducted. for assessing spectral variability patterns and fractal analysis was used to examine scale-dependent variation in the data. Results indicate that spectral variability from fields of ice seracs can exhibit fractal characteristics, although most surface features on the glacier exhibit a change in the fractal dimension over different ranges in scale. The fractal dimension was found to be useful for differentiating between glacier surfaces such as white ice and debris-covered ice. Characteristics of the debris-load and the scale-dependent nature of calculating the fractal dimension ultimately determined the potential of class separability.     

The consequences of land-cover changes on soil erosion distribution in Slovakia

Soil erosion is a complex process determined by mutual interaction of numerous factors. The aim of erosion research at regional scales is a general evaluation of the landscape susceptibility to soil erosion by water, taking into account the main factors influencing this process. One of the key factors influencing the susceptibility of a region to soil erosion is land cover. Natural as well as human-induced changes of landscape may result in both the diminishment and acceleration of soil erosion. Recent studies of land-cover changes indicate that during the last decade more than 4.11% of Slovak territory has changed. The objective of this study is to assess the influence of land-cover and crop rotation changes over the 1990–2000 period on the intensity and spatial pattern of soil erosion in Slovakia. The assessment is based on principles defined in the Universal Soil Loss Equation (USLE) modified for application at regional scale and the use of the CORINE land cover (CLC) databases for 1990 and 2000. The C factor for arable land has been refined using statistical data on the mean crop rotation and the acreage of particular agricultural crops in the districts of Slovakia. The L factor has been calculated using sample areas with parcels identified by LANDSAT TM data. The results indicate that the land-cover and crop rotation changes had a significant influence on soil erosion pattern predominately in the hilly and mountainous parts of Slovakia. The pattern of soil erosion changes exhibits high spatial variation with overall slightly decreased soil erosion risks. These changes are associated with ongoing land ownership changes, changing structure of crops, deforestation and afforestation.     

Mapping badland areas using LANDSAT TM/ETM satellite imagery and morphological data

Soil erosion is the most influential component of land degradation for its strong impacts on both natural and agricultural environments. In order to support effective intervention and recovery policies for eroded areas, monitoring techniques should take into account the space–time variability of the processes involved, and make use of assessed mapping methodologies as baseline criteria for studying the dynamics of landform development. When using multispectral data for mapping eroded areas, low spectral separability is a significant limit in areas with complex features, where soil materials are frequently remixed by surface runoff. Since multispectral satellite images are a valuable data source for multi-temporal analyses of erosion processes at the medium scale, we assessed how accurately a badland area can be identified from LANDSAT TM and ETM data. A protocol for an optimal mapping was built up by testing the performance of different supervised algorithms and input layers (spectral and morphological). Tests were carried out in a well-known badland area of Basilicata, Italy, with an extension of  8000 ha. Results obtained from the use of spectral bands (with and without thermal channel) and principal components returned an overall accuracy ranging from 53% (for classification on first three components) to 72% (for classification on all bands from TM), with low values for the kappa coefficient (0.30–0.50), showing that the spectral information alone are insufficient to accurately identify badland areas. In order to improve mapping, we found that the integration of slopes and aspects derived from a Digital Elevation Model (DEM) can overcome problems inherent to the low separability of spectral signatures. The use of morphological data was tested for different classification algorithms and integration approaches. In discriminating badlands, the better performing algorithm was MLC (Maximum Likelihood Classifier) and the best results were obtained by integrating all seven bands (including TIR) with slope and aspect maps as input within the classifier (A > 0.85 and K  0.75 for both the sensors). We selected such parameters because they play an important role in characterising badlands of study area from a morphological perspective but the proposed approach is also conceptually simple and can be easily exported to other areas. The obtained results support the hypothesis that the combined use of remote sensing imagery and auxiliary morphological data significantly improves the mapping of badlands over large areas with heterogeneous features, thus providing a useful methodology for long-term studies on soil erosion processes.     

The Mekong from satellite imagery: A quick look at a large river

Physical datasets on rivers, thousands of kilometres long, can be efficiently and rapidly acquired through satellite imagery. As large rivers commonly cross several tectonic and climatic zones, their channels are composed of a number of units, each with characteristic morphology and behaviour. Separating a large river into such units provides a framework for understanding the geomorphology of the river concerned, allowing acquisition of information for environmental impact analysis and river management.About 2500 km of the Mekong River, from the China border to the sea, has been examined with SPOT satellite images with a selection of MODIS and IKONOS scenes in support. Hydrological data of the Mekong River Commission, hydrographic maps of the Commission, and field observations have been used to verify and extend the findings from the satellite imagery. The technique provides a rapid and holistic conceptualisation of forms and functions along the derived eight-unit classification of the Mekong River. Such a framework is valuable for (1) determining selected aspects of the geomorphological behaviour of a large river system, (2) rapidly analysing project-related environmental impacts, and (3) examining the geological evolution of the river. Investigating large rivers is difficult and resource consuming, but satellite imagery provides an easy and rapid tool. The cost of the images, however, may be high as a number of them are required for this type of analysis.     

Estimating the prediction performance of spatial models via spatial k-fold cross validation

In machine learning, one often assumes the data are independent when evaluating model performance. However, this rarely holds in practice. Geographic information datasets are an example where the data points have stronger dependencies among each other the closer they are geographically. This phenomenon known as spatial autocorrelation (SAC) causes the standard cross validation (CV) methods to produce optimistically biased prediction performance estimates for spatial models, which can result in increased costs and accidents in practical applications. To overcome this problem, we propose a modified version of the CV method called spatial k-fold cross validation (SKCV), which provides a useful estimate for model prediction performance without optimistic bias due to SAC. We test SKCV with three real-world cases involving open natural data showing that the estimates produced by the ordinary CV are up to 40% more optimistic than those of SKCV. Both regression and classification cases are considered in our experiments. In addition, we will show how the SKCV method can be applied as a criterion for selecting data sampling density for new research area.     

经济途径对地缘政治格局的影响机制及其空间表现研究进展

随着中国崛起,世界主要大国将战略中心转移至亚洲并通过各种经济组织和经济联盟等经济途径重塑中国周边环境,由此导致中国周边地缘环境形势变得极其复杂。中国则通过共建“一带一路”、亚洲基础设施投资银行、金砖国家开发银行等经济途径化解。本文按照全球—区域—国家间的尺度,探讨了国际经济组织、经济联盟和国家间经济相互依赖等3种主要经济途径对地缘政治格局的影响机制及空间表现。结果表明：通过国家间经济依赖的不对称性改变各国权力大小,进而影响地缘格局中地缘体的权力分配,其空间表现主要为影响地区冲突和改变国家地缘空间影响范围。而国际经济组织和经济联盟则通过权力再平衡影响地缘政治格局,其空间表现主要为数量增长、交错分布和空间范围扩大。中国学者对如何通过经济途径增强地缘政治影响力、改善中国地缘环境的研究不够充分。中国作为崛起中的大国,应更为关注经济实力的增强对周边乃至世界地缘政治格局的影响。为此,应加强以下几个方面研究：①将经济相互依赖转化成为有效的地缘影响力;②利用国际经济组织和经济联盟拓展中国地缘政治空间;③“一带一路”空间走向及沿线国家;④多尺度经济途径影响效应分析;⑤运用定量模拟方法探求国家地缘影响力演变的影响因素以及驱动机制等,为中国的地缘战略提供理论支撑和研究实证。     

Integrating algebraic multigrid method in spatial aggregation of massive trajectory data

The advanced technologies in location-based services and telecom have yield large volumes of trajectory data. Understanding these data effectively requires intuitive yet accurate visual analysis. The visual analysis of massive trajectory data is challenged by the numerous interactions among different locations, which cause massive clutter. This paper presents a new methodology for visual analysis by integrating algebraic multigrid (AMG) method in data aggregation. The non-parametric method helps to build a multi-layer node representation from a graph which is extracted from trajectory data. The comparison with AMG and other methods shows that AMG method is more advanced in both the spatial representation and the importance of nodes. The new method is tested with real-world dataset of cell-phone signalling records in Beijing. The results show that our method is suitable for processing and creating abstraction of massive trajectory dataset, revealing inherent patterns and creating intuitive and vivid flow maps.     

A treatise on the use of geostatistics for the characterization of nonrenewable resources

Five decades of geostatistical development are reviewed to summarize the state of the art for spatial interpolation vis-à-vis kriging or a form thereof. Although a search of the literature reveals a variety of kriging methods, there are but two infrastructures for geostatistical interpolation: simple cokriging, for estimating a single variable using two variables, and generalized cokriging, for estimating one or more variables using the same number of variables that are estimated. The many forms of kriging are varieties of these two interpolation infrastructures. This notion is emphasized to aid the selection of an appropriate interpolation model for a nonrenewable resource. These models are discussed, and literature for the models and for applicable software is cited. Additionally, all aspects of spatial interpolation are discussed, including the adequacy of spatial sampling, distribution characteristics of spatial samples, semivariograms, search parameters, and selection of interpolation models in conformance with spatial data characteristics. Finally, the relationship between interpolation and raster-based geographic information systems is emphasized.