A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level

Optical Earth Observation data with moderate spatial resolutions, typically MODIS (Moderate Resolution Imaging Spectroradiometer), are of particular value to environmental applications due to their high temporal and spectral resolutions. Time-series of MODIS data capture dynamic phenomena of vegetation and its environment, and are considered as one of the most effective data sources for land cover mapping at a regional and national level. However, the time-series, multiple bands and their derivations such as NDVI constitute a large volume of data that poses a significant challenge for automated mapping of land cover while optimally utilizing the information it contains. In this study, time-series of 10-day cloud-free MODIS composites and its derivatives – NDVI and vegetation phenology information, are fully assessed to determine the optimal data sets for deriving land cover. Three groups of variable combinations of MODIS spectral information and its derived metrics are thoroughly explored to identify the optimal combinations for land cover identification using a data mining tool.The results, based on the assessment using time-series of MODIS data, show that in general using a longer time period of the time-series data and more spectral bands could lead to more accurate land cover identification than that of a shorter period of the time-series and fewer bands. However, we reveal that, with some optimal variable combinations of few bands and a shorter period of time-series data, the highest possible accuracy of land cover classification can be achieved.     

我国森林植被遥感调查及建库研究

我国森林植被的覆盖程度受到土壤、气候、海拔、人为等各种因素的互相影响,为了能够遵循＂适地适树＂原则而更加精确在不同地区栽种适宜的树种从而提高我国森林覆被率,利用遥感技术对全国森林植被及其所生长的条件进行调查是重中之重。本文以全国范围为研究区域,利用MODIS数据对土壤、植被及气候数据进行提取,引入梯形网格,对全国进行等经纬度的网格划分,利用ArcGIS,ENVI等软件绘出我国土壤类型、N、P元素及有机质含量、年降雨量、年最高温度及最低温度的温度等值线,再利用数据挖掘技术提取与森林植被覆盖率相关的影响因子得出我国现阶段森林植被覆盖图,计算出我国现有林地面积并对影响森林植被覆盖的因子进行数据库的录入。     

管道穿越采空区沉陷监测方法初探

长距离油气管道运行压力大,沿线地质、地理、生态环境复杂。由于成本控制、地理环境等因素,油气管道常常不可避免地铺设在矿山采空区上。针对油气管道穿越煤层采空区受地表沉陷灾害的威胁这一问题,对矿山采空区沉陷监测的几种方法进行了比较,分析了它们的优势及不足,对矿山沉陷监测方法的选择和保护油气管道的正常运营具有指导意义。     

面向矿产行业的GIS卓越工程师培养模式研究

针对现有GIS专业培养方案难以适应社会发展需求的问题,本文以国家"卓越工程师教育培养计划"为契机,依托我校矿物资源工程优势学科的矿产行业背景,对GIS专业的实践教学计划与内容进行改革,探索了校内特色专业实验室、校外典型行业实践基地和双师制行业实践的3种教学模式,构建出培养具备高素质矿产行业背景GIS卓越工程师培养的"四四"实践教学平台。并在开放式矿业教学平台的支持下,研究了"学习—实践—研究—创新—再学习"为主线的实践教学方法,达到提升GIS专业学生行业应用能力的目的。     

Remote sensing image classification with GIS data based on spatial data mining techniques

Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification. Two learning granularities are proposed for inductive learning from spatial data, one is spatial object granularity, the other is pixel granularity. We also present an approach to combine inductive learning with conventional image classification methods, which selects class probability of Bayes classification as learning attributes. A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data. Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning. Comparing with the results produced only by Bayes classification, the overall accuracy increased by 11% and the accuracy of some classes, such as garden and forest, increased by about 30%. The results indicate that inductive learning can resolve spectral confusion to a great extent. Combining Bayes method with inductive learning not only improves classification accuracy greatly, but also extends the classification by subdividing some classes with the discovered knowledge.     

Method and application of comprehensive knowledge discovery

This paper proposes the principle of comprehensive knowledge discovery. Unlike most of the current knowledge discovery methods, the comprehensive knowledge discovery considers both the spatial relations and attributes of spatial entities or objects. We introduce the theory of spatial knowledge expression system and some concepts including comprehensive knowledge discovery and spatial union information table (SUIT). In theory, SUIT records all information contained in the studied objects, but in reality, because of the complexity and varieties of spatial relations, only those factors of interest to us are selected. In order to find out the comprehensive knowledge from spatial databases, an efficient comprehensive knowledge discovery algorithm called recycled algorithm (RAR) is suggested.     

Analysing topographical changes in open cast coal-mining region of Patratu,Jharkhand using CARTOSAT-I Stereopair satellite images

This study provides an assessment of changes in the terrain topography due to opencast coal mining in the Patratu region of Jharkhand state during the period of 1962–2007. It demonstrated the potential of digital elevation model (DEM) differencing technique using Cartosat-I satellite (2007) derived DEM with reference to DEM derived from contours obtained from Survey of India topographical map (1962). The topographical changes through DEM differencing revealed positive relief changes (up to 49 m) due to coal-mining overburden dumps. The dumping of overburden near the banks of perennial Damodar River also caused positive topographic changes (up to 20 m) indicating adverse effects on its hydrological regime. Negative relief changes are represents by deep depressions (up to 66 m) created within coal mines due to the extraction of coal. These depression areas within the abundant mines generally become the zones of water accumulation causing wastage of surface and ground water resources.     

A graph-based approach to detecting tourist movement patterns using social media data

ABSTRACT

Understanding the characteristics of tourist movement is essential for tourist behavior studies since the characteristics underpin how the tourist industry management selects strategies for attraction planning to commercial product development. However, conventional tourism research methods are not either scalable or cost-efficient to discover underlying movement patterns due to the massive datasets. With advances in information and communication technology, social media platforms provide big data sets generated by millions of people from different countries, all of which can be harvested cost efficiently. This paper introduces a graph-based method to detect tourist movement patterns from Twitter data. First, collected tweets with geo-tags are cleaned to filter those not published by tourists. Second, a DBSCAN-based clustering method is adapted to construct tourist graphs consisting of the tourist attraction vertices and edges. Third, network analytical methods (e.g. betweenness centrality, Markov clustering algorithm) are applied to detect tourist movement patterns, including popular attractions, centric attractions, and popular tour routes. New York City in the United States is selected to demonstrate the utility of the proposed methodology. The detected tourist movement patterns assist business and government activities whose mission is tour product planning, transportation, and development of both shopping and accommodation centers.     

The resource boom and socio-economic well-being in Australian resource towns: a temporal and spatial analysis

The economy of Western Australia has long been characterized by a heavy dependence on extractive industries. The past decade, however, has seen the mining industry expand very rapidly, with significant implications for the small towns that support the sector. In this article, we consider the socio-economic performance of these towns through an assessment of unemployment, welfare dependence and incomes. In contrast to many other studies of resource boomtowns that typically focus on a single locality or time period, in this study we focus explicitly on spatial and temporal variability. We examine the ways in which place-specific characteristics—the commodity produced, local economic diversity and basic demographic features of a town—interact and have contributed to change in socio-economic well-being of mining communities across Western Australia over a 10-year period.     

Using spatial analysis and Bayesian network to model the vulnerability and make insurance pricing of catastrophic risk

Vulnerability refers to the degree of an individual subject to the damage arising from a catastrophic disaster. It is affected by multiple indicators that include hazard intensity, environment, and individual characteristics. The traditional area aggregate approach does not differentiate the individuals exposed to the disaster. In this article, we propose a new solution of modeling vulnerability. Our strategy is to use spatial analysis and Bayesian network (BN) to model vulnerability and make insurance pricing in a spatially explicit manner. Spatial analysis is employed to preprocess the data, for example kernel density analysis (KDA) is employed to quantify the influence of geo-features on catastrophic risk and relate such influence to spatial distance. BN provides a consistent platform to integrate a variety of indicators including those extracted by spatial analysis techniques to model uncertainty of vulnerability. Our approach can differentiate attributes of different individuals at a finer scale, integrate quantitative indicators from multiple-sources, and evaluate the vulnerability even with missing data. In the pilot study case of seismic risk, our approach obtains a spatially located result of vulnerability and makes an insurance price at a finer scale for the insured buildings. The result obtained with our method is informative for decision-makers to make a spatially located planning of buildings and allocation of resources before, during, and after the disasters.