基于GIS的呼和浩特市近百年街道时空演变及其特征分析

以历史文化名城——呼和浩特市城区为研究区,以GIS为技术平台,运用现代地图、古地图、遥感影像数据以及史料和现代文献,制作了6个典型时期历史街道GIS数据,定量与定性相结合分析了研究区近百余年的街道时空演变过程及其特征。结果显示：研究区近百年的街道时空演变具有“摊饼式”空间扩展特征、先集聚后分散的空间演变特征;解放初期至改革开放时期的街道演变具有向内填充式发展与向北缓慢扩展相结合演变的特点,而快速城市化时期研究区新增街道以原归化、绥远城为中心向四周延伸,呈现出向外扩展的空间演变特征;百余年后的今天,原归化城内的街巷基本消失,而绥远城内的街道基本上保持了清代的棋盘式格局;人口增加、城市规划与建设、集聚与分散以及地质地貌条件等是研究区街道时空演变的主要驱动因素。     

Damage evaluation of soybean chilling injury based on Google Earth Engine (GEE) and crop modelling

Journal of Geographical Sciences - Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain yields. Timely and accurate measures are...     

Efficient joint-probability methods for hurricane surge frequency analysis

The Joint-Probability Method (JPM) was adopted by federal agencies for critical post-Katrina determinations of hurricane surge frequencies. In standard JPM implementations, it is necessary to consider a very large number of combinations of storm parameters, and each such combination (or synthetic storm) requires the simulation of wind, waves, and surge. The tools used to model the wave and surge phenomena have improved greatly in recent years, but this improvement and the use of very large high-resolution grids have made the computations both time-consuming and expensive. In order to ease the computational burden, two independent approaches have been developed to reduce the number of storm surge simulations that are required. Both of these so-called JPM-OS (JPM-Optimal Sampling) methods seek to accurately cover the entire storm parameter space through optimum selection of a small number of parameter values so as to minimize the number of required storm simulations. Tests done for the Mississippi coast showed that the accuracy of the two methods is comparable to that of a full JPM analysis, with a reduction of an order of magnitude or more in the computational effort.     

Predicting gully densities at sub-continental scales: a case study for the Horn of Africa

Despite its environmental and scientific significance, predicting gully erosion remains problematic. This is especially so in strongly contrasting and degraded regions such as the Horn of Africa. Machine learning algorithms such as random forests (RF) offer great potential to deal with the complex, often non-linear, nature of factors controlling gully erosion. Nonetheless, their applicability at regional to continental scales remains largely untested. Moreover, such algorithms require large amounts of observations for model training and testing. Collecting such data remains an important bottleneck. Here we help to address these gaps by developing and testing a methodology to simulate gully densities across Ethiopia, Eritrea and Djibouti (total area: 1.2 million km²). We propose a methodology to quickly assess the gully head density (GHD) for representative 1 km² study sites by visually scoring the presence of gullies in Google Earth and then converting these scores to realistic estimates of GHD. Based on this approach, we compiled GHD observations for 1,700 sites. We used these data to train sets of RF regression models that simulate GHD at a 1 km² resolution, based on topographic/geomorphic, land cover, soil and rainfall conditions. Our approach also accounts for uncertainties in GHD observations. Independent validations showed generally acceptable simulations of regional GHD patterns. We further show that: (i) model performance strongly depends on the amount of training data used, (ii) large prediction errors mainly occur in areas where also the predicted uncertainty is large and (iii) collecting additional training data for these areas results in more drastic model performance improvements. Analyses of the feature importance of predictor variables further showed that patterns of GHD across the Horn of Africa strongly depend on NDVI and annual rainfall, but also on normalized steepness index (k_sn) and distance to rivers. Overall, our work opens promising perspectives to assess gully densities at continental scales. © 2020 John Wiley & Sons, Ltd.     

基于谷歌地球引擎平台的海上养殖信息提取方法研究——以福建省平潭县为例

海上养殖业对粮食安全有着至关重要的作用。然而,海上养殖的无序扩张和开发,阻碍了海上交通,同时也造成了海洋环境问题。为及时、准确地获取海上养殖信息,满足海岸带调查以及推进海上养殖规范化、科学化,提出一种基于谷歌地球引擎(Google Earth Engine, GEE)平台实现长时间序列下海上养殖区信息快速提取的方法。本研究构建了一个基于随机森林分类的海上养殖区信息提取模型,该模型综合利用了Sentinel 1卫星SAR影像数据的VV和VH极化波段,以及Sentinel 2卫星的多光谱影像数据。此外,模型还融合了4个用于增强养殖区特征的指数,以提高养殖区域信息提取的准确性和效率。这种方法的应用旨在优化海上养殖区的识别过程,通过精确分析和利用不同数据源的互补优势,展现了〖JP2〗遥感技术在海洋养殖监测领域的巨大潜力。本研究对2017—2021年平潭县海上养殖区域进行判定与提取,实验结果表明,以养殖密度较低,养殖特征不明显为特征的海上养殖区,基于GEE平台的海上养殖区信息提取方法精度在90%以上,表明在复杂水体背景下对养殖区快速识别取得较好的效果,可为海上养殖科学规划与规范化管理提供有效的参考依据。     

Self‐Organized Cognitive Maps

This paper argues cognitive mapping is a learning process that can be simulated by a self‐organizing neural network. The learning of city locations was considered in two studies. One study focused on the learning of city locations on four continents. Results indicated the neural network aligned the cities producing systematic errors similar to those in human cognitive maps. A second study had a neural network learn a biased sample of city locations in the United States. Results indicated a non‐linear relationship between cognitive and physical distances. Self‐organized cognitive maps naturally produce this non‐linear relationship when information from more than one scale is mapped into one space.     

基于Arc/Info的地貌晕渲法的技术和实践

根据地貌晕渲基本理论和技术,利用Arc/Info制作了彩色晕渲图,并探讨了不同参数下的晕渲效果,最后还尝试了将晕渲图加载到表面视图,以从不同的角度来观察地貌形态。     

Map Use by Adults with Visual Impairments

In this paper we summarize five experiments that were designed to investigate how tactile maps contribute to the cognitive maps of people with visual impairments. In two experiments we demonstrated that tactile maps can contribute to peoples' ability to learn a route through an unfamiliar area. From the results of two further studies, we argue that compared to people with sight, people with visual impairments have greater difficulty encoding information from a map. We considered this directly in a fifth experiment by investigating how people with visual impairments search and learn a map. We found that they used less efficient strategies than people with sight. We conclude that tactile maps are an important source of geographical information when people need to learn about new environments, but we also point out that to benefit most from tactile maps people need to encode the map information as effectively as possible. As people with visual impairments do not always use the most appropriate encoding strategies we suggest that tactile map users might benefit from greater experience and training in map encoding strategies.     

A comparison of alternative criteria for defining fuzzy boundaries on fuzzy categorical maps

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EVIDENCE OF A RESPONSE-BIAS EXPLANATION OF NONEUCLIDEAN COGNITIVE MAPS*

Two groups of subjects with varying amounts of experience with the city in which they lived judged from memory distances between familiar locations. The distance judgments were processed by a multidimensional scaling algorithm (MULTISCALE) that estimates the parameters of a power function transformation of the response scale and the coordinates of the locations in k-dimensional euclidean space. The results for both subject groups suggested that inaccuracies of distance cognition do not reflect the noneuclidean nature of urban cognitive maps but are caused by response bias.