Tygobar Processing approach and data handling procedures ultimately determine the efficiency and capabilities of an integrated system. Much of the data input and processing was done with ArcInfo AML, but the indices calculation was done with C programs, i. Right-mouse click on the layer name EAplusand select Properties. These tools readily extend landscape structure analysis to other spatially dependent variables, such as the distribution of animal populations. Fragstats was developed to look at patterns present in areal patches data and, while it has been used principally to look at environmental data, the methodology can be applied to urban landscapes e. However, the tools must be tightly integrated into a system designed for use by resource managers, scientists and other end-users with minimal procedural knowledge of the programs involved.
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Creating a Model. Selecting Input Layers.. Specifying Common Tables [optional]. Setting Analysis Parameters. Browsing and Saving the Results. Getting Help. Version 4 is a major internal architectural revision to accommodate several upcoming new features in version 4. The graphical user interface is entirely new and is described in detail in the user guidelines, but has the same functionality as version 3.
The ancillary tables used to specify the class properties i. Supported image formats beginning with version 4. Support for the latter six image formats is via the GDAL library. The model file i. You can no longer input a unique patch ID grid. We realized too many conflicts with the user-provided patch ID file not being consistent with the user-specified model; e.
For the time being, we removed the option of inputting a patch ID file, but maintained the option of outputting a patch ID file. The habitats in which organisms live, for example, are spatially structured at a number of scales, and these patterns interact with organism perception and behavior to drive the higher level processes of population dynamics and community structure Johnson et al.
Anthropogenic activities e. A disruption in landscape patterns may therefore compromise its functional integrity by interfering with critical ecological processes necessary for population persistence and the maintenance of biodiversity and ecosystem health With For these and other reasons, much emphasis has been placed on developing methods to quantify landscape patterns, which is considered a prerequisite to the study of pattern-process relationships e.
This has resulted in the development of literally hundreds of indices of landscape patterns. This progress has been facilitated by recent advances in computer processing and geographic information GIS technologies. Unfortunately, according to Gustafson , the distinction between what can be mapped and measured and the patterns that are ecologically relevant to the phenomenon under investigation or management is sometimes blurred.
What Is a Landscape? Landscape ecology by definition deals with the ecology of landscapes. Surprisingly, there are many different interpretations of the term landscape. The disparity in definitions makes it difficult to communicate clearly, and even more difficult to establish consistent management policies.
Definitions of landscape invariably include an area of land containing a mosaic of patches or landscape elements see below.
Forman and Godron defined landscape as a heterogeneous land area composed of a cluster of interacting ecosystems that is repeated in similar form throughout. The concept differs from the traditional ecosystem concept in focusing on groups of ecosystems and the interactions among them.
There are many variants of the definition depending on the research or management context. For example, from a wildlife perspective, we might define landscape as an area of land containing a mosaic of habitat patches, often within which a particular "focal" or "target" habitat patch is embedded Dunning et al.
In-other-words, because each organism scales the environment differently i. This definition most likely contrasts with the more anthropocentric definition that a landscape corresponds to an area of land equal to or larger than, say, a large basin e. Indeed, Forman and Godron suggested a lower limit for landscapes at a "few kilometers in diameter", although they recognized that most of the principles of landscape ecology apply to ecological mosaics at any level of scale.
While this may be a more pragmatic definition than 4 the organism-centered definition and perhaps corresponds to our human perception of the environment, it has limited utility in managing wildlife populations if you accept the fact that each organism scales the environment differently.
From an organism-centered perspective, a landscape could range in absolute scale from an area smaller than a single forest stand e. If you accept this organism-centered definition of a landscape, a logical consequence of this is a mandate to manage habitats across the full range of spatial scales; each scale, whether it be the stand or watershed, or some other scale, will likely be important for a subset of species, and each species will likely respond to more than one scale.
Ke y Po in t It is not our intent to argue for a single definition of landscape. Rather, we wish to point out that there are many appropriate ways to define landscape depending on the phenomenon under consideration. The important point is that a landscape is not necessarily defined by its size; rather, it is defined by an interacting mosaic of patches relevant to the phenomenon under consideration at any scale. It is incumbent upon the investigator or manager to define landscape in an appropriate manner.
The essential first step in any landscape-level research or management endeavor is to define the landscape, and this is of course prerequisite to quantifying landscape patterns. Classes of Landscape Pattern Real landscapes contain complex spatial patterns in the distribution of resources that vary over time; quantifying these patterns and their dynamics is the purview of landscape pattern analysis.
Landscape patterns can be quantified in a variety of ways depending on the type of data collected, the manner in which it is collected, and the objectives of the investigation. Broadly considered, landscape pattern analysis involves four basic types of spatial data corresponding to different representations of spatial heterogeneity, although in practice these fundamental conceptual models of landscape structure are sometimes combined in various ways.
These basic classes of landscape pattern look rather different numerically, but they share a concern with the characterization of spatial heterogeneity: 1 Spatial point patterns. Spatial point patterns represent collections of entities where the geographic locations of the entities are of primary interest, rather than any quantitative or qualitative attribute of the entity itself. A familiar example is a map of all trees in a forest stand, wherein the data consists of a list of trees referenced by their geographic locations.
Typically, the points would be labeled by species, and perhaps further specified by their sizes a marked point pattern. Linear network patterns represent collections of linear landscape elements that intersect to form a network. A familiar example is a map of shelterbelts in an agricultural landscape, wherein the data consists of nodes intersections of the linear features and segments linear features that connect nodes ; the intervening area is considered the matrix and is typically ignored i.
Often, the nodes and segments are further characterized by composition e. As with point patterns, it is 5 the geographic location and arrangement of nodes and segments that is of primary interest. The goal of linear network pattern analysis with such data is to characterize the physical structure e. Surface patterns represent quantitative measurements that vary continuously across the landscape i.
Hence, this type of spatial pattern is also referred to as a landscape gradient. Here, the data can be conceptualized as representing a three-dimensional surface, where the measured value at each geographic location is represented by the height of the surface.
A familiar example is a digital elevation model, but any quantitative measurement can be treated this way e. Analysis of the spatial dependencies or autocorrelation in the measured characteristic is the purview of geostatistics, and a variety of techniques exist for measuring the intensity and scale of this spatial autocorrelation Legendre and Fortin , Legendre and Legendre Techniques also exist that permit the kriging or modeling of these spatial patterns; that is, to interpolate values for unsampled locations using the empirically estimated spatial autocorrelation Bailey and Gatrell These geostatistical techniques were developed to quantify spatial patterns from sampled data n.
When the data is exhaustive i. All of these geostatistical techniques share a goal of describing the intensity and scale of pattern in the quantitative variable of interest. In all cases, while the location of the data points or quadrats is known and of interest, it is the values of the measurement taken at each point that are of primary concern. Here, the basic question is, "Are samples that are close together also similar with respect to the measured variable?
Alternatively, What is the distance s over which values tend to be similar? While the geostatistical properties of surface patterns has been the focus of nearly all surface pattern analysis in landscape ecology, recently it was revealed that surface metrology derived from the field of structural and molecular physics offers a variety of surface metrics for quantifying landscape gradients akin to the more familiar patch metrics described below for categorical maps McGarigal and Cushman Like their analogous patch metrics, surface metrics describe both the nonspatial and spatial character of the surface as a whole, including the variability in the overall height distribution of the surface nonspatial and the arrangement, location or distribution of surface peaks and valleys spatial.
Here, the goal of the analysis is to describe the spatial structure of the entire surface in a single metric, and a variety of surface metrics have been developed for this purpose McGarigal et al. Categorical map patterns represent data in which the system property of interest is represented as a mosaic of discrete patches. Hence, this type of spatial pattern is also referred to as a patch mosaic.
From an ecological perspective, patches represent relatively discrete areas of relatively homogeneous environmental conditions at a particular scale. The patch boundaries are distinguished by abrupt discontinuities boundaries in environmental character states from their surroundings of magnitudes that are relevant to the ecological 6 phenomenon under consideration Wiens , Kotliar and Wiens A familiar example is a map of land cover types, wherein the data consists of polygons vector format or grid cells raster format classified into discrete land cover classes.
There are a multitude of methods for deriving a categorical map patch mosaic which has important implications for the interpretation of landscape pattern metrics see below. Patches may be classified and delineated qualitatively through visual interpretation of the data e. Alternatively, with raster grids constructed of grid cells , quantitative information at each location may be used to classify cells into discrete classes and to delineate patches by outlining them, and there are a variety of methods for doing this.
The most common and straightforward method is simply to aggregate all adjacent touching areas that have the same or similar value on the variable of interest. An alternative approach is to define patches by outlining them: that is, by finding the edges around patches Fortin , Fortin and Drapeau , Fortin et al.
An edge in this case is an area where the measured value changes abruptly i. An alternative is to use a divisive approach, beginning with a single patch the entire landscape and then successively partitioning this into regions that are statistically homogeneous patches Pielou A final method to create patches is to cluster them hierarchically, but with a constraint of spatial adjacency Legendre and Fortin While these patch metrics are quite familiar to landscape ecologists, scaling techniques for categorical map data are less commonly employed in landscape ecology.
This is because in applications involving categorical map patterns, the relevant scale of the mosaic is often defined a priori based on the phenomenon under consideration. In such cases, it is usually assumed that it would be meaningless to determine the so-called characteristic scale of the mosaic after its construction. However, there are many situations when the categorical map is created through a purely objective classification procedure and the scaling properties of the patch mosaic is of great interest.
Lacunarity analysis is one technique borrowed from fractal geometry by which class-specific aggregation can be characterized across a range of scales to examine the scale s of clumpiness Plotnick et al.
Patch-Corridor-Matrix Model Landscapes are composed of elementsthe spatial components that make up the landscape. A convenient and popular model for conceptualizing and representing the elements in a categorical map pattern or patch mosaic is known as the patch-corridor-matrix model Forman Under this model, three major landscape elements are typically recognized, and the extent and configuration of these elements defines the pattern of the landscape.
Landscapes are composed of a mosaic of patches Urban et al. Landscape ecologists have used a variety of terms to refer to the basic elements or units that make up a 7 landscape, including ecotope, biotope, landscape component, landscape element, landscape unit, landscape cell, geotope, facies, habitat, and site Forman and Godron Any of these terms, when defined, are satisfactory according to the preference of the investigator.
Like the landscape, patches comprising the landscape are not self-evident; patches must be defined relative to the phenomenon under consideration. For example, from a timber management perspective a patch may correspond to the forest stand. From an ecological perspective, patches represent relatively discrete areas spatial domain or periods temporal domain of relatively homogeneous environmental conditions where the patch boundaries are distinguished by discontinuities in environmental character states from their surroundings of magnitudes that are perceived by or relevant to the organism or ecological phenomenon under consideration Wiens From a strictly organism-centered view, patches may be defined as environmental units between which fitness prospects, or "quality", differ; although, in practice, patches may be more appropriately defined by nonrandom distribution of activity or resource utilization among environmental units, as recognized in the concept of "Grain Response".
A patch at any given scale has an internal structure that is a reflection of patchiness at finer scales, and the mosaic containing that patch has a structure that is determined by patchiness at broader scales Kotliar and Wiens Thus, regardless of the basis for defining patches, a landscape does not contain a single patch mosaic, but contains a hierarchy of patch mosaics across a range of scales.
Para la matriz de adyacencias tenemos una matriz de doble entrada, donde cada clase de parches tiene un ID correspondiente al valor asignado a tal clase y cada celda de la matriz contiene el valor de adyacencias de la celda para cada par combinatorio de clases. However, and this is quite tricky, interior background is in essence excluded from the total landscape area in a number of class and landscape metrics that involve summarizing patch or class metrics. For example, mean patch area is based on the average size of patches at the class or landscape level. If interior background is present, mean patch size as computed by FRAGSTATS will not equal the total landscape area divided by the number of patches, because the total landscape area includes background area not accounted for in any patch. Similarly, the area-weighted mean of any patch metric i.
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