A scale-explicit model for checking directional consistency in multi-resolution spatial data

Multi-resolution spatial data always contain the inconsistencies of topological, directional, and metric relations due to measurement methods, data acquisition approaches, and map generalization algorithms. Therefore, checking these inconsistencies is critical for maintaining the integrity of multi-resolution or multi-source spatial data. To date, research has focused on the topological consistency, while the directional consistency at different resolutions has been largely overlooked. In this study we developed computation methods to derive the direction relations between coarse spatial objects from the relations between detailed objects. Then, the consistency of direction relations at different resolutions can be evaluated by checking whether the derived relations are compatible with the relations computed from the coarse objects in multi-resolution spatial data. The methods in this study modeled explicitly the scale effects of direction relations induced by the map generalization operator – merging, thus they are efficient for evaluating consistency. The directional consistency is an essential complement to topological and object-based consistencies.     

城市地表温度与NDVI空间相关性的尺度效应

城市地表温度与NDVI的空间相关性已被广泛研究,但是其尺度效应常常被忽略,给研究结果带来不确定性。本文以郑州市为例,基于四幅Landsat8影像,经辐射传导法反演地表温度(LST),运用半变异函数识别地表温度的空间相关性分析尺度,并结合空间相关指数Moran's I,从多尺度、多季节、多邻接范围3个方面对地表温度与植被覆盖的空间相关性的尺度效应进行了探讨,结果表明：①LST和NDVI的单变量空间自相关和双变量空间相关尺度均在300 m左右;②300 m相关尺度内,单变量空间自相关性存在显著尺度效应,相比之下双变量空间相关性尺度效应较为微弱;③LST和NDVI的单变量空间自相关性和双变量空间相关性尺度效应均表现出明显的季节差异;④随着邻接范围增大,LST和NDVI的空间自相关性减弱,尺度效应更明显。因此度量LST和NDVI的空间相关性要考虑时空尺度效应,本文研究结果有助于进一步认识LST和NDVI间空间相关性的尺度效应。     

Effects of spatial aggregation approaches on classified satellite imagery

Spatial aggregations of raster data based on the majority rule have been typically used in landscape ecological studies. It is generally acknowledged that (1) dominant classes increase in abundance while minor classes decrease in abundance or even disappear through aggregation processes; and (2) spatial patterns also change with aggregations. In this paper, we examined an alternative, random rule-based aggregation and its effects on cover type abundance and landscape patterns, in comparison with the majority rule-based aggregation. We aggregated a classified TM imagery (about 1.5 million ha) from 30m (4231 × 3717 pixels) incrementally to 990m resolution (132 pixels × 116 pixels). Cover type proportion, mean patch size ratio, aggregation index (AI), and fractal dimension (FD) were used to assess the effects of aggregation. To compare landscapes under different resolutions, we assumed that the landscapes were least distorted if (1) the cover type proportions and mean patch size ratios among classes were maintained, and (2) all cover types responded in the same way for a given index as aggregation levels increased. For example, distortion is introduced by aggregation if some cover types increase their AI values with increasing aggregation levels while other cover types decrease. Our study indicated that the two spatial aggregation techniques led to different results in cover type proportions and they altered spatial pattern in opposite ways. The majority rule-based aggregations caused distortions of cover type proportions and spatial patterns. Generally, the majority rule-based aggregation filtered out minor classes and produced clumped landscapes. Such landscape representations are relatively easy for interpreting and, therefore, are suitable for land managers to conceptualize spatial patterns of a study region. By contrast, the random rule-based aggregations maintained cover type proportions accurately, but tended to make spatial patterns change toward disaggregation. Overall, the measurements of landscape indices used in this study indicated that     

Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation: a simulation study

ABSTRACT

The objective of this paper is to investigate uncertainties surrounding relationships between spatial autocorrelation (SA) and the modifiable areal unit problem (MAUP) with an extensive simulation experiment. Especially, this paper aims to explore how differently the MAUP behaves for the level of SA focusing on how the initial level of SA at the finest spatial scale makes a significant difference to the MAUP effects on the sample statistics such as means, variances, and Moran coefficients (MCs). The simulation experiment utilizes a random spatial aggregation (RSA) procedure and adopts Moran spatial eigenvectors to simulate different SA levels. The main findings are as follows. First, there are no substantive MAUP effects for means. However, the initial level of SA plays a role for the zoning effect, especially when extreme positive SA is present. Second, there is a clear and strong scale effect for the variances. However, the initial SA level plays a non-negligible role in how this scale effect deploys. Third, the initial SA level plays a crucial role in the nature and extent of the MAUP effects on MCs. A regression analysis confirms that the initial SA level makes a substantial difference to the variability of the MAUP effects.     

基于Landsat数据的2种地表温度空间降尺度算法比较研究

以2003年5月29日福州市LandsmETM＋影像为数据源,对2种地表温度空间降尺度算法——EM算法和HUTS算法进行实验、比较与分析,EM算法是利用高空间分辨率的地表比辐射率对低空间分辨率的亮度温度影像进行调节,从而达到提高热红外影像空间分辨率的目的;HUTS算法则是引入了归一化差异植被指数NDVI和地表反照率d,通过在低空间分辨率拟合二者与地表温度LST之间的关系,然后根据其尺度不变性,将该关系应用到高空间分辨率的影像上,从而达到提高热红外影像空间分辨率的目的．研究结果表明：1）2种算法所得结果影像都能在有效提高空间分辨率的同时较好地保持了原始地表温度影像的空间分布特征,达到了较好的降尺度效果;2）以RMSE为定量评价指标,HUTS算法要略优于EM算法,其中,EM算法的RMSE为1．2494,而HUTS算法仅为0．9869．     

模型驱动数据空间分辨率对模拟生态水文过程的影响

设定两种独立的调参方案,检验将高/低分辨率下的调参值应用到低/高分辨率中驱动模型模拟的可行性;同时分析驱动数据分辨率的差异对模型模拟生态水文过程的影响;此外,设置9种集水面积阈值,分析驱动数据分辨率与集水面积阈值对模拟结果的影响。结果表明两种调参方案下模拟的水文与生态数据相关性强、差异小,说明调参方案受驱动数据分辨率的影响不明显;分辨率对土地覆被和土壤类型面积影响小,而对高程和坡度影响大;驱动数据分辨率降低,模拟径流的均值减小、峰值增大、生态变量值增大;驱动数据分辨与集水面积阈值之间不存在交互作用;集水面积阈值增大,模拟径流的均值、峰值均减小,生态变量值也减小,但阈值变化对峰现时间无影响。     

基于地理探测器下的川西高原地表温度空间格局及影响因子分析——以西昌市为例

以西昌市为例,选取2010年和2015年 Landsat系列遥感影像,在“3S”技术的支持下,采用监督分类中的最大似然法,并结合Google Earth高分辨率影像和GPS野外实地验证数据获取西昌市2010年和2015年土地利用信息;再利用大气校正法反演获得西昌市2010年和2015年地表温度,并对反演结果中的高温异常区进行实地调研验证;最后,采用地理探测器定量分析了坡度、总辐射、坡向、海拔、年均降水量、年均气温、植被类型、土壤类型以及土地利用类型9个影响因子对地表温度影响的解释力。结果表明：（1）地表温度空间分布存在显著差异。（2）影响因子对地表温度的影响存在差异,其中海拔和年均气温对地表温度的影响较大,而总辐射影响最小。（3）不存在单一因素或是单一性质的因素影响地表温度。不同影响因子之间存在交互作用,对地表温度的影响是相互增强或非线性增强的。（4）部分影响因子对地表温度具有显著性差异影响,并且地表温度均值最大时,对应着影响因子不同的范围或类型。     

Sensitivity of disease cluster detection to spatial scales: an analysis with the spatial scan statistic method

ABSTRACT

The spatial scan statistic method has been widely used for detecting disease clusters. Its results may be affected by scales, including the aggregation level of the input data and the population threshold used in the detection. Previous studies offered inconsistent findings, and few had considered both types of scales at the same time. Using 24 simulated datasets and two real disease datasets, we investigated the method’s sensitivity to the two types of scales. We aggregated the individual-level data into areal units of three levels, including county, town, and a 900 m grid. We detected clusters with three population thresholds, including 10%, 25%, and 50%. We used two measurements, distance between cluster centres and the Jaccard index, to quantify the consistency of clusters detected with different scale settings. We find: (1) the method is not greatly sensitive to the data aggregation level when the cluster is strong and in a place with high population density; (2) the method’s sensitivity to the population threshold is determined by the actual size of the true cluster; and (3) a regular grid with fine resolution is advantageous over the subjectively defined areal units. The process and findings may have broader meanings to similar spatial analyses.     

空间分辨率对总初级生产力模拟结果差异的影响

利用模型分析气候变化对陆地生态系统功能的影响,是当前全球变化生态学的研究热点,然而模型模拟不确定性来源之一就是空间异质性的问题。空间异质性是尺度的函数,基于气象和遥感数据驱动的生态系统过程模型（BEPS模型）,分别模拟2003-2005年中国生态系统通量观测与研究网络（ChinaFLUX）长白山站、千烟洲站、海北站及当雄站在1 km和8 km空间分辨率下的总初级生产力（GPP）的时间动态变化,并结合土地覆盖类型及叶面积指数(LAI)的差异,探讨两种空间分辨率输入数据对GPP模拟结果的影响。结果表明：① 差异性主要是由于8 km范围内混合像元导致LAI的不同,4个站点月均差异值分别为0.85、1.60、0.13及0.04;② 两种空间分辨率均能较好地反映各站点GPP的季节动态变化,与GPP观测值的相关性R²为0.79~0.97 (1 km)、0.69~0.97(8 km),月均差异值为11.46~29.65 gC/m²/month (1 km)、11.87~24.81 gC/m²/month (8 km);③ 4个通量站点在两种空间分辨率下的GPP月均差异值分别为14.43,12.05,4.79,3.22 gC/m²/month,不同空间分辨率的模拟结果在森林站的差异大于草地站,且生长季的差异大于非生长季。因此,模型在模拟大尺度、长时间序列GPP时,为了提高模型模拟效率,适度降低空间分辨率是可行的,但应尽量减小低空间分辨率对于森林生态系统以及生长季GPP模拟上的误差。     

地形信息对确定DEM适宜分辨率的影响

分辨率会直接影响基于栅格数字高程模型（DEM）的数字地形分析结果,因此在实际应用中,需要选择适宜的DEM分辨率。目前采取的基本方法,基于某种地形信息定量刻画尺度效应曲线,从而确定DEM适宜分辨率,但对于采用不同地形信息时所产生的影响尚缺乏研究。本文针对该方法中通常采用的坡度、剖面曲率、水平曲率等3 种地形信息,每种地形信息提取时,分别使用两种不同的常用算法,在3 个不同地形特征的研究区中,逐一计算其在不同分辨率下的局部方差均值,以刻画尺度效应曲线,确定相应的DEM适宜分辨率,并进行对比分析。结果表明：① 采用剖面曲率或水平曲率所得适宜分辨率结果基本相同,但采用坡度所得出的适宜分辨率结果则有明显差别,后者所得的适宜分辨率更粗;② 采用不同地形信息时,越是在平缓地形为主的研究区,所得的适宜分辨率结果越相近,在复合地形特征的研究区所得到的适宜分辨率区间均明显较宽;③ 地形属性计算时所用的算法对适宜分辨率结果的影响不明显。     

Aggregation in continuous space coverage modeling

Uncertainties and errors associated with aggregation have long been recognized in the study of spatial problems. In facility location modeling, while much has been done to examine the aggregation of large datasets of discrete points, errors and uncertainties involved in aggregating continuous spatial units are not well understood. This study focuses on the effects of aggregating continuous spatial units into discrete points within the context of the location set covering problem. We propose new measures to understand and quantify errors associated with a continuous aggregation scheme. In a real-world application, the proposed methods can be used to suggest an appropriate aggregation scheme before the application of the location model. We demonstrate the concepts developed here with an empirical study of siting emergency warning sirens in the city of Dublin, OH.     

应用遥感技术模拟净初级生产力的尺度效应研究进展

采用遥感技术估算地表植被净初级生产力(NPP),已经成为模拟NPP的主要发展方向。遥感技术以及数据处理能力的迅速发展和基于遥感观测生理生态理论研究的进展使大尺度生态系统格局和过程的定量、动态观测成为可能。多种卫星传感器提供了丰富的多尺度对地观测数据,从而形成了不同空间分辨率的影像数据层次体系,使得从定量遥感出发的NPP估算必然存在多尺度的问题。NPP遥感估算模型以不同分辨率的遥感数据(代表不同的研究尺度)作为输入参数时,得到的NPP模拟值差异明显。为了提高NPP的估算精度,需要充分认识不同分辨率的遥感数据对NPP估算结果的影响差异,即NPP的尺度效应问题。本文介绍了遥感尺度效应研究进展,多分辨率遥感数据监测NPP变化的多尺度研究进展,以及NPP估算尺度效应问题的研究进展。并分析了应用遥感技术研究NPP尺度效应的发展趋势。     

基于地理标记照片的城市旅游意象空间分异研究

城市旅游感知意象与投射意象的匹配程度是城市旅游形象建设与宣传效果的重要评价,景观尺度下旅游感知与投射意象的空间分异特征可以有效评估游客对城市旅游意象的感知偏差,以此把握旅游者的旅游动机与景观偏好,更好地提升旅游地形象建设。本研究以五大世界著名旅游城市为例,以Flickr网站的地理标记照片数据和政府旅游网站宣传的旅游信息为数据源,利用ArcGIS空间分析技术分析城市旅游感知意象与投射意象的要素构成和空间分异特征。结果表明,城市旅游感知与投射意象的差异既体现在要素构成的偏差上,也体现在空间分布特征的差异方面。除东京旅游感知与投射意象要素结构基本一致以外,其他城市均存在一定程度的偏差。局部空间聚集异常值可视化了五大城市旅游感知意象与投射意象的空间分布偏差,相比城市旅游投射意象的空间集聚,城市旅游感知意象的范围更为离散。同时,双变量空间自相关指数分析结果验证了五大城市旅游感知意象与投射意象均呈显著的空间正相关,但是相关程度和依赖度并不高,说明了从感知与投射视角进行旅游意象空间特征分析的重要性,应以需求为导向进行城市旅游景观规划与宣传。结合地理标记照片数据的空间分析方法为城市旅游感知意象与投射意象的空间分异研究提供了普适性的方法,以服务于城市可持续发展和旅游形象建设。     

多传感器不同分辨率遥感数字图像的尺度转换

针对日益增多的多传感器不同分辨率的遥感数字图像数据,提出其综合利用和尺度转换的有效方法。其中,作为尺度上推 (scaling-up) 技术,本研究选择IHS和小波分解变换两种数据融合方法,应用Landsat ETM+数据和IRS-1C数据进行了实例研究。发现: IHS方法操作简单,对两种待融合的图像的像元大小和像元数 (bit数) 的依赖不是很高,而对原始图像光谱信息的依赖很高,因此适用于不同传感器之间的数据融合上,其缺点就是不能够更多的保留原始多光谱数据的光谱信息。小波分解变换 (WD) 方法可以很好地保留多光谱传感器的所有光谱信息,但是在重采样中对两种待融合的图像的像元配准的要求很高,因此比较适合于对同一传感器的多光谱数据与全色光谱数据的融合上。作为尺度下退 (scaling-down) 技术,通过探讨将札幌野外实验站点数据镶嵌到高时间分辨率遥感图像的问题,提出像元级尺度扩展的具体操作方法和对NDVI图像进行扩展时出现混合像元的识别方法。应用AVHRR, MODIS, ETM+和ASTER的NDVI图像数据进行了像元尺度的扩展研究。得出: 对不同传感器的不同分辨率的数据之间进行像元级的尺度扩展后,能够较好地识别出不同地物的分布边界,较好的分辨混合像元,因此其结果可用于对不同地物分布特征的分类研究或土地覆盖变化中的时间序列变化的研究中。     

Image data fusion for the remote sensing of freshwater environments

Remote sensing based mapping of diverse and heterogeneous freshwater environments requires high-resolution images. Data fusion is a useful technique for producing a high-resolution multispectral image from the merging of a high-resolution panchromatic image with a low-resolution multispectral image. Given the increasing availability of images from different satellite sensors that have different spectral and spatial resolutions, data fusion techniques that combine the strengths of different images will be increasingly important to Geography for land-cover mapping. Different data fusion methods however, add spectral and spatial distortions to the resultant data depending on the geographical context; therefore a careful selection of the fusion method is required. This paper compares a technique called subtractive resolution merge, which has not previously been formally tested, with conventional techniques such as Brovey transformation, principal component substitution, local mean and variance matching, and optimised high pass filter addition. Data fusion techniques are grouped into spectral and spatial centric methods. Subtractive resolution merge belongs to a new class of data fusion techniques that uses a mix of both spatial and spectral centric approaches. The different data fusion techniques were applied to a QuickBird image of a semi-aquatic freshwater environment in New Zealand. The results were compared both qualitatively and quantitatively using spectral and spatial error metrics. This research concludes that subtractive resolution merge performed better than all the other techniques and will be a valuable technique for enhancing images for freshwater land-cover mapping.     

闽东北沿海罗源县土地利用空间分布格局的多尺度分析

在不同的空间尺度上,制约土地利用空间分布的影响因子及其影响程度并不相同,因此区域空间分布格局 分析应优先考虑制约空间分布的影响因子识别及其影响程度的尺度依赖性的研究。本文以闽东沿海的罗源县为研 究区域,采样统计方法与GIS 技术,选取20 个候选影响因子,研究了该县主要土地利用空间分布格局的影响因子 及其空间尺度相关性。研究表明模型的解释能力、影响因子及其影响系数均会随研究尺度发生不同程度的变化,回 归模型的解释能力以及主要影响因子的制约程度总体上均随研究尺度增大呈增强趋势。除受坡度、海拔高程等地 形条件的严格制约外,罗源县主要地类均在一定程度上受到人口因素以及若干可达性因素的影响。     

The role of local land-use on the urban heat island effect of Tel Aviv as assessed from satellite remote sensing

Climate change in cities has received much focus in the past few decades. Heat stress in urban areas has an adverse effect on human health and is expected to worsen in the future due to the global warming. Vegetation has been shown to mitigate this effect, but introducing ‘green’ areas into the metropolitan space is a challenging task. We assessed the thermal load in terms of surface temperature in Tel Aviv, the biggest metropolitan area of Israel. The thermal effect of four different urban land uses was estimated. Specifically, we compared the cooling effect of residential areas with high vegetation cover (referred here as ‘green’ residential) to that of small to medium size (2–40 ha) public parks. To this end, we used satellite data of land surface temperature (LST) and the Normalized Difference Vegetation Index (NDVI), as a surrogate for vegetation cover. High-temporal data were combined with high spatial resolutions data to produce 10-year average LST and NDVI maps at high spatial resolution over Tel Aviv. As expected, industrial areas had the highest LST due to lowest ratio of vegetation to free space area (1%), while ‘green’ areas displayed the lowest LST. Green residential and small-medium public parks had comparable thermal loads, with green residential having slightly lower LST (by 0.5 °C). In general, small-medium public parks displayed higher LST than expected. Inefficient use of free spaces for vegetation, i.e., relatively low vegetation cover to free space ratio, was probably the main cause for this. Public parks had a higher local cooling effect, but a less continuous one on the proximate surrounding (30–90 m from the park), probably due to their relative location in the urban fabric. Our results suggest that ‘greening’ areas within the private urban space should be encouraged at the expense of building new small-medium parks in metropolitan areas that lack the sufficient free space for larger parks. The outcome of this study may have key implications for urban planners seeking to mitigate urban heat island effects under the limitation of existing dense urban layout.     

不同分辨率DEM提取地面坡度的不确定性模拟:以在黄土高原的试验为例

Slope is one of the crucial terrain variables in spatial analysis and land use planning,especially in the Loess Plateau area of China which is suffering from serious soil erosion. DEM based slope extracting method has been widely accepted and applied in practice. However slope accuracy derived from this method usually does not match with its popularity. A quantitative simulation to slope data uncertainty is important not only theoretically but also necessarily to applications. This paper focuses on how resolution and terrain complexity impact on the accuracy of mean slope extracted from DEMs of different resolutions in the Loess Plateau of China. Six typical geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1:10,000 scale topographic maps. Field survey results show that 5 m should be the most suitable grid size for representing slope in the Loess Plateau area. Comparative and math-simulation methodology was employed for data processing and analysis. A linear correlativity between mean slope and DEM resolution was found at all test areas,but their regression coefficients related closely with the terrain complexity of the test areas. If taking stream channel density to represent terrain complexity, mean slope error could be regressed against DEM resolution (X) and stream channel density (S) at 8 resolution levels and expressed as (0.0015S2+0.031S-0.0325)X-0.0045S2-0.155S+0.1625, with a R2 value of over 0.98. Practical tests also show an effective result of this model in applications. The new development methodology applied in this study should be helpful to similar researches in spatial data uncertainty investigation.     

基于FSDAF模型的干旱区典型绿洲城市夏季地表热岛效应时空演变研究

全球城市化进程加快引起的干旱区绿洲城市热岛效应变化及其生态环境问题已成为当前国内外城市气候、环境和生态等领域的研究热点之一.基于遥感热红外通道反演的地表温度(Land surface temperature,LST)是开展城市地表热岛(Surface urban heat island,SUHI)效应监测研究的关键参数...     

Investigation of aggregation effects in vegetation condition monitoring at a national scale

Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. Vegetation indices (VIs), mathematical transformations of reflectance bands, have played an important role in vegetation monitoring, as they depict the abundance and health of vegetation. Instead of storing raster VI maps, aggregated statistics can be derived and used in long-term monitoring. The aggregation schemes (zonations) used in Greece are the forest service units, the fire service units, and the administrative units. The purpose of this work was to explore the effect of the modifiable areal unit problem (MAUP) in vegetation condition monitoring at the above-mentioned aggregation schemes using 16 days Normalized Difference Vegetation Index (NDVI) composites acquired by the moderate resolution imaging spectroradiometer satellite sensor. The effects of aggregation in the context of MAUP were examined by analyzing variance, from which the among polygon variation (objects' heterogeneity) and the within polygon variation (pixels' homogeneity) were derived. Significant differences in objects' heterogeneity were observed when aggregating at the three aggregation schemes; therefore there is a MAUP effect in monitoring vegetation condition on a nationwide scale in Greece with NDVI. Monitoring using the fire service units has significantly higher pixels' homogeneity; therefore there is indication that it is the most appropriate for monitoring vegetation condition on a nationwide scale in Greece with NDVI. Results were consistent between the two major types of vegetation, natural and agricultural. According to the statistical validation, conclusions based on the examined years (2003 and 2004) are justified.