# Presentation: 668488

## Methods for Generating  Patch and Landscape Metrics

- Biodiversity and Spatial Information Center
- North Carolina State University
- Raleigh, NC
- Ed_Laurent@ncsu.edu

- Conservation Design Workshop St. Louis, MO  April 11, 2006

## What Are Landscape and Patch Metrics?

- Algorithms for quantifying spatial heterogeneity.
- Efforts to measure landscape patterns are often driven by the premise that patterns are linked to ecological processes
- 			  Edges           Predation
- 		Fragmentation           Energy Expenditure

## Pattern-Process 

## Pattern-Process 

- Pattern-Process

## Why Are Landscape and Patch Metrics Useful?

- More and more maps are becoming available for pattern-process predictions over large areas
- Permit a coarse approximation of various          landscape processes
- Faster and less expensive than extensive surveys
- Facilitate efficient sampling for research and monitoring
- Many more...

## Definitions

- Landscape: “Area that is spatially heterogeneous in at least one factor of interest.”
- Patch: “Surface area that differs from its surroundings in nature or appearance.”
- Scale: “...the spatial or temporal dimension of an object or a process.”
- Grain: Smallest sampling unit (e.g., 30m pixel)
- Extent: Entire area or time of consideration (e.g., a study region or state)
- Level: “...a place within a biotic hierarchy” or a relative precision of pattern characterization.

- Turner et al. 2001. Landscape Ecology in Theory and Practice. Springer-Verlag

## Examples of Metrics

- Patch metrics: summarize the shape or size of patches
- Area, perimeter, width
- Core area: requires a threshold distance to edge
- Landscape metrics: quantify the spatial relationships among patches within the landscape
- Composition
- Fractional Cover: what proportion of the landscape is occupied by a given class
- Richness: the number of classes
- Evenness: the relative abundance of classes
- Configuration
- Contagion and Dispersion: distinguish between landscapes with clumped or evenly distributed patches
- Isolation: based on the distances between similarly classified patches
- Neighbor metrics: quantify spatial relationships among objects
- Calculate distances between similarly classified features (patches, lines)
- Quantify distance road or water (distance to edge can be difficult)

## Data Types

- Vector: each object explicitly represented as points, lines or polygons.
- Pros: small files; permits topology (i.e., explicit spatial relationships between connecting or adjacent objects)
- Cons: complex data structure (Slow!); can require much more time to create; manipulations require complex algorithms
- Raster: data is divided into a grid consisting of individual cells or pixels. Each cell holds a numeric (e.g., elevation in meters) or descriptive (e.g., land use) value.
- Pros: simple data structure; easy to represent continuous variables (e.g., intensity); filtering and mathematical modeling is relatively simple
- Cons: Large files; no topology; objects are generalized (limited by cell size)

## Vector vs. Raster 

## Vector vs. Raster 

- Inaccuracies due to less spatial precision

## Vector vs. Raster 

- Explicitly defined as two objects

- Two objects?

## Vector vs. Raster 

- Shift in study region boundary

## Software

- Stand alone
- Various GIS & RS packages (e.g., ArcGIS, GRASS, Imagine)
- FRAGSTATS http://www.umass.edu/landeco/research/fragstats/fragstats.html
- APACK http://landscape.forest.wisc.edu/projects/apack/
- IAN http://landscape.forest.wisc.edu/projects/IAN/
- GIS extensions
- Patch Analyst for ArcView 3.x http://flash.lakeheadu.ca/~rrempel/patch/
- r.le programs that interface with GRASS

## Anthropocentric vs. Functional Landscape Descriptions

***“******...the choice of categories to include in a pattern analysis is critical.”*** (Turner et al. 2001)
- Anthropocentric: human defined landscape heterogeneity
- How would you divide the landscape?
- Data limitations (e.g., sensor resolution, spectral variability)
- Functional: Heterogeneity defined by the process of interest
- Example: descriptions that reflect how other species’ behaviors or population rates differ across the landscape
- Knowledge limitations

## Crosswalk Anthropocentric          to Functional

| Avian Habitat Types | NC-GAP Map Units |
| --- | --- |
| Estuarine emergent marsh | Tidal Marsh |
| Open Fresh Water | Open water |
| Atlantic white cedar | Seepage and Streamhead Swamps |
| Maritime forest | Maritime Forests and Hammocks |
| Early-successional hardwood and pine | Coniferous Regeneration |
| Pine plantations | Coniferous Cultivated Plantation (natural / planted) |
| Cypress-tupelo | Cypress-Gum Floodplain Forests |
| Early-successional hardwood and pine | Successional Deciduous Forests |
| Atlantic white cedar | Peatland Atlantic White-Cedar Forest |
| Pine sandhills | Xeric Longleaf Pine |
| Pine hardwoods | Xeric Oak - Pine Forests |
| Bottomland hardwood | Coastal Plain Oak Bottomland Forest |

**Notes:**
Develop translations of different ways for describing the landscape. People often have different ways of describing landscapes, especially people from different agencies. Crosswalking is done before model development to link expert opinion to land cover description. It is done again to translate model output into descriptions for management.

## Avicentric Land Cover

## Example of Documenting     and Using Patch and Neighborhood Metrics            by SE-GAPMap AlgebraStating AssumptionsSources of Errors

## Literature Review Database

## Habitat Suitability

## Landscape Modifiers

## Spatially Explicit Population Descriptions

## Queries

- Each record is one entry in the previous form

## Map Algebra

- Logistic (S-shaped)
- 1/(1+ a *EXP(- b *( [Map Value] / c )))
- Example: 1 / (1+ 40 *EXP(- 6 *( [Dist_Edge] / 90 )))
- a affects where upturn begins.
- b affects slope of the “S”. Larger numbers shrink the curve.
- c also affects slope of the “S” but less so. Larger numbers stretch the curve.

## Mapping Suitability Relationships

## Habitat Suitability Prediction

_**Input**_
**Avicentric land cover**

- 6 km

## Lump Classes of Similar Suitability

_**Input**_
**Flycatcher-centric land cover**

- 6 km

- Acadian
- Flycatcher

## Calculate and Weight Distance to Edge

_**Input**_
**Distance to Edge**

- 6 km

- Acadian
- Flycatcher

## Map Algebra 2: Combine Maps

- Suitability ranked from 0 to 1:
- Suitability under all conditions = Map1 * Map2 * Map3
- Abundance/Density Modeling
- Extrapolate research results from sample locations (e.g., Logistic Regression)
- Population modeling
- Combine maps of vital population rates that vary under different spatial conditions:
-  *dR/dt = a*R - b*R*F *
*dF/dt = e*b*R*F - c*F*
- Where:
- R are the number of prey
- F are the number of predators
- and the parameters are defined by:
*a* is the natural growth rate of prey in the absence of predation,
*c* is the natural death rate of predators in the absence of prey,
*b* is the death rate per encounter of prey due to predation,
*e* is the efficiency of turning predated prey into predators.

## Habitat Suitability Prediction

_**Multiply suitability given:**_
**Land cover**
**Distance to water**
**Distance to edge**

- 1 km

- Acadian
- Flycatcher

## Explicitly State Assumptions!

- Allows testing to validate and refine predictions
- Example assumptions
-  Land cover, distance to water and distance to edge are all equally important considerations for mapping habitat suitability
- Density, nesting success and predation rates are all equally relevant indications of habitat suitability
- Relationships between patch/landscape/neighbor descriptions and habitat suitability are similar everywhere.

## Some Sources of Error

- Age of data
- Precision and availability of information
- Positional accuracy
- Classification appropriateness and accuracy
- Inconsistencies during data creation
- Different interpreters or methods
- Different classification schemes
- Different scales of precision

## Example: Digital Line Graphs

- Used in the National Hydrographic Dataset

## Distance to Water

## Different Scales of Precision

## www.basic.ncsu.edu/segap