Landscape dynamics and their effect on the functional connectivity of a Mediterranean landscape in Chile

Land use and cover changes have been identified as a major factor contributing to shape landscape structure and biodiversity patterns, particulary in areas with a long history of human occupation and habitat fragmentation, such as the Mediterranean landscapes. However, the existing studies on landscape change indicators for Mediterranean areas have mostly focused in Europe, while for other Mediterranean zones, and especially for South America, there is a serious lack of knowledge concerning the impact of landscape dynamics on ecological processes. Further research on this topic is urgently needed, given the high biodiversity levels and the rapidly increasing rates of human modification in the Mediterranean landscapes of South America. For this purpose, we investigated the dynamics of a landscape in the semiarid region of the Mediterranean zone of Chile, and measured the effect of those dynamics on functional connectivity, during a period of about four decades (1975–2011). Landscape connectivity indicators were extracted from a series of Landsat images. The Equivalent Connnected Area index (ECA) was used as indicator of connectivity trends, and was evaluated for three representative distances of seed dispersal in the study area (150 m, 500 m and 1000 m). In addition, the patches that most contribute to maintain the present connectivity, and their roles as connectivity providers, were identified through a set of commensurable indicators: betweenness centrality and the fractions (intra, flux connector) of the Integral Index of Connectivity. We found that these indicators were useful to detect and summarize a number of previously unreported trends in these Mediterranean landscapes. First, population growth and economic development were compatible with an increase in functional connectivity for forest habitats, mainly because the abandonment of marginal agricultural lands and their subsequent conversion to espinals (Acacia caven) triggered vegetation succession towards secondary forests. Second, increased forest connectivity was not associated to a decrease in the characteristic heterogeneity of Mediterranean landscapes. Third, many patches of espinal, despite being commonly regarded as of poor conservation value, were crucial to promote connectivity by acting as stepping stones among other patches with higher habitat quality. The approach here presented provides a combined assessment of landscape structure, function and change that should be valuable and applicable to deliver operational indicators in dynamic landscapes in South America and other Mediterranean regions.     

Comparing semantically-blind and semantically-aware landscape similarity measures with application to query-by-content and regionalization

Local landscapes can be assessed for a degree of mutual similarity between their patterns using a methodology pioneered in the domain of Content-Based Image Retrieval (CBIR). They are represented by multivariate histograms of selected pattern features and their similarity is calculated as the similarity between those histograms. Potential similarity functions include semantically-blind measures — designed to compare only the patterns without taking into account intrinsic similarities between the features, and semantically-aware measures — that also take into account intrinsic (semantic) alikeness between landscape features. In this paper we compare the performance of these two types of landscape similarity measures. Two measures are evaluated, the semantically-blind Jensen–Shanon (JS) and the semantically-aware Earth Mover's Distance (EMD). The performance of these two measures is evaluated empirically by running tests pertaining to their potential practical applications: a query-by-content and regionalization into landscape types. Variants of the JS and EMD corresponding to 1D and 2D histograms are applied to the two test sites taken from the NLCD 2006. Each test site is divided into 400 3 × 3 km areal units serving as local landscapes. For each test site extensive query-by-content and regionalization calculations are run using different variants of JS and EMD. The results are compared using statistics and visual assessment. The EMD has been found to perform better than the JS, but the difference in performance is not pronounced, especially if a 1D histogram representation of landscapes is used. For the 2D histogram representation of landscapes the EMD consistently yields better results than the JS but suffers from high computational cost. The EMD also enables the comparison of landscapes having different legends.     

Designing ecological corridors in a fragmented landscape: A fuzzy approach to circuit connectivity analysis

Landscape connectivity analysis is a major tool in supporting biodiversity conservation. Several methodologies have been developed to tackle it by following two main paths. The first path exploits graph approaches and models focal nodes’ connections on a resistance/conductance matrix depending on focal species’ movement potential. The second path considers geometrical pattern analyses based on the calculation of structural landscape metrics. These approaches separately investigate functional and structural features of the landscape, and may come short of a total definition if used separately. Here we propose a new scalable, modular, participative and open-source procedure based on Fuzzy logic to combine the functional and structural aspects of connectivity. We applied this method on the highly fragmented landscape of the Po Plain, focusing on its rare and endangered plain springs named fontanili. We identified an expert panel and involved it in the assignation of permeability values of land use classes with respect to the capacity of movement of animal species typical of fontanili. We concurrently performed a quantitative evaluation of the landscape fragmentation with a moving window. We found that the functional and structural evaluations were poorly correlated in the area under study (Pearson's r = −0.35, p < 0.001). We thus integrated these two non-overlapping analyses of the landscape by Fuzzy logic using thresholds and combination weights obtained from questionnaires proposed to the expert panel. The resulting index, termed Fuzzy Functionality Index (FFI), improved the level of information associated with landscape classification. By merging functional and structural aspects of the landscape, the FFI allowed us to discriminate different functional values of equally permeable parcels and vice versa. We demonstrate that FFI may act as a conductance measure in a circuit theory approach, highlighting ecological corridors between focal points of species’ distribution. We present FFI as an effective predictive index to inspect complex and non-linear landscape dynamics.     

Spatial metrics for detecting ecosystem degradation in the ridge-slough patterned landscape

Indicators of landscape condition should be selected based on their sensitivity to environmental changes and their capacity to provide early warning detection of those changes. We assessed the performance of a suite of spatial-pattern metrics selected to quantify the condition of the ridge-slough landscape in the Everglades (South Florida, USA). Spatial pattern metrics (n = 14) that describe landscape composition, geometry and hydrologic connectivity were enumerated from vegetation maps of twenty-five 2 × 2 km primary sampling units (PSUs) that span a gradient of hydrologic and ecological condition across the greater Everglades ecosystem. Metrics were assessed in comparison with field measurements from each PSU of landscape condition obtained from regional surveys of soil elevation, which have previously been shown to capture dramatic differences between conserved and degraded locations. Elevation-based measures of landscape condition included soil elevation bi-modality (BI_SE), a binary measure of landscape condition, and also the standard deviation of soil elevation (SD_SE), a continuous measure of condition. Metric performance was assessed based on the strength (sensitivity) and shape (leading vs. lagging) of the relationship between spatial pattern metrics and these elevation-based measures. We observed significant logistic regression slopes with BI_SE for only 4 metrics (slough width, ridge density, directional connectivity index – DCI, and least flow cost – LFC). More significant relationships (n = 8 metrics) were observed with SD_SE, with the strongest associations for slough density, mean ridge width, and the average length of straight flow, as well as for a suite of hydrologic connectivity metrics (DCI, LFC and landscape discharge competence – LDC). Leading vs. lagging performance, inferred from the curvature of the association obtained from the exponent of fitted power functions, suggest that only DCI was a leading metric of the loss of soil elevation variation; most metrics were indeterminate, though some were clearly lagging. Our findings support the contention that soil elevation changes from altered peat accretion dynamics precede changes in landscape pattern, and offer insights that will enable efficient monitoring of the ridge-slough landscape as part of the ongoing Everglades restoration effort.     

Validating Landsat-based landscape metrics with fine-grained land cover data

ContextModerate-grained data may not always represent landscape structure in adequate detail which could cause misleading results. Certain metrics have been shown to be predictable with changes in scale; however, no studies have verified such predictions using independent fine-grained data.ObjectivesOur objective was to use independently derived land cover datasets to assess relationships between metrics based on fine- and moderate-grained data for a range of analysis extents. We focus on metrics that previous literature has shown to have predictable relationships across scales.MethodsThe study area was located in eastern Connecticut. We compared a 1 m land cover dataset to a 30 m resampled dataset, derived from the 1 m data, as well as two Landsat-based datasets. We examined 11 metrics which included cover areas and patch metrics. Metrics were analyzed using analysis extents ranging from 100 to 1400 m in radius.ResultsThe resampled data had very strong linear relationships to the 1 m data, from which it was derived, for all metrics regardless of the analysis extent size. Landsat-based data had strong correlations for most cover area metrics but had little or no correlation for patch metrics. Increasing analysis areas improved correlations.ConclusionsRelationships between coarse- and fine-grained data tend to be much weaker when comparing independent land cover datasets. Thus, trends across scales that are found by resampling land cover are likely to be unsuitable for predicting the effects of finer-scale elements in the landscape. Nevertheless, coarser data shows promise in predicting fine-grained for cover area metrics provided the analysis area used is sufficiently large.     

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.     

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.     

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.     

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。     

Integrating geo-biodiversity features in the analysis of landscape patterns

Spatial patterns are deeply linked to ecological processes and this relationship lies at the core of landscape ecology. In turn, landscape patterns are influenced by physical, biological and anthropogenic factors. The aim of this study was to explore how specific physical and biological factors, namely geo- and biodiversity features influence landscape patterns. The focus was on microscale relationships and we chose as our focus area a small scale study site covering 3091 ha characterized by vegetation mosaics with multiple patterns. We considered geology, soil and altitude (for geodiversity) and land cover classes (for biodiversity) as superposed layers and we aggregated their elements into a new combined mosaic. Several landscape metrics related to patterns such as landscape fragmentation, connectivity of habitats and ecotone properties were computed at the class level for the new mosaic and were used in multivariate statistical analyses. We determined the most important parameters by Principal Component Analysis. The first component was mainly linked to metrics related to size variability, while the second one was related to border complexity. In the reduced space, we delineated three clusters of objects that were characterized by different landscape patterns. We analyzed the underlying geology, soil structure and occurring land cover classes for each cluster. We then performed Redundancy Analysis using geo- and biodiversity features as predictor variables and metrics as response variables. While the land cover acted as explanatory variable for the first principal axis of variation, the geodiversity features (geology and soil) were related to the second one. Specifically, the occurrence of limestone yields more complex borders of patches; some phenomena are visible in situ, such as limestone appearing at the surface as outcrops (lapis) that induce irregular shapes of the patches. Overall, the analyses hinted that, besides the land cover class, the underlying geology plays an important role in defining landscape patterns, and this relationship can be revealed through the use of appropriate statistical tools. On the other hand, the study area is an agro-silvopastoral landscape, where local traditional management practices are also an important driver for the occurrence of specific patterns. Therefore, understanding the links between geo- and biodiversity characteristics and landscape features can contribute to developing appropriate management and planning strategies.     

Modeling landscape condition for biodiversity assessment—Application in temperate North America

Conservation decisions are well supported by predictive spatial models that indicate the relative ecological condition of a given place. The intent of this 90 m pixel landscape condition model is to use nationally available spatial data from the USA, Mexico, and Canada to express assumptions regarding the relative ecological effects of land uses on terrestrial natural communities and species. This approach emphasizes and updateable and transparent design which takes advantage of empirical biodiversity data from the USA to both calibrate and validate the model. Map layers depicting infrastructure, land use, and modified vegetation were each scored for site impact and distance decay, and then combined into one map surface. Field observations from Natural Heritage Programs, each scored for relative ecological condition (in categories A = excellent to D = poor), were used to calibrate distance decay parameters. Some 90,000 observations for at-risk species, invasive plant species, and natural communities were used for model validation. Statistically significant distinctions in ecological condition among validation samples were predicted by the resultant spatial model. Variation in landscape condition was then summarized by regional U.S. Landscape Conservation Cooperatives (LCCs) in terms of areas approximating A–D condition. Montane and desert LCCs scored on average much higher in area approximating “A” and “B” landscape condition, while LCCs with more substantial agricultural and urban footprints scored overwhelmingly within the “D” range of condition. Similar analyses illustrated range-wide scoring of landscape condition for major vegetation types across temperate North America.     

Impact of landscape pattern at multiple spatial scales on water quality: A case study in a typical urbanised watershed in China

Buffer zones along rivers and streams can provide water quality services by filtering nutrients, sediment and other contaminants from the surface. Redundancy analysis was used to determine the influence of the landscape pattern at the entire catchment scale and at multiple buffer zone scales (100 m, 300 m, 500 m, 1000 m and 1500 m) on the water quality in a highly urbanised watershed. Change-point analysis was further applied to estimate the specific locations along a gradient of landscape metric that result in a sudden change in the water quality variable. The landscape characteristics for 100 m buffer zones appeared to have a slightly greater influence on the water quality than the entire catchment. The patch density of urban land and the large patch index of water were recognised as the dominant variables influencing the water quality for a 100 m buffer zone. The result of change-point analysis indicated key interval values of the two landscape metrics within the 100 m buffer zone. When the patch density of urban land was >30–40 n/100 ha and the largest patch index of water was >2.5–3.5%, the watershed water quality appeared to be better protected.     

Interpreting multiscale domains of tree cover disturbance patterns in North America

Spatial patterns at multiple observation scales provide a framework to improve understanding of pattern-related phenomena. However, the metrics that are most sensitive to local patterns are least likely to exhibit consistent scaling relations with increasing extent (observation scale). A conceptual framework based on multiscale domains (i.e., geographic locations exhibiting similar scaling relations) allows the use of sensitive pattern metrics, but more work is needed to understand the actual patterns represented by multiscale domains. The objective of this study was to improve the interpretation of scale-dependent patterns represented by multiscale domains. Using maps of tree cover disturbance covering North American forest biomes from 2000 to 2012, each 0.09-ha location was described by the proportion and contagion of disturbance in its neighborhood, for 10 neighborhood extents from 0.81 ha to 180 km². A k-means analysis identified 13 disturbance profiles based on the similarity of disturbance proportion and contagion across neighborhood extent. A wall to wall map of multiscale domains was produced by assigning each location (disturbed and undisturbed) to its nearest disturbance profile in multiscale pattern space. The multiscale domains were interpreted as representing two aspects of local patterns – the proximity of a location to disturbance, and the interior-exterior relationship of a location relative to nearby disturbed areas.     

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.     

Effect of landscape composition and arrangement on biological control agents in a simplified agricultural system: A cost–distance approach

Landscape simplification has been clearly demonstrated to have negative impacts on the in-crop density and biological-control activity of natural enemies in agricultural landscapes. The role of spatial arrangement of the landscape, however, has not been investigated in agroecosystems. We applied cost–distance modeling to investigate the relationship between the in-crop density of natural enemies and the structural connectivity of non-crop land uses surrounding crops within Australian cotton landscapes. We further compared the explanatory power of this approach with the more commonly used spatially specific proportional-area approach, which considers landscape composition in terms of the proportional area of a given land use within a given radius. Cost–distance metrics offered a more significant explanation of in-crop density for the predatory beetle Dicranolaius bellulus (Coleoptera: Melyridae) than did the proportional-area approach. The in-crop density for this species was positively and significantly correlated with the connectivity of wooded land uses within a 3000 m radius. However, for natural enemy taxa that responded to landscape characteristics at smaller spatial scales (within a 750 m radius), namely Oxyopes spp. (Araneae: Oxyopidae) and Trichogramma spp., (Hymenoptera: Trichogrammatidae), the proportional-area approach gave a more significant explanation of in-crop density. Herbivore taxa responded weakly to proportional area at all scales and showed no correlation to cost–distance metrics. Findings indicate potential for simplified agricultural landscapes to be ‘selectively’ manipulated to enhance colonization of the crop by natural enemies, but not herbivores, by improving connectivity between crops and non-crop resources, through the presence of woody vegetation.     

Landscape diversity and forest edge density regulate stream water quality in agricultural catchments

It is well known that the composition of land cover within a watershed plays a large role in regulating stream water quality. However, there remains significant uncertainty regarding the effect of spatial configuration of different types of land cover on water quality. Using periphytic algae (diatoms) as indicators of stream trophic state, we investigated the relationship between landscape configuration and water quality in a large number of watersheds (590) at varying catchment scales in Eastern Canada. Variation partitioning analysis showed that landscape configuration explained 48% of the variation in water quality. However, since the physiographic setting constrains most agricultural activities, most of the variation was attributed to the shared influence of surficial deposits, land cover and landscape configuration (34%). The results from regression models showed that the geomorphological setting of watersheds (surficial deposits and slopes) and the proportion of different land cover types (mainly forests, wetlands, crops and urban areas) have a major impact on stream water quality. Nevertheless, a few configuration metrics emerged as important factors. Landscape diversity appeared to have a negative impact on water quality, whereas forest and wetland edge densities had a positive impact. Moreover, the influence of these lanscape metrics seems to occur at certain thresholds. In areas of intensive farming, streams with a forest area that covers at least 47% of the watershed have a better water quality. Below this threshold, eutrophic and meso-eutrophic conditions are more frequent in streams and rivers. The shape and location of forested patches were also found to be relevant. Woodlands and wetlands with an edge density higher than 36 m/ha and located along streams and gullies have a positive impact on water quality. For the same proportion of forest, complex patches will be more efficient filters than large regular patches. Forest edge density seems to control the extent of the interface with the agricultural sources and thus promotes the “sink” effect of forests on nutrients.     

Introducing a new method for assessing spatially explicit processes of landscape fragmentation

Landscape fragmentation is commonly defined as having five distinct phases (perforation, dissection, subdivision, shrinkage, and attrition). Previous studies focus on using landscape pattern metrics to interpret the phase of the landscape. A critical but underinvestigated aspect is that these five phases are also spatially explicit processes. This study proposes a new method to map and measure the different spatially explicit processes of landscape fragmentation. Unlike previous studies that are based on landscape pattern metrics, this current study measures landscape change directly. The new method is applied to Bao’an District in Shenzhen, China, to investigate forest land fragmentation under rapid urbanization. Landsat data were used to map the land use distributions from 1978 to 2009. Results show that different spatial processes occur simultaneously throughout the study area as well as in different study periods of forest fragmentation. Shrinkage was the dominant spatial process in the early period. In the later period, subdivision played a more important role, followed by attrition. Interestingly, urban land was not the leading land use that directly encroached on forest land, as many other studies have shown. Instead, forest land was first converted to orchard land, and orchard land was converted to urban land. The study contributes to Forman's general model of spatially explicit processes of landscape fragmentation and holds implications for urban planning policy and practice. The study concludes that the new method is effective in understanding and assessing forest land fragmentation, especially in the context of rapid urbanization.     

Self-similar land cover heterogeneity of temperate and tropical landscapes

Landscapes are complex objects often showing self-similar properties. For example, some power law exponents and fractal dimensions have been extensively used as global metrics for describing spatial arrangements of landscape land covers. This paper presents some newly found invariance properties for rural landscapes, with the hope to prepare further theory capable to link these properties mechanistically with generating processes. For this purpose, I propose a new surface-pattern analysis computing heterogeneity metrics into moving windows in a specific way. This generic method provides Multiscale Heterogeneity Maps (MHMs) and Heterogeneity Profiles (HPs) of almost any kind of landscape. Six landscapes, covering a wide range of observed mosaics have been analyzed by this way: four of them exhibit strong self-similar evenness (diversity) heterogeneity over two orders of magnitude with an apparent universality of the power law exponents (γ = ?0.19 ± 0.02). Such landscape self-organization is interpreted in terms of spatial arrangements of numerous land covers and fine scale patchy mosaics. This study suggests that most of terrestrial landscapes could exhibit power law behaviors in terms of evenness heterogeneity and could be the result of a hidden optimization of ecological and/or socio-economic processes.     

The link between landscape pattern and vegetation naturalness on a regional scale

The land use and land cover pattern of landscapes are key elements of basic landscape structure; accordingly, this pattern has an important role in landscape management, nature conservation and preservation. In Hungary, the naturalness of the vegetation was surveyed between 2003 and 2006, and the vegetation-based Natural Capital Index (NCI) was calculated for almost the entire area of the country. This field-based database gave us the unique opportunity to analyse the statistical connection between the naturalness of the vegetation and the landscape (land cover) pattern on a regional scale. In our study, we analysed the efficiency of the regional-level CORINE Land Cover (CLC) database for the estimation of the naturalness of the vegetation. This connection was analysed at the country scale using every (2272) Flora Mapping Unit (FMU), or 5.5 × 6.5 km quadrate, of Hungary. We calculated the shape-, edge- and size-related landscape indices for all FMUs on a landscape level (including all CLC patches) and a class level (the land cover polygons were classified according to their land cover characteristics and their level of hemeroby). We determined the Spearman’s correlations to reveal the statistical connections between the landscape metric parameters and the NCI values. All of the investigated area-weighted landscape indices: Main Patch Size, (MPS), Main Fractal Dimension Index, (MFDI), Total Edge (TE), Main Shape Index (MSI) and Number of Shape Characteristic Points (NSCP) on the landscape level showed a significant statistical connection with the NCI, but the sign of its correlation with the NCI contrasted with the findings from previous studies on a larger scale. Our study shows that scale has a strong impact on the sign of the correlation between the naturalness of the vegetation and the landscape structure. On a class level, particularly the shape-related landscape indices of the “Forest and semi-natural areas” showed statistically significant correlations with the NCI. The correlation strongly depended on the method of classification of the CLC polygons. Furthermore, the spatial pattern of the land-cover-type-based CLC polygon categories showed higher correlation values with the NCI than CLC polygon classes, which were categorized according to their hemeroby state. These results show that although the sign of the spatial pattern change in the main land cover classes is scale-dependent, they can be used to estimate the increase or decrease in the naturalness of the vegetation better than the spatial changes of the hemeroby-level-based landscape pattern. We can predict the change in the naturalness of vegetation based on the spatial changes in the land cover pattern.