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.     

黑河流域中游地区景观变化研究

使用GIS技术和景观结构分析软件FRAGSTATS,分析了黑河流域中游地区近20年间景观结构的变化。结果表明,该地区的各类景观元素在近20年间发生了十分复杂的结构变化和相互转换,但整体景观仍保持荒漠化与绿洲景观强烈分异的鲜明格局。人类有目的性的干扰活动改变了干旱区有限的水资源的分布与分配,加剧了本地区荒化进程与绿洲化进程的对立斗争,而绿洲与荒漠的交错地带是斗争的最敏感部位,香农多样性指数和香农均度指数的下降,充分证明人类对本地区景观改造程度的不断加强,其结果一方面显著提高了本地区的社会和经济效益,另一方面也导致了局部地区生态环境效益的下降,对本论文的研究方法和技术也进行了讨论。     

Landscape heterogeneity and the use of space by elephants in the Kruger National Park, South Africa

Landscape heterogeneity may influence ranging behaviour of mammals. Here we relate the home range size of elephants living in the Kruger National Park to the number of patches, proportion of each patch, spatial arrangement of patches, patch shape, and contrast between neighbouring patches. Home range sizes decreased exponentially with an increase in the number of patches per 100 km² and the home range sizes of bulls were in general more strongly related to measures of heterogeneity. This may reflect differences in perception of heterogeneity between the sexes.     

Spatial correlograms of soil cover as an indicator of landscape heterogeneity

Soil cover, which is one of the most informative and integrative landscape factors, can be used for the analysis of landscape patterns. We studied the spatial autocorrelation (Moran's I) of raster format soil maps (1:10,000; 10 m pixel size) in 35 study areas representing all landscape regions in Estonia. The carbonate concentration of soils, volumetric soil moisture (%) and the depth of the groundwater table were taken into consideration in compiling a scale of contrast of 17 soil groups. We introduce a simple characteristic based on spatial correlograms: a half-value distance lag, h_I = 0.5—a distance where Moran's I drops below 0.5. Spatial autocorrelation decreased very rapidly in the case of heights with a very heterogeneous landscape composition, showing low values of h_I = 0.5 (<100 m in all 6 study areas). In uplands and depressions, the spatial autocorrelation also decreased relatively rapidly (h_I = 0.5 < 200 m). In most of the plains, coastal lowlands, sea islands and inland paludified lowlands, the values of Moran's I did decrease slowly with increasing lag, being >200 m in all forest and bog areas with complex topographical conditions due to the variety of glacial landforms and peatlands. All of the eight FRAGSTATS landscape metrics studied demonstrated significant correlations with h_I = 0.5, whereas five of them – Contrast Weighted Edge Density (CWED); Percentage of Like Adjacencies (PLADJ), Edge Density (ED), Patch Density (PD) and Mean Patch Area Distribution (AREA_MN) – had Spearman Rank Order Correlation values higher than 0.8. Landscapes with high ED, PD, and CWED values have a low autocorrelation: PD, ED, and CWED correlated negatively with h_I = 0.5. PD, ED, and CWED decreased and PLADJ increased with the power-law relationship with increasing h_I = 0.5. Spatial autocorrelation is lower in landscapes with complex structure and high contrast. The positive relationship with PLADJ indicates the same. Thus, spatial correlograms of potential landscape structure based on soil cover analysis can be used for the characterization of human-influenced landscape (land use) structure.     

黑河流域景观结构分析

在黑河流域土地利用分类的基础上,利用地理信息系统进行流域景观制图,划分出6个景观区。利用景观结构分析软件FRAGSTATS在流域尺度上计算了各景观区的多种景观结构指标。结果表明：山区的景观结构特征突出表现为连通性强,拼块形状复杂。绿洲－荒漠区的景观结构特征表现为,绿洲主要沿河流和渠道分布,是镶嵌在荒漠基质上的景观异质体。绿洲是干旱区景观结构最复杂,类型最丰富,景观多样性最高的地区,但不同景观区的绿洲其景观结构有显著的差异。荒漠和绿注的过渡地带的过渡生态类型丰富,其共同的景观结构特征是拼块破碎、呈团聚状分布,在极端干旱的北部阿拉善高平原,裸露戈壁是景观模地,占据绝对优势,蔓延度极高,其它类型是面积相对很小的异质镶嵌体。文章最后指出,在使用FRAGSTATS的栅格版本时要特别注意尺度的影响。     

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.     

基于遥感数据的晋陕蒙交汇区景观格局定量分析——以榆林幅1／25万TM影像分析为例

1　引　　言景观生态学的产生与发展 ,给传统生态学与地理学带来了活力与许多新思想 .其研究方法与成果为资源开发和环境生态保护提供了新的科学方法和决策依据 .景观异质性是景观生态学的核心概念之一 .景观格局是景观异质性的表现[2 ,3 ,5,9] .景观格局分析是景观生态学研究任务之一 ,是定量研究斑块在景观中的分布规律 .空间格局分析的目的是从无序的景观上发现潜在的有意义的秩序和规律[5] .而景观格局分析是因为景观格局对其中元素流产生影响 ,不同景观格局或景观格局动态演变导致区域景观功能发生变化[7] ,景观格局会影响到物种的丰度…     

空间粒度变化对景观格局分析的影响

认识空间异质性的多尺度依赖性和景观格局特征对尺度效应关系的影响是进行空间尺度推绎的基础。以2种真实景观(中国广东粤北植被景观与美国凤凰城城市景观)和SIMMAP景观中性模型产生的27种模拟景观为对象。利用景观格局分析软件FRAGSTATS对18种常用景观指数的尺度效应进行了系统的分析。根据这些指数对空间粒度变化的响应曲线和尺度效应关系,18种景观指数可分为3类。第1类指数随空间粒度的增大单调减小。具有比较明确的尺度效应关系(幂函数下降),尺度效应关系受景观空间格局特征的影响较小;这类指数包括缀块数、缀块密度、边界总长、边界密度、景观形状指数、缀块面积变异系数、面积加权平均缀块形状指数、平均缀块分维数和面积加权平均缀块分维数。第2类指数随空间粒度的增大将最终下降。但不是单调下降的;尺度效应关系比较多样,可表现为幂函数下降、直线下降或阶梯形下降。主要受缀块空间分布方式和缀块类优势度的交互影响;这类指数有5种：平均缀块形状指数、双对数回归分维数、缀块丰度、缀块丰度密度和Shannon多样性指数。第3类指数随空间粒度的变粗而增加。随缀块类优势度均等性的增加。尺度效应关系由阶梯形增加、对数函数增加、直线增加向幂函数增加过渡。尺度效应关系主要受缀块类优势度的影响;此类指数包括平均缀块面积、缀块面积标准差、最大缀块指数与聚集度。景观指数随空间粒度变化是一种1临界现象,当粒度大于或小于1临界值时,景观指数对空间粒度变化非常敏感。变化速率非常大。绝大部分情况下。真实景观粒度效应关系和曲线形状与模拟景观所得分析结果相似。说明模拟景观具有很好的代表性。文中也讨论了本研究结果与前人研究的异同。分析了造成差异的原因。景观指数的粒度效应关系与指数本身所反映的景观格局信息有一定关系,总体上来说。随粒度增加。缀块数、边界长度、缀块形状的复杂性、多样性将减小,而平均缀块面积和聚集度将增加。一系列的尺度效应图和不同景观指数的尺度效应关系可作为景观格局分析时指数选择、分析结果的解释和进行空间尺度推绎的参考。