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.     

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.     

Gypsy moth response to landscape structure differs from neutral model predictions: implications for invasion monitoring

Simulations of dispersal across computer-generated neutral landscapes have generated testable predictions about the relationship between dispersal success and landscape structure. Models predict a threshold response in dispersal success with increasing habitat fragmentation. A threshold is defined as an abrupt, disproportionate decline in dispersal success at a certain proportion of habitat in the landscape. To identify potential empirical threshold responses in invasion success to landscape structure, we quantified the relationship between progression of the gypsy moth (Lymantria dispar) invasion wavefront across Michigan (1985–1996) and the structure of the Michigan landscape using two indices of invasion success and six landscape metrics. We also examined the effect of scale of analysis and choice of land cover characterization on our results by repeating our analysis at three scales using two different land cover maps. Contrary to simulation model predictions, thresholds in invasion success did not correspond closely with thresholds in landscape structure metrics. Increased variation in invasion success indices at smaller scales of analysis also suggested that invasion success should be studied at larger spatial extents (≥75 km²) than would be appropriate for characterizing individual dispersal events. The predictions of individual dispersal models across neutral landscapes may have limited applications for the monitoring and management of vagile species with excellent dispersal capabilities such as the gypsy moth.     

Landscape heterogeneity metrics as indicators of bird diversity: Determining the optimal spatial scales in different landscapes

Species distribution models are often used to study the biodiversity of ecosystems. The modelling process uses a number of parameters to predict others, such as the occurrence of determinate species, population size, habitat suitability or biodiversity. It is well known that the heterogeneity of landscapes can lead to changes in species’ abundance and biodiversity. However, landscape metrics depend on maps and spatial scales when it comes to undertaking a GIS analysis.We explored the goodness of fit of several models using the metrics of landscape heterogeneity and altitude as predictors of bird diversity in different landscapes and spatial scales. Two variables were used to describe biodiversity: bird richness and trophic level diversity, both of which were obtained from a breeding bird survey by means of point counts. The relationships between biodiversity and landscape metrics were compared using multiple linear regressions. All of the analyses were repeated for 14 different spatial scales and for cultivated, forest and grassland environments to determine the optimal spatial scale for each landscape typology.Our results revealed that the relationships between species’ richness and landscape heterogeneity using 1:10,000 land cover maps were strongest when working on a spatial scale up to a radius of 125–250 m around the sampled point (circa 4.9–19.6 ha). Furthermore, the correlation between measures of landscape heterogeneity and bird diversity was greater in grasslands than in cultivated or forested areas. The multi-spatial scale approach is useful for (a) assessing the accuracy of surrogates of bird diversity in different landscapes and (b) optimizing spatial model procedures for biodiversity mapping, mainly over extensive areas.     

The Role of Landscape Patterns of Habitat Types on Plant Species Diversity of a Tropical Forest in Mexico

The relationships among landscape characteristics and plant diversity in tropical forests may be used to predict biodiversity. To identify and characterize them, the number of species, as well as Shannon and Simpson diversity indices were calculated from 157 sampling quadrats (17,941 individuals sampled) while the vegetation classes were obtained from multi-spectral satellite image classification in four landscapes located in the southeast of Quintana Roo, Mexico. The mean number of species of trees, shrubs and vines as well as the mean value of the total number of species and the other two diversity indices were calculated for four vegetation classes in every one of the four landscapes. In addition, the relationships between landscape patterns metrics of patch types and diversity indices were explored. The multiple statistical analyses revealed significant predictor variables for the three diversity indices. Moreover, the shape, similarity and edge contrast metrics of patch types might serve as useful indicators for the number of species and the other two diversity variables at the landscape scale. Although the association between the three diversity indices and patch types metrics showed similar behavior, some differences were appreciated. The Shannon diversity index, with its greater sensitivity to rare species, should be considered as having a greater importance in interpretation analysis than Simpson index.     

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.     

红松存在/不存在数据的多尺度空间分布格局

红松属小兴安岭地区地带性植被优势种,该地区也是其分布的北缘。在景观尺度上开展红松的分布格局研究有利于进一步了解红松分布机理、未来迁移过程等问题,对其经营和保护有重要意义。将景观指数法与点格局分析法结合,设定8个空间尺度,利用红松存在/不存在数据,通过计算各空间尺度上红松聚集程度和景观指数,分析小兴安岭地区红松种群在多尺度上的分布格局。研究结果表明,小尺度上红松聚集分布明显,随机分布区多处于其聚集分布区的边缘,均匀分布区则散布在其聚集分布区内。景观指数研究表明,通过景观指数可判断红松聚集分布格局趋势,而不能判断均匀分布、随机分布格局趋势,因为它们在多尺度下景观指数波动大,不能用景观指数来描述分布格局。研究得出如下结论:1)红松主要分布在其分布区的核心区域内,在分布区边缘和过渡带上呈随机分布,2)存在/不存在数据能够用来分析种群的多尺度空间分布格局,3)空间尺度的变化会引起树种分布格局的变化,随机分布随尺度增加,边缘化程度加强,4)单一尺度上,景观格局指数不能完全描述种群分布格局;而在多尺度上,变化趋势稳定的景观指数表明聚集分布存在,而波动剧烈的景观指数常与随机分布和均匀分布联系在一起,5)地形因子中,红松对坡度和海拔两个因子变化敏感。     

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.     

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。     

基于高分辨率卫星图的川中丘陵区村级景观格局特征研究

以Erle C.Ellis 建立的村级景观分类和景观制图方法,利用IKONOS高分辨率（1 m）卫星遥感图,进行典型抽样和地形→土地利用→土地覆被→生态立地的景观分类和分层制图,研究了四川盆地中部丘陵地区村级景观的构成和格局特征,并就有关方法作了讨论.结果表明,川中盆地丘陵村级景观类型多样,其多样性指数从地形、土地覆被、土地利用到生态立地变化在1.08～2.26之间,丰富度除土地覆被较高,达到85%以外,其余都较低,在42.22%～58.62%之间.分布普遍的生态立地类型为12.5%,其余88.5%的类型都以较大差异分布在各村级景观内.景观破碎化指数较高,不同村级景观之间为2.93～4.27,地形到生态立地的破碎度为2.86～5.63.村级景观构成中,人口密度、道路面积和农家院落面积与景观格局指数存在较大程度的线性相关关系,但以农家院落面积与景观分形指数、景观破碎度的线性相关显著,分别为0.957*和0.991**.运用景观4级分类和制图方法研究村级景观,多数景观格局指标都表现出显著的统计学差异,表明运用高分辨率卫星遥感图研究村级景观,以多级景观单元分类研究比单一分类的更能够反映相关信息,增加对村级景观格局的认识和理解.     

上海市城市土地利用景观的空间尺度效应

在RS与GIS技术支持下,基于2002年上海市5m分辨率的SPOT遥感影像和上海城市土地利用的景观类型,定量分析了几种常用景观格局指数随尺度的变化规律．结果表明：上海市土地利用景观格局指数对粒度和幅度变化都很敏感,景观格局具有明显的尺度依赖性,不同指数对尺度的响应特征不同;40m分辨率是上海城市土地利用景观的本征观测尺度,小于这个尺度范围往往表现出随机性特征;24km的幅度是一个特征操作尺度,与上海市建成区与非建成区边界的范围相吻合,说明对于上海城市景观而言,距离城市中心12km的幅度可能是一个本征的操作尺度;上海市城市结构的复杂性和城市空间扩展的不对称性,说明上海城市景观格局的本征操作尺度并不是一个规则的形状,24km的正方形范围仅是一个近似的操作尺度．     

Complex effects of scale on the relationships of landscape pattern versus avian species richness and community structure in a woodland savanna mosaic

Landscape pattern metrics are widely used for predicting habitat and species diversity. However, the relationship between landscape pattern and species diversity is typically measured at a single spatial scale, even though both landscape pattern, and species occurrence and community composition are scale‐dependent. While the effects of scale on landscape pattern are well documented, the effects of scale on the relationships between spatial pattern and species richness and composition are not well known. Here, our main goal was to quantify the effects of cartographic scale (spatial resolution and extent) on the relationships between spatial pattern and avian richness and community structure in a mosaic of grassland, woodland, and savanna in central Wisconsin. Our secondary goal was to evaluate the effectiveness of a newly developed tool for spatial pattern analysis, multiscale contextual spatial pattern analysis (MCSPA), compared to existing landscape metrics. Landscape metrics and avian species richness had quadratic, exponential, or logarithmic relationships, and these patterns were generally consistent across two spatial resolutions and six spatial extents. However, the magnitude of the relationships was affected by both resolution and extent. At the finer resolution (10‐m), edge density was consistently the best predictor of species richness, followed by an MCSPA metric that measures the standard deviation of woody cover across extents. At the coarser resolution (30‐m), NDVI was the best predictor of species richness by far, regardless of spatial extent. Another MCSPA metric that denotes the average woody cover across extents, together with percent of woody cover, were always the best predictors of variation in avian community structure. Spatial resolution and extent had varying effects on the relationships between spatial pattern and avian community structure. We therefore conclude that cartographic scale not only affects measures of landscape pattern per se, but also the relationships among spatial pattern, species richness, and community structure, often in complex ways, which reduces the efficacy of landscape metrics for predicting the richness and diversity of organisms.     

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.     

Interactions between landcover pattern and geospatial processing methods: Effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric “number of patches” than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty.     

Importance of estimating matrix quality for modeling species distribution in complex tropical landscapes: a test with Atlantic forest small mammals

Information to guide decision making is especially urgent in human dominated landscapes in the tropics, where urban and agricultural frontiers are still expanding in an unplanned manner. Nevertheless, most studies that have investigated the influence of landscape structure on species distribution have not considered the heterogeneity of altered habitats of the matrix, which is usually high in human dominated landscapes. Using the distribution of small mammals in forest remnants and in the four main altered habitats in an Atlantic forest landscape, we investigated 1) how explanatory power of models describing species distribution in forest remnants varies between landscape structure variables that do or do not incorporate matrix quality and 2) the importance of spatial scale for analyzing the influence of landscape structure. We used standardized sampling in remnants and altered habitats to generate two indices of habitat quality, corresponding to the abundance and to the occurrence of small mammals. For each remnant, we calculated habitat quantity and connectivity in different spatial scales, considering or not the quality of surrounding habitats. The incorporation of matrix quality increased model explanatory power across all spatial scales for half the species that occurred in the matrix, but only when taking into account the distance between habitat patches (connectivity). These connectivity models were also less affected by spatial scale than habitat quantity models. The few consistent responses to the variation in spatial scales indicate that despite their small size, small mammals perceive landscape features at large spatial scales. Matrix quality index corresponding to species occurrence presented a better or similar performance compared to that of species abundance. Results indicate the importance of the matrix for the dynamics of fragmented landscapes and suggest that relatively simple indices can improve our understanding of species distribution, and could be applied in modeling, monitoring and managing complex tropical landscapes.     

Mapping changes to vegetation pattern in a restoring wetland: Finding pattern metrics that are consistent across spatial scale and time

Tidal salt marshes in the San Francisco Estuary region display heterogeneous vegetation patterns that influence wetland function and provide adequate habitat for native or endangered wildlife. In addition to analyzing the extent of vegetation, monitoring the dynamics of vegetation pattern within restoring wetlands can offer valuable information about the restoration process. Pattern metrics, derived from classified remotely sensed imagery, have been used to measure composition and configuration of patches and landscapes, but they can be unpredictable across scales, and inconsistent across time. We sought to identify pattern metrics that are consistent across spatial scale and time – and thus robust measures of vegetation and habitat configuration – for a restored tidal marsh in the San Francisco Bay, CA, USA. We used high-resolution (20 cm) remotely sensed color infrared imagery to map vegetation pattern over 2 years, and performed a multi-scale analysis of derived vegetation pattern metrics. We looked at the influence on metrics of changes in grain size through resampling and changes in minimum mapping unit (MMU) through smoothing. We examined composition, complexity, connectivity and heterogeneity metrics, focusing on perennial pickleweed (Sarcocornia pacifica), a dominant marsh plant. At our site, pickleweed patches grew larger, more irregularly shaped, and closely spaced over time, while the overall landscape became more diverse. Of the two scale factors examined, grain size was more consistent than MMU in terms of identifying relative change in composition and configuration of wetland marsh vegetation over time. Most metrics exhibited unstable behavior with larger MMUs. With small MMUs, most metrics were consistent across grain sizes, from fine (e.g. 0.16 m²) to relatively large (e.g. 16 m²) pixel sizes. Scale relationships were more variable at the landcover class level than at the landscape level (across all classes). This information may be useful to applied restoration practitioners, and adds to our general understanding of vegetation change in a restoring marsh.     

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。     

耦合种群动态的生境格局变化分析粒度与景观因子选择——以盐城越冬丹顶鹤及其生境的变化为例

选择盐城珍禽国家级自然保护区,以丹顶鹤越冬生境景观与种群动态这一格局-过程关系为切入点,尝试从丹顶鹤最小存活面积特征与种群动态视角探讨一下最佳粒度、景观格局变化分析的指数遴选方法。结果发现:根据丹顶鹤最小生存面积确定200m为最大转换粒度,综合景观指数随空间粒度变化的规律和粒度转换精度损失评价的结果,确定最佳分析粒度为70m。在众多景观因子中,运用Spearman秩相关分析,再利用逐步回归分析建立起景观与丹顶鹤数量指标间的联系,最终筛选出反映景观面积(CA)和空间格局(IJI,ENN_MN)的3种影响显著景观因子,其解释贡献率(累计R2)达到70.5%,且其所代表的生境景观的组成和结构信息较为客观地反映出研究区丹顶鹤种群动态分布的显示状态。与纯粹的景观指数分析相比,这种方法更能反应景观格局演变特征的特定生态学意义。