传统景观格局指数在城市热岛效应评价中的适用性

以北京部分城区为研究对象,以QuickBird影像制作景观类型图,基于同年4个季节的Landsat ETM+数据反演地表温度;将120 m×120 m作为固定窗口,计算其中的景观格局指数,探寻传统景观格局指数解释地表温度的适用性.结果表明：在景观水平计算的24个景观格局指数中,只有景观组成百分比(PLAND)、斑块密度（PD）、最大斑块指数(LPI)、欧氏距离变异系数（ENN_CV）和分离度(DIVISION)与3月、5月、11月的地表温度具有稳定的显著相关.在类型水平计算的24个景观格局指数中,PLAND、LPI、DIVISION、相似邻接百分比、分散与并列指数与4个时相(3月、5月、7月和12月)的温度显著相关,且与7月温度的相关性最强;斑块密度、边界密度、聚簇度、凝聚度、有效MESH大小、分裂度、聚合度、归一化景观形状指数依据不同景观类型而与地表温度呈现相关性.传统景观格局指数可能并不适合评估河流对地表温度的影响.一些景观格局指数可以用来表征城市地表温度,辅助分析城市地表热岛效应,但需要对其进行筛选和甄别.     

An ArcMap plug-in for calculating landscape metrics of vector data

Landscape metrics are quantitative indices that describe the abundant information about landscape patterns and specific characteristics of the landscape composition and configuration. Landscape metrics have extensive applications such as land evaluation, ecosystem services, forest monitoring, urban sprawl control, and regional biodiversity conservation. Although numerous software packages, such as FragStats, Patch Analyst, etc., are available for calculating the landscape metrics, most of them process only raster data, while those applicable to vector data provide inadequate landscape metrics for calculations, making them insufficient for landscape analysis. The proposed plug-in can solve this task for ArcMap10.2 software called Arc_LIND, which computes the landscape metrics of vector data. This paper presents an overview of Arc_LIND, which can calculate three levels, six groups, and the total of 195 landscape metrics, including 13 ones at the patch level, 87 ones at the class level, and 95 ones at the landscape level. These features make it lucrative for the teaching and scientific research in the landscape ecology and other relevant fields. The comparative study on the scale effect of landscape metrics was performed using data in different formats and different landscapes, which confirms the feasibility and efficiency of Arc_LIND.     

不同斑块类型的景观指数粒度效应响应——以无锡市为例

斑块类型与景观格局粒度效应响应关系密切。以无锡市为研究区域,针对地区社会经济活动频繁、人为干扰强烈和生态脆弱等特性,以土地利用类型、热力等级和生态贡献为斑块类型划分依据,构建出对应的3种不同景观格局。在相同粒度变化情况下,选用了19个景观水平指数和Moran's I指数,分析了不同景观格局粒度效应的响应情况。结果表明,随粒度变粗:1)土地利用类型、热力等级和生态贡献斑块的部分景观指数响应曲线变化剧烈程度依次减弱;2)3类斑块的Moran's I指数均存在尺度效应。其中,土地利用类型和生态贡献斑块的Moran's I指数存在负相关,热力等级斑块没有。生态贡献斑块响应曲线在正相关区域内变化相对平缓,土地利用类型与热力等级斑块响应曲线变化趋势相反;3)指数反映的第一临界粒度基本一致,但景观指数响应曲线的临界现象更为明显。总体上,不同类型斑块在同一研究区构成的景观格局、指数响应曲线变化趋势和第一临界粒度都较为相似;斑块类型对景观指数粒度效应响应存在影响,但还有待深入探讨。     

On using landscape metrics for landscape similarity search

Landscape similarity search involves finding landscapes from among a large collection that are similar to a query landscape. An example of such collection is a large land cover map subdivided into a grid of smaller local landscapes, a query is a local landscape of interest, and the task is to find other local landscapes within a map which are perceptually similar to the query. Landscape search and the related task of pattern-based regionalization, requires a measure of similarity – a function which quantifies the level of likeness between two landscapes. The standard approach is to use the Euclidean distance between vectors of landscape metrics derived from the two landscapes, but no in-depth analysis of this approach has been conducted. In this paper we investigate the performance of different implementations of the standard similarity measure. Five different implementations are tested against each other and against a control similarity measure based on histograms of class co-occurrence features and the Jensen–Shannon divergence. Testing consists of a series of numerical experiments combined with visual assessments on a set of 400 3 km-scale landscapes. Based on the cases where visual assessment provides definitive answer, we have determined that the standard similarity measure is sensitive to the way landscape metrics are normalized and, additionally, to whether weights aimed at controlling the relative contribution of landscape composition vs. configuration are used. The standard measure achieves the best performance when metrics are normalized using their extreme values extracted from all possible landscapes, not just the landscapes in the given collection, and when weights are assigned so the combined influence of composition metrics on the similarity value equals the combined influence of configuration metrics. We have also determined that the control similarity measure outperforms all implementations of the standard measure.     

Using landscape metrics to analyze micro-scale soil erosion processes

Methods of recording soil erosion using photographs exist but they are not commonly considered in scientific studies. Digital images may hold an expressive amount of information that can be extracted quickly in different manners. The investigation of several metrics that were initially developed for landscape ecology analysis constitutes one method. In this study we applied a method of landscape metrics to quantify the spatial configuration of surface micro-topography and erosion-related features, in order to generate a possible complementary tool for environmental management. In a 3.7 m wide and 9.7 m long soil box used during a rainfall simulation study, digital images were systematically acquired in four instances: (a) when the soil was dry; (b) after a short duration rain for initial wetting; (c) after the first erosive rain; and (d) after the 2nd erosive rain. Thirteen locations were established in the box and digital photos were taken at these locations with the camera positioned at the same orthogonal distance from the soil surface under the same ambient light intensity. Digital photos were converted into bimodal images and seven landscape metrics were analyzed: percentage of land, number of patches, density of patches, largest patch index, edge density, shape index, and fractal dimension. Digital images were an appropriate tool because they can generate data very quickly. The landscape metrics were sensitive to changes in soil surface micro-morphology especially after the 1st erosive rain event, indicating significant erosional feature development between the initial wetting and first erosive rainfall. The method is considered suitable for spatial patterns of soil micro-topography evolution from rainfall events that bear similarity to landscape scale pattern evolution from eco-hydrological processes. Although much more study is needed for calibrating the landscape metrics at the micro-scale, this study is a step forward in demonstrating the advantages of the method.     

景观指数之间的相关分析

应用辽宁省1997~1998年的TM 5影像数据,编制了景观类型图,以78个县市区为单位,分割成78个景观,共计算39个景观格局指数,对它们进行了相关分析。总面积是最基本的景观指数,它决定景观总边界长度、斑块数、类型密度等基本指数,同时与多个指数有显著的相关关系(相关系数绝对值大于0.75)。形状指数的独立性强,极少数指数与其它指数有显著的相关关系;多样性指数和蔓延度指数之间信息重复量最多,都表示景观的异质性,但多样性指数以面积百分比表示景观异质性,而蔓延度指数以类型之间相邻边界的百分比表示景观异质性。研究发现,如果两个指数之间存在显著的相关关系,而由它们两个构成的指数与它们之间没有显著的相关关系。如果指数平均值之间存在显著的相关关系,则它们的变异系数之间不存在显著的相关关系。景观指数间的相关系数不仅与景观格局本身有关,还与空间尺度,分类系统、计算公式及其参数、计算单元和生态学意义关系密切。指数之间影响因子的相同之处越多,它们之间存在显著相关关系的概率越大。     

Scale dependence of landscape metrics and their indicatory value for nutrient and organic matter losses from catchments

We investigated scale dependence of landscape metrics and the relationship between land use parameters and FRAGSTATS-based landscape metrics (edge density (ED), patch density (PD), mean shape index (SHAPE_MN), mean euclidean nearest neighbor index (ENN_MN), contagion (CONTAG), patch richness density (PRD), and Shannon's diversity index (SHDI)) and nutrient/organic-matter-based water quality indicators (BOD₇ and COD_KMnO4 values, total-N and total-P concentrations in water) in 24 catchments with various land use patterns in Estonia. We used the Basic Map of Estonia (1:10,000), the Base Map of Estonia (1:50,000) and the CORINE Land Cover Map (1:100,000). In scale analysis, we calculated landscape metrics on artificial and real landscapes. Scale analysis showed that responses of landscape metrics to changing grain size vary among landscapes and metrics. Analysis of artificial landscapes showed that mean euclidean nearest neighbor distance and contagion are directly dependent on grain size and should therefore be used carefully. When finding relationships between landscape metrics and water quality indicators, significant differences between the relationships derived from the Base Map and the CORINE Land Cover Map were found. In the case of the Base Map, landscape metrics correlated strongly with land use and showed no expected relationships with water quality data. This underlines the importance of land use classification in such kind of analysis. Correlation between the landscape metrics calculated on the basis of the CORINE Land Cover Map and water quality data was stronger than in the case of the Base Map. The COD_KMnO4 value significantly correlated with all land use types. For instance, the COD_KMnO4 values are higher when fens and natural areas form a higher proportion of the catchments’ land use. Except for the BOD₇ value, all the water quality indicators showed significant correlation with urban land use proportions. Strong relationship between the patch density and the COD_KMnO4 value is most likely caused by the fact that both parameters were significantly correlated with the proportion of natural areas. As the landscape metrics depend on pixel size, topographic scale, and land use classification, and as the effect of land use on water quality in catchments is the most significant of the factors, it was impossible to separate the influence of land use pattern from the influence of FRAGSTATS-based landscape metrics.     

Evaluating the effectiveness of landscape metrics in quantifying spatial patterns

The effectiveness of landscape metrics in quantifying spatial patterns is fundamental to metrics assessment. Setting 36 simulated landscapes as sample space and focusing on 23 widely used landscape metrics, their effectiveness in quantifying the complexity of such spatial pattern components as number of patch types, area ratio of patch types and patch aggregation level, were analyzed with the application of the multivariate linear regression analysis method. The results showed that all the metrics were effective in quantifying a certain component of spatial patterns, and proved that what the metrics quantified were not a single component but the complexity of several components of spatial patterns. The study also showed a distinct inconsistency between the performances of landscape metrics in simulated landscapes and the real urban landscape of Shenzhen, China. It was suggested that the inconsistency resulted from the difference of the correlation among spatial pattern components between simulated and real landscapes. After considering the very difference, the changes of all 23 landscape metrics against changing of number of patch types in simulated landscapes were consistent with those in the real landscape. The phenomenon was deduced as the sign effect of spatial pattern components on landscape metrics, which was of great significance to the proper use of landscape metrics.     

landscapemetrics: an open‐source R tool to calculate landscape metrics

Quantifying landscape characteristics and linking them to ecological processes is one of the central goals of landscape ecology. Landscape metrics are a widely used tool for the analysis of patch‐based, discrete land‐cover classes. Existing software to calculate landscape metrics has several constraints, such as being limited to a single platform, not being open‐source or involving a complicated integration into large workflows. We present landscapemetrics, an open‐source R package that overcomes many constraints of existing landscape metric software. The package includes an extensive collection of commonly used landscape metrics in a tidy workflow. To facilitate the integration into large workflows, landscapemetrics is based on a well‐established spatial framework in R. This allows pre‐processing of land‐cover maps or further statistical analysis without importing and exporting the data from and to different software environments. Additionally, the package provides many utility functions to visualize, extract, and sample landscape metrics. Lastly, we provide building‐blocks to motivate the development and integration of new metrics in the future. We demonstrate the usage and advantages of landscapemetrics by analysing the influence of different sampling schemes on the estimation of landscape metrics. In so doing, we demonstrate the many advantages of the package, especially its easy integration into large workflows. These new developments should help with the integration of landscape analysis in ecological research, given that ecologists are increasingly using R for the statistical analysis, modelling and visualization of spatial data.     

基于模拟景观的城市森林景观格局指数选取

基于现实的沈阳城市森林景观,模拟了4个景观格局梯度,并选取了1个与之相应的现实景观格局梯度,分析了28个景观格局指数在各梯度对景观破碎化和景观斑块形状复杂性的反映,从而筛选出描述这两种景观格局特征的适宜景观格局指数.结果表明：斑块密度（PD）和平均斑块面积（AREA_MN）在城市森林景观破碎化方面表现出较规律的变化趋势,斑块密度随破碎化程度的增加而增加,平均斑块面积随破碎化程度的增加而减小;面积加权平均周长面积比（PARA_AM）在描述景观斑块形状复杂性方面与景观格局梯度相吻合,且随斑块形状复杂性的增加而增加,能够较为准确地描述景观斑块形状的复杂性.     

Sensitivity of correlation structure of class- and landscape-level metrics in three diverse regions

Sensitivity of landscape metrics to selection of spatial scale (i.e., resolution or areal extent), land-use categories, and different landscapes has led to unreliable conclusions for practitioners of landscape analysis and modeling. Unlike previous studies that mostly considered such metrics and assessed the effect of each factor separately, our study focuses on the sensitivity of the correlation structure of different sets of landscape metrics as a whole under different situations via principal component analysis (PCA). We used the congruence coefficient (r_c) to calculate the changes in factor structures under different situations. We used 16 class-level and 15 landscape-level metrics of 900 village-based and 150 town-based samples that were collected from three regions. Five cell sizes, two land-use classes, and two sets of land-use metrics were also considered. We did not control the cell sizes, sample extent, and different landscapes in the sensitivity analysis to study the interactive relationships between different factors. All factors strongly influence the correlation structure of the landscape metrics, with each factor demonstrating a unique influence. Changing cell size significantly affects the correlation structures in the plain region, especially in croplands and built-up lands. Town-based results show a relatively more stable correlation structure than village-based results (except in land-use categories). Different land-use classes show different responses to changing cell size, sample extent, and sets of landscape metrics in different regions. These results show the great interactive influences of these factors, which have often been overlooked in previous studies. The conclusions drawn from fixed factors may be conditional and inapplicable to other situations. The sensitivity of the correlation structure in diverse regions may improve our understanding of landscape metrics as a whole and can provide further insights into the correlation structure of landscape metrics for land-use management and monitoring.     

Construction of 3D landscape indexes based on oblique photogrammetry and its application for islands

Landscape indexes are quantitative indicators that reflect the composition and spatial configuration of landscape structures. However, the current two-dimensional (2D) spatial analysis methods lack accuracy in assessing patch characteristics due to the lack of three-dimensional (3D) information. Following the “Patch-Class-Landscape” framework, eight 3D landscape indexes were built to quantitatively describe spatial landscape features and two sensitivity indexes were developed to identify the differences between 2D and 3D structures. Based on two types of oblique photogrammetry data, validation and comparison studies were conducted for Tianheng Island and Sichang Island. The results found the following: (1) At the patch level, the 3D shape index (TPSI) of vegetation was generally higher than that of buildings, with an R² of 0.634, and the classification index (TCI) showed remarkable performance in identifying patch type. The patch type was likely to be building or vegetation when TCI approached 33, respectively, with a classification accuracy of 90% after verification. (2) At the class level, the 3D percentage of landscape (TPLAND) of grassland and arbor types on the two islands were quite different, reflecting significant differences in the dimensionality of the vegetation landscapes, as influenced by different climatic zones. Moreover, the 3D landscape shape index (TLSI) and other shape-related indexes had higher exponential sensitivity coefficient (ESC) values, due to the higher amount of 3D shape information they carry. (3) At the landscape level, the two 3D Shannon indexes (TSHDI and TSHEI) did not significantly change compared with their 2D counterparts, implying that these two indexes, as larger-scale landscape indicators, had lower sensitivity when extra-dimensional information was added. Overall, the 3D landscape indexes can better present 3D information at different landscape levels. As a potential and effective assessment tool and it will be applied to improve existing spatial planning and landscape management.     

景观指数耦合景观格局与土壤侵蚀的有效性

景观格局分析是景观生态学中揭示景观变化及其生态效应的主要方法,而景观指数是景观格局分析中广泛使用的工具。土壤侵蚀是土壤物质在景观中的迁移和再分配过程,受地形、植被和人类活动及其空间格局的调控。运用景观格局分析揭示景观格局变化特别是土地利用/覆被格局变化对土壤侵蚀的影响是土壤侵蚀研究中应用景观生态学原理和方法的典型。在当前的研究中,斑块-廊道-基质范式下建立的景观指数对侵蚀过程的解释能力不断受到质疑,建立筛选适用的景观指数的原则和方法十分必要。以延河流域碾庄沟小流域为例,利用WATEM/SEDEM模型模拟多个年份流域侵蚀产沙和输沙量;基于土地利用/覆被数据,利用Fragstat4.2软件,计算了相应年份流域斑块、边界密度、形状、集聚与分散和斑块类型多样性4个方面的代表性景观指数。在此基础上,分析了景观指数与流域侵蚀产沙和输沙之间的关系,讨论了景观指数在土壤侵蚀研究中的有效性,在景观和斑块类型水平上分析了景观指数表达"源"、"汇"两大类景观类型的空间格局与侵蚀产沙和输沙之间的关系的一致性。结果表明:斑块-廊道-基底范式下发展的景观指数在指示景观格局的土壤侵蚀效应时存在局限。相对而言,斑块类型尺度的景观指数更能有效表达景观格局与土壤侵蚀的关系。基于景观类型在土壤侵蚀过程中的"源"、"汇"功能,提出了在土壤侵蚀研究中筛选适用的景观指数的原则:(1)对"源"、"汇"两类景观类型,景观指数与土壤侵蚀状况表征变量的相关系数符号相反;(2)对同为"源"或"汇"景观类型的多个景观类型,景观指数与土壤侵蚀表征变量的相关系数应具有符号一致性。尽管景观指数在斑块类型水平上具有一定的有效性,但用其预测景观格局变化的侵蚀效应有很大的不确定性。因此,基于土壤侵蚀过程与景观格局的作用机制发展新型的景观指数是增强景观格局分析预测土壤侵蚀过程的能力的途径。     

景观格局指数间相关关系对植被覆盖度等级分类数的响应

运用景观指数进行景观格局分析已成为景观生态学的主要研究方法。但是,由于景观本身的复杂性以及景观格局指数之间的相关性,其应用与发展受到制约。选用面积/周长/密度、形状和蔓延度等3类共28个指数,以广州市中心城区为研究区域,以TM遥感影像为数据源形成植被覆盖度等级图,在景观类型水平上探讨各类型内指数之间的相关性及其对景观分类数的敏感程度。相关性分析表明:景观格局指数间都存在一定的相关性,但是相关程度差异较大,其中面积/周长/密度类指数间的相关性最强,蔓延度类指数其次,形状类指数间独立性相对较强。敏感性分析结果显示:117个指数对的相关关系都随景观分类数发生变化,根据其变化类型和程度,可以分为简单型、分段型、复杂型等3种指数关系响应的类型,分别包括12、31和74对指数;敏感曲线还表明4或5是指数对之间相关关系最敏感的分类数;不仅如此,分类数对景观格局指数之间相关关系的影响程度因景观格局指数所属的类别而异,面积/周长/密度类型指数不敏感,其次是蔓延度类指数,最敏感的是形状类指数;最后,研究结果表明景观格局指数间的相关性对分类数的敏感性存在较大的空间变异。     

黄土丘陵沟壑区景观格局对流域侵蚀产沙过程的影响——斑块类型水平

在黄土丘陵沟壑区,景观格局对侵蚀产沙过程有着复杂的影响,且与尺度密切相关。选取河口-龙门区间内42个水文站控制流域为研究对象,以侵蚀模数、输沙模数和泥沙输移比作为表征各流域单元内土壤侵蚀、产沙及泥沙输移过程的特征指标,运用景观指数和CCA排序,系统分析了斑块类型水平上景观格局对流域侵蚀产沙过程的影响。结果表明:流域侵蚀产沙及泥沙输移过程中,空间分异特征随景观类型不同而异;对于不同用地类型,影响"过程"空间分异的景观格局指标不同,显著影响流域侵蚀产沙及泥沙输移过程的景观指数有草地平均斑块面积(AREA_MN3)、居民建设用地景观面积百分比(PLAND5)、居民建设用地和其它类型用地景观的斑块密度(PD5和PD6),其中斑块密度(PD)是影响流域侵蚀产沙及泥沙输移过程的共性指标;草地、居民建设用地、其它类型用地的景观格局特征对"过程"变化的解释程度要高于其它景观类型。开展景观格局与生态过程关系研究时,不仅需要考虑景观格局的整体效应,更应关注单一景观类型及其格局特征对一些生态过程的指示意义。     

Seeking parsimony in landscape metrics

Numerous metrics describing landscape patterns have been used to explain landscape-scale habitat selection by birds. The myriad metrics, their complexity, and inconsistent responses to them by birds have led to a lack of clear recommendations for managing land for desired species. The amount of a target land cover type in the landscape (percentage cover) often has been a useful indicator of the likelihood of species occurrence or of habitat selection; is it also a more adequate and parsimonious measure for explaining species distributions than patch size or more complex measures of landscape configuration? We examined responses of 6 woodland-interior bird species to the percentage tree cover within prescribed areas and to patch size, edge density, and other metrics. We examined responses in 2 landscapes: a mixed woodland-savanna and an eastern deciduous forest. For these 6 species, percentage tree cover explained bird occurrence as well as or better than other measures in both study areas. We then repeated the analysis on a larger group of woodland species, including those associated with woodland edges. The bird species we studied had varied responses to landscape metrics, but percentage tree cover was the strongest explanatory variable overall. Although percentage cover estimated from remotely sensed data is an inexact representation of habitat in the landscape, it does appear to be reliable and easy to conceptualize, relative to other measures. We suggest that, at least for woodland habitat, percentage cover is a broadly useful measure that can be helpful in pragmatic questions of explaining responses to landscapes or in anticipating responses to landscape change. © 2011 The Wildlife Society.     

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.     

Fractal dimension as an indicator for quantifying the effects of changing spatial scales on landscape metrics

While geographers and ecologists are well aware of the scale effects of landscape patterns, there is still a need for quantifying these effects. This paper applies the fractal method to measure the scale (grain or cell size) sensitivity of landscape metrics at both landscape and class levels using the Gold Coast City in Southeast Queensland, Australia as a case study. By transforming the original land use polygon data into raster data at eleven aggregate scales, the fractal dimensions of 57 landscape metrics as defined in FRAGSTATS were assessed. A series of linear log–log regression models were constructed based on the power law to obtain the coefficient of determination (COD or R²) of the models and the fractal dimension (FD) of the landscape metrics. The results show that most landscape metrics in the area and edge, shape and the aggregation groups exhibit a fractal law that is consistent over a range of scales. The six variations of several landscape metrics that belong to both the area/edge and shape groups show different scale behaviours and effects. However, the metrics that belong to the diversity group are scale-independent and do not accord to fractal laws. In addition, the scale effects at the class level are more complex than those at the landscape level. The quantitative assessment of the scale effect using the fractal method provides a basis for investigating landscape patterns when upscaling or downscaling as well as creating any scale-free metric to understand landscape patterns.     

Mediterranean drylands: The effect of grain size and domain of scale on landscape metrics

This study first sought to isolate a select group of landscape metrics particularly well-suited for describing dryland Mediterranean landscapes in Jordan. We examined the response of 50 landscape metrics to a large range of imagery grain sizes. Most of the metrics exhibited an expected behavior, similar to what has been previously reported in literature such as (a) a predictable (linear or power law) response to changing grain size, and (b) an unpredictable (staircase-like or erratic) response to changing grain size. Some metrics, however, exhibited a domain of scale effect, in particular the core area metrics. Using correlation analysis, the original 50 metrics were placed into 19 groups such that all metrics within a group were strongly correlated with each other, and were represented by a single representative metric. Using these representative metrics in the context of principal components analysis, we then found that six factors explained 95.35% of the total variation found in the landscape pattern. The highest loadings for these six factors, in order, were the number of patches (NP), mean proximity index (PROX_MN), largest patch index (LPI), patch cohesion index (COHESION), total core area (TCA), and the proximity index coefficient of variation (PROX_CV). It was concluded that east Mediterranean landscapes with a long history of anthropogenic-driven change showed a domain of scale for core area metrics. We also recommend that the majority of the pattern in dry Mediterranean landscapes, particularly those in Jordan, can be described with six metrics. We suggest that our procedure for landscape metric selection can be utilized in other regions of study as well.