Presentation at session on methods for mapping and monitoring biodiversity at the RGS international conference, London, August 2005

1. Fragmentation revisited
The use of landscape metrics:
Definitions, concepts,
calculation and interpretation
Niels Chr. Nielsen, SDU-Esbjerg, Denmark
nichni@mail.dk
Alan Blackburn, Lancaster University
alan.blackburn@lancaster.ac.uk

2. Fragmentation in landscapes

3. - 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 ?

4. Increasing fragmentation
Habitatloss
Ecological impact of fragmentation

5. 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?

6. 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
1SqP
Edge
A* forest
−=
)*(#
PPU
sizepixelpixels
NP
=
M
forestwindow A*A
Edge
10*=

7. 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

8. Metrics maps

9. Analysis of results
Various 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)

10. - 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

11. Forest concentration – calculation flow
Cover fraction Masking (criteria)
1
scapeCover_land
Cover_mask
FC win
win −=
FC20 = 0.086257
FC10 = 0.156578
FC5 = 0.226093

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

13. Forest concentration profiles
Italian regions

14. Forest concentration profiles
Watersheds in North and Middle Italy

15. 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 ??

16. Management uses – local display
M-index
Cover
fraction
PPU
Red
Green
Blue
Central Umbria
50*50 km, 25 m pixels
Landsat TM
June 1996

17. Management uses – regional display
Northern Italy
700*500 km,
200m pixels
Classified WiFS
Mosaic 1997
M-index
Cover
fraction
PPU

18. 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.

19. - 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