Spatial Pattern Metrics Research Articles

Measuring Moran's I in a Cost-Efficient Manner to Describe a Land-Cover Change Pattern in Large-Scale Remote Sensing Imagery

Detection and analysis of a land-cover change pattern from remotely sensed imagery have gained increasing research interests in recent years. A number of spatial statistics and landscape pattern metrics have been explored for this purpose. Moran's index (Moran's I) of spatial autocorrelation is one such spatiostatistical measure, which has been proved to be useful in characterizing the land-cover change, especially in Landsat data. However, since the Moran's I estimation needs to deal with spatial weight between each pair of spatial data objects, it becomes almost unfeasible to apply Moran's I in the case of large-scale remote sensing data, containing several millions of pixels. This paper proposes a method for computing Moran's I in the Hadoop MapReduce framework and thereby helps in describing spatial patterns in large-scale remotely sensed data. The contributions of the work include: 1) the exhaustive description of the Mapper and Reducer implementation for cost-effective estimation of Moran's I, and 2) the computational complexity analysis of the respective algorithms. Furthermore, two case studies have been presented, considering both the rook case and the queen case of spatial contiguity. Case Study 1 demonstrates the computational efficiency of the proposed implementation, and Case Study 2 illustrates an application of Moran's I in describing the urban sprawling pattern in two large spatial zones in Kolkata, India.

Global Spatial–Temporal Variability in Terrestrial Productivity and Phenology Regimes between 2000 and 2012

The productivity and phenology of vegetation are spatially and temporally variable ecosystem functions. Monitoring spatial–temporal patterns in these functions can improve our understanding of global change and natural ecosystem variability and inform management actions. Researchers typically focus on temporal changes within or among static regions and omit dynamics of spatial configuration. Our goal was to assess global spatial–temporal variability in productivity and phenology regimes between 2000 and 2012 using a temporally dynamic functional type classification. Fourteen functional types were defined for each year by clustering the annual sum and annual variability (seasonality) of the fraction of photosynthetically active radiation (fPAR)—a biophysical proxy for vegetation greenness or productivity—from the Moderate Resolution Imaging Spectrometer (MODIS). The fourteen functional types ranged from tundra (low cumulative fPAR and highly seasonal) to tropical forests (high cumulative fPAR and low seasonality). Variability in the mean of the fPAR metrics and in two spatial pattern metrics was assessed for each functional type. Many pixels changed from one cluster to another then back again, suggesting considerable short-term variability. Temporal variability in the mean of the fPAR metrics was relatively low, with changes instead primarily manifested in spatial pattern. Spatial pattern was most variable within tundra, grasslands, shrublands, and savannas. A dynamic classification demonstrated the variability in spatial patterns of primary productivity and can be used for future monitoring.

Individual-tree- and stand-based development following natural disturbance in a heterogeneously structured forest: A LiDAR-based approach

Large-scale severe natural disturbance events drive spatial and temporal patterns of forests by altering forest structure, composition, and functions. In the Bavarian Forest National Park in Germany, windthrow events led to large disturbances caused by the European bark beetle (Ips typographus L.). Until recently, it was assumed that at the initial stage of regeneration, trees tend to form a homogeneous stand structure, whereas structural heterogeneity is an attribute of later developmental stages. Yet recent studies provide evidence that under certain conditions structural heterogeneity can arise much earlier in stand development. Here we combined LiDAR data and forest growth modeling based on individual trees to develop a workflow for studying forest development in post-disturbed areas in the upper montane regions of the national park. The current forest structure was derived from LiDAR data of individually detected trees and a set of forest structural attributes were derived. The results served as input to simulate tree development spatio-temporally for a period of 80years. Several spatial statistics, including landscape and spatial point pattern metrics, were calculated to assess the structural heterogeneity. The results showed that naturally regenerating forests on post-disturbed sites reveal structural heterogeneity already at the early-seral stage. Moreover, a significant portion of the eventual old-growth structural heterogeneity might already be determined in the early successional stages. Our workflow highlights the use of multi-sensor aerial remote sensing to provide detailed structural information useful for the investigation of early-phase forest 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 2×2km 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 (BISE), a binary measure of landscape condition, and also the standard deviation of soil elevation (SDSE), 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 BISE 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 SDSE, 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.

Seascape structure and complexity influence temperate seagrass fish assemblage composition

Understanding how spatial patterning relates to ecological processes is fundamental to define important species–environment associations at broader scales. Analyses targeting habitat structure (i.e. composition and configuration) in terrestrial landscapes are increasing, but similar studies in marine landscapes are still relatively uncommon. In this study, we explored how seascape structure and complexity (determined from significant spatial pattern metrics) influenced summer and autumn fish assemblage composition in 30 seagrass (Zostera marina) meadows along the west coast of Sweden. Species density was not influenced by seascape structure in any season. In contrast, the majority of significant fish assemblage variables were influenced by seascape structure during the summer (i.e. abundance and proportion of juveniles, abundance of Labridae and abundance of occasional shallow‐water visitors) whilst fewer in the autumn (i.e. abundance of occasional shallow‐water visitors and Synganthidae). For instance, less complex seascapes were more suitable for juvenile assemblages in summer, as these seascapes exhibit larger patch sizes of appropriate habitat (e.g. Z. marina) and less edge boundaries providing refuges from predators and food resources. Abundances of migrating fish, such as the sea trout Salmo trutta, also responded positively to a less complex seascape in the summer though perhaps ecological processes, such as prey availability, were additional contributing factors driving this relationship. High complexity seascapes only had a positive influence on the abundance of taxa using multiple habitats (Labridae during the summer). Our study shows that fish assemblages in temperate marine environments are significantly linked to spatial habitat patterning and seascape complexity. This offers valuable insights into species–habitat–seascape linkages, information important for coastal conservation and marine spatial planning.

Using Multispectral Imagery to Compare the Spatial Pattern of Injury to Wheat Caused by Russian Wheat Aphid1and Greenbug1

Abstract. The Russian wheat aphid, Diuraphis noxia (Kurdjumov), and greenbug, Schizaphis graminum (Rondani) (Hemiptera: Aphididae), are important insect pests of wheat, Triticum aestivum L. Outbreaks of both pests in commercial wheat fields occur almost every year in the Great Plains of the United States. Infestations of both pests in wheat fields are clumped in distribution, and as a result so is the injury to plants in the field. The spatial pattern of patches of plants injured by the two aphid pests can be detected using airborne multispectral imaging. This study used multispectral imagery of wheat fields infested by the two species of aphids analyzed using spatial-pattern metrics with the purpose of determining differences between species in the spatial pattern of injury to wheat plants in fields. Analyses using Students t-tests revealed patches of wheat within fields injured by Russian wheat aphid were larger, more complex in shape, and more isolated than patches of wheat injured by greenbug. Our fin...

Expressing the spatio-temporal pattern of farmland change in arid lands using landscape metrics

Identifying, recording and monitoring land cover change on the Earth's surface is a complex procedure. Spatio-temporal modelling is an effective approach to simplifying and simulating the process. Existing spatio-temporal modelling methods are typically based on either, an overlay of multi-temporal land cover maps, or temporal trend analysis of spatial pattern indices. Consequently, an understanding of the spatial dynamics of any changes is either fragmental in the former case, or invisible in the latter case, due to the lack of adequate geographical location information.In the arid zone of western China, a widely accepted belief is that rapid farmland expansion and the subsequent abandonment of the farms, or their mis-management, would lead to soil salinisation and desertification. In order to better understand the spatio-temporal pattern of farmland change, this paper proposes an integrated approach that combines the two methods of pixel-based trajectory analysis and class-level spatial pattern metrics. Multi-temporal remote sensing images were collected beginning in 1994, a year that captured the initial effects of the period of rapid farmland expansion. Historical change trajectories were established for each pixel and categorized according to change types (i.e. expanding or shrinking). The spatial dynamics of farmland change can then be illustrated by mapping the change trajectory classes. Spatial patterns of farmland change were quantified by employing distribution-related landscape metrics, such as indices of interspersion (IJI), connectivity (COHESION) and isolation (ENN), to analyse farmland development models of the two river basins in the study area. Shape indices, including overall shape (nLSI) and edge shape (FRAC), were applied to appraise the structural stability of the farmlands over time. Results indicate that, over the past two decades, the area subject to farmland expansion was significantly larger than that experiencing farmland abandonment. The relatively rapid expansion of farmland exhibited a concentrated pattern, and generally followed a layer-based development model. The study showed that the proposed research method effectively visualized and quantified the spatio-temporal dynamics of farmland change.

Processed multispectral imagery differentiates wheat crop stress caused by greenbug from other causes

The greenbug, Schizaphis graminum (Rondani) (Hemiptera:Aphididae) is an important pest of small grains such as winter wheat (Triticum aestivum). The objective of this study was to determine the potential for multispectral imagery analyzed using spatial pattern metrics subjected to discriminant function analysis to differentiate patches of wheat plants within wheat fields infested by greenbug from stressed patches caused by other factors. Multispectral images of wheat fields were acquired using a Duncantech MS3100-CIR multispectral camera. Stress observed to wheat plants in wheat fields was grouped into categories: greenbug, drought and agricultural conditions. ERDAS Imagine software was used to process and analyze images, and FRAGSTATS was used to quantify spatial pattern. A set of 10 spatial pattern metrics were computed at the patch level for each stress factor. The analysis of spatial pattern metrics by discriminant function analysis revealed that the three types of stress could be reliably differentiated. The combination of multispectral data and spatial pattern metrics made it possible to differentiate patches in wheat fields infested by greenbug from patches caused by drought and agronomic conditions. The detection and differentiation of stressed patches may help in mapping stress within fields for the purpose of site-specific pest management and for monitoring systems to identify greenbug infestations at individual field and regional scales.

Climate, fire size, and biophysical setting control fire severity and spatial pattern in the northern Cascade Range, USA.

Warmer and drier climate over the past few decades has brought larger fire sizes and increased annual area burned in forested ecosystems of western North America, and continued increases in annual area burned are expected due to climate change. As warming continues, fires may also increase in severity and produce larger contiguous patches of severely burned areas. We used remotely sensed burn-severity data from 125 fires in the northern Cascade Range of Washington, USA, to explore relationships between fire size, severity, and the spatial pattern of severity. We examined relationships between climate and the annual area burned and the size of wildfires over a 25-year period. We tested the hypothesis that increased fire size is commensurate with increased burn severity and increased spatial aggregation of severely burned areas. We also asked how local ecological controls might modulate these relationships by comparing results over the whole study area (the northern Cascade Range) to those from four ecological subsections within it. We found significant positive relationships between climate and fire size, and between fire size and the proportion of high severity and spatial-pattern metrics that quantify the spatial aggregation of high-severity areas within fires, but the strength and significance of these relationships varied among the four subsections. In areas with more contiguous subalpine forests and less complex topography, the proportion and spatial aggregation of severely burned areas were more strongly correlated with fire size. If fire sizes increase in a warming climate, changes in the extent, severity, and spatial pattern of fire regimes are likely to be more pronounced in higher-severity fire regimes with less complex topography and more continuous fuels.

Remote Sensing and Land Use/Land Cover Trajectories

Remotely sensed data is the most important data source for land cover change trajectories over the past 40 years. This research explores the temporal composition of the main Land-use/land-cover (LULC) trajectories. Examine the spatial configuration of the trajectory derive the probability of transitions in the Olomouc region, Czech Republic. Multi-temporal satellite data from 1991, 2001 and 2013 were used to extract land use/cover types by object oriented classification method. To achieve the objectives, three different aspects were used: (1) Calculate the quantity of each transition; (2) Allocate location based landscape pattern (3) Compare land use/cover evaluation procedure. Land cover change trajectories show that 16.69% agriculture, 54.33% forest and 21.98% other areas (settlement, pasture and water-body) were stable in all three decade. Approximately 30% of the study area maintained as a same land cove type from 1991 to 2013. The results suggest that spatial pattern metrics of land cover change trajectory can provide a good quantitative measurement for better understanding of the spatio-temporal pattern of land cover change due to different causes.

Assessing connectivity in a tropical embayment: Fish migrations and seascape ecology

Seascape connectivity and configuration of multiple habitats are important features to include in marine spatial planning, and protecting seascapes with high connectivity is recommended. The present study examines the potential connectivity of reef fish assemblages in a shallow-water conservation area in Zanzibar (Tanzania) by analysing relationships between a set of habitat variables and fish diversity and density for different functional groups (based on diet) and life stages of fish using PLS-analysis. We combined spatial pattern metrics (habitat type, patch size, distance to patch) and dispersal abilities of a number of fish species using buffer radius to answer the questions; (i) do coral reefs with high connectivity to seagrass habitats have higher abundances and higher species richness of fish that undertake routine migrations during their life-history? and (ii) do coral reefs closer to mangrove forest support higher abundances of nursery species (i.e., fish species that use mangrove and seagrass beds as juvenile habitat)? Habitat mosaics surrounding fish survey sites and within-patch measurements inside fish survey sites were quantified at multiple scales (meters to kilometers) using aerial photography and scuba. Fish data was collected using a standardized point census method. We found that both fine- and broad-scale variables were important in structuring fish communities and connectivity with surrounding habitats, where predominantly seagrass beds within a 750mradius had a positive influence on fish abundances of invertebrate feeders/piscivores (especially for lutjanids and lethrinids). Additionally, fine-scale seagrass cover had a positive influence on nursery species. Depth also had a positive influence on total species richness and the abundance of invertebrate feeders/piscivores. This study highlights the importance of combining connectivity and habitat configuration at different scales to fully understand and manage the tropical seascape.

Effects of heterogeneity of pre-fire forests and vegetation burn severity on short-term post-fire vegetation density and regeneration in Samcheok, Korea

This study investigated the combined effects of heterogeneity of pre-fire forest cover and vegetation burn severity on post-fire vegetation density and regeneration at an early stage in Samcheok, Korea. To measure the spatial heterogeneity of pre-fire forests, spatial pattern metrics at a landscape level and class level were adopted, and a regression tree analysis for post-fire vegetation density and regeneration was used to avoid spatial autocorrelation. Two regression tree models were estimated for post-fire vegetation density and post-fire vegetation regeneration with the same independent variable sets, including heterogeneity of pre-fire forest cover and vegetation burn severity. The estimated model suggested that the percentage of Japanese red pine and burn severity were the most significant variables for post-fire vegetation density and regeneration, respectively. The compositional and spatial heterogeneity of pre-fire forest and burn severity, as well as the degree of burn severity, was found to have significant impacts on post-fire vegetation density and regeneration. Overall, more rapid vegetation regeneration can be expected in more severely burned areas. However, this rapid vegetation regeneration at an early stage is due mostly to perennials and shrubs, not to the sprouting or regrowth of trees. The study results strongly indicated that a susceptible forest cover type and its spatial patterns directly influence the heterogeneity of burn severity and early vegetation density and regeneration. Hence, the management of susceptible forest cover types is particularly critical for establishing more fire-resilient forests and for post-fire forest restoration.

Differentiating stress to wheat fields induced by Diuraphis noxia from other stress causing factors

The objective of this study was to develop a method to differentiate two categories of stress to wheat fields, stress induced by the Russian wheat aphid, Diuraphis noxia (Mordvilko), and stress caused by other factors. The study used a set of spatial pattern metrics derived from multispectral imagery acquired with an MS3100-CIR digital camera. Thematic maps were generated from the subset of multispectral image of each wheat field. A discriminant function analysis was used to determine whether the set of spatial pattern metrics derived from thematic maps could differentiate the categories of stress. The study concluded that it was possible to use class-based spatial pattern metrics to differentiate stress induced by D. noxia from other stress factors.

Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors

The Russian wheat aphid (RWA) Diuraphis noxia (Mordvilko) is a major pest of winter wheat and barley in the United States. RWA induces stress to the wheat crop by damaging plant foliage, lowering the greenness of plants, and affecting productivity. The utilization of multispectral remote sensing is effective at detecting plant stress in agricultural crops. Stress to wheat plants detected in fields can be caused by several factors that can vary spatially in their presence and intensity across a field. Stress can result from factors such as nutrient deficiency, drought, diseases, and pests that can occur individually or collectively. The present study investigated the potential of using spatial pattern metrics derived from multispectral images in combination with topographic and edaphic variables to identify a set of variables to differentiate the stress induced by RWA from other stress causing factors. A discriminant function analysis was applied to 15 discriminating variables. A set of 13 variables were retained to develop a model to differentiate the three types of stress. Overall, 97 percent of patches of stress used to validate the model were correctly categorized. Stressed patches caused by RWA were 98 percent correctly classified, patches caused by drought were 94 percent correctly classified, and patches caused by agronomic conditions were 99 correctly classified. It is possible to discriminate stress induced by RWA from other stress causing factors in multispectral data when spatial attributes of the stress causing factors are incorporated in the analysis.

Diel movements of fishes linked to benthic seascape structure in a Caribbean coral reef ecosystem

Many common fishes associated with Caribbean coral reef ecosystems use resources from more than 1 patch type during routine daily foraging activities. Few studies have provided direct evidence of connectivity across seascapes, and the importance of benthic seascape structure on movement behavior is poorly known. To address this knowledge gap, we coupled hydro-acoustic technology to track fish with seafloor mapping and pattern analysis techniques from landscape ecol- ogy to quantify seascape structure. Bluestriped grunts Haemulon sciurus and schoolmaster snapper Lutjanus apodus were tracked over 24 h periods using boat-based acoustic telemetry. Movement pathways, and day and night activity spaces were mapped using geographical information system (GIS) tools, and seafloor structure within activity spaces was mapped from high-resolution aerial photography and quantified using spatial pattern metrics. For both fish species, night activity spaces were significantly larger than day activity spaces. Fish exhibited a daytime preference for seascapes with aggregate coral reef and colonized bedrock, then shifted to night activity spaces with lower- complexity soft sediment including sand, seagrass, and scattered coral/rock. Movement path com- plexity was negatively correlated with seascape complexity. This demonstrates direct connectivity across multiple patch types and represents the first study to apply quantitative landscape ecology techniques to examine the movement ecology of marine fish. The spatially explicit approach facili- tates understanding to the linkages between biological processes and the heterogeneity of the land- scape. Such studies are essential for identifying ecologically relevant spatial scales, delineating essen- tial fish habitat and designing marine protected areas.

Quantifying seascape structure: extending terrestrial spatial pattern metrics to the marine realm

Spatial pattern metrics have routinely been applied to characterize and quantify struc- tural features of terrestrial landscapes and have demonstrated great utility in landscape ecology and conservation planning. The important role of spatial structure in ecology and management is now commonly recognized, and recent advances in marine remote sensing technology have facilitated the application of spatial pattern metrics to the marine environment. However, it is not yet clear whether concepts, metrics, and statistical techniques developed for terrestrial ecosystems are relevant for marine species and seascapes. To address this gap in our knowledge, we reviewed, synthesized, and evaluated the utility and application of spatial pattern metrics in the marine science literature over the past 30 yr (1980 to 2010). In total, 23 studies characterized seascape structure, of which 17 quan- tified spatial patterns using a 2-dimensional patch-mosaic model and 5 used a continuously varying 3-dimensional surface model. Most seascape studies followed terrestrial-based studies in their search for ecological patterns and applied or modified existing metrics. Only 1 truly unique metric was found (hydrodynamic aperture applied to Pacific atolls). While there are still relatively few studies using spatial pattern metrics in the marine environment, they have suffered from similar misuse as reported for terrestrial studies, such as the lack of a priori considerations or the problem of collinearity between metrics. Spatial pattern metrics offer great potential for ecological research and environmental man- agement in marine systems, and future studies should focus on (1) the dynamic boundary between the land and sea; (2) quantifying 3-dimensional spatial patterns; and (3) assessing and monitoring seascape change.

Development of a method using multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused by Diuraphis noxia

The Russian wheat aphid, Diuraphis noxia, is an important pest of winter wheat, Triticum aestivum, and barley, Hordeum vulgare that has caused an annual economic loss estimated at over 1 billion dollars since it first appeared in the United States. The objective of this study was to determine the potential of combining multispectral imagery with spatial pattern recognition to identify and spatially differentiate D. noxia infestations in wheat fields. Multispectral images were acquired using an MS3100-CIR multispectral camera. D. noxia, drought, and agronomic conditions were identified as major causes for stresses found in wheat fields. Seven spatial metrics were computed for each stress factor. The analysis of spatial metrics quantitatively differentiated the three types of stress found within wheat fields. Detection and differentiation of wheat field stress may help in mapping stress and may have implications for site-specific monitoring systems to identify D. noxia infestations and help to target pesticide applications.

Effects of landscape patterns of fire severity on regenerating ponderosa pine forests (Pinus ponderosa) in New Mexico and Arizona, USA

Much of the current effort to restore southwestern ponderosa pine forests to historical conditions is predicated upon assumptions regarding the catastrophic effects of large fires that are now defining a new fire regime. To determine how spatial characteristics influence the process of ponderosa pine regeneration under this new regime, we mapped the spatial patterns of severity at areas that burned in 1960 (Saddle Mountain, AZ) and (La Mesa, NM) 1977 using pre- and post-fire aerial photography, and quantified characteristics of pine regeneration at sample plots in areas where all trees were killed by the fire event. We used generalized linear models to determine the relationship of ponderosa pine stem density to three spatial burn pattern metrics: (1) distance to nearest edge of lower severity; (2) neighborhood severity, measured at varying spatial scales, and (3) scaled seed dispersal kernel surfaces. Pine regeneration corresponded most closely with particular scales of measurement in both seed dispersal kernel and neighborhood severity. Spatial patterns of burning remained important to understanding regeneration even after consideration of subsequent disturbance and other environmental variables, with the exception of a few cases in which simpler models were equally well-supported by the data. Analysis of tree ages revealed slow progress in early post-fire years. Our observations suggest that populations spread in a moving front, as well as by remotely dispersed individuals. Based on our results, recent large fires cannot be summarily dismissed as catastrophic. We conclude that management should focus on the value and natural recovery of post-fire landscapes. Further, process centered restoration efforts could utilize our findings in formulating reference dynamics under a changing fire regime.

Freshwater fish community structured more by dispersal limitation than by environmental heterogeneity

Abstract – We examined the relative contribution of environmental heterogeneity and dispersal limitation on freshwater fish community composition in 18 Greek protected lakes and reservoirs. Environmental heterogeneity was measured by spatial pattern metrics (calculated by digital map processing, e.g., patch richness density, area‐weighted mean patch area), altitude, maximum lake depth and trophic status. Dispersal limitation was measured by straight‐line distances among lake centres. Ecosystems were clustered according to species composition. We examined the correlation of similarity in species composition among ecosystems with that of environmental heterogeneity and with straight‐line distances, for the entire dataset, as well as for the occurring clusters. Fish species richness increased with ecosystem area and decreased with altitude. The clusters identified (aquatic ecosystems of Northern vs. ecosystems of Western Greece), implied an underlying biogeographical pattern as defined, with Pindus range acting as a natural barrier. Between ecosystems similarity, based on fish species composition, showed a weak to insignificant correlation with environmental heterogeneity, but was significantly correlated to dispersal limitation for the entire dataset as well as within each occurring cluster. Thus, natural barriers, species biogeography and dispersal limitation played a more significant role in shaping freshwater fish communities than environmental heterogeneity.

Relationship between landscape structure and burn severity at the landscape and class levels in Samchuck, South Korea

Fuel treatments for reducing fire risk are necessarily tied to the landscape structure including forest composition and configuration. Thus understanding the relationships between landscape structure and burn severity is important for developing guidelines and management strategies for fire-resilient forests. The goal of this study was to investigate the relationship between landscape structure as described by spatial pattern metrics and burn severity at the landscape and class levels. In 2000, a mostly severe fire burned 16,210 ha of dense forest located in Samchuck on the east coast of the Korean peninsula. Spatial pattern metrics including patch density, largest patch index, mean shape index, area-weighted mean shape index, Euclidean nearest neighborhood distance, and Shannon's diversity index, as well as topographic characteristics of slope and elevation, were correlated with burn severity based on delta Normalized Burn Ratio (dNBR) assessments. Regression tree analysis was also carried out with the same variables to avoid spatial autocorrelation and to reveal the relative importance of variables to burn severity. The results of this study strongly suggest that both composition and configuration of the forest cover patches are closely tied to burn severity. In particular, both the correlation analysis and regression tree analysis indicated that the area of red pine tree forest cover was the most significant factor in explaining the variance of burn severity. Topography and spatial configuration of forest cover patches were also significantly related to burn severity. The heterogeneity of forests also had a significant influence on burn severity. To reduce fire risk and increase the fire resilience of forests, forest managers and agencies need to consider enhancing the heterogeneity of forests when implementing fuel treatment schemes. However, such fuel treatments for landscape structure may only be effective under moderate weather conditions.