Fragmentation revisited

The use of landscape metrics: Definitions, concepts,

calculation and interpretation

Niels Chr. Nielsen, SDU-Esbjerg, Denmarknichni@mail.dk

Alan Blackburn, Lancaster Universityalan.blackburn@lancaster.ac.uk

Fragmentation in landscapes

- Low or diminishing forest cover (Mayaux et al)- Breaking up of habitat (Forman)- Landscape transformation (Kouki & Lofman)- Loss of connectivity (Delbaere & Gulinck)- Presence of isolated patches (Skole & Tucker)- Perforation (Riitters and Coulston)

along with other forest patterns: patch, transitional, edge, interior (core)

Definitions of Fragmentation

However, is it always or necessarily harmful ?

Equally important (harmfull) as habitat loss ?

Increasing fragmentation

Habitat loss

Ecological impact of fragmentation

Process and/or pattern

Fragmented state Land Cover

Fragmentation process (agents, decisions, information)

Land Cover Change

(rate, locations)

Feedback possible – though not necessarily

Feedback: Cellular models, GIS with supplementary information

LANDSCAPE

Remote Sensing, mappingProperty (observed)

DRIVES

SEEN IN

SCALE?

Cell size?MMU?

Choice of spatial metrics..the ideal shape index should :

From Forman (1995)

• be easy to calculate,

• unambiguously and quantitatively differentiate between different shapes, and finally• permit the shape to be drawn based on knowledge of the index number alone

•work over the whole domain of interest,

4

1 SqPEdge

A* forest

)*(#

PPUsizepixelpixels

NP

Mforestwindow A*A

Edge 10*

Moving Windows approach

Map 1: Window (user choice): Map 2:

Grain = pixel size = 30m Size (extent) = 9 pixels = 270 m Grain = pixel size = 90 m

Extent = 30*30 pix = 900*900 m Step = 3 pixels = 90 m Extent = 8*8 pixels = 720*720 m

• As implemented with calculation of Fragstats-derived and other spatial metrics for “sub-landscapes”

INPUT: “cover type” map(1) OUTPUT: metrics/index value map(2)

DeterminesApplied to

equals

1 2 3 4 5

Calculate

(e.g.)

Patch type

Richness

Metrics maps

Analysis of resultsVarious types of scale-dependence and plots/diagrams quantifying and illustrating it :

Tools for selection of most robust and representative metrics of fragmentation

1. Response of metrics to window – or pixel – size (scalograms)2. Variability and autocorrelation of metric, with changing window

size: plots (”variograms”)3. Relationships between different metrics, with changing window

size: scattergrams (single), tables (multiple window sizes)4. Relationships between same metric derived from different data

sources: scattergrams (single), correlograms (multiple window sizes)

- If single shape index required, use Matheron index- Count or density of background patches (perforating forest), also as alternative to Lacunarity measures

- Window sizes around 5 km acceptable for regional monitoring (pixel sizes 100–200 m)

- Patch count metrics are highly sensitive to grain/pixel size- If possible normalise, and compensate for window-size effects

Recommendations from study of metrics

behaviour

Forest concentration – calculation flowCover fraction Masking (criteria)

1scapeCover_land

Cover_maskFC winwin

FC20 = 0.086257FC10 = 0.156578FC5 = 0.226093

Forest concentration profile – combined for 50*50 km study area

Forest concentration profiles Italian regions

Forest concentration profiles Watersheds in North and Middle Italy

Management uses – statistical description of

fragmentation • Overview of (differences in) landscape structure

Recommendataions for..

• Monitoring temporal changes in points or regions • Overcoming/bypassing the MAUP by being multi-scalar and with the region of calculation being user-chosen (F.C. profiles as well as average metrics values) • Meeting the need for indicators of sustainable forest and landscape management?

• Targets – threshold values ??

Management uses – local display

M-index

Cover fraction

PPU

Red

Green

Blue

Central Umbria50*50 km, 25 m

pixels

Landsat TM June

1996

Management uses – regional display

Northern Italy700*500 km, 200m pixels

Classified WiFSMosaic 1997

M-index

Cover fraction

PPU

Conclusions 1- The “moving-windows” approach has made it possible to calculate metrics values throughout the study areas and to visualise and statistically analyse regional differences.

- Limiting to the use of spatial metrics as indicators is the quality of the input data, i.e. maps or satellite images. Often a higher thematic resolution than what is normally available from LUC data is needed for meaningful comparisons for assessment of forest and nature/habitat diversity. It was however found that binary forest-non-forest maps constitute a sufficient input for analysis of forest fragmentation.

- Spatial metrics have the potential to function as indicators of landscape structure and diversity. Forest Concentration profiles facilitates comparison of regions. - Which specific metrics to use for a particular environmental assessment will depend on the management objectives for the landscape, forest or nature area of interest.

- ToDos: Test, sensitivity analysis. Neutral Landscapes, agent based approaches..

Conclusions 2