Swelter in the city: Urban greenery and its effects on temperature in Guwahati,India

Changing urban landscape with multistoried high rises, roads and pavements is continuously reducing urban green space. These structures result in high surface temperature variation within cities. To explore the relationship between surface temperature and normalized difference vegetation index (NDVI), this study estimates two models—geographically weighted regression (GWR) and a fixed effect panel data model in relation to the Guwahati Metropolitan Area (GMA), a secondary city in north east India. The results indicate the superiority of GWR regression in presence of spatial dependence. Panel data analysis shows that the densely populated urban areas in the GMA with less than 10 per cent greenery are 1°C warmer than the sub-urban areas with 50 per cent greenery.     

都市新区住宅地价空间异质性驱动因素研究 ——基于空间扩展模型和GWR模型的对比

以南京市江宁区为例,基于2004~2011年住宅用地出让数据,利用空间扩展模型和GWR模型对都市新区住宅地价空间异质性及其驱动因素进行研究。结果表明：① 空间扩展模型与GWR模型分别可解释采样区63%、61%的住宅地价变化,较全局回归模型(47%)有显著提升,更有利于研究土地市场的空间异质性。② 空间扩展模型可有效表征各解释变量及其交互项对住宅地价作用的空间结构总体趋势,其拟合效果相对较优。GWR模型则在局部参数估计方面存在优势,借助GIS可将各变量的地价作用模式可视化,从而比空间扩展模型更能有效刻画住宅地价影响因素的空间非平稳性特征,各因素对地价的平均边际贡献排序为水域> 地铁> 大学园区> CBD> 商业网点> 医院,且商业网点、 医院系数值具有方向差异性。③ 距地铁站点、水域、大学园区以及CBD的距离是研究区住宅地价的关键驱动因素,各自存在特有的地价空间作用模式,可为研究区住宅土地市场细分提供科学依据。     

中国与美国大都市区城市内部土地覆盖结构时空差异

城市内部土地覆盖结构对城市生态服务功能和人居环境质量产生重要影响。中国与美国不同发展阶段城市不透水地表和绿地时空分布格局存在显著差异。本文基于Landsat TM/MSS影像获取1978、1990、2000、2010 年城市内部土地覆盖和不透水地表分类信息,监测并比较中国和美国六个特大城市扩展时空动态、土地覆盖结构差异及城市不同功能区特征。研究表明,在过去30 多年以来,中国城市以相对紧凑形态发展,美国城市较为离散;美国三大城市植被所占的比例是中国的2.21 倍;中国城市内部结构土地利用功能类型更加复杂,不透水地表密度更高,而美国城市CBD和居住区功能相对独立,特别居住区以镶嵌式低不透水地表和高绿地比例结构为主。     

Spatio-temporal characteristics of intra-urban land cover in the cities of China and USA from 1978 to 2010

Urban land cover has major impacts on a city''s ecosystem services and the inherent quality of its urban residential environment. The spatio-temporal distribution of impervious surface area and green areas in Chinese cities has exhibited a significantly marked difference in comparison with USA cities. This study focused on monitoring and comparing the spatio-temporal dynamics, land cover patterns and characteristics of functional regions in six Chinese (n=3) and USA (n=3) cities. The study data were collated from Landsat TM/MSS imagery during the period 1978-2010. Results indicate that Chinese cities have developed compactly over the past three decades, while development has been notably dispersed among USA cities. Mean vegetation coverage in USA cities is approximately 2.2 times that found amongst Chinese urban agglomerations. Land use types within Chinese cities are significantly more complex, with a higher density of impervious surface area. Conversely, the central business district (CBD) and residential areas within USA cities were comprised of a lower proportion of impervious surface area and a higher proportion of green land. Results may be used to contribute to future urban planning and administration efforts in both China and the USA.     

PERCEPTION OF COLOR SEQUENCES ON MAPS OF ATMOSPHERIC PRESSURE

Impervious surface cover is often associated with low socioeconomic status (SES) and vegetation with high SES in urban areas in both developed and developing countries. This study documents the relationships among land cover, number of years urbanized (YR), topographic slope, and socioeconomic indicators, including a socioeconomic marginality index (MI), in Tijuana, Mexico. Unlike in other urban areas, vegetation cover decreased slightly and impervious cover increased significantly with increasing SES in Tijuana. This pattern was due to (1) slightly higher vegetation cover in tracts with high MI (low SES), where informal settlements were sometimes on steep slopes with remnant patches of vegetation; (2) significantly higher soil cover and low impervious cover in tracts with high MI due to unpaved roads and vacant lots, and (3) low vegetation cover and high impervious cover in tracts with low MI, which included the city center and new suburban developments with high population densities. YR, slope, and population density were important secondary predictors of land cover and SES. Approximately half of the variance in the proportion of the population with drainage and with piped water supply was explained by a multiple regression with land cover, slope, and YR, whereas fertility and infant mortality correlated with but were not predicted well by land cover. The combination of rapid population growth, variable topography, semiarid climate, and history of service development in Tijuana resulted in unexpected associations between SES and land cover, with implications for the environment and public health.     

厦门市热岛强度与相关地表因素的空间关系研究

以厦门市为例,基于遥感影像与建筑普查数据,分析了各局部气候区中相关地表因素与热岛强度之间的空间响应规律,以及厦门市各局部气候区中热岛强度与相关地表因素的空间关系。结果表明：研究区热岛强度有显著的空间自相关性,高值区集中于东南部的建设用地及耕地和裸地,低值区聚集于湖泊、河流等水体、湿地以及北部、西北部的林地;普通回归模型不能有效解释空间中相关地表因素与热岛强度之间的关系;空间误差模型的拟合效果优于空间滞后模型,可以更准确分析地表因素与热岛强度之间的空间关系;各局部气候区中可以作为回归模型自变量的地表因素有所不同。作为回归模型的自变量时,植被指数、水体指数、天空视域因子与热岛强度呈负相关关系,建筑密度、不透水面比例与热岛强度呈正相关关系,而建筑体积密度、建筑平均高度、建筑高度差与热岛强度的相关性在各气候区中并不一致。根据研究结论建议保护“补偿区”、分隔“作用区”,综合考虑规划实施策略的可行性,以有效缓解热岛效应。     

1982-2013年中国植被NDVI空间异质性的气候影响分析

为研究气候变化与植被活动之间的复杂关系,采用1982-2013年GIMMS NDVI与气象站点温度与水分的监测资料,应用基于像元的地理加权回归方法,探究了中国植被NDVI及其动态特征对气候变化响应的空间格局。中国植被NDVI与地表温度呈空间非平稳关系,在空间上的负相关关系主要集中在东北、西北及东南部分地区,空间正相关则更为集中和连片;针对不同气候指标的标准化系数对比可知,植被NDVI受水分控制作用较为显著的区域主要集中在北方地区以及青藏高原,温度的主导作用区域则分布在华东、华中及西南地区,其中年均最高气温对NDVI的主导区域范围最广;植被NDVI动态与气候变率的回归结果表明,增温速率的升高会通过加剧干旱等机制对植被活动产生抑制作用,水分变率对植被活动的强弱起到了重要的调节作用。     

深圳市不透水表面的遥感监测与时空格局

定量监测城市不透水表面扩展过程,是分析城市景观格局动态,深入剖析城市地区空间镶嵌体内部异质性景观相互转化的重要基础。以深圳市为例,通过定量划分城市不透水表面等级体系,计算不透水表面的转移矩阵与景观格局指数,分析不透水表面的时空动态,结果表明:1990～2005年间,高盖度及极高盖度的不透水表面持续增加,中盖度及低盖度不透水表面先增加后减少,而自然地表及极低盖度的不透水表面则为先减少后恢复,反映了城市化进程对城市地表覆被的结构性影响。景观格局变化显示景观多样性程度较高,且经历了低-高-低-高的变化;斑块在1990年以自然地表为主,1995年以后以中盖度等级为主,高盖度等级显著增加。自然地表和中等盖度的不透水表面变化分别主导着前期和中后期的变化过程。城市建设的迅速扩张,将林地、农田、水体及滩涂等自然地表快速地转变为不同功能的建设用地,主导着不透水表面等级分布的梯度性变化。     

Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.     

Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.     

Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression

Scientific interpretation of the relationships between agricultural landscape patterns and urbanization is important for ecological planning and management. Ordinary least squares (OLS) regression is the primary statistical method in previous studies. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between several urbanization indicators (urbanization intensity index, distance to urban centers and distance to road) and changes in metrics describing agricultural landscape patterns (total area, patch density, perimeter area ratio distribution and aggregation index) at two block scales (5 km and 10 km). Results denoted that GWR was more powerful than OLS in interpreting relationships between agricultural landscape patterns and urbanization, since GWR was characterized by higher adjust R², lower Akaike Information Criterion values and reduced spatial autocorrelations in model residuals. Character and strength of the relationships identified by GWR varied spatially. In addition, GWR results were scale-dependent and scale effects were particularly significant in three aspects: kernel bandwidth of weight determination, block scale of pattern analysis, and window size of local variance analysis. Homogeneity and heterogeneity in the relationships between agricultural landscape patterns and urbanization were subject to the coupled influences of the three scale effects. We argue that the spatially varying relationships between agricultural landscape patterns and urbanization are not accidental but nearly universal. This study demonstrated that GWR has the potential to provide references for ecological planners and managers to address agricultural landscapes issues at all scales.     

Factors Affecting Domestic Water Consumption on the Spanish Mediterranean Coastline

During the last decades on the Spanish Mediterranean coastline there has been a great development of low-density urban areas, as well as a change in the sociodemographic structures, especially in the municipalities that have developed a residential tourism model. Likewise, urban and tourist development have stressed the balance between the availability of water resources and urban water demands, generating situations of scarcity that might be aggravated by climate change. This study identifies the determinants of water consumption on the Spanish Mediterranean coastline, focusing on the variables related to urban land uses and socioeconomic and sociodemographic variables at the municipal level using an ordinary least square (OLS) and a geographically weighted regression (GWR) model. The GWR model results substantially improved the results of the OLS model, explaining 88.27 percent of the variance in domestic water consumption and solving the spatial autocorrelation problem of some independent variables. The most influential variables include the percentage of second homes or the percentage of residential properties with swimming pools at the municipal level. These characteristics must be considered to develop demand management policies and an updated hydrological planning to ensure urban supply in a future with less available water resources.     

Geomorphic effects of rural-to-urban land use conversion on three streams in the Central Redbed Plains of Oklahoma

This research evaluates the impact of rural-to-urban land use conversion on channel morphology and riparian vegetation for three streams in the Central Redbed Plains geomorphic province (central Great Plains ecoregion) of Oklahoma. The Deep Fork Creek watershed is largely urbanized; the Skeleton Creek watershed is largely rural; and the Stillwater Creek watershed is experiencing a rapid transition from rural to urban land cover. Each channel was divided into reaches based on tributary junctions, sinuosity, and slope. Field surveys were conducted at transects in a total of 90 reaches, including measurements of channel units, channel cross-section at bankfull stage, and riparian vegetation. Historical aerial photographs were available for only Stillwater Creek watershed, which were used to document land cover in this watershed, especially changes in the extent of urban areas (impervious cover).The three streams have very low gradients (< 0.001), width-to-depth ratios < 10, and cohesive channel banks, but have incised into red Permian shales and sandstone. The riparian vegetation is dominated by cottonwoods, ash, and elm trees that provide a dense root mat on stream banks where the riparian vegetation is intact. Channels increased in width and depth in the downstream direction as is normally expected, but the substrate materials and channel units remained unchanged. Statistical analyses demonstrated that urbanization did not explain spatial patterns of changes in any variables. These three channels in the central Redbed Plains are responding as flumes during peak flows, funneling runoff and the wash-load sediment downstream in major runoff events without any effect on channel dimensions. Therefore, local geological conditions (similar bedrock, cohesive substrates and similar riparian vegetation) are mitigating the effects of urbanization.     

Exploring spatially varying and scale-dependent relationships between soil contamination and landscape patterns using geographically weighted regression

Landscape pattern is an important determinant of soil contamination at multiple scales, and a proper understanding of their relationship is essential for alleviating soil contamination and making decisions for land planners. Both soil contamination and landscape patterns are heterogeneous across spaces and scale-dependent, but most studies were carried out on a single scale and used the conventional multivariate analyses (e.g. correlation analysis, ordinary least squared regression-OLS) that ignored the issue of spatial autocorrelation. To move forward, this paper examined spatially varying relationships between agricultural soil trace metal contamination and landscape patterns at three block scales (i.e. 5 km × 5  km, 10 km × 10 km, 15 km × 15 km) in the Pearl River Delta (PRD), south China, using geographically weighted regression (GWR). This paper found that GWR performed better than OLS in terms of increasing R square of the model, lowering Akaike Information Criterion values and reducing spatial autocorrelation. GWR results revealed great spatial variations in the relationships across scales, with an increasing explanatory power of the model from small to large block scales. Despite a few negative correlations, more positive correlations were found between soil contamination and different aspects of landscape patterns of water, urban land and the whole landscape (i.e. the proportion, mean patch area, the degree of landscape fragmentation, landscape-level structural complexity, aggregation/connectivity, road density and river density). Similarly, more negative correlations were found between soil contamination and landscape patterns of forest and the distance to the river and industry land (p < 0.05). Furthermore, most significant correlations between soil contamination and landscape variables occurred in the western PRD across scales, which could be explained by the prevailing wind, the distribution of pollutant sources and the pathway of trace metal inputs.     

地理单元划分对城市热环境响应规律的影响——以北京为例

尺度效应及空间异质性决定了同一种影响因子在不同地理单元中对生态过程的作用程度存在差异.论文选择城市热环境效应这一典型城市生态过程为研究对象,针对地理单元划分对热环境效应响应规律研究较少的现状,选择土地利用分类、网格、街区、局地气候区4种地理单元,选取常用景观组分指标——归一化差值植被指数(normalized diff...     

Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R² for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected.     

城市不同下垫面的能量平衡及温度差异模拟

城市能量平衡是研究城市热岛效应的物理基础。利用北京市教学植物园2010年的实测数据,设置不同类型下垫面(植被覆盖类型:林地、草地和不透水层覆盖类型:道路、房屋),利用局地尺度城市气象参数化方案模拟并分析了相同气象条件和净辐射通量输入下,不同类型下垫面的显热、潜热通量及蒸散降温效应的差异。结果显示:(1)不同类型下垫面的各能量支出项有明显差异,植被覆盖区域和不透水层覆盖区域的波文比年均值分别为0.28和4.60,且在植被生长季差异较大;(2)城市扩展过程中道路、房屋替换林地、草地的过程,也是显热增加而潜热减少的过程。植被层向不透水层转换的过程中,显热通量年均增加32.74W/m²,潜热通量减少38.87W/m²,储热通量增加7.95W/m²;(3)理论上,植被蒸散的年降温效应使单位面积植被覆盖区域的气温比不透水层区域可低2.63℃。     

Drivers of afforestation in Northern Vietnam: Assessing local variations using geographically weighted regression

This article identifies drivers of forest transition in a province of Northern Vietnam between 1993 and 2000 by applying geographically weighted regression (GWR) analysis to remotely sensed and statistical data. The regression model highlighted the spatial variation of the relationship between the percentage of land afforested and its proximate causes. Factors identified as having a major impact on afforestation are: the presence or proximity of a wood-processing industry, the distance to highways, and land allocation to households. Whereas the two former variables are in most areas of the province positively correlated with afforestation, an unexpected negative correlation was observed for the latter. The analysis of these results, supported by an in-depth knowledge of the area and of the political context, leads to the conclusion that, during the time period considered, afforestation was largely driven by state organisations on protected state-owned land, and forestry was not a significant component of household economic activities.