基于百度街景图像的行人视角城市街道植被绿化格局分析

城市街道绿化植被作为城市景观的重要组成部分,其分布格局对城市景观美学发展及行人身心健康有显著影响,立足行人视角准确监测街道绿植分布信息对城市规划与管理有明确的辅助作用。该文针对已有研究多采用沿天底方向垂直向下观测的遥感影像监测地表植被而对行人视角的绿色植被分布格局研究涉及不多的现状,基于免费获取的百度街景图像,选取绿植覆被典型的泰安市区为案例区,结合网络信息抓取与空间地理信息处理技术,分析百度街景图像提取侧视绿植信息的可行性,统计并对比其计算结果与遥感影像提取结果的关系,以期为城市规划与管理提供辅助参考信息。网络抓取案例区273个样点共3276幅百度街景图像,利用计算机监督分类提取图像中的绿植区域;基于空间分析模型分析街道绿色植被的分布格局;利用SPSS软件趋势拟合模块分析百度街景图像与遥感影像提取的植被信息的相关性。主要结果为:百度街景图像可作为主数据源提取城市街道的侧视绿植分布情况;案例区不同区域植被分布指数区别较大,空间格局差异明显;百度街道植被分布指数与基于遥感图像提取的10、20、50 m缓冲距离范围内植被覆盖面积呈显著正相关关系,但两者的变化趋势并非完全一致。百度街道植被分布结果可作为遥感监测结果的辅助信息更好地指导城市绿色景观规划与精准管理。     

基于图像融合与混合像元分解的城市植被盖度提取

城市植被盖度提取对于开展城市绿色空间保护和城市规划具有重要意义。随着遥感技术的发展,混合像元分解模型被广泛用于从中等分辨率的多光谱影像提取城市植被盖度,但较低的影像空间分辨率限制了该模型的应用领域。为此,以杭州市为例,首先引入Gram-Schmidt(GS)方法对Landsat ETM+的多光谱波段和全色波段进行融合,再通过混合像元分解模型从ETM+融合影像上提取城市植被盖度,最后利用SPOT影像进行精度检验。结果发现,采用GS方法对影像进行融合后,标准差、信息熵、平均梯度提高,相对偏差小于0.07,说明在保留多光谱信息的基础上提高了其空间分辨率。与SPOT影像相比,在融合影像上75%以上样本的植被盖度值相似,误差较大的区域是市区植被特别稀疏或茂盛的像元。与源影像相比,从融合影像上提取的植被盖度的均方根误差和系统误差降低了0.01。该方法在降低城市植被监测成本、提高监测精度方面具有潜力。     

街道绿化品质的人本视角测度框架—基于百度街景数据和机器学习的大规模分析

新技术条件下测度街道绿化品质,实现人眼视角绿化可见度与街道可达性的整合分析。抓取上海的大规模街景数据,基于机器学习算法提取绿化可见度,将其与基于空间网络分析的街道可达性开展叠合分析,并与基于卫星遥感影像的绿化率比较,发现绿化率难以准确展现市民日常生活中绿化接触度。运用新技术和新数据推动精细化规划导控,实践上能实现大规模分析并保证高精度结果,理论上能为规划政策的人本视角转型提供支撑。     

黄河三角洲湿地不同景观类型影响下植被与土壤因子的空间分布特征

以黄河三角洲湿地为研究区,结合遥感影像和实地调查数据,构建湿地景观类型和主要影响因子的空间分布格局,解析不同景观类型下的植被及土壤因子的空间分布特征以及耦合关系.结果表明:农田、湿地植被区是研究区内面积最大的景观类型,分别占有46.87％、20.6％,而植被覆盖贡献率59.07％、37.62％,生物量贡献率59.08％...     

基于街景数据的绿视率模型构建及其影响因子研究

以行人的视觉直观感受为出发点, 以匈牙利塞克什白堡为研究区, 基于街景数据, 深入研究植被信息提取方法, 针对传统像素级分类容易造成过度提取的现象, 提出一种面向对象的街景图像分类方法, 构建了基于街景数据的绿视率模型, 并分析总结了街景图像拍摄时的水平视角、垂直视角、水平方向范围等镜头参数对绿视率计算的影响。研究结果表明: 面向对象的分类方法提升了街景图像分类的精度和效率, 为绿视率计算模型提供了新的数据源和计算方法; 采集街景图像时, 增加水平视角和垂直视角、扩大水平方向范围能使绿视率计算结果更加真实地反映行人视觉感受。构建的绿视率计算模型能从行人角度为街道绿化的布局和空间结构优化提供依据, 可为城市绿地规划设计、居住区视觉生态设计等提供参考依据。     

基于中分辨率TM数据的湿地水生植被提取

利用湿地水生植被生长旺盛、光谱反射较强、光谱信息比较丰富的8月份中分辨率Landsat TM和ETM+多光谱遥感影像,采用面向对象的分类方法,进行野鸭湖湿地水生植被的提取。研究表明:在提取过程中,通过对原始影像进行主成分变换和穗帽变换,将主要信息与噪声分离,不仅减小了数据冗余和波段间的相关性,而且增大了影像上湿地水生植被与其他地物类型光谱和空间信息的差异性,并结合野外水生植被光谱特征分析,选择归一化植被指数NDVI与归一化水体指数NDWI辅助分类,构建特征波段或波段组合,然后,确定适当的隶属度函数和阈值范围,构建分类决策树,完成湿地水生植被的自动分类,提高了影像分割与面向对象分类的精度,取得了较为理想的湿地水生植被提取结果。2002年和2008年两景影像的总体分类精度分别达到86.5%和85.44%,表明中分辨率TM影像可以满足湿地水生植被提取的需要,又因为其具有较高的波谱分辨率、极为丰富的信息量、相对较低的价格、长时间序列,可以作为近20a湿地水生植被提取和动态变化监测的主要数据源。     

基于TM NDVI的武功山山地草甸植被覆盖度时空变化研究

以江西省武功山山地草甸为研究区,基于4期TM(Thematic Mapper,专题测图仪)卫星遥感影像,提取NDVI(Normalized Difference Vegetation Index,归一化植被指数),采用像元二分模型,运用ENVI 5.1和Arc GIS 10.0软件计算得到武功山山地草甸的植被覆盖度分布格局及动态变化。研究结果表明:(1)研究期间山地草甸面积减少了9.72%,呈递减趋势。20年来随着武功山风景区成立—旅游业发展—山地草甸生态修复,山地草甸植被覆盖度增加和减少交替,总体呈上升趋势;(2)山地草甸植被覆盖度呈现东南高西北低的空间分布特征。低覆盖度草甸区集中在武功山山脉的西北侧坡面的崖壁和部分山脊线上,而高覆盖度草甸区多分布在武功山山脉的东南坡面;(3)研究区山地草甸退化与改善并存,山地草甸最北端和白鹤峰-九龙山区域的东南坡、南坡低海拔处植被总体呈退化特征;发云界南部的东坡植被总体呈现改善特征。研究期间山地草甸退化面积比改善面积多出1.78%。(4)山地草甸植被覆盖度的分布格局和地形因子存在较高的相关性(P0.05):植被覆盖度随着坡向的变化而呈规律性的变化,总体上山地草甸植被覆盖度的分布为阳坡平坡阴坡;植被覆盖度先是随着坡度的上升而升高,在坡度15°—25°时达到峰值,然后随坡度的上升而下降,在45°—90°最低;植被覆盖度随海拔升高呈波浪式下降,1000—1200m最高,在主峰山顶海拔1800—1918.3m最低。遥感解译检验结果证明采用此方法对大面积山地草甸覆盖度分布及变化进行反演可行而准确;在后续研究中将采用不同季相的多期影像数据提取NDVI对研究区植被覆盖度进行长期监测,以便更准确可靠地分析山地草甸演化过程和趋势。     

面向对象的优势树种类型信息提取技术

森林植被优势树种类型信息的提取是遥感影像分类中的难点.面向对象分类方法是用高空间分辨率遥感数据实现精确类型信息提取的新方法.本文以2013年Quickbird影像作为基础数据,选择福建省三明市将乐林场为研究区,采用面向对象多尺度分割方法提取耕地、灌草地、未成林造林地、马尾松、杉木和阔叶树等类型信息.分类特征融合植被的光谱、纹理和多种植被指数3类特征信息,建立类层次结构,对不同层次分别用隶属度函数和决策树分类规则,最终完成分类,并与只用纹理与光谱特征相结合的方法进行对比.结果表明:融合纹理、光谱、多种植被指数的面向对象的分类方法提取研究区优势树种类型信息的精度为91.3％,比只用纹理和光谱的方法精度提高了5.7％.     

基于无人机遥感的“莫兰蒂”台风前后植被变化分析——以鼓浪屿为例

台风是沿海地区危害最为严重的自然灾害之一,高效、准确的灾害监测与评估为灾情的管理和决策提供了必不可少的条件。以“莫兰蒂”台风为例,利用无人机遥感影像提取台风前后的植被覆盖信息,通过计算、对比各自景观格局指数分析植被分布格局的变化,进一步通过对比图斑差异和计算受灾图斑的景观格局指数,分析鼓浪屿受灾图斑的分布情况与受灾细节。研究结果表明,鼓浪屿景观虽然存在较多小面积斑块,但整体格局呈集聚分布。台风未影响岛上景观的整体分布特征,台风前植被景观与非植被景观比例大致相等,分布均衡,台风后植被减少使非植被景观成为鼓浪屿的优势景观。受台风影响景观格局发生了较大改变,由台风前非植被斑块的景观破碎化程度高转变为台风后植被斑块景观破碎程度高,且台风后植被聚集程度有所减小。受灾区域呈现规律性分布,位于山体之上及周边无遮挡的开敞地区受灾更为严重,建筑密集区和受山体遮挡的区域受灾相对较轻。     

基于LSMM和改进的FCM提取城市植被覆盖度——以北京市海淀区为例

植被是城市生态系统的重要组成部分,及时获取植被覆盖信息对城市生态环境监测具有重要意义。利用中分辨率Landsat TM遥感数据,采用线性光谱分解模型(LSMM)开展城市植被覆盖度提取;同时,通过改进训练样本选择方式,在最小噪声变换(MNF)、像元纯净指数分析(PPI)、N维可视化分析基础上得到端元样本,再运用模糊C-均值(FCM)获取植被覆盖度;最后以高分辨率SPOT5遥感数据对两种方式的提取结果进行精度检验。结果显示,基于LSMM和改进的FCM提取的城市植被覆盖度与检验数据相关系数分别为0.8252和0.9381,后者可以较好地处理其他要素的非线性影响,因而具有较高精度。     

中国35个城市行道树树种组成特征研究

明确城市森林树种组成是城市森林建设与管理的基础。以往研究多局限在特定城市或区域，难以准确揭示其普遍规律。本研究基于百度街景选取全国35个主要城市，共设置88 632个样点。并根据地理区域将这些城市划分为北方、南方、青藏和西北城市，比较不同区域城市行道树树种组成差异。结果表明：①基于街景调查我国常用行道树共99种，最常见的是杨树（Populus spp.）、柳树（Salix spp.）、香樟（Cinnamomum camphora）、雪松（Cedrus deodara）和国槐（Sophora japonica）；②我国南方城市行道树种多选用阔叶树如香樟，北方和西北城市行道树则以杨树、柳树和槐树为主，青藏地区行道树以杨树和针叶树为主；③依据10/20/30经验法则，仅有昆明和桂林城市行道树在种的水平上配置较为合理，其他城市植物配置均不合理，尤其克拉玛依市的杨树相对多度高达61.2%，长沙市和杭州市的樟树比例超过树种总数的50%，单个树种优势明显，4个区域行道树树种水平上配置均不合理。今后在行道树种选择时北方、西北和青藏区域城市应减少杨树、柳树、松树和槐树等的利用，南方地区应增加适宜本土生长的其他树木种类。本研究为我国合理规划城市行道树，系统开展多城市对比和综合提升生态服务功能提供了重要参数。     

成都青羊区背街小巷行道树孢粉致敏风险分析

为研究背街小巷行道树的孢粉致敏风险,以成都市青羊区410条背街小巷为例,在记录背街小巷行道树特征的基础上,计算其孢粉致敏风险指数(I_UGZA)并叠加区域人口密度图,获得青羊区背街小巷的孢粉致敏风险地图。结果表明: 410条背街小巷共有行道树32461棵,属27科、41属、52种。树种分布严重不均,过度使用小叶榕(占比31.8%)、银杏(12.9%)和香樟(8.5%)。背街小巷的I_UGZA均值为2.61,致敏风险较高,春季为首要风险季。其中,175条背街小巷的致敏程度低(I_UGZA为0～1),174条致敏程度相对较低(I_UGZA为1～5),6条致敏风险极高(I_UGZA为15～20)。相关性分析显示,背街小巷平均树高和树冠与街道面积比是影响其I_UGZA的关键因素。叠加人口密度图后显示,青羊区行道树孢粉致敏风险较高的街区主要是少城街道、草市街道、西御河街道、府南街道和苏坡街道片区。     

Spatial pattern of riparian vegetation in desert of the lower Tarim River basin

AimsRevealing the spatial pattern of riparian vegetation in hyper-arid regions can improve our understanding on the water relations of riparian vegetation in the desert watershed ecosystem, and also can provide valuable scientific guidance for desertification control and water resources management of watershed of the arid region in northwestern China. This research objective is to show the spatial distribution and structures of typical riparian vegetation in hyper-arid desert watershed from regional and overall perspective.Methods Based on Landsat-8 OLI remote sensing images and a large number of field vegetation surveys, the supervised classification method was used to distinguish three main vegetation categories in the lower Tarim River basin: Tamarix thicket, Populus euphratica woodland, and Phragmites australis grassland. The leaf area index (LAI) of Tamarix thickets and Populus euphratica woodlands were inverted by using the remote-sensed LAI inversion empirical model that we developed.Important findings Supervised classification supporting abundant information of ground objects by remote sensing was an effective method to determine desert riparian vegetation categories in arid desert regions. The area was 336.4 km² for the Populus euphratica woodlands and 405.3 km² for the Tamarix thickets, respectively. The Tamarix thickets had a wider distribution range while the Populus euphratica woodlands grew near the river channel. The overall LAI of the riparian vegetation was low. The average LAI value was 0.253 for the Tamarix thickets and 0.252 for the Populus euphratica woodlands. The areas of vegetation with the LAI value of less than 0.5 accounted for 92.4% and 90.1% of the total area of the Tamarix thickets and the Populus euphratica woodlands, respectively. The statistic results showed that large spatial variability of the riparian vegetation LAI existed. The spatial variability of the Populus euphratica woodlands was larger than that of the Tamarix thickets. The LAI values of the riparian vegetation had a significant negative exponential relationship with the distances away from the river channel. The LAI values declined rapidly within the distance of 1 km from the river channel and they were generally lower than 0.1 when the distances beyond 1 km, which indicated that the riparian vegetation was mainly distributed within 1 km from both side of the river. This research indicated three basic characteristics of the spatial pattern in riparian vegetation from hyper-arid desert regions, including overall sparse spatial distribution, high spatial variability and negative exponential relationship between LAI and distance away from the river channel.     

基于深度学习街景影像解译和景感生态学的视域环境定量解读

城市物理环境带给居民丰富而生动的视觉意象,目前许多文献结果表明其宜人性与公共福祉以及健康状况息息相关。景感生态为探究城市物理环境与居民感知信息之间的联系提供了指导依据,通过人本尺度的定量手段解读城市环境中视觉、听觉和味觉等多维度感知信息。秉承景感生态学的基本原理,引入一种结合街景大数据和深度学习的城市环境量化手段,以北京市六环范围为例,将景感视率作为测度对人本视角下的城市环境展开定量解读。在全面把控多维景感要素的同时,旨在实现以人为本的城市物理环境优化设计,从而满足人们对生活品质提升的实际需求。实验结果显示:(1)从视觉感受的宏观表现来看,北京四环路范围内建成环境的"闭合感"较强,而对绿植的感知程度相对偏弱,因而需要开展存量环境设计并优化视域界面结构;(2)以景感视率作为特征值进行聚类得出3类主导空间(绿色空间、灰色空间和蓝色空间),可针对灰色空间着重部署垂直绿化资源,提高城市视觉绿化的可感知性,从而营造舒适宜人的绿色氛围、促进公众身心健康;(3)为景感生态学提供了基于大数据思维的数据集和定量方法补充。综上,以街景影像和景感生态视角对北京市中心城区的视域环境展开定量分析,采用了先进的深...     

Research on characteristics of biomass distribution in urban forests of Shanghai metropolis based on remote sensing and spatial analysis

Aims
Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing.
Methods
An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting (IDW) to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations and management practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4 (denoted Band 234), and the sum of TM3, TM4 and TM5 (denoted Band 345) were used as alternative auxiliary parameters .
Important findings
The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t·hm^-2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing’an and Huangpu, mostly ranging from 35 to 70 t·hm^-2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t·hm^-2. The biomass density of Cinnamomum camphora trees across the Shanghai metropolis varied between 20 and 110 t·hm^-2. The spatial biomass distribution of urban forests displayed a tendency of higher densities in northeastern areas and lower densities in southwestern areas. The total biomass was 3.57 million tons (Tg) for urban forests and 1.33 Tg for C. camphora trees. The overall forest biomass was also found to be distributed mostly in the suburban areas with a fraction of 93.9%, whereas the urban areas shared a fraction of only 6.1%. In terms of the areas, the suburban and urban forests accounted for 95.44% and 4.56%, respectively, of the total areas in the Shanghai metropolis. Among all the administrative districts, the Chongming county and the new district of Pudong had the highest and the second highest biomass, accounting for 20.1% and 19.18% of the total forest biomass, respectively. In contrast, the Jing’an district accounted for only 0.11% of the total forest biomass. The root-mean-square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the model for estimating urban forest biomass in this study were 8.39, 6.86 and 24.22%, respectively, decreasing by 57.69%, 55.43% and 64.00% compared to the original simple regression model and by 62.21%, 58.50%, 65.40% compared to the spatial analysis method. Our results indicated that a more efficient way to estimate urban forest biomass in the Shanghai metropolis might be achieved by combining spatial analysis with regression analysis. In fact, the estimated results based on the proposed model are also more comparable to the up-scaled forest inventory data at a city scale than the results obtained using regression analysis or spatial analysis alone.     

Exploring the influence of soil types underneath the canopy in winter wheat leaf area index remote estimating

Aims Remote sensing is an effective and nondestructive way to retrieve leaf area index (LAI) from plot, regional and global range. Soil background is one of the confounding factors limiting remotely estimating LAI. And soil type contains a large proportion of soil background information, which can influence the optical properties of vegetation canopy and soil. However, our knowledge on the effects stemmed from soil types underneath the canopy on LAI remote estimating have been in shortage. Thus, this study aims to explore the influences of soil types underneath the canopy on winter wheat LAI remote estimating. Methods We analyzed the sensitivity variation of eight spectral indices, named normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption ratio index 2 (MCARI₂), red edge inflection point (REIP), red edge amplitude (Dr), red edge area (SDr), red edge symmetry (RES), normalized difference spectral index (NDSI), to LAI in different soil types, and then we identified some spectral intervals or parameters that were insensitive to soil type variations underneath the canopy. We also compared the accuracy of two commonly used regression models, partial least squares regression (PLSR) and random forest regression (RFR), in estimating LAI for different soil types. We also explored the problems arising from applying the regression model developed in single soil type area to complex soil types area in retrieving LAI. Important findings This paper demonstrates the effects of soil types underneath the canopy on LAI retrieving. 1) The sensitivity of spectral indices to LAI is significantly different due to the soil type variation, but REIP has the least effects from soil type variation among the eight spectral indices. Meanwhile, the band selection algorithm of lambda-by-lambda not only chooses the most sensitive spectral interval for LAI, but also provides a feasible way to construct the spectral index that exhibits strong resistances to the effects of soil types underneath the canopy. 2) The accuracy of LAI estimation by regression models differs under soil type considered or not. So we suggest that in small scale researches, especially in a field scale, the ability of regression models in explaining variables is the priority consideration, while the PLSR is superior to RFR in this respect. Under the premise of unknown priori knowledge of land surfaces, the RFR is more suitable for retrieving LAI than PLSR, but land surface priori knowledge is still necessary. These findings provide the theoretical basis and methods for developing remotely sensing estimating LAI models adapted to various land surfaces. Further analysis is needed in applying the findings in more crop types, cultivars and growth stages.     

Application and comparison of remote sensing GPP models with multi-site data in China

Aims Estimation of gross primary productivity (GPP) of vegetation at the global and regional scales is important for understanding the carbon cycle of terrestrial ecosystems. Due to the heterogeneous nature of land surface, measurements at the site level cannot be directly up-scaled to the regional scale. Remote sensing has been widely used as a tool for up-saling GPP by integrating the land surface observations with spatial vegetation patterns. Although there have been many models based on light use efficiency and remote sensing data for simulating terrestrial ecosystem GPP, those models depend much on meteorological data; use of different sources of meteorological datasets often results in divergent outputs, leading to uncertainties in the simulation results. In this study, we examines the feasibility of using two GPP models driven by remote sensing data for estimating regional GPP across different vegetation types. Methods Two GPP models were tested in this study, including the Temperature and Greenness Model (TG) and the Vegetation Index Model (VI), based on remote sensing data and flux data from the China flux network (ChinaFLUX) for different vegatation types for the period 2003-2005. The study sites consist of eight ecological stations located in Xilingol (grassland), Changbaishan (mixed broadleaf-conifer forest), Haibei (shrubland), Yucheng (cropland), Damxung (alpine meadow), Qianyanzhou (evergreen needle-leaved forest), Dinghushan (evergreen broad-leaved forest), and Xishuangbanna (evergreen broad-leaved forest), respectively. Important findings All the remote sensing parameters employed by the TG and VI models had good relationships with the observed GPP, with the values of coefficient of determination, R², exceeding 0.67 for majority of the study sites. However, the root mean square errors (RMSEs) varied greatly among the study sites: the RMSE of TG ranged from 0.29 to 6.40 g·m^-2·d^-1, and that of VI ranged from 0.31 to 7.09 g·m^-2·d^-1, respectively. The photosynthetic conversion coefficients m and a can be up-scaled to a regional scale based on their relationships with the annual average nighttime land surface temperature (LST), with 79% variations in m and 58% of variations in a being explainable in the up-scaling. The correlations between the simulated outputs of both TG and VI and the measured values were mostly high, with the values of correlation coefficient, r, ranging from 0.06 in the TG model and 0.13 in the VI model at the Xishuangbanna site, to 0.94 in the TG model and 0.89 in the VI model at the Haibei site. In general, the TG model performed better than the VI model, especially at sites with high elevation and that are mainly limited by temperature. Both models had potential to be applied at a regional scale in China.     

公共空间安全感研究：以上海城市街景感知为例

在文献基础上梳理了街道安全感影响因素,并采用上海样本检验了“街道眼”等西方街道安全理论。邀请30位学生和30位市民对上海5个不同发展时期社区的300张百度街景图片进行安全感评定。实验发现绿视率、管理程度、车道数等都对安全感起着显著作用,并分别建立了单双车道和多车道街道空间的安全感回归模型。其中发现绿视率（单双车道相关系数R=0.728,p<0.01;多车道相关系数R=0.471,p<0.01）、管理程度（单双车道相关系数R=0.766,p<0.01;多车道相关系数R=0.450,p<0.01）、车道数量因素（相关系数R=0.502,p<0.01）对安全感均有显著的积极作用,界面透明度（单双车道相关系数R=0.222,p<0.01）、独立自行车道（相关系数R=0.309,p<0.01）及设计美感（相关系数R=0.432,p<0.01）等因素在单双车道空间中具有积极影响,而助动车与自行车（单双车道相关系数R=-0.327,p<0.01;多车道相关系数R=-0.281,p<0.01）在对安全感知评价具有消极影响,机动车（单双车道相关系数R=0.251,p<0.01;多车道相关系数R=-0.327,p<0.01）在单双车道与多车道空间中呈现相反的作用。