Analysis of Categorical Maps with Landscape Metrics

Analysis of Categorical Maps with Landscape Metrics
Introduction1
In this conceptual exercise, you will use landscape metrics to analyze landscape patterns in the
region of Madison, Wisconsin, USA (Fig. 1), from a land-use / land-cover classification derived
from Landsat Thematic Mapper satellite imagery (Fig. 2). We will focus on forest cover, which
was mapped out separately for deciduous and evergreen forest (Fig. 3).
The map is broken up into four “landscapes” as study areas (Fig. 3): Landscape 2 contains part of
the urban area of Madison, whereas the other Landscapes are of rural character with some
differences in topography: Landscape 4 is the flattest and Landscape 1 has the highest
topographic variability (Fig. 1).
We will interpret landscape metrics for these four landscapes to address the following questions:
• How fragmented is this landscape for a forest species?
• How much do the results depend on the input map and choice of neighbor rule?

Figure 1: Map with shaded relief of the study area near Madison, WI (left). Location of Wisconsin within
the US (top) and location of the study area within Wisconsin (bottom right).

1 This exercise is partly based on a lab outline in S.E. Gergel and M.G. Turner, editors (2001) Learning
Landscape Ecology: A Practical Guide to Concepts and Techniques Springer-Verlag, NY

Lab Week 3: Landscape Ecology. 2 H. Wagner, University of Toronto
Figure 2: Land-use/land-cover map of the study area near Madison, WI.

Figure 3: Map of forest cover (derived from land-use/land-cover map in Fig. 2, same map extent)
showing subdivision of study area into four landscapes.

Lab Week 3: Landscape Ecology. 3 H. Wagner, University of Toronto
Sensitivity analysis of landscape metrics
Let’s look at some landscape metrics to practice the interpretation and get a feel for the
sensitivity to various sources or uncertainty.
We will be using the following class-level metrics (see page 5 for description of metrics):
Research question Selected class-level metric
What proportion of total area does each type occupy? Percentage of Landscape (PLAND)
How fragmented is each type? Patch Density (PD)
How aggregated is each type? Landscape Shape Index (LSI)
How large is the largest patch? Largest Patch Index (LPI)
How well are patches connected? Patch Cohesion Index (COHESION)
1) Table 1 shows key landscape metrics for all cover types in Landscape 1 (classification 1).
a) Which land-use type would you consider the matrix in this landscape?
b) Which is the most fragmented habitat type, based on this map analysis?
c) Is the landscape more fragmented for species depending on evergreen forest or for those depending
on deciduous forest?
d) Do you think such conclusions are valid? What factors, relating to the organisms, the input map or
the analysis methods need to be considered?
Table 1: Class-level landscape metrics for ‘madison1_landscape1’ using 8-Neighbor-Rule.
TYPE PLAND PD (e-6) LSI LPI (e-3) COHESION
2 – Evergreen 1.5 % 5.9 29.0 0.9 59.2
7 – Water 0.2 % 0.23 4.6 0.7 77.2
11 – Other veg. 25.9 % 39.4 104.1 7.1 84.9
17 – Alfalfa 8.1 % 8.3 42.7 1.8 82.4
20 – Deciduous 17.1 % 22.6 74.1 10.3 87.4
28 – Corn 37.8 % 23.7 82.7 36.7 95.9
29 – Impervious 9.4 % 25.7 61.5 6.0 78.3
We often take GIS data for granted, i.e., we think they are perfect because we lack own
experience of all the challenges of generating them. Here, the same satellite image has been
classified independently by two different researchers, applying the same definition of land-use/
land-cover type. The resulting land-use/land-cover maps differ somewhat. How and to what
degree does this uncertainty affect our landscape analysis?
2) Table 2 shows the same landscape metrics calculated for the same cover types in the same
Landscape 1 but using two different land-use/land-cover classifications of the same Landsat 7
satellite image. Discuss:
a) Which land-use/land-cover types were most sensitive to the classification?
b) Which aspects of landscape pattern (i.e., which landscape metrics) were most sensitive to
the classification?

Lab Week 3: Landscape Ecology. 4 H. Wagner, University of Toronto
Table 2: Class-level landscape metrics for ‘madison2_landscape1’ using 8-Neighbor-Rule.
TYPE PLAND PD (e-6) LSI LPI (e-3) COHESION
2 – Evergreen 3.2 % 9.5 38.9 1.2 68.5
7 – Water 0.1 % 11.6 3.7 0.5 73.9
11 – Other veg. 31.4 % 31.7 101.6 21.8 92.0
17 – Alfalfa 9.8 % 6.7 34.7 2.6 85.5
20 – Deciduous 22.9 % 16.5 67.2 18.9 93.9
28 – Corn 22.3 % 27.6 76.7 11.9 88.1
29 – Impervious 10.3 % 18.4 50.4 12.3 87.8
Available land-use/land-cover maps may not classify cover types in the way most relevant to
our study organism and research question. For instance, a forest classification may be too
broad (unsuitable habitat lumped with suitable habitat into one cover type) or too narrow
(suitable habitat split into multiple cover types).
3) We often have to rely on available maps. The spatial, temporal and topical resolution may not
always match the habitat requirements and spatial scale of our study organisms.
a) Look at Fig. 3. If we couldn’t distinguish evergreen and deciduous forest, how would this
affect our assessment of forest fragmentation? Which results would change most?
b) Assume that we are interested in a species depending on old-growth deciduous forest, but
the map only lists “deciduous” and does not discern between different seral stages of forests.
How and to what degree would this affect our landscape analysis?
The choice of neighbor rule for delineating patches may also have an important effect on the
quantification of landscape pattern. It is unlikely to affect composition but may have a largeeffect
on configuration metrics!
4) Table 3 shows key landscape metrics for two forest types calculated from the same map
(Landscape 1, classification 1), but with two different neighbor rules. Discuss:
a) Which results changed when using the 4-neighbor rule instead of the 8-neighbor rule?
b) How did they change, and why?
c) What does this mean biologically? Which rule would you use and why?
Table 3: Landscape metrics using different neighbour rules (based on classification 1).
TYPE RULE PLAND PD (e-6) LSI LPI (e-3) COHESION
2 – Evergreen 8-neighbor 1.5 % 5.9 29.0 0.9 59.2
4-neighbor 1.5 % 7.8 29.0 0.7 47.0
20 – Deciduous 8-neighbor 17.1 % 22.6 74.1 10.3 78.3
4-neighbor 17.1 % 44.1 74.1 2.9 72.6

Lab Week 3: Landscape Ecology. 5 H. Wagner, University of Toronto
5) Refer to Table 5 to evaluate whether forest fragmentation in the Madison region is affected by
gradients of urban development or topography. Discuss:
a) How does forest fragmentation differ between the urban area near Madison (Landscape 2)
and the rural areas to the South (Landscape 4) and West (Landscape 3)?
b) How does forest fragmentation differ between the flatter landscapes in the East
(Landscapes 2 & 4) and the more rugged terrain in the West (Landscapes 1 & 3)?

Table 5: Summary of class-level results with R for all four landscapes (using classification 1).

Class-level metrics Units Range Description Comments
Percentage of Landscape
PLAND

Percent 0 < PLAND ≤ 100 PLAND equals the sum of the areas (m2

) of all patches
of the corresponding patch type, divided by total
landscape area (m2

), multiplied by 100 (to convert to a
percentage); in other words, PLAND equals the
percentage the landscape comprised of the
corresponding patch type. Note, total landscape area
(A) includes any internal background present.

Percentage of landscape quantifies the proportional
abundance of each patch type in the landscape. Like total
class area, it is a measure of landscape composition important
in many ecological applications. However, because PLAND
is a relative measure, it may be a more appropriate measure of
landscape composition than class area for comparing among
landscapes of varying sizes.

Patch Density
PD

Number per
100 hectares

PD > 0, constrained
by cell size.

PD equals the number of patches of the corresponding
patch type divided by total landscape area (m2
),
multiplied by 10,000 and 100 (to convert to 100
hectares). Note, total landscape area (A) includes any
internal background present.

Patch density is a limited, but fundamental, aspect of
landscape pattern. Patch density has the same basic utility as
number of patches as an index, except that it expresses
number of patches on a per unit area basis that facilitates
comparisons among landscapes of varying size. Of course, if
total landscape area is held constant, then patch density and
number of patches convey the same information. Like
number of patches, patch density often has limited
interpretive value by itself because it conveys no information
about the sizes and spatial distribution of patches. Note that
the choice of the 4-neighbor or 8-neighbor rule for
delineating patches will have an impact on this metric.

Landscape Shape Index
LSI

None LSI ≥ 1, without
limit.

LSI equals the total length of edge (or perimeter)
involving the corresponding class, given in number of
cell surfaces, divided by the minimum length of class
edge (or perimeter) possible for a maximally
aggregated class, also given in number of cell surfaces,
which is achieved when the class is maximally clumped
into a single, compact patch.

Landscape shape index provides a simple measure of class
aggregation or clumpiness and, as such, is very similar to the
Aggregation index. The differences lie in whether
aggregation is measured via class edge (or perimeter) surfaces
(as in LSI) or via internal like adjacencies (as in AI). Since
these surface counts are inversely related to each other (i.e.,
holding area constant, as the perimeter count increases, the
internal adjacency count must decrease, and vice versa), these
metrics largely measure the same thing. Note, previous
versions of FRAGSTATS used a slightly different definition
of LSI; hence, the results will differ from previous runs.

Largest Patch Index
LPI

Percent 0 < LPI ≤ 100 LPI equals the area (m2

) of the largest patch of the
corresponding patch type divided by total landscape
area (m2
), multiplied by 100 (to convert to a
percentage); in other words, LPI equals the percentage
of the landscape comprised by the largest patch. Note,
total landscape area (A) includes any internal
background present.

Largest patch index at the class level quantifies the
percentage of total landscape area comprised by the largest
patch. As such, it is a simple measure of dominance.
LPI approaches 0 when the largest patch of the corresponding
patch type is increasingly small. LPI = 100 when the entire
landscape consists of a single patch of the corresponding
patch type; that is, when the largest patch comprises 100% of
the landscape.

Patch Cohesion Index
COHESION

None COHESION equals 1 minus the sum of patch perimeter
(in terms of number of cells) divided by the sum of
patch perimeter times the square root of patch area (in
terms of number of cells) for all patches in the
landscape, divided by 1 minus 1 over the square root of
the total number of cells in the landscape, multiplied by
100 to convert to a percentage. Note, total landscape
area (A) excludes any internal background present.

Patch cohesion index at the class level measures the physical
connectedness of the corresponding patch type. However, at
the landscape level, the behavior of this metric has not yet

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