城市景观格局的空间尺度分析

景观生态学研究的核心内容是景观格局与生态过程。因为景观格局与生态过程中都存在的尺度多样性问题,导致尺度成为理解景观格局和生态过程相互作用的关键,但是由于理论和方法的限制,目前对景观生态学的尺度研究还不够深入,特别是景观格局对尺度变化的响应特征。目前GIS与RS技术的发展为不同尺度下的空间格局特征和空间异质性研究提供了有效的工具。论文以上海市为背景,用景观生态学方法研究了不同粒度下景观格局特征,同时用半变异函数分析了不同幅度下景观多样性的格局特征。结果显示:(1)不同的指数对粒度的响应不同;(2)空间多样性在小幅度内变化较复杂;(3)不论粒度还是幅度都揭示出景观格局具有尺度依赖性规律。     

上海城市土地利用格局的景观生态学分析

在RS(遥感)与GIS(地理信息系统)技术支持下,本文以上海市外环以内的城市中心区为研究区域,利用景观生态学方法研究了在不同粒度下城市景观镶嵌结构的空间特征,并结合半变异函数,对不同研究幅度下的城市景观空间格局进行分析研究。结果显示:不同粒度下所得的景观指数有明显差异。就本研究区域而言,在20 m粒度下,具有明显的城市景观特征;不同幅度下的景观指数空间格局总体相似,但较小的研究幅度对于小尺度的格局变化比较敏感,而较大尺度下的空间格局变化更易从较大幅度的图中表现出来;不同的景观指数对尺度变化响应不同;各景观指数受不同过程影响的大小不同,从而使得不同景观指数的空间格局表现不同;从总体上看,该区域范围内N45°E和E0°方向上景观空间变异明显。     

城市景观空间自相关与自相似的尺度特征研究

在RS与GIS技术支持下,以上海市外环以内城市中心区为区域背景,研究城市景观格局空间自相关与分形结构在不同粒度下的变化特征,揭示其对粒度的响应特征和敏感程度。结果表明,城市景观格局的空间自相关性和自相似性依赖于粒度的选择,MoranⅠ与各种景观类型的分维数均存在尺度效应;城市景观格局空间自相关性与自相似性在一定尺度范围内具有尺度依赖性,但二者对尺度的依赖程度不同。空间自相关性具有较高的尺度依赖性;而不同景观类型的自相似性随尺度的变化没有统一规律;城市景观格局的空间自相关性和自相似性对尺度变化的敏感点不同。居住景观的MoranⅠ对尺度变化的敏感点为50m,其他景观类型无明显的敏感点;分维数对尺度的敏感点因景观类型不同而异。     

遥感影像空间分辨率及粒度变化对城市景观格局分析的影响

尺度是景观生态学和遥感领域的重要问题,已有研究主要考虑景观的粒度效应,很少涉及遥感影像空间分辨率对景观格局,尤其是对城市景观格局的影响.基于2000年和2002年分别由ETM 和IRS-PAN影像解译得到的土地利用图,从景观和类型两个层次分析了不同粒度下上海市一城市化样带的景观格局.结果表明,城市景观有明显的尺度效应,空间分辨率和粒度变化都会影响城市景观格局,而道路等线性廊道对尺度变化的敏感则是造成这一影响的主要原因.斑块密度、边界密度、平均斑块分维度、景观聚集度和最大斑块指数的粒度效应比较明显,而景观比例、Shannon多样性指数对粒度变化并不敏感.高分辨率影像适用于城市景观格局,尤其是破碎度的分析,其用于分析的合适粒度是5～10m.     

基于无人机航测的漯河市土地利用景观格局尺度效应

景观格局的尺度效应一直是景观生态学研究的核心内容,对于揭示景观空间格局变化规律及其生态过程具有重要意义。以漯河市中心城区为研究对象,基于景观生态学原理,采用无人机航测技术获取空间分辨率为0.09 m的无人机影像,结合GIS空间分析法,量化分析了漯河市土地利用景观格局的尺度效应。结果表明:(1)漯河市中心城区土地利用景观格局具有明显的粒度和幅度效应。(2)粒度越小,景观格局指数随空间粒度的变化趋势越稳定,其表达的生态过程越真实;景观水平上景观格局的粒度效应是由建筑、道路和绿地景观在景观优势度、破碎度和聚集度等方面的变化导致的;35 m和3 m分别为研究粒度效应的临界阈值和最佳粒度。(3)景观优势度、破碎度和蔓延度随空间幅度的增加而降低,景观复杂程度和聚集度随空间幅度的增加而增加;景观格局具有明显的空间梯度分布特征——从市中心往外由不透水地面向透水地面过渡;建筑和道路在城市中心区聚集度较高,而绿地景观在城市内部破碎度较高,进而主导了整体景观格局的梯度变化;景观组分的稳定与城市规模有关。(4)无人机航测技术可以更快速、准确地获取城市尺度上的景观生态信息,揭示景观格局对尺度效应的响应特征,可为景观格局优化和城乡景观规划提供科学依据。     

城市景观多样性的空间尺度分析——以上海市外环线以内区域为例

景观生态学研究的就是某一空间尺度范围内的景观格局与生态过程。因为景观格局与生态过程中存在的尺度多样性 ,导致尺度成为理解景观格局和生态过程相互作用的关键 ,其已经成为景观生态学的一个重要概念 ,但是由于理论和方法的限制 ,对景观生态学的尺度研究还不够 ,特别是景观格局综合性指标在不同幅度上的变化特征和效应。在 GIS与 RS技术支持下 ,采用基准分辨率为 5 m的 SPOT遥感图像作为数据源 ,对不同幅度下的城市景观多样性的空间分布格局进行了分析 ,并进一步利用半变异函数对其空间异质性进行定量描述。结论揭示 :随着空间尺度的增加 ,景观多样性程度也不断增加 ,另外多样性的空间分布格局也具有显著变化 ,由于受城市发展历史和目前城市扩展方向的影响 ,多样性在总体上是不平衡的 ,尺度越大 ,不平衡越明显 ;不同尺度下景观多样性空间格局的变化 ,与城市景观的特点和城市景观的功能息息相关 ,不过其受经济效益和社会文化效益的影响更大 ;随着尺度增加由于掩盖了更小尺度上的变异 ,导致块金效应增强 ,空间自相关部分对系统总的变异则明显下降 ;景观多样性具有尺度依赖性 ,可以说景观多样性也是尺度的函数 ,在不同的尺度上 ,结果差异显著 ,所以在景观生态学的研究中绝对不能忽略尺度对格局的影响     

三峡库区低山丘陵区多尺度景观指数响应及适宜粒度

景观格局具有典型的空间异质性和尺度依赖性。在对景观格局进行分析研究时需跨越多个尺度,空间粒度大小在尺度聚合分析中至关重要。三峡库区低山丘陵区斑块破碎、景观格复杂性显著、景观格局特征及尺度变异规律仍待明确,本文以三峡库区秭归县为研究区,设定1—400m内23个粒度梯度水平,定量评估县域、乡镇以及小流域不同幅度上的景观格局指数以及拟合函数,探讨多空间幅度景观格局指数随粒度大小的变化特征。基于景观指数的粒度效应特征和拟合函数的曲线特征(最大曲率点、极值点),明确适宜不同幅度的山地景观格局指数研究的粒度阈值,以揭示库区低山丘陵区景观结构的复杂性和变异性。结果表明：不同景观指数对空间粒度变化和空间幅度变化的响应存在差异,不同景观指数的空间粒度响应主要呈现增加、降低、波动和无明显规律变化的趋势,其中斑块密度、最大斑块面积等指数对斑块形状和大小的变化敏感,而多样性指数的粒度变化敏感度较低;部分指数如斑块密度、边缘密度、周长面积分维数等对空间幅度的变化并不敏感,而最大斑块面积、景观形状指数、散布与并列指数、分离度等指数对空间幅度的变化敏感,适合进行不同幅度适宜粒度阈值的推定;边缘密度、平均斑块大小、景...     

黔中喀斯特山地城市景观格局指数粒度效应——以安顺市为例

景观格局对生态过程的影响具有强烈的尺度依赖性,尺度的合理确定是决定景观格局与生态过程研究的关键。以黔中喀斯特典型山地城市安顺市为对象,在GIS与RS技术的支持下,从类型和景观两个层次分析景观格局的空间粒度效应,揭示不同景观格局指数随粒度变粗的变化特征,进而探明喀斯特山地城市景观生态研究的适宜空间粒度。结果表明:除SIDI、SIEI、TA外其余景观格局指数受空间粒度的影响显著,随粒度变粗,各指数随粒度变粗的响应可分为单调递减、单调递增、先上升后下降及基本稳定类型;大部分景观格局指数在20或100 m粒度下出现明显跃变,存在"临界阈"现象;对于安顺市中心规划区而言,进行景观格局分析的最佳尺度范围为5~20 m,适宜粒度为20 m;山体绿地在喀斯特山地城市绿地系统中占主导地位,其CA、TE、DE等7个景观格局指数的粒度响应曲线相似。综上,基于高分辨率影像的黔中喀斯特山地城市景观生态研究的适宜粒度为20 m。     

景观生态学发展现状

景观生态学主要研究空间格局和生态过程的相互作用,与生态学有着良好的结合。过去的15年,景观生态学得到迅猛发展。土地利用历史和自然干扰对目前生态系统具有持久影响。格局指标（pattern metrics）的发展已保持相对稳定,并且广泛应用于反映景观格局与生态响应的关系。在研究生物类群的景观格局时,多尺度分析具有重要意义,同时,物种间的空间联系也日益受到生态学家的关注。干扰是景观生态学研究的一个重点,当前研究注重干扰的相互作用。尽管景观生态学与系统生态学之间的结合仍然面临挑战,但对景观功能的认识仍需要加强。景观生态学应继续立足于空间异质性进行研究,严格验证有关概念的通用性,同时更加深刻地认识景观格局与过程关系的机理     

扎龙自然保护区不同空间尺度景观格局时空变化及其生态风险

景观格局变化下的区域生态风险研究可以为保护区域景观生态系统健康,实现区域资源的合理和可持续利用提供重要科学依据。以扎龙自然保护区为研究区,利用遥感手段提取景观类型信息,定量分析不同空间尺度下研究区景观格局时空变化特征和演变规律。根据研究区景观格局变化特点构建景观生态风险指数,对研究区不同时期空间尺度变化过程中景观生态风险时空变化特征进行分析。研究表明:1995—2010年研究区不同空间尺度下景观总体格局和各景观类型格局变化显著,呈现不同变化特征;景观生态风险随空间粒度的增大而减小。对比分析两期数据,研究区景观生态风险加剧,其空间分布均呈环形扩散特征。根据本研究区区域面积大小和景观格局复杂程度,确定了景观格局和景观生态风险研究的适宜尺度域。     

Analysis of Large-Scale Spatial Heterogeneity in Vegetation Indices among North American Landscapes

We analyzed the spatial heterogeneity in vegetation indices among 13 North American landscapes by using full Landsat Thematic Mapper images. Landscapes varied broadly in the statistical distribution of vegetation indices, but were successfully ordinated by using a measure of central tendency (the mean) and a measure of dispersion (the standard deviation or the coefficient of variation). Differences in heterogeneity among landscapes were explained by their topographic relief and their land cover. Landscape heterogeneity (standard deviation of the Normalized Difference Vegetation Index, NDVI) tended to increase linearly with topographic relief (standard deviation of elevation), but landscapes with low relief were much more heterogeneous than expected from this relationship. The latter were characterized by a large proportion of agricultural land. Percent agriculture, in turn, was inversely related to topographic relief. The strength of these relationships was evaluated against changes in image spatial resolution (grain size). Aggregation of NDVI images to coarser grain size resulted in steady decline of their standard deviation. Although the relationship between landscape heterogeneity and explanatory variables was generally preserved, rates of decrease in heterogeneity with grain size differed among landscapes. A spatial autocorrelation analysis showed that rates of decrease were related to the scale at which pattern is manifested. On one end of the spectrum are agricultural, low-relief landscapes with low spatial autocorrelation and small-scale heterogeneity associated with fields; their heterogeneity decreased sharply as grain size increased. At the other end, desert landscapes were characterized by low small-scale heterogeneity, high spatial autocorrelation, and almost no change in heterogeneity as grain sized was increased—their heterogeneity, associated with land forms, was present at a large scale. Received 1 October 1997; accepted 11 February 1998.     

Can we measure ecological sustainability? Landscape pattern as an indicator for naturalness and land use intensity at regional,national and European level

European landscapes have been shaped over the centuries by processes related to human land use, which are reflected in regionally distinct landscape patterns. Since landscape pattern has been linked to biodiversity and other ecological values of the landscapes, this paper explores landscape pattern as a tool for ecological sustainability assessments at the regional (Austrian Cultural Landscapes), national (Austria) and European (European Union + Norway, Switzerland) level with focus on agricultural landscapes. A set of landscape metrics served as a basis to assess naturalness and geometrisation of Austrian and European landscapes as a proxy for their sustainability. To achieve an accurate spatially explicit assessment, we applied a spatial reference framework consisting in units that are homogeneous in biophysical and socio-economic contexts, adapted the regional approach for its application at European level, and developed relative sustainability thresholds for the landscape metrics. The analyses revealed that several landscape metrics, particularly the “Number of Shape Characterising Points” showed a high correlation with the degree of naturalness. The sustainability map of Austria based on an ordinal regression model revealed well-known problem regions of ecological sustainability. At the European level, the relative deviation from the average pattern showed clearly the simplification processes in the landscapes. However, a better spatial resolution of land cover data would add to the refinement of pattern analysis in regions and therefore the assessment of sustainability. We recommend the combination of information of different scales for the formulation and implementation of sustainability policies.     

Overlapping landscapes: A persistent,but misdirected concern when collecting and analyzing ecological data

A primary focus of wildlife ecology is studying how the arrangement, quality, and distribution of habitat influence wildlife populations at multiple spatial scales. A practical limitation of conducting wildlife–habitat investigations in the field, however, is that sampling points tend to be close to one another, resulting in spatial clustering. Consequently, when ecologists seek to quantify the effects of environmental predictors surrounding their sampling points, they encounter the issue of using landscapes that are partially or completely overlapping. A presumed problem of overlapping landscapes is that data generated from these landscapes, when used as predictors in statistical modeling, might violate the assumption of independence. However, the independence of error is the critical assumption, not the independence of predictor variables. Nonetheless, many researchers strive to avoid such overlaps through sampling design or alternative analytical procedures and specialized software programs have been created to assist with this. We present theoretical arguments and empirical evidence showing that changing the amount of overlap does not alter the degree of spatial autocorrelation. Using data derived from 2 broad-scaled avian monitoring programs, we quantified the relationship between forest cover and bird abundance and occurrence at multiple landscapes ranging from 100 m to 24 km across. We found no clear evidence that increasing overlap of landscapes increased spatial autocorrelation in model residuals. Our results demonstrate that the concern of overlapping landscapes as a potential cause of violation of spatial independency among sampling units is misdirected and represents an oversimplification of the statistical and ecological issues surrounding spatial autocorrelation. Overlapping landscapes and spatial autocorrelation are separate issues in the modeling of wildlife populations and their habitats; non-overlapping landscapes do not ensure spatial independency and overlapping landscapes do not necessarily lead to greater spatial autocorrelation in model errors. © 2011 The Wildlife Society.     

生态学中的尺度问题：内涵与分析方法

尺度问题已成为现代生态学的核心问题之一.尺度问题主要涉及3个方面：尺度概念、尺度分析和尺度推绎.主要评述前两个方面.生态学尺度有三重概念：维数、种类和组分,其中每重概念又包含了多个定义,有必要进行澄清、分类和统一.尺度分析涉及尺度效应分析和多尺度空间格局分析.格局、过程及它们之间的关系,以及某些景观特性均表现出尺度效应,因此多尺度研究非常必要和重要.多尺度空间格局分析（尤其是特征尺度的识别）是进行尺度效应分析和跨尺度推绎的基础.多尺度分析需要特定的方法,景观指数法是最常用和最简单的方法,但也常产生误导;空间统计学方法（如半方差分析法、尺度方差分析法、空隙度指数法和小波分析法等）和分维分析法在最近十几年发展起来,并逐渐应用于生态学,在尺度分析上具有很大的应用潜力.各种方法在尺度分析上各有优势和不足,有必要同时使用两种或两种以上方法进行比较和评估.总之,有关尺度分析的研究需要进一步加强,从而为下一步的尺度推绎提供可靠的依据.     

基于生态安全格局理论的国土空间生态修复分区模拟——以粤港澳大湾区为例

国土空间生态修复分区有助于推动生态修复工作的整体性和系统性。利用国土空间生态安全格局理论进行生态修复分区是一种较为经典的模式，但现有分区技术方案对高强度人类活动导致的土地利用景观变化考虑不足，不能很好的适应城市群尺度国土空间生态安全格局的塑造需求。设计了一种基于情景模拟的国土空间生态修复分区方案，该框架首先通过耦合空间马尔科夫链与元胞自动机模型模拟城镇化发展末期的土地利用景观格局；然后利用"生态源地-生态廊道-生态网络"理论构建国土空间生态安全格局基本骨架；最后根据最小累积阻力模型划分出生态修复关键区、调节区和双修区等类型。粤港澳大湾区案例研究表明：（1）土地利用景观变化对生态修复分区方案影响较大，不同景观格局导致的分区规模差异可达国土空间的10%左右；（2）国土空间生态修复分区需要充分考虑高强度人类活动对景观格局变化的潜在影响，进而使得生态修复分区政策更有利于国土空间生态安全格局塑造目标的实现。研究构建的国土空间生态修复分区技术方案可进一步深化生态安全格局理论在国土空间规划应用上的认知。     

粒度变化对城市热岛空间格局分析的影响

尺度是景观格局和生态过程研究中的关键问题。综合目前城市热岛效应研究来看,景观格局指数的引入极大推进了热岛格局的定量研究,然而其尺度效应仍未得到重视。由于热岛空间格局与形成过程的复杂性和人类认识的局限性,对其尺度问题有待深入讨论。基于Landsat TM影像反演地表温度,采用均值-标准差分类方法划分热力等级,对珠三角城市热岛格局特征的粒度效应进行了研究。结果显示:随粒度增加,弱势热力斑块类型下降,向相邻斑块转移;景观指数在类型水平和景观水平均受空间粒度影响明显,"临界粒度"现象明显;总体而言,粒度150 m是城市热岛格局特征的临界粒度,对热岛格局进行景观指数计算的适宜粒度范围为30—150 m;不同景观指数粒度效应曲线有所差异,其中斑块密度和平均分维数在两个水平指数上都有较强的规律性;根据各景观指数变化特征,研究区尺度域主要在(30 m,150 m),表明在该尺度范围内构建的热岛效应预测模型可经简单推绎后使用。分析热岛格局随空间粒度变化特征对了解热岛格局的形成机制及进行尺度推绎具有重要意义。     

The effect of landscape pattern on Mediterranean vegetation dynamics: A modelling approach using functional types

Abstract. In the framework of land use changes in the Mediterranean area, I asked to what extent different landscape structures might determine long‐term dynamics in Mediterranean ecosystems. To answer this question, a spatially explicit model was developed (the Melca model), incorporating two functional types of woody species dominant in Mediterranean ecosystems: a resprouter (R) and a non‐resprouter fire‐recruiter (seeders, S). The model was used as a tool for generating hypotheses on the possible consequences of different landscape scenarios. Thus, five different hierarchically structured random landscapes were generated, all having the same cover for the two functional types but different landscape structure (ranging from highly heterogeneous to homogeneous landscapes). After a 100‐yr simulation, plant cover and spatial pattern had changed and the changes were different for the different initial spatial configurations, suggesting that long‐term vegetation dynamics is spatially dependent (the resultant dynamics are sensitive to the initial spatial structure). In the landscapes where R‐type species had a low number of large patches and S‐species had a large number of small patches, the number of R‐patches increased and their size decreased, while the number of S‐patches decreased. In these cases, the final cover of the two types changed little from the initial cover. Landscapes with a large number of small R‐patches interspersed with S‐patches had a decrease in the number of R‐patches, an increase in the number of S‐patches and a decrease in the size of S‐patches. In these landscapes, final cover was significantly changed, increasing in R‐type and decreasing in S‐type species. These results suggest that low spatial autocorrelation (low aggregation) favours R‐type species. Implications for land management are also discussed.     

Sensitivity of landscape measurements to changing grain size for fine-scale design and management

Landscape pattern quantities are affected by issues of scale, namely extent and resolution. The grain size (resolution) of fine-resolution geographic information system (GIS) data for two highly fragmented landscapes in USA and Italy were altered to evaluate the effect of grain size changes on landscape pattern metrics and cost-surface model outputs. Beginning with 3 m resolution data and resampling the data to 300 m resolution, we applied pattern metrics and cost-surface models (available in GIS software) and evaluated the types of behaviors in resulting quantities. Results showed that some pattern metrics are robust to changes in grain size (such as area, cohesion, interspersion and juxtaposition metrics), while others exhibit staircase-like or erratic responses. Compared to previous studies, we identified behavioral responses that differ from grain-size changes at coarser resolutions. Cost-surface models demonstrated robust or consistent responses to grain size changes in most cases. For both types of pattern measurement, however, we found that behaviors could differ contextually; that is, there could be different types of behaviors for different landscapes, classifications, or grain sizes. Results indicate that comparing spatial data collected at different scales (such as historical data to more recent, high-resolution sensed data) is complicated by different types of responses to changes in grain size. This may limit the applicability of tools for the measurement of landscape change over time if landscapes are represented by differently scaled data.