Reflections and speculations on the progress in Geographic Information Systems (GIS): a geographic perspective

Great strides have been made in Geographic Information Systems (GIS) research over the past half-century. However, this progress has created both opportunities and challenges. From a geographic perspective, certain challenges remain, including the modelling of geographic-featured environments with GIS data model, the enhancement of GIS’s analysis functions for comprehensive geographic analysis and achieving human-oriented geographic information presentation. Several basic theoretical and technical ideas that follow the workflow and processes of geographic information induction, geographic scenario modelling, geographic process analysis and geographic environment representation are proposed to fill the gaps between GIS and geography. We also call for designing methods for big geographic data-oriented analysis, making best use of videos and developing virtual geographic scenario-based GIS for further evolution.     

1992-2013年巢湖流域土壤侵蚀动态变化

基于GIS和RS技术,利用修正的通用土壤流失方程（RUSLE）模型,结合遥感影像、DEM数据、土壤类型数据及相关统计确定了模型中参数因子,计算出巢湖流域1992-2013年土壤侵蚀模数,分析了土壤侵蚀强度的时空动态变化特征。结果表明：巢湖流域土壤侵蚀区域主要呈东北至西南方向分布。微度、轻度、中度、强度、极强和剧烈侵蚀占土壤侵蚀总面积百分比分别是93.46%、6.25%、0.68%、0.19%、0.01%、0.01%。1992-2006年土壤侵蚀模数由510.70 t/（km²·a）减少到129.79 t/（km²·a）,降幅为74.59%,同时植被覆盖率由37.0%增至47.80%,土壤侵蚀的面积比例变化明显,轻度、中度、强度、极强和剧烈侵蚀由8.93%、2.33%、1.32%、0.09%、0.05%分别减少为4.74%、1.39%、0.28%、0.02%、0.01%,微度侵蚀由87.88%增加到94.16%。但2013年土壤微度侵蚀又减少为93.46%,土壤微度侵蚀有向高一级转换趋势。2006-2013年土壤侵蚀模数也由129.79 t/（km²·a）增加到149.44 t/（km²·a）,增幅为15.14%。     

A Landscape Ecology Approach to Assessing Development Impacts in the Tropics: A Geothermal Energy Example in Hawaii

Geographic information systems (GIS) are increasingly being used in environmental impact assessments (EIA) because GIS is useful for analysing spatial impacts of various development scenarios. Spatially representing these impacts provides another tool for landscape ecology in environmental and geographical investigations by facilitating analysis of the effects of landscape pattern on ecological processes and examining change over time. Landscape ecological principles are applied in this study to a hypothetical geothermal development project on the Island of Hawaii. Some common landscape pattern metrics were used to analyse dispersed versus condensed development scenarios and their effect on landscape pattern. Indices of fragmentation and patch shape did not appreciably change with additional development. The amount of forest to open edge, however, greatly increased with the dispersed development scenario. In addition, landscape metrics showed that a human disturbance had a greater simplifying effect on patch shape and also increased fragmentation than a natural disturbance. The use of these landscape pattern metrics can advance the methodology of applying GIS to EIA.     

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.