基于轮廓小波变换的航空影像建筑物识别

在对航空影像中的建筑物进行识别提取时,建筑物顶部的轮廓信息是一个重要的判断依据。基于航空影像建筑物个数繁多、形状复杂,且存在较多的干扰信息,提出一种新颖有效的建筑物识别方法:首先,利用改进标记分水岭算法提取建筑物区域。然后,对每个分割得到的建筑物区域,提取其轮廓,对轮廓进行基于平稳小波变换的仿射不变量计算,并构造建筑物模型数据库,利用相关系数实现了建筑物的有效识别。     

Spatial analysis of boil water advisories issued during an extreme weather event in the Hudson River Watershed,USA

Water infrastructure in the United States is aging and vulnerable to extreme weather. In August 2011, Tropical Storm Irene hit the eastern part of New York and surrounding states, causing great damage to public drinking water systems. Several water supply districts issued boil water advisories (BWAs) to their customers as a result of the storm. This study seeks to identify the major factors that lead water supply systems to issue BWAs by assessing watershed characteristics, water supply system characteristics and treatment plant parameters of water districts in the Mohawk-Hudson River watershed in New York. Logistic regression model suggests that the probability of a BWA being issued by a water supply district is enhanced by higher precipitation during the storm, high density of septic systems, lack of recent maintenance and low population density. Interviews with water treatment plant operators suggested physical damage to water distribution systems were the main causes of boil water advisories during storms. BWAs result in additional costs to residents and communities, and the public compliance of the advisory instructions is low, so efforts must be made to minimize their occurrence. Prior investments in infrastructure management can proactively address municipal water supply and quality issues.     

基于地理量化方法的四川省A级旅游景区空间分布结构研究

利用数理统计和地理信息空间分析方法,从定量和定性两方面探讨四川省221个A级旅游景区的空间分布结构,分析其空间分布规律。结果表明：四川省A级旅游景区的空间分布类型属于集聚型,且区域间差异较大,空间分布均匀度较低,不均衡性显著;四川省A级旅游景区规模等级结构表现为中间大两头小的纺锤形结构,中端的市场供给占据上风;四川省A级旅游景区空间分布受地理区位、交通条件、景区地域组合状况和经济发展水平等多种因素综合影响。     

Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery

Accelerated soil erosion, high sediment yields, floods and debris flow are serious problems in many areas of Iran, and in particular in the Golestan dam watershed, which is the area that was investigated in this study. Accurate land use and land cover (LULC) maps can be effective tools to help soil erosion control efforts. The principal objective of this research was to propose a new protocol for LULC classification for large areas based on readily available ancillary information and analysis of three single date Landsat ETM+ images, and to demonstrate that successful mapping depends on more than just analysis of reflectance values. In this research, it was found that incorporating climatic and topographic conditions helped delineate what was otherwise overlapping information. This study determined that a late summer Landsat ETM+ image yields the best results with an overall accuracy of 95%, while a spring image yields the poorest accuracy (82%). A summer image yields an intermediate accuracy of 92%. In future studies where funding is limited to obtaining one image, late summer images would be most suitable for LULC mapping. The analysis as presented in this paper could also be done with satellite images taken at different times of the season. It may be, particularly for other climatic zones, that there is a better time of season for image acquisition that would present more information.     

基于 GVF 和 Snake 模型的高分辨率遥感图像四元数空间道路提取

基于Snake模型的优势及缺陷,提出一种高分辨率多光谱图像道路提取方法。通过建立多光谱图像信息的四元数表达式,解决Snake模型应用于多光谱图像的局限性;通过多光谱图像四元数空间标记分水岭分割,获取道路初始轮廓,解决道路初始边界设置问题;通过梯度矢量流模型迭代求解,解决道路凹陷位置逼近问题,最终实现道路提取。用QuickBird 高分辨率多光谱遥感数据验证方法的可行性,与直接采用Snake 模型进行道路提取结果相比,本文所提出的方法对于高分辨率影像上的高等级道路提取具有较好的精度。     

利用小波变换的高分辨率多光谱遥感图像多尺度分水岭分割

为了减少仅用分水岭变换而导致的过分割问题,本文提出利用小波变换的多尺度处理方式用于融合后多光谱QuickBird图像的分割。整个分割过程包括多尺度图像表示、图像分割、区域合并和结果映射等过程。首先,依据原始图像的大小确定分解尺度并用小波变换产生各波段的低尺度图像。采用相位一致模型提取各近似系数的梯度,并逐尺度地融合各梯度图。分析不同尺度下的不同地物的局部梯度方差,以选择最佳的小波分解尺度。然后,通过移动阈值与扩展最小变换,利用多层次标记提取方法标记均质区域。进而,在梯度重建的基础上利用标记分水岭变换得到分割图像。其次,采取空间相邻关系、面积、光谱与纹理等多约束策略,以搜索最小合并代价的方式合并最初分割区域中的邻接区域对。最后,修改细节子图并进行小波逆变换将最初分割结果投影到更高尺度图像,同时处理边界上的像元以保持区域边界直至原始图像。实验结果表明本文方法不仅能够用于高分辨率多光谱遥感图像的分割,而且缓解了过分割问题且取得了较准确的分割效果。     

一种区域自适应的遥感影像分水岭分割算法

提出了一种区域自适应的标记分水岭分割方法。该方法利用高斯低通滤波和概率统计相结合的方法,对梯度影像进行区域自适应阈值分割,提取分割标记,然后采用Meyer算法进行标记分水岭分割。实验结果表明,该算法能够有效解决遥感影像不同区域的分割问题,达到比较理想的水平。     

流域植被对径流调节作用的实验研究

根据河北省邢台市西部山区两个小流域实验站的水文监测资料,研究了不同流域内的不同植被对径流的影响。分析了流域植被对径流的年内分配、径流系数、削减洪峰以及枯季径流量等的影响。结果表明,流域植被对涵养水源、调节径流量、增大枯季径流量起到重要作用。     

Streamflow Modelling: A Primer on Applications,Approaches and Challenges

This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

[Traduit par la rédaction] Le présent article examine la pratique actuelle en modélisation d’écoulement fluvial, un domaine qui évolue depuis plus d'un siècle. Nous présentons un échantillon de la vaste gamme d'applications d’évaluation et de planification des modèles d’écoulement fluvial. La diversité dans l'utilisation de ces modèles est le reflet de la diversité dans la complexité des modèles, et nous décrivons à l'aide d'exemples les approches de modélisation qui peuvent être empiriques ou basées sur la physique ou encore localisées ou entièrement réparties. Plusieurs sources d'erreur peuvent affecter les prévisions issues des modèles hydrologiques; nous discutons de ces sources d'erreur de même que des méthodes de réduction ou d'anticipation des erreurs. L'erreur du modèle est généralement quantifiée à l'aide d'un ensemble de prévisions servant à échantillonner la grandeur de l'incertitude prévisionnelle. Cet ensemble peut servir à produire des prévisions probabilistes fiables des grandeurs hydrologiques si toutes les sources d'erreur sont prises en compte. Jusqu’à maintenant, les applications des méthodes d'ensemble à la prévision des écoulements fluviaux n'ont généralement tenu compte que d'une ou deux sources d'erreur. Ce sera un défi de mettre au point des prévisions d'ensemble d’écoulement fluvial qui échantillonnent un plus large éventail d'incertitude prévisionnelle.     

流域水文模型对土壤数据响应的多尺度分析

流域水文模拟对输入数据空间详细程度的要求受流域面积大小的影响,而流域面积影响作用的定量描述有助于模拟时的数据选取.本文以美国Brewery Creek流域(约19.5 k㎡)为例,在逐级连续的汇流面积上,分析了SWAT模型基于1:2.4万的SSURGO和10m分辨率的SoLIM土壤数据模拟的径流量的差别随汇流面积的变化...