A microscale model for air pollutant dispersion simulation in urban areas: Presentation of the model and performance over a single building

A microscale air pollutant dispersion model system is developed for emergency response purposes. The model includes a diagnostic wind field model to simulate the wind field and a random-walk air pollutant dispersion model to simulate the pollutant concentration through consideration of the influence of urban buildings. Numerical experiments are designed to evaluate the model's performance, using CEDVAL(Compilation of Experimental Data for Validation of Microscale Dispersion Models) wind tunnel experiment data, including wind fields and air pollutant dispersion around a single building. The results show that the wind model can reproduce the vortexes triggered by urban buildings and the dispersion model simulates the pollutant concentration around buildings well. Typically, the simulation errors come from the determination of the key zones around a building or building cluster. This model has the potential for multiple applications; for example, the prediction of air pollutant dispersion and the evaluation of environmental impacts in emergency situations; urban planning scenarios;and the assessment of microscale air quality in urban areas.     

1971—2010年京津冀大城市热岛效应多时间尺度分析

利用1971—2010年均一化的京津冀区域逐日气温数据与质量控制后的2011年自动站逐时气温数据,分析了北京、天津和石家庄热岛效应的多尺度时间变化特征。结果表明,三个城市平均、最高和最低气温的热岛效应呈非对称性特征,最强为最低气温的热岛效应,其次为平均气温的热岛效应,最弱为最高气温的热岛效应。北京平均气温的热岛效应最强,其次为天津,石家庄相对较弱,石家庄平均气温的热岛效应近40年呈显著上升趋势,每10年达0.13℃。石家庄最高气温的热岛效应最强,其次为北京,最小为天津,近40年北京最高气温的热岛效应呈缓慢上升趋势,每10年增加0.06℃,石家庄变化不明显,天津呈微弱下降趋势。最低气温的热岛在北京最强,其次为天津,最小为石家庄,近40年最低气温热岛效应天津呈明显上升趋势,每10年增加0.18℃,其次为石家庄,北京呈微弱下降趋势。三个城市的平均气温、最低气温的热岛效应季节变化通常表现为夏季较弱,冬季最强。三个城市最高气温的热岛效应季节变化差异较大,北京10月热岛效应最弱,其他月份变化不大;天津热岛效应6月最弱,在1或12月最强;石家庄4和5月热岛效应最强,10月热岛效应最弱。由2011年自动站数据得到的平均气温热岛效应与1971—2010年的40年平均得到的平均气温的热岛效应季节变化具有类似的规律。2011年自动站热岛效应在一天中表现为白天热岛强度较低,而夜间热岛强度较高。     

城区地表形变差分TomoSAR监测方法

压缩感知技术(CS)的差分TomoSAR技术解决了中高分辨率SAR数据在城区出现的叠掩问题,实现了城区地表形变信息的重构,但是该方法仅利用了目标的稀疏特性并没有考虑目标的结构特性,对具有这两种特性的目标进行重构时其性能较差。针对这一问题,本文采用联合Khatri-Rao子空间和块压缩感知(KRS-BCS),提出了一种差分SAR层析成像方法。该方法依据目标的结构特性和重构观测矩阵具有的Khatri-Rao积性质,将稀疏结构目标的差分TomoSAR问题转化为Khatri-Rao子空间下的BCS问题,然后对目标进行块稀疏的l1/l2范数最优化求解,最后通过理论分析和仿真试验对分辨能力和重构估计性能进行了定性和定量评价,仿真结果表明本文所采用的KRS-BCS方法不仅保持了高分辨率的优点,而且有效地降低了虚假目标出现的概率,大幅度提高了散射点准确重构概率,切实可行地解决了CS方法的不足。应用实例研究中,利用34景Envisat卫星ASAR时间序列影像对日本千叶县茂原市城区进行地表形变监测,并以一等水准点和实时测量的GPS站点观测数据作为参考形变结果进行验证,试验结果表明采用KRS-BCS方法反演的结果与参考形变结果保持了良好的一致性且形变速率整体偏差也较小,实现了较高精度的城区地表形变估计。     

顾及邻域居民地群组相似性的道路网匹配方法

现有多源同比例尺道路网匹配方法中,大多只利用道路自身特征进行匹配,而较少顾及道路周边要素对匹配过程的影响和约束,从而影响了道路网匹配效果的进一步提高,特别是对系统误差改正后仍存在一定位置或旋转偏差的道路数据进行匹配时,这种影响尤为明显。本文借鉴人类对陌生环境的空间认知特点,提出了一种顾及邻域居民地群组相似性的道路网匹配方法。该方法通过构建城市骨架线网确定与道路相邻的居民地群组,进而计算居民地群组空间关系和几何特征相似度来获得对应道路的匹配结果。其特点在于:对存在位置或旋转偏差的道路数据匹配,以其邻域空间内居民地群组的整体相似性指标来带动道路自身匹配,实际上是增加了周边居民地群组对道路匹配过程的约束,更具鲁棒性。试验及对比分析表明,本方法能够较好地解决系统误差改正后仍存在较大位置和旋转偏差的道路数据间的匹配问题,提高匹配的正确率。     

影像与激光雷达数据的城市地物信息提取

针对城市地物信息提取中地物边界难以确定、分类精度不高的问题,该文提出一套综合利用影像及激光雷达点云高程信息的面向对象分类方法。在分割中,各类地物的最佳分割尺度由监督法分割精度评价确定,最终分割结果利用粒度理论下的分割尺度综合方法进行合成,能兼顾不同地物最优分割尺度,获得准确地物边界;在分类中,采用ReliefF特征选择算法度量从影像及点云数据提取的对象特征重要度,选择最佳特征组合,并采用多分类器组合方法进行分类,以消除Hughes现象,提高分类精度。选择德国斯图加特市两块实验区进行分类实验,结果表明:该方法有利于提高大范围城市地物精细信息提取的精度和效率,具有较高的应用价值。     

The use of WorldView-2 satellite imagery to model urban drainage system with low impact development (LID) Techniques

Within a wide range of best management practices for stormwater management in urban areas, there has been an increasing interest in source control measures. Source controls such as low-impact development (LID) techniques are potentially attractive as retrofit options for older developed areas that lack available land to implement conventional measures such as stormwater management ponds. Hence, distributed urban drainage models requiring detailed representation of developed drainage areas should be developed to accurately estimate the benefits that LIDs may provide. This study (1) presents a two-stage classification process on a high-resolution WorldView-2 image, and (2) demonstrates how to use the extracted land cover information in the subsequent hydrologic modelling and assessment of different LIDs’ performance. The proposed two-stage classification method achieved an overall accuracy of 80.6%, whereas a traditional pixel-based achieved 68.4% in classifying the same urban area into six land cover classes. From the classification results, the hydrologic properties of micro-subcatchments were imported in the United States Environmental Protection Agency Storm Water Management Model to assess the performance of LIDs. A reduction of run-off volume 18.2% and 37.1% was found with the implementation of porous pavement and bioretention, respectively, in a typical low-rise residential area located in the city of San Clemente, California, US. The study demonstrates the use of high-resolution remote sensing image to aid in evaluating LID retrofit options, and thus benefits in situations where detailed drainage area information is not available.     

生态敏感性限制下的城市建设用地适宜性评价

针对当前城市规划中对建设用地适宜性评价框架的不尽合理问题,以河南省西峡县为例构建了包含主导性和限制性逻辑的综合评价方案。利用遥感与地理信息系统相结合的方法,将生态敏感性作为限制性因素进行城市建设用地适宜性研究。研究区的分析结果显示:西峡县整体新增城镇建设区呈现南北区域不适宜而中间适宜的特征,而适宜和较适宜城市建设的用地主要分布在鹳河及其支流形成的河谷地带。但在中心城区外缘某些区域生态敏感性强,应禁止开发活动。研究结果有效体现出建设用地和生态用地的空间格局,对城市总体规划具有一定的参考价值。     

Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.     

Use of an inside buffer method to extract the extent of urban areas from DMSP/OLS nighttime light data in North China

Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) nighttime imagery provides a valuable data source for mapping urban areas. However, the spatial extents of large cities are often overestimated because of the effect of over-glow from nighttime light if a fixed thresholding technique is used. In the work reported here, an inside buffer method was developed to solve this issue. The method is based on the fact that the area overestimated is proportional to the extent of the lit area if a fixed threshold is used to extract urban areas in a region/county. Using this method, the extents of urban areas in North China were extracted and validated by interpretations from Landsat Thematic Mapper images. The results showed that the lit areas had a significant linear relationship with the urban areas for 120 representative cities in North China in 2000, with an R² value of over 0.95. This demonstrates that the inside buffer method can be used to extract urban areas. The validation results showed that the inside buffer model developed in 2000 can be directly used to extract the extent of urban areas using more recent nighttime light imagery. This is of great value for the timely updating of urban area databases in large regions or countries.     

Influence of urban expansion on the urban heat island effect in Shanghai

The urban heat island (UHI) effect resulting from rapid urbanization is attracting increasing attention among the global scientific community. This research analyzed the relationship between urban expansion and the UHI effect utilizing an integrated approach, including urban land interpretation and retrieving land surface temperature based on remote sensing, and spatial overlay analysis for revealing the relationship for different time periods between 1984 and 2014 in Shanghai, China. The results show that (1) the spatiotemporal changes in UHI are consistent with the expansion of urban land, and rapid urban expansion leads to an expansion of the UHI, in particular along roadways. (2) The mode of urban expansion is an important factor influencing the UHI effect. Urban sprawl (urban expansion in the edge-expansion way) is a form of typical expansion that leads to the rapid increase in the UHI. When the urban compactness ratio is less than 0.15, a compact design can effectively control the expansion rate of the UHI and mitigate its range of influence and intensity. However, when the urban compactness ratio is greater than 0.15, the urban design has a marked influence on the UHI ratio index: a more compact form produces a stronger UHI effect. So, finding an equilibrium between urban compactness ratio and urban expansion rate is good for effective urban management and planning.