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Characterizing Forest Fragmentation and
Vulnerability Based on Patch Characteristics 1
K. Bruce Jones2
Timothy G. Wade2
James D. Wickham 2
Kurt H. Riitters 3
Curtis M. Edmonds4
Abstract-Loss and fragmentation ofnaturalforests due to human
activities represents one of the greatest threats to global biodiversity
and the sustainability of the biosphere. Although we are aware of
declines in natural forests, we lack comprehensive knowledge ofthe
extent and magnitude of forest loss and fragmentation. Moreover,
we lack methodology to assess the vulnerability offorests to human
activities. This paper highlights a simple 2-step method to assess
forest fragmentation and vulnerability due to human activities over
a range of scales. The method is demonstrated in tropical forest
zones of Central America, South America, and Mrica, using l-kro
global land cover data.
The shrinking and fragmentation of intact forests has
become a major environmental concern worldwide (Turner
et al. 1990; Groom and Schumaker 1993; Houghton 1994;
Imhoff 1994; Ojima et al. 1994). These two factors have and
continue to contribute to the loss of important forest resources and associated ecological services, including timber
production (Franklin 1992), forest plant and animal diversity (Van Dorp and Opdam 1987; Bierregaard et al. 1992;
Whitmore and Sayer 1992; Stouffer and Bierregaard 1995;
Jullien and Thiollay 1996; Metzger 1997), and the processes
ofwater interception, infiltration, and runofftha t determine
the magnitude of flooding, water storage, and the quality of
drinking water (Peterjohn and Correll 1984; Saunders et al.
1991; Franklin 1992).
Shrinking and fragmentation of forests results primarily
from human activities that convert natural land cover to
anthropogenic uses, including agriculture, urban, and residential uses (Zipperer 1993; Ojima et al. 1994). As an area is
developed, continuous forest stands are divided into smaller
and smaller patches, and the proportion of individual forest
lpaper presented at the North American Science Symposium: Toward a
Unified Framework for Inventorying and Monitoring Forest Ecosystem
Resources, Guadalajara, Mexico, November 1-6,1998.
2K. Bruce Jones is a Senior Ecologist, Timothy G. Wade is a Geographer,
and Curtis M. Edmonds is an Environmental Engineer with the U.S. EPA in
Las Vegas, NY; Phone (Bruce Jones): (702) 798-2671. Fax: (702) 798-2208.
Email: jones.bruce@epamail.epa.gov
3J ames D. Wickham is an Ecologist with the U.S. EPA in Research Triangle
Park,NC.
4Kurt H. Riitters is a Landscape Ecologist with the USGS Biological
Resources Division located on the North Carolina State University campus in
Raleigh, NC.
USDA Forest Service Proceedings RMRS-P-12. 1999
patch edges juxtaposed to anthropogenic land cover increases until the patches are fully imbedded in a sea of
human land use. Small patches that become imbedded
within a sea of human land use are the most likely to be lost,
or to become sufficiently small and/or isolated enough such
that they no longer support an array of interior forests
species. Unless checked by biophysical (e.g., slopes or geology), economic (e.g., a downturn in the economy) , or social
(e.g., preservation land) constraints, forests adjacent to
developing areas continue to shrink in size, become more
fragmented, and eventually are lost (Zipperer 1993).
By analyzing the size ranges of individual patches, the
spatial pattern in the range of sizes, and the types of edges
surrounding individual patches, it is possible to see fragmentation events and evaluate relative vulnerabilities of
individual patches from composite images of land cover.
Moreover, new digital coverages ofland cover and advances
in Geographic Information Systems (GIS) and other computer tools permit analysis ofland cover pattern and forest
fragmentation over relatively large areas (Jones et al. 1997;
Riitters et al. 1997).
We developed a 2-step analysis process that uses a relatively simple set oflandscape pattern statistics to evaluate
the status of forest loss, fragmentation, and threats due to
human use. We also developed a simple index offorest patch
vulnerability based on patch sizes and edge characteristics.
These indicators can be calculated and interpreted from a
wide range of remote sensing imagery in a GIS. We demonstrate the application of these indicators and a vulnerability
index in tropical zones of Central America, South America,
and Africa using l-km land cover databases distributed by
the U.S. Geological Survey's EROS Data Center.
Methods _ _ _ _ _ _ _ _ _ __
A set of three continental images of land cover were
obtained from the U.S. Geological Survey through the Global Land Cover Characteristics (GLCC) project and converted to ArclInfo GRID format. These data have a nominal
one-kilometer spatial resolution, and were derived from
satellite imagery (Advanced Very High Resolution Radiometer or AVHRR) from the time period of April 1992 to March
1993. We used the 17-class land cover grid (International
Geosphere Biosphere Program, Lambert equal-area projection) for Central America (on the North American coverage),
South America, and Mrica (Belward and Loveland 1995).
359
,.:'
To identify forest land cover, and to determine if forest
land cover was adjacent to or divided by natural non-forest
land cover, or anthropogenic land cover, we reclassified the
17 land cover classes into three classes (forest, anthropogenic, and non-forest natural, Table 1).
We used a two-step process to assess tropical forest fragmentation on three different continents; an initial step at the
continental-scale to identify areas where forest fragmentation might be occurring, and a second step to evaluate the
extent and nature of forest fragmentation and risk to individual forest patches in those areas identified in the first step.
The first step involved converting the 3-class land cover
grid into individual patches using the Regiongroup command in GRID. This command grouped cells with the same
land cover class into patches with unique values. Cells may
be connected in any direction (including diagonals). Figure
1 provides an example for South America. We then identified
three areas, one in central America (southern Honduras),
one in South America (central Amazon), and one in Mrica
(southeastern Ivory Coast and southwest Ghana), where
large block tropical rain forests have been fragmented into
smaller patches by human land uses. We also used this
process to identify an area in South America where little
fragmentation was evident (control).
We zoomed into the smaller, subarea within each continent by clipping them out of the larger continental grids in
ArclInfo GIS. Grided versions of patches for each of the three
areas were then converted into polygons. Using the relationship between arc and polygon topologies, the proportion of
adjacent land cover was calculated for each forest polygon.
The proportion of anthropogenic/forest edge was calculated as: anthropogenic edge/ (anthropogenic edge + natural
edge) x 100. We included the entire area of all patches
located at the edges of the study areas rather than artificially cutting them off. This reduced the impact of the study
area boundary on the patch statistics. For example, a patch
of several hundred cells might have been clipped so that only
a few patches were in the study area. In this case, the edge
values (and area) for the entire patch were used in the
Table 1.-Classification of the 17 IGBP land cover classes into forest,
natural non-forest, and anthropogenic land cover
IGBP class
New classification
Evergreen needleleaf forest
Evergreen broad leaf forest
Deciduous needleleef forest
Deciduous broadleaf forest
Mixed forest
Closed shrublands
Open shrublands
Woody savannas
Savannas
Grasslands
Permanent wetlands
Croplands
Urban and built-up
Cropland/natural mosaic
Snow and ice
Barren or sparsely vegetated
Water bodies
Forest
Forest
Forest
Forest
Forest
Non-forest natural
Non-forest natural
Non-forest natural
Non-forest natural
Non-forest natural
Non-forest natural
Anthropogenic
Anthropogenic
Anthropogenic
Non-forest natural
Non-forest natural
Non-forest natural
360
calculations. Shorelines of rivers, bays, and oceans were
counted as natural edge (Figure 1).
Quintiles were calculated for the edge values, with anthropogenic edge proportions of 20% and less being classified as 1 (least risk) to proportions of over 80% which were
classified as 5, or highest risk.
Patch area was also split into categories on the assumption that large patches are at lower risk than small patches,
even if they have a high proportion of anthropogenic edge.
Patches of 3 pixels (300 Hectares) or less were classified as 3,
or highest risk, patches between 3 and 300 pixels were classified as 2, and patches over 300 pixels were classified as l.
An index was generated by mul tiplying the values for area
and edge. The resulting index values ranged from 1 to 15,
and were displayed in 5 colors. Darker green represents the
lowest risk, values 1-3, light green shows values 4-6, yellow
7-9, pink 10-12, and red 13-15 (greatest risk, see Figure 2).
Natural land cover was displayed as light blue, and anthropogenic use as black. It is possible to evaluate and display
forest vulnerability across an entire continent and then
zoom into areas of interest. Figure 3 illustrates how one can
zoom into the central Amazon Basin from an entire view of
South America.
Results ____________
Identification of individual forest patches through the
GRID Regiongroup command permits an initial screen of
forest fragmentation across an entire continent. Figure 1
illustrates how one can use the continental-scale forest
patch map as an initial screen to find areas that might be
undergoing significant forest loss and fragmentation. The
example provided in Figure 1 is of South America and it
clearly shows significant patterns offorest loss and fragmentation in the central Amazon. A similar method was used to
screen areas of significant forest fragmentation in Central
America and Mrica. As a result of this screening, central
Amazonia of South America, the southern Honduras area of
Central America and the southeast area of the Ivory Coast
and southwest Ghana of Mrica were selected for the second
level of analysis.
Comparison of patch characteristics for forested areas in
the central Amazon, southern Honduras, and southeast
Ivory Coast/southwest Ghana show that the three areas
differ in terms offorestlhuman spatial pattern and risk. All
three of these areas contain tropical forests whose loss and
fragmentation due to human activities is of primary concern. Figure 4 illustrates the spatial distribution of forest
patches by degree of risk (five quintiles or levels of risk). As
expected, based on our methods, forest patches at greatest
risk were small and fully imbedded within anthropogenic
land cover (Figure 4). Forest patches classified as moderatehigh and moderate risk varied in size from a 5 or 6 pixels up
to 35-40 pixels, and have from approximately 50% to 100%
of their edges adjacent to anthropogenic cover (Figure 4).
Larger forest patches that fell into moderate-high and moderate risk classes tended to be fully imbedded within anthropogenic cover whereas smaller patches in these two classes
tended to have a lower percentage of edge adjacent to
anthropogenic cover (Figure 4). Forest patches in the two
lowest risk classes tended to be large (>50 pixels) and had a
USDA Forest Service Proceedings RMRS-P-12. 1999
Figure 1.-lndividual patches at the continent scale and the Amazonia study area boundary.
USDA Forest Service Proceedings RMRS-P-12. 1999
361
Three-class rankings of forest patches
based on area.
_
Over 3,000 hectares
300 to 3,000 hectares
300 hectares or less
Natural
Anthropogenic
_
Quintile rankings of forest patches
based on the proportion of edge
adjacent to anthropogenic cover.
_
0-20%
20 - 40%
40 - 600/0
60 - 80%
80 -100%
; Natural
Anthropogenic
_
_
Patch risk index map derived by
multiplying area and edge maps.
_
Lowest Risk
Low-Moderate Risk
Moderate Risk
Moderate-High Risk
Highest Risk
;~\j~~e;i~j Natural
_
Anthropogenic
!IIIII
8
a
8
16 Kilometers
Figure 2.--Combining landscape themes to evaluate forest vulnerability in Central Amazonia.
362
USDA Forest Service Proceedings RMRS-P-12. 1999
o
200
20
0
_
_
20
40 Kilometers
Lowest Risk
Low-Moderate Risk
Moderate Risk
Moderate-High Risk
Highest Risk
Natural
Anthropogenic
_
_
8
200 Kilometers
o
8
16 Kilometers
Figure 3.-Forest patch vulnerability in Central Amazonia at three scales.
USDA Forest Service Proceedings RMRS-P-12. 1999
363
South America
(Central Amazonia)
_
_
Lowest Risk
Low-Moderate Risk
Moderate Risk
Moderate-High Risk
Highest Risk
Natural
Anthropogenic
_
Central America
(Southern Honduras)
Africa
(Southeast Ivory Coast!
Southwest Ghana)
;
-:
-....
8
o
8
16 Kilometers
Figure 4.-Forest patch vulnerability in three tropical zones of the world.
364
USDA Forest Service Proceedings RMRS-P-12. 1999
;,
,:
significant proportion of their edges adjacent to natural nonforest land cover, such as woodland savannah (Figure 4).
Of the three areas, forest patches in the central Amazon
had the highest average risk score (Table 2). It also had a
relatively high proportion of its forest patches in the maximum risk class, as did the Mrican site (Table 2). Figure 4
also shows that the Amazon site has the greatest amount of
anthropogenic cover of the three sites and has a large
number of forest patches fully imbedded within anthropogenic cover. The central American site had the lowest percentage offorest patches in the maximum risk category and
adjacent to anthropogenic cover, but it had a higher average
patch risk score than the Mrican site (Table 2). All three
areas had a considerably higher percentage offorest patches
in the high risk category than the relatively undisturbed site
(Table 2). Based on average risk scores offorest patches, one
could conclude that forest patches in the central Amazon are
at greatest risk, followed by the southern Honduras, and the
Mrican site. However, the Mrican si te had a greater number
of high risk forest patches than did the southern Honduras
site (Table 2).
Discussion ___________
The two-step analysis process highlighted in this paper
provides a way to evaluate forest fragmentation across
entire continents, and to evaluate the extent and nature of
fragmentation in specific areas. Moreover, the approach
evaluates the relative risk of forest loss and fragmentation
based a simple index offorest patch size and the percentage
of an individual patch edge that is adjacent to anthropogenic
cover. This assumes that areas with numerous small forest
patches imbedded in a sea of anthropogenic cover are more
likely to be undergoing rapid forest fragmentation and forest
loss than larger forest patches imbedded within natural
land cover (patches that are naturally fragmented). Based
on patterns of deforestation reported in other studies
(Zipperer 1993), this assumption seems reasonable. Unlike
more traditional remote sensing methods that evaluate
forest loss from a set of imagery obtained over a range of
dates (Tucker et al. 1984; Iverson et al. 1989; Hall et al. 1991;
Vogelmann 1994; Jones et al. 1997), our method provides a
way to evaluate ongoing fragmentation from a single land
cover product, such as the global1-km land cover database
produced by the U.S. Geological Survey's EROS Data Center.
Because of costs associated with data collection and processing at continental scales, geographic "targeting" approaches, such as that highlighted in this paper, are needed
to assess forest resource conditions over large areas at
multiple scales. Jones et al. (1997) used landscape data to
evaluate watershed conditions at a regional scale, and to
target those areas that potentially needed additional evaluation. Using a regional scale land-cover database, Wickham
et al. (1998) ranked watersheds relative to their potential for
forest restoration and increased connectivity.
The approach can be applied to land cover data at varying
scales, including land cover derived from satellites, as well
as aerial photography. However, because the scale of the
imagery can affect index value for a given area (see Lillesand
and Kiefer 1994), comparisons among areas should be limited to a single scale of imagery.
This paper makes no attempt to analyze the consequences
of forest patch vulnerability on ecological resources or associated processes. To do so would require the application of
ecological, biological, and hydrological process models, depending upon the ecological resource of concern. For example, one could evaluate the consequences of losing high
risk forest patches on biota through the use ofmetapopulation
and biogeography process models (Whitcomb et al. 1981;
Fahrig and Merriam 1985; Schumaker 1996; Keitt et al.
1997). Similarly, watershed models are needed to assess the
consequences of forest patch loss on water quality.
The vulnerability index applied in this paper could be
improved by adding thematic data layers on slope and
elevation and protected areas. For example, some forest
patches with high amounts of anthropogenic edge might be
less likely to be developed because they are on areas with
steep slopes and hence are unsuitable for development, or
because they are protected by law. Cost grids of these
additional characteristics could be calculated in a GIS and
incorporated into the index. For example, forests in protected areas would have risk values at or near zero because
these sites are not likely to be developed. Similarly, forests
on steep slopes would have lower scores than forests in
relatively flat areas. The threshold for slopes (the point at
which development probably would drop substantially) could
be determined from existing patterns of development. Additionally, it may be possible to improve the index by separating internal from external edge, since the former represents
an internal erosion of forest patches and the later a peripheral erosion (Zipperer 1993).
Finally, it may be possible to improve the initial screening
process of each continent by applying a spatially filtering
approach (Riitters et al. 1997). For the purpose ofthis study,
we located the three study sites by visually inspecting patch
maps for the three continents. However, fragmented forests
Table 2.-Comparison of forest patch statistics and risk for central Amazonia (CA). southern Honduras (SH). southeast Ivory Coast!
southwest Ghana (IC). and a relatively undisturbed forest area in the Amazon (CN) . Maximum risk patches were those with
index values between 13 and 15 (see Methods section).
Area
Number forest patchesl
total number patches
Average forest
patch area
km2
SH
CA
IC
CN
84/210
94/164
57/220
21109
13.87
17.86
34.60
2111.5
Average anthro edge
......
- .. -- ...
64.25
85.56
79.00
4.72
Forest patches
at maximum risk
Average index
value of patches 1
percent
19.05
35.11
35.09
0.0
3.09
2.51
1.25
1.00
lWeighted by area.
USDA Forest Service Proceedings RMRS-P-12. 1999
365
are difficult to see at the continental scale because the image
is relatively fine-scaled (see Figure 1). The spatial filtering
approach utilizes a "windowing" concept which can be implemented in a GIS. One sets the window size based on the scale
at which wants to analyze fragmentation. For example, a
window of 100 x 100 pixels would analyze forest patch
characteristics on area of 10,000 km2 around each pixel
(assuming a one Km pixel). The window starts from the top
left part of the land cover patch map and moves one pixel at
a time until it reaches the bottom right pixel in the grid.
Next, one could calculate the average patch size or edge to
area ratio or number of patches in the window. The result
would be a new map of average patch size or edge to area
ratios for forest patches in the window. The new map would
result in a better visualization offorest fragmentation across
each continent, and a way to target areas for more detailed
analysis. The same spatial filtering approach could be used
to display forest patch risk at a continental scale.
Acknowledgments
We thank the USGS EROS Data Center for the creation
and dissemination of global land cover products.
Notice: The research described in this paper has been
funded in part by the United States Environmental Protection Agency (EPA). This work has not been reviewed by the
EPA, and no official endorsement should be inferred.
Literature Cited
Belward, AS. and T. Loveland. 1995. The IGBP-DIS 1km land cover
project: remote sensing inaction. Pp. 1099-1106, in Proceedings,
21st Annual Conference of the Remote Sensing Society,
Southampton, United Kingdom.
Bierregaard, R.O., T.E. Lovejoy, V. Kapos, AA dos Santos, and
R. W. Hutchings. 1992. The biological dynamics of tropical rain
forest fragmentation. Bioscience 42:859-866.
Fahrig, L, and G. Merriam. 1985. Habitat patch connectivity and
populations survival. Ecology 66:1762-1768.
Franklin, J. F. 1992. Scientific basis for new perspectives in forests
and streams. Pp. 25-72, in R. J. Naiman (ed.) Watershed
Management. Springer-Verlag, NY.
Groom, M.J., andN. Schumaker. 1993. Evaluating landscape change:
patterns of worldwide deforestation and local fragmentation. Pp.
24-44, in P.M. Kareiva, J.G. Kingsolver, and R.B Huey (eds.),
Biotic interactions and global change. Sinauer, Sunderland,
Massachusetts, USA
Hall, F.G., D.B. Botkin, D.E. Strebel, KD. Woods, and S.J. Goetz.
1991. Large-scale patterns of forest secession as determined by
remote sensing. Ecology 72:628-640.
Houghton, R.A 1994. The worldwide extent of land-use change.
BioScience 44:305-313.
Imhoff, M.L. 1994. Mapping human impacts on the global biosphere.
Bioscience 44:598.
Iverson, L.R., R.L. Graham, and E.A Cook. 1989. Applications of
satellite remote sensing to forest ecosystems. Landscape Ecology
3:131-143.
366
Jones, KB., KH. Riitters, J.D. Wickham, R.D. Tankersley, Jr., R.V.
ONeill, D.J. Chaloud,
E.R. Smith, and A.C. Neale. 1997. An Ecological Assessment of the
United States Mid-Atlantic Region: A Landscape Atlas. EPAl600/
R-97/130.
Jullien,M.,andJ.M. Thiollay.1996.Effectsofrainforestdisturbance
and fragmentation: comparative changes ofthe raptor community
along natural and human-made gradients in French Guiana. J.
Biogeogr.23:7-25.
Keitt, T.H., D.L. Urban, and B.T. Milne. 1997. Detecting critical
scales in fragmented landscapes. Conserv. Ecol. 1(1):1-20.
Lillisand, T.M., and R.W. Kiefer (eds). 1994. Remote sensing and
image interpretation. John Wiley and Son, Inc., 750 pp.
Metzger, J.P. 1997. Relationships between landscape structure and
tree species diversity in tropical forests of southeast Brazil.
Landscape and Urban PI. 37:29-35.
Ojima, D.S., KA Galvin, and B.L. Turner II. 1994. The global
impact of land-use change. BioScience 44:300-304.
PeteIjohn, W. T. and D. L. Correll 1984. Nutrient dynamics in an
agricultural watershed: Observations on the role of a riparian
forest. Ecology 65:1466-1475.
Riitters, KH., R.V. O'Neill, and KB. Jones. 1997. Assessing habitat
suitability at multiple scales: a landscape-level approach.
Biological Conservation 81:191-202
Saunders, D.A, R.J. Hobbs, and C.R. Margules. 1991. Biological
consequences of ecosystem fragmentation: a review. Conserv.
BioI. 5: 18-32.
Schumaker, N. H. 1996. Using landscape indices to predict habitat
connectivity. Ecology 77:1210-1225.
Stouffer, P.C., and R.O. Bierregaard. 1995. Effects of forest
fragmentation on understory hummingbirds in Amazonia Brazil.
Conserv. BioI. 9:1085-1094.
Townshend,J.R.G., C.O. Justice, and V. Kalb.1987. Characterization
and classification of South American land cover types using
satellite data. Int. J. Rem. Sens. 8:1189-1207.
Tucker, C.J., B. Holben, and T.E. Goff. 1984. Intensive forest
clearing in Rondonia, Brazil, as detected by satellite remote
sensing. Rem. Sens. Env. 15:225-261.
Turner,B.L.,II, W.C. Clark,R.W. Kates,J.F. Richards,J.T. Mathews,
and W.B. Meyers (eds.). 1990. The earth as transformed by
human action. Cambridge Univ. Press, Cambridge, UK.
Van Dorp, D., and P.F.M. Opdam. 1987. Effects of patch size,
isolation and regional abundance on forest bird communities.
Landscape Ecol. 1:59-73.
Vogelmann, J.E. 1994. Assessment of forest fragmentation in
southern New England using remote sensing and geographic
information systems technology. Conserv. BioI. 9:439-449.
Whitcomb, R.F., J.F. Lynch, M.K Klimkiewicz, C.S. Robbins, B.L.
Whitcomb, and D. Bystrak. 1981. Effects offorests fragmentation
on avifauna of the eastern deciduous forest. Pp. 125-206, in R.L.
Burgess and D.M. Sharpe (eds.), Forest island dynamics in mandominated landscapes. Springer-Verlag, New York, New York.
USA
Whitmore, T.C., and J.A Sayer (eds.). 1992. Tropical deforestation
and species extinction. Chapman and Hall, London.
Wickham,J.D.,KB. Jones,KH. Riitters,T. Wade, andR.V. O'Neill.
1998. Transitions in forest fragmentation: implications for
restoration opportunities at regional scales. Landscape Ecology,
in press.
Zipperer, W.C. 1993. Deforestation patterns and their effects on
forest patches. Landscape Ecol. 8:177-184.
USDA Forest Service Proceedings RMRS-P-12. 1999