Landscape Ecology vol. 3 no. 2 pp 97-109 (1989)
SPB Academic Publishing bv, The Hague
Tropical deforestation and species endangerment: the role of remote
sensing
Walter E. Westman', Laurence L. Strong2 and Bruce A. Wilcox3
'Applied Science Division, Lawrence Berkeley Laboratory, Bldg. 90-3 125, I Cyclotron Road, Berkeley,
CA 94720, USA; 2TGS Technology, Inc., N A S A Ames Research Center, Mail Stop 242-4, Moffett Field,
CA 94035, USA; 31nstitutefor Sustainable Development, 3000 Sand Hill Rd, Bldg I , Suite 102, Menlo
Park, CA 94025, USA
Keywords: tropical deforestation, biodiversity, remote sensing, Uganda, Queensland, rainforests, change
detection
Abstract
Initial results of a pilot study to link remotely-sensed information on tropical forest loss to field-based information on species endangerment are reported here. LANDSAT multispectral scanner (MSS) imagery from
1973 and 1988 were used to estimate net forest removal (29% of forest area), regrowth (7% of forest area,
including possible artifactual errors), and forest edges in Mabira Forest in southeastern Uganda during the
15-year period. Of the forest remaining, the percentage that was heavily disturbed increased from 18% to
42%. This change in forest density was observable with the MSS imagery. The total forest edge-to-area ratio
(including edges interior to the forest boundary) increased by 29% over the period. Although four distinct
types of closed tropical forest, based on structure or dominance, could be recognized on the ground, the types
could not be distinguished by differences in spectral reflectance in the four MSS bands. Closed tropical forest
could be readily distinguished from exotic conifer plantations, banana plantations, and other non-forest
vegetation types. Field measurements in Mabira and other Ugandan rain forests, and in rain forest isolates
on the Atherton Tableland of North Queensland, are being made to relate changes in forest fragmentation
to resulting changes in species abundance, structural form of the forests, and morphological diversity of target populations. Possible applications of conservation biology theory and modeling to these data are briefly
discussed.
Introduction
Current international concern over the global loss
of species by habitat alteration results in part from
the rapid rate of clearing of species-rich tropical
forests (Lewin 1986a, b). While the rate of tropical
deforestation in the middle and late 1970's has been
estimated in two major studies (Myers 1980; Lanly
1982), the estimates were necessarily first-order approximations. In many instances deforestation estimates were derived solely from estimates of population growth and assumed per capita land requirements. Further, the data used to make the estimates
are now as much as 15-20 years old. While the
FAO/UNEP study (Lanly 1982) employed some
satellite imagery (mostly LANDSAT Multispectral
Scanner (MSS)) in its analysis, the utilization was
limited to a 6% area sample of the 76 countries surveyed, employing visual analysis of 1:1,000,000
transparencies of MSS data. There is a three-fold
difference in the estimates of rates of tropical forest
clearing from the two studies (7.4 million ha/yr
(Lanly 1982); 22 million ha/yr (Myers 1980)) for the
same period in the late 1970's. Some of this discrepancy has been attributed to differences in definitions used in the two surveys (Melillo et al. 1985),
98
and the limited extent to which remotely-sensed
data were used (Grainger 1984).
Many national governments and international
agencies are increasing their efforts to formulate effective policies to stem the predicted loss of species
resulting from tropical forest clearing (e.g., World
Resources Institute 1985; Office of Technology Assessment 1987). At the same time, the international
scientific community has identified the need for
ongoing monitoring of land surface changes in
order to meet the challenges of the International
Geosphere-Biosphere Program (e.g., Bretherton
1986). The need for information on land surface
changes at the global scale currently derive both
from the desire on the part of international environmental agencies to assess and prioritize needs for
biological conservation efforts, and from the needs
of scientists for global-scale information for studying global processes of biogeochemical cycling and
climate change.
Woodwell et al. (1984) have noted that global
coverage of the earth once by LANDSAT MSS imagery would require 12,000 frames. At 1988 prices,
image acquisition would cost U.S. $7.92 million;
image analysis would require several person-years
and significant amounts of computer time. Clearly
it would be desirable to have more cost-efficient approaches to remote sensing of large areas. In recent
years, some work has focused on use of smaller spatial resolution imagery for this purpose (1,4 and 15
km resolution from NOAA AVHRR satellite; e.g.,
Tucker et al. 1984; Malingreau 1986).
To relate habitat alteration at regional or global scales to ecological effects, and ultimately to
species endangerment, information on landscape
change is needed at a range of scales from global to
regional and local. Global or regional-scale data are
needed in order to assess the numerical effects of
habitat loss on species loss, initially as a direct result
of the smaller number of species that are typically
found in reduced areas of habitat due to the heterogeneous spatial distribution of species (Coleman
et al. 1982; Connor and McCoy 1979; Simberloff
1978). A further reduction in species is expected
once species have reequilibrated with their reduced
and often fragmented environments, in which extinction processes may be enhanced (e.g., Boecklen
and Simberloff 1986; Lovejoy et al. 1984; Simberloff 1986; Wilcox 1980).
Large-scale information on habitat change is
needed if the broad predictions of number of species endangered are to be translated into specific
predictions about species at risk. Whether derived
from detailed field survey, broader regional Sampling, or a combination of field data and modeling,
information is needed about habitat type, rates of
habitat change, disturbance pattern and extent for
the biologist to make predictions of the extent of
resulting endangerment to particular species.
In this article we review some recent research on
the use of remote sensing both for small-scale
regional assessment of tropical forest clearing, and
for determining habitat pattern and change at larger scales. We illustrate some abilities and limitations of LANDSAT MSS satellite imagery for detecting tropical forest changes, using data from a
pilot study in Uganda. We also discuss some approaches we are exploring for linking remotelysensed information on tropical forest habitat to
predictions of species endangerment, both in Uganda and in northern Australia.
Remote sensing of tropical forest habitat: a review
Global and regional scales
At a biome-wide or regional scale the principal objective of remote sensing for studying tropical
deforestation has been to differentiate closed forest
from bare ground and early secondary successional
phases at frequent intervals.
Optical remote sensing in the tropics has been
hindered by the high frequency and extent of cloud
cover. In our study of two rainforest patches in
southern Uganda (Kibale, Mabira), for example,
we found only three LANDSAT MSS frames of Kibale forest with <20% cloud cover available during the period 1972-1987. This scarcity of imagery
resulted from a combination of high cloud cover
over the forests, low frequency of overflights (22
times per year), and lack of retention of data (only
20 frames were archived and currently available
over this area).
99
An innovation to minimize this problem has involved the use of Advance Very High Resolution
Radiometer (AVHRR) data, which is collected once
every 12 hours over each part of the globe. By
selecting cloud-free pixels from daily scenes and
compositing them on a weekly or monthly basis,
95- 100% cloud-free scenes can be obtained (e.g.,
Colwell and Hicks 1985). While global composites
at 4 and 15 km scale have been retained by NOAA's
National Satellite Data Services Center (Suitland,
MD) since October, 1978, the 1 km data over tropical areas are only acquired upon request. Copies of
previously-requested scenes are archived by the
Center. The NASA Goddard Space Flight Center
has retained some 1300 computer-compatible tapes
of digital imagery at 1 km scale from the AVHRR
satellite, largely over Amazonia and equatorial
Africa, since 1981 (C.J. Tucker, pers. comm.); this
is presently the largest collection of 1 km AVHRR
imagery for tropical coverage.
Nelson and Holben (1986) evaluated the utility of
AVHRR data at the 1 and 4 km scale, and 0.9 km
Visible Infrared Spin Scan Radiometer (VISSR)
data onboard the GOES satellite in detecting forest
clearings in Amazonia. They compared results to
imagery from LANDSAT MSS (79 m resolution).
They found that 4 km data were too coarse to detect
linear clearings up to 2 km wide reliably. VISSR
data exhibited excessive noise. One km AVHRR
data performed well in estimates when all spectral
bands were used in the analysis. When only the
near-infrared (NIR) and red-sensing (R) bands were
used in a normalized difference vegetation index
(NDVI) of the form (NIR - R)/(NIR + R) at 1 km
scale, results were not as good because of the poorer
discrimination of primary forest from regrowth
vegetation.
One of the thermal-sensing bands of AVHRR
(Band 3, 3550-3930 nm) is sensitive to temperatures in the 0"- 100°C range. This channel has been
used effectively by Tucker et al. (1984) and Malingreau and Tucker (1988) to distinguish forested
areas from bare areas, pastureland, cropland, or
shrubland in Amazonia. The basis for this differentiation is the significant difference in reradiation of
heat from bare soil or low biomass vegetation vs.
mature tropical forest. Band 3 has also been used to
identify areas of active forest clearing, by identification of fires (Malingreau and Tucker 1988). The
method was used to generate estimates of land
clearing in the Rondonia area of Amazonia (Malingreau and Tucker 1988), producing results consistent with earlier trends derived from LANDSAT
MSS (Tardin et al. 1979, 1980). Tucker et al. (1984)
found that of the five AVHRR bands, Band 3 gave
the largest and apparently most accurate estimate
of cleared forest in Rondonia.
Because AVHRR and LANDSAT MSS or
Thematic Mapper (TM; 30 m resolution) data offer
different advantages, their combined use can sometimes prove beneficial. Nelson et al. (1987) stratified the Mato Grosso area of Amazonia into forest
and non-forest sites using a 1:5,000,000 vegetation
map (UNESCO 1980). They then used 1 km
AVHRR thermal data to identify active fires in
forest. Using the frequency of spot fires as a basis
for stratifying the scene, scene subsamples for MSS
and TM analysis were chosen at random (with
replacement), with probability of selection proportional to observed fire activity. The accuracy of this
method relies on the tightness of correlation between forest clearing activity as observed with
MSS, and fire activity as measured with AVHRR.
In the Mato Grosso study, a correlation of r = 0.64
between the two variables was observed, leading to
the possibility of high variation in final estimates of
forest clearing activity. As a result, Nelson et al.
(1987) recommend that the AVHRR data be used to
stratify the scene by fire activity, and MSS scenes
then be chosen by stratified random sampling,
rather than by probability proportional to fire activity. Additional sources of error in this procedure
include misregistration of AVHRR to MSS scenes,
and inaccuracies in forest-nonforest classification
(Nelson et al. 1987).
Malingreau (1986) showed that repeated temporal observations of the NDVI of an AVHRR image (every three weeks for approximately three
years) can yield useful discriminatory ability between tropical forest and agricultural types, even
when data are aggregated to 15 km2scale. The latter
technique relies on a knowledge of the phenological
changes of the vegetation or crop types of interest.
Townshend et al. (1985) used principal components
100
analysis to highlight underlying sources of temporal
variation in NDVI in Africa and North America.
They found that the first component corresponded
closely to the NDVI integrated over the year, the second to seasonality in the NDVI index. Townshend
et al. (1987) examined the utility of maximum
NDVI values, integrated over 20 km2 (GVI) on a
monthly basis for 13 months, in differentiating 16
land cover classes over the continent of South
America. A maximum-likelihood classification, using the 13 monthly NDVI values, produced a classification accuracy above 90% for all but one land
cover class.
Radar imagery holds promise for tropical remote
sensing studies, since radio waves penetrate clouds,
and are independent of solar illumination. Work is
in progress to assess the utility of shuttle imaging
radar (L-band, HH) to detect forest clearing in a
portion of Amazonia (Mato Grosso, Rondonia;
Stone and Woodwell 1985, 1988; Tucker et al.
1983). Previous radar images of portions of the
Amazon also exist (Projeto Radambrazil 1978).
Stone and Woodwell (1988) found that the brightest SIR-A radar backscatter occurred on recently
cleared sites, where remaining slash may be acting
to increase backscatter. They noted a non-linear
relationship between radar backscatter and LANDSAT MSS-derived NDVI, enabling enhanced definition of successional stages of forest by combined
use of radar and MSS data. At present, radar imagery is available only from irregular aircraft or
shuttle flights. Continuous radar coverage awaits
the launching of a civilian radar satellite, such as
that planned for the polar-orbiting Earth Observing
System (EOS) in the mid-1990’s.
Townshend and Justice (1988) examined the
question of optimal spatial resolution for change
detection on a global scale, for use in designing new
sensors aboard EOS. They suggested that 500 m
resolution may provide the best balance between
detail of changes detected and volume of data
generated.
Local scales
Over smaller areas (of the order of tens and
hundreds, rather than thousands, of meters), key
issues of concern to ecologists center on both temporal changes and the accuracy of discrimination of
habitat types. At present, LANDSAT MSS offers
certain advantages over LANDSAT TM or SPOT
data for assessing tropical forest change over an extended period. MSS data are available from 1972 to
the present, whereas TM data were first collected in
1982, and SPOT in 1986. In addition, in part because of its coarser spatial resolution (79 m for MSS
vs. 30 m for TM and 20 m for multispectral SPOT),
the costs per unit of scene area, both for acquisition
and processing, are least for MSS.
In urban or agricultural areas, the accuracy of
maps produced by automated classification of imagery can increase as spatial resolution is decreased
from TM to MSS scale (Williams et al. 1984;
Wrigley et al. 1983). Whether this phenomenon will
apply to certain tropical forest scenes remains to be
fully explored. Finer spatial resolutions may be
necessary for discrimination in landscapes exhibiting high levels of fragmentation (e.g., Wehde
1983).
The challenge of distinguishing floristic, structural, or disturbance types within tropical forest
has only begun to be addressed. Singh (1987),
working in northeastern India, found that LANDSAT MSS imagery was able to distinguish closed
from open forest by differences in mean gray values
of particular bands, as measured in sensor units
termed ‘digital numbers’ or DN values. He found
that closed forest differed from dense mixed bamboo in the two near-infrared bands. Forest regrowth, however, could not be distinguished from
open forest or shifting cultivation. Some shifting
cultivation plots were also indistinguishable from
grassland or bare soil.
Methods
Remote detection of deforestation
LANDSAT MSS images from February 2, 1973,
and March 12, 1988 over the Mabira tropical forest
reserve in southeastern Uganda were used to determine changes in total forest cover over the 15 year
period. The LANDSAT MSS sensor measures the
103
Table 1. Forest cover (km2), edge (km), and edge/area ratio (km-’) in Mabira Forest, Uganda in 1973 and 1988, based on analysis of
MSS imagery described in text.
1973
1988
Lightly or
moderately
disturbed
forest
Heavily
disturbed
forest
Total
forest
present
Area of
forest
present on
one date only
Total
length
of forest
edge
Edge/
area
235.1
119.4
50.3
84.8
285.4
204.2
101.4
20.1
847.2
783.5
2.97
3.84
nated forest; Celtis-dominated forest; poor, wet
forest; and young, mixed forest). All four types
have closed canopies. Six other land cover types
(pine plantation, swamp, pasture, tea, banana,
other agriculture) were identified from the 1987
map (Fig. 2).
A test of the spectral separability of land cover
types was performed by delineating 1-6 areas on
each image corresponding to central portions of
each of the 10 land cover classes, using the groundbased maps as guide. Means, variances and covariances were computed for DN values for each MSS
band and cover class.
As an alternative approach to discriminating
cover types, a classification of the destriped 1973
Mabira image was undertaken without initial reference to known cover types (‘unsupervised’ classification). Ten clusters were created from a subsample
of 23,500 pixels classed as forest, using an iterative
Euclidean distance clustering algorithm. A Bayesian maximum-likelihood algorithm was used to classify all forest pixels in each image into one of the 10
unsupervised classes, assuming equal prior probabilities for membership in each class. In order to
ascertain the cover class or classes comprising each
unsupervised spectral class, spectral values from 10
known cover classes, derived from the 1958 and
1987 maps (Fig. 2), were compared to the unsupervised classes by means of a contingency table.
Results
Remote detection of deforestation
A comparison of changes in forest cover between
the coregistered images of Mabira is presented in
Table 1. The amount of forest cover present in 1973
that had been cleared by 1988 was 35.5% (101.4
km2). Some 7.0% (20.1 km’) of new forest cover
had appeared in the 1988 image, however, due to
regrowth and possible artifactual errors of omission in forest classification in the 1973 image. The
net loss of forest cover was therefore estimated at
29% (81.3 kmz) during the 1973-1988 period. The
amount of heavily-disturbed forest, as a percentage
of forest remaining, was estimated to increase from
18% to 42% during the 15 year period.
The net reduction in forest cover over the 15 year
period, counting regrowth as forest, can be expressed as an annual rate of geometric decline
(Fearnside 1982). If ais the total forest cover initially, b is the total forest cover (including regrowth) at
the end of the measurement period, and n is the total time elapsed between measurements, then the
annual rate of geometric decline, X, can be expressed as b/a = X”. The value for Mabira forest
(2.2%) is compared to other values for Africa in
Table 2.
The value for Mabira forest is higher than for
Uganda as a whole, or other parts of East Africa,
perhaps reflecting the proximity of Mabira to the
major city of Kampala, increased clearing pressures
in the 1980’s, or both. If the annual rate of geometric decline is computed for changes in the area of
lightly- or moderately-disturbed forest, comparable to the definition of ‘forest conversion’ used by
Myers (1980), the rate for Mabira forest is 4.4%.
The annual geometric rate of gross deforestation
(total area clearcut, not counting regrowth) is
2.9%.
While the total area of forest declined by 29%
over 15 years, the edge/area ratio increased by an
equal percentage (Table l), with significant impli-
104
Table 2. Annual rates of geometric decline of forest cover (Oro),
measured by LANDSAT in Mabira Forest, Uganda, during
1973-1988, compared with rates for other parts of Africa
projected for 1980-1985 (FAOIUNEP 1981). Rates reflect conversion to non-forest; recent regrowth is counted as forest.
Mabira Forest, Uganda
Congo
Gabon
Zaire
Sudan
Tanzania
2.2
0.1
0.1
0.2
0.6
0.7
Uganda
Ghana
Kenya
Rwanda
Nigeria
Ivory Coast
20Yo CONCENTRATION ELLIPSES
PINE AND MAESOPSIS
PLANTATION
1.3
1.3
1.7
2.7
0.
SWAMP
MAESOPSIS-ALBIZIA
5 .O
6.5
CELTIS-HOLOPTELEA
POOR WET
YOUNG MIXED
cations for the relative abundance of edge vs. interior forest species.
Y
L 421
0
5
10
15
RED (0.6-0.7rm11 RELATIVE RESPONSE
20
Fig. 3. Supervised spectral classes (Bands 2 and 4 on x- and
Differentiation of forest types
The closed forest types delineated on MSS imagery
by reference to the forest-type map were not very
different in spectral reflectance characteristics.
Figure 3 shows the range of DN values in Bands 3
and 4 for six of the cover classes, with 20% probability of including in the ellipse all DN values found
in the cover class. Even with this restrictive criterion, it is evident from Fig. 3 that the four closed
forest types are not readily separable. Other band
combinations did not improve separability. The
pairwise transformed divergence, a measure of the
difference between classes based on all spectral
bands (Swain and Davis 1978), ranged from 0.05 to
0.52 between the forest types, whereas divergence
between any of the forest types and pine plantation
or swamp exceeded 0.9. The index ranges from 0.00
for maximum similarity to 2.00 for minimum
similarity.
The differentiation between clusters in the unsupervised classification was higher than for the
spectral classes created from known cover categories, with an average pairwise transformed divergence of 1.89 (range: 1.38-2.00). The 10 unsupervised classes each contained representation from
several of the cover types, however. No unsupervised class contained more than 25% of the pixels
from any one forest cover type. An image derived
from the 10 unsupervised classes suggested that
topographic features accounted for some of the
y-axes, respectively) for four closed forest types, plus exotic pine
plantation and swamp, from the 1988 Mabira MSS image.
differentiation between classes.
From the analysis to date we conclude that clear
differentiation of closed forest types in southeastern Uganda is not feasible with MSS Band 1-4
data of a single date alone. The possibility exists
that multitemporal data, or the combined use of
MSS with TM, SPOT, radar or digital elevation
data might yet achieve the desired differentiation.
At the same time, MSS spectral data are capable of
differentiating closed native forest from a variety
of agricultural types, including tea and pine plantations. Of potentially greater significance ecologically, the degree of forest disturbance (light or moderate vs. heavy), and the change in length of exterior
and interior forest edge, can be differentiated using
a combination of MSS imagery and ground data.
Discussion
Linking remotely sensed data to ecological data
Several approaches for utilizing remotely-sensed
data in predicting species endangerment are being
tested in our Ugandan and northeast Queensland
study sites.
Known responses to disturbance. Once levels of
disturbance to forest cover have been established by
105
-
Cercocebus olbigeno
I I .o
Colobus badius
8.8
6.6
4.4
2.2
n
"
E
Cercopithecus osconius
UL LL HL
Colobus guereza
Q,
C
0
Cercopithecus l'hoesti
Pan troalodvtes
2.0
I .6
I .2
0.8
0.4
n
"
2
UL LL H L
Cercopithecus mitis
U L = Unlogged Plot
L L =Lightly Logged Plot
HL= Heavily Logged Plot
Fig. 4. Effects of logging activity on abundance of seven primate
species in Kibale Forest, southwestern Uganda. Abundances
that are significantly lower than the values for the unlogged
forest are indicated by diagonal hatching. Reprinted from
Skorupa (1986) with permission of author and publisher.
remote sensing, such information can be useful in
assessing the differential impact of disturbance on
a taxonomic group, using field data on tolerance to
disturbance. An example of the latter possibility for
rainforests of East Africa can be seen in Fig. 4.
Data for changes in primate abundance with forest
density in Kibale Forest (Skorupa 1986) suggest
that all but Colubus guereza and Cercopithecus mitis would likely decrease in abundance with heavy
logging disturbance of the type differentiable by
MSS imagery at Mabira forest.
Species-area curves. Ecologists and biogeographers have long recognized that the number of species
encountered in an area increases with the area surveyed (Preston 1948). This extensively-documented
empirical generalization is the basis for the prediction that as the area of tropical forest biome is
reduced, a certain proportion of species endemic to
this biome type will become extinct. A powerfunction curve (log S = log c + z log A, where S
is number of species, A is area sampled, and c and
z are constants) often fits data obtained in sampling
areas of several hectares or more, although a semilog or linear relation of species to area may fit best
in particular cases (Connor and McCoy 1979). The
coefficient c will typically vary with taxon and biogeographic region.
Clearly any estimate of number of species lost
from a region based on a species-area relationship
will be subject to variations due to uncertainties in
the exact curve shape; these uncertainties will in
turn derive from small sample size and inherent
variability in the relationship (Westman 1985; Simberloff 1986). Furthermore, other ecological and
cultural factors affecting species extinction rates,
and time since isolation, must be taken into account
in compiling and interpreting such curves.
As part of the pilot study in Uganda, Buechner
and Wilcox (in prep.) have compiled species-area
curves for several forest mammal groups (primates,
ungulates, squirrels, all carnivores), based on
ground data from each tropical forest isolate in
Uganda. They are analyzing the influences of a
range of anthropogenic disturbance factors on
these data (e.g., 070 of adjacent cultivated land; density of adjacent human population; intensity of
agricultural encroachment into the forest). To obtain forest area changes, C . Hlavka is analyzing
Band 3 data from 1 km. AVHRR data for Uganda
for 1987, based on imagery registered to a map base
by C. J. Tucker. Estimates of forest celaring derived
from this analysis can be compared to historical
maps of forest area (e.g., Atlas of Uganda 1962) to
determine forest losses. By linking these two sets of
data, estimates of initial losses of species richness
in mammalian groups due to habitat removal can
be derived.
Structural predictors of species response to deforestation. One challenge for work at the regional
level is to find generalized predictors of taxonomic
response to habitat loss. If such predictors could be
found, they could be applied to spatially-based
106
species databases. In some regions of the tropics,
such as northern Queensland, Australia, the rainforest vegetation has been mapped by means of aerial photography and ground survey (Tracey and
Webb 1975), and entered on a geographic information system (GIS) (E. Saxon, pers. comm. 1988).
The occurrence of known bird and mammal species
in these forests (Winter et al. 1984) has been entered
in the GIs, as has the location of rare and endemic
plant species, using computerized data from
500,000 herbarium labels in the Queensland Herbarium (R. Johnson, pers. comm. 1988). Additional predictor attributes of these species (e.g., morphology, dispersal-mode) could be added in a relational database system.
To predict the differential risk to species from
landscape fragmentation, it would be desirable to
identify catalogued or readily-measurable attributes of species that indicate their vulnerability to
such disturbance. We have recently pursued this issue for vascular plants in the simple notophyll vine
forests of North Queensland (Atherton Tableland).
By obtaining data on structural attributes (e.g., leaf
size, orientation, branching pattern), dispersal
mode, and life form as well as taxonomic identity
within a set of recolonization zones of decreasing
age, we have determined that the early successional,
edge species have certain structural, reproductive,
or higher-order taxonomic features in common
(Westman 1989). If such predictors were to prove
robust throughout North Queensland rainforests,
they might be used, in association with the computerized database of Queensland plants, and
known structural features of the species from the
Flora of Australia and other floras, to identify likely increasers, decreasers and invaders as the edgeto-interior ratio changes across the landscape.
Modeling. As an additional part of field efforts
in Queensland in our pilot study, J. Weishampel,
working with H. Shugart (University of Virginia),
is examining the morphological diversity of centipede (Rhysida nuda) populations in 17 sites of complex mesophyll/notophyll vine forest that differ in
size, age, and extent of isolation of the fragment.
Using LANDSAT MSS data to characterize features of the isolates and their change over time, a
deterministic model is being constructed to relate
observed population-level differences in phenotypic characters to changes in forest patch characteristics.
Simulation modeling can also contribute insights
into the effects of ecological and cultural features
on species-area curves and species behavior in fragmented landscapes. Seagle and Shugart (1985) have
used simulation models to study the role of patch
dynamics and competition on species extinctions
and species-area curves. Simulation models have
also been used to study the role of ecological attributes and microhabitat and landscape patchiness
on species survival in birds (Shugart and Urban
1986; Urban and Shugart 1986).
Stamps et al. (1987) and Buechner (1987) have
modeled the effects of edge-to-area ratio and edge
permeability on species movement between isolated
patches. Edge ‘permeability’ refers to the extent to
which a transition to a neighboring habitat acts as
a barrier to emigration. Once field studies have
identified the types of adjacent habitats that are
more and less favorable to migration by a species,
potential routes of species migration could be
mapped on remotely-sensed images of the habitat
mosaic.
A further link of satellite data to models can be
made by examining rates of temporal transition between habitat states using a Markov matrix approach. Hall et al. (1987) have illustrated the potential of this approach in the boreal forest.
Conclusions
At a large regional scale, multitemporal AVHRR 1
km. data alone can provide information on current
deforestation patterns over a region at reasonable
cost (one AVHRR scene costs approximately U.S.
$100). To obtain detailed information on conversion of tropical closed forest to agriculture or tree
plantation seems readily achievable with LANDSAT MSS data.
Information on changes in the relative abundances of closed forest types is not readily achieved
by this imagery, however, at least based on studies
in India of Singh (1987) and in Uganda, reported
here. For the present, utilization of MSS for this
107
purpose is likely to be most successful when combined with either larger-scale resolution imagery
(TM, SPOT, aerial photography), existing vegetation maps, or both. Of significant promise, however, is the ability to differentiate disturbance regimes, regrowth, and exterior and interior edges of
tropical closed forest by the combined use of MSS
imagery and ground data.
Linking remotely sensed data to biological data,
in order to predict species at risk from deforestation, is an area with promising research opportunities. Structural or behavioral features that predict
species’ behavior in fragmented landscapes can
potentially be used to identify classes of species at
differing risk from forest conversion. This categorization can aid in assessing landscape-level impacts
when linked with existing georeferenced databases
on the regional biota. The extent of potential endangerment by taxon at a regional level can be
characterized by the use of species-area curves in association with remotely-sensed data on habitat loss,
with caution. Simulation modeling can contribute
much to our ability to assess the sensitivity of
species-area curves to a variety of ecological and
cultural features. With coordinated research by
ecologists and remote sensing scientists on the questions outlined here, the potential exists to determine
global-scale changes in patterns of biotic occurrence and species endangerment.
Acknowledgments
J. Holmes and S. Kramer conducted field work in
Uganda and prepared drafts of Figs 1 and 2, under
the supervision of B.A.W. We thank C. Hlavka
(NASA Ames Research Center) and J. Skorupa
(Univ. California, Davis) for their extensive help
and contributions to the work and to the manuscript. J. Dungan (NASA Ames Research Center)
also provided suggestions on the manuscript. Research reported here was conducted with funding
from the National Science Foundation through Interagency Agreement No. BSR-87 17168 with the
Department of Energy, and from the NASA Earth
Sciences Division (UPN-677-80-06-05). Any opinion, findings, conclusions, or recommendations ex-
pressed in this publication are those of the authors
and do not necessarily reflect the views of the National Science Foundation, NASA, or the Department of Energy.
References
Atlas of Uganda, 1962. Government Printer’s. Kampala,
Uganda.
Boecklen, W.J. and Simberloff, D.S. 1986. Area-based extinction models in conservation. In Dynamics of Extinction. pp.
247-276. Edited by D.K. Elliott. Wiley, New York.
Bretherton, F., chr. 1986. Earth System Science. A Program for
Global Change. Earth System Sciences Comm, NASA,
Washington, D.C.
Buechner, M. 1987. Conservation in insular parks: simulation
models of factors affecting the movement of animals across
park boundaries. Biol. Conserv. 41: 57-76.
Coleman, B.D., Mares, M.A., Willig, M.R. and Hsieh, Y.-H.
1982. Randomness, area, and species richness. Ecology 63:
1121-1133.
Colwell, J.E. and Hicks, D.R. 1985. NOAA satellite data: a useful tool for macro inventory. Environ. Manage. 9: 463-470.
Connor, E.F. and McCoy, E.D. 1979. The statistics and biology
of the species-area relationshi;. Am. Nat. 113: 791-833.
ESL 1988. IDIMS Functional Guide, Vols. 1,2. Electromagnetic Systems Lab., Sunnyvale, CA.
FAOAJNEP 1981. Tropical Forest Resources Assessment
(GEMS): Tropical Africa. Vol. 2. UN Food Agric. Org.,
Rome.
Fearnside, P.M. 1982. Deforestation in the Brazilian Amazon:
how fast is it occurring? Interciencia 7: 82-88.
Grainger, A. 1984. Quantifying changes in forest cover in the
humid tropics: overcoming current limitations. J. World
Forest Res. Mgmt. 1: 3-63.
Hall, F.G., Strebel, D.E., Goetz, S.J., Woods, K.D. and Botkin, D.B. 1987. Landscape pattern and successional dynamics
in the boreal forest. In Remote Sensing: Understanding the
Earth as a System. IGARSS ’87, Vol. 1. pp. 473-482. IEEE,
New York.
Hlavka, C. 1986. Destriping AIS data using Fourier filtering
techniques. In Proc. 2nd Airborne Imaging Spectrometer
Data Workshop. pp. 74-80. Edited by G. Vane. Calif. Inst.
Tech. and NASA. JPL Publ. 86-35.
Lanly, J.-P. 1982. Tropical Forest Resources. F A 0 Forestry
Paper 30. With UN Environ. Progr., UN Food Agric. Org.,
Rome.
LAS 1986. LAS User’s Manual, Version 1.0. EROS Data
Center, Sioux Falls, SD.
Lewin, R. 1986a. A mass extinction without asteroids. Science
234: 14-15.
Lewin, R. 1986b. Damage to tropical forests, or why were there
many kinds of animals? Science 234: 149-150.
Lovejoy, T.E., Rankin, J.M., Bierregaard, R.O., Brown, K.S.
108
Jr. Emmons, L.H. and Van der Voort, M. 1984. Ecosystem
decay of Amazon forest remnants. In Extinctions, pp.
295-325. Edited by M.H. Nitecki. Univ. Chicago Press,
Chicago.
Malingreau, J.-P. 1986. Global vegetation dynamics: satellite
observations over Asia. Int. J. Remote Sens. 7: 1121-1146.
Malingreau, J.-P. and Tucker, C.J. 1988. Large-scale deforestation in the southeastern Amazon basin of Brazil. Ambio 17:
49-55.
Melillo, J.M., Palm, C.A., Houghton, R.A., Woodwell, G.M.
and Myers, N. 1985. A comparison of two recent estimates of
disturbance in tropical forests. Environ. Conserv. 12: 37-40.
Moik, J.G. 1980. Digital processing of remotely sensed images.
NASA Spec. Publ. SP-431.
Myers, N. 1980. Conversion of Tropical Moist Forests. Natl.
Acad. Sci., Washington, D.C.
Nelson, R. and Holben, B. 1986. Identifying deforestation in
Brazil using multiresolution satellite data. Int. J. Remote
Sens. 7: 429-448.
Nelson, R., Homing, N. and Stone, T.A. 1987. Determining the
rate of forest conversion in Mato Grosso, Brazil, using Landsat MSS and AVHRR data. Int. J . Remote Sens. 8:
1767- 1784.
Office of Technology Assessment 1987. Technologies to Maintain Biological Diversity. Report to U.S. Congress, Washington, D.C.
Preston, F.W. 1948. The colplmonness, and rarity, of species.
Ecology 29: 254-283.
Projeto RADAMBRAZIL 1978. Folha Sc. 20 Porto Velho,
Ministerio das Minas e Energia, Departmento Nacional de
Producao Mineral, Rio de Janeiro.
Seagle, S.W. and Shugart, H.H. 1985. Faunal richness and turnover on dynamic landscapes: a simulation study. J. Biogeogr. 12: 499-508.
Shugart, H.H. and Urban, D.L. 1986. Overall summary: a
researcher’s perspective. In Modeling habitat relationships of
terrestrial vertebrates. pp. 425-429. Edited by J. Verner,
M.L. Morrison and C.J. Ralph. Univ. Wisconsin Press,
Madison.
Simberloff, D.S. 1978. Colonisation of islands by insects: immigration, extinction, and diversity. In Diversity of Insect
Fauna. pp. 139-153. Edited by C.A. Mound and N. Waloff.
Symp. Roy. Entomol. SOC.No. 9, London.
Simberloff, D.S. 1986. Are we on the verge of a mass extinction
in tropical rain forests? In Dynamics of Extinction. pp.
165-182. Edited by D.K. Elliott. Wiley, New York.
Singh, A. 1987. Spectral separability of tropical forest cover
classes. Int. J. Remote Sens. 8: 971-979.
Skorupa, J. 1986. Responses of rainforest primates to selective
logging in Kibale Forest, Uganda: a summary report. In Primates: The Road to Self-sustaining Populations. pp. 57-70.
Edited by K. Benirschke. Springer-Verlag, New York.
Stamps, J.A., Buechner, M. and Krishnan, V.V. 1987. The effects of edge permeability and habitat geometry on emigration from patches of habitat. Am. Nat. 129: 533-552.
Stone, T.A. and Woodwell, G.M. 1985. Analysis of deforestation in Amazonia using Shuttle-imaging Radar. Proc. Intl.
Geoscience Remote Sens. Symp. p. 574. Amherst, Mass.
Stone, T.A. and Woodwell, G.M. 1988. Shuttle imaging radar
A analysis of land use in Amazonia. Int. J. Remote Sens. 9:
95-105.
Swain, P.H. and Davis, S.M. (ed.) 1978. Remote Sensing: the
Quantitative Approach. McGraw Hill, New York.
Tardin, A.T., dos Santos, A.P., Lee, D.C.L., Maia, F.C.S.,
Mendoncza, F.J., Assunczio, G.V., Rodrigues, J.E., de
Moura Abdon, M., Novaes, R.A., Chen, S.C., Duarte, V.
and Snimabukuro, Y.E. 1979. Levantamento de areas de desmatamento na Amazonia Legal atraves de imagens do satellite Landsat. Relatoria INPE 41 1-NTE/142. Instituto Nacional de Pesquisas Espaciais, San Jose dos Campos.
Tardin, A.T., Lee, D.C.L., Santos, R.J.R., de Assis, O.R., dos
Santos Barbosa, M.P., de Lourdes Moreira, M. Pereira,
M.T., Silva, D. and dos Santos Filho, C.P. 1980. Relatoria
INPE 1649RPE/103. Instituto Nacional de Pesquisa Espaciais, San Jose dos Campos.
Townshend, J.R.G., Goff, T.E. and Tucker, C.J. 1985. Multitemporal Dimensionality of Images of Normalized Difference Vegetation Index at Continental Scales. IEEE Trans. Geoscience Remote Sens. GE-23, 888-895.
Townshend, J.R.G. and Justice, C.O. 1988. Selecting the spatial
resolution of satellite sensors required for global monitoring
of land transformations. Int. J. Remote Sens. 9: 187-236.
Townshend, J.R.G., Justice, C.O. and Kalb, V. 1987. Characterization and classification of South American land cover
types using satellite data. Int. J. Remote Sens. 8: 1189-1207.
Tracey, J.G. and Webb, L.J. 1975. Vegetation maps of the
rainforests of North Queensland. 1:lOO 000. CSIRO, Melbourne.
Tucker, C.J., Holben, B.N. and Goff, T.E. 1983. Forest clearing in Rondonia, Brazil as detected by NOAA AVHRR imagery. NASA/GSFC TM 85018, Greenbelt, MD.
Tucker, C.J., Holben, B.N. and Goff, T.E. 1984. Intensive
forest clearing in Rondonia, Brazil, as detected by satellite remote sensing. Remote Sens. Environ. 15: 255-261.
UNESCO 1980. Vegetation map of South America, 1:5 000 000.
FAO/UNESCO, Rome.
Urban, D.L. and Shugart, H.H. 1986. Avian demography in
mosaic landscapes: modeling paradigm and preliminary
results. In Modeling Habitat Relationships of Terrestrial Vertebrates. pp. 273-279. Edited by J . Verner, M.L. Morrison
and C.J. Ralph. Univ. Wisconsin Press, Madison.
Westman, W.E. 1985. Ecology, Impact Assessment, and Environmental Planning. Wiley, New York.
Westman, W.E. 1989. Structural and floristic attributes of
recolonizing species in large rainforest gaps, North Queensland. Biotropica: in review.
Winter, J., Bell, F.C., Pahl, L.I. and Atherton, R.G. 1984. The
specific habitats of selected Australian mammals. Report to
World Wildlife Fund, Australia.
Wilcox, B.A. 1980. Insular ecology and conservation. In Conservation Biology: an Evolutionary-Ecological Perspective.
pp. 95-118. Edited by M.E. SoulCand B.A. Wilcox. Sinauer,
Sunderland, MA.
Williams, D.L., Irons, J.R., Markham, B.L., Nelson, R.F.,
109
Toll, D.L., Latty, R.S. and Stauffer, M.L. 1984. A statistical
evaluation of the advantages of LANDSAT Thematic Mapper data in comparison to Multispectral Scanner data. IEEE
Trans. Geoscience Remote Sens. GE-22, 294-301.
Williams, J.G. 1967. A Field Guide to the National Parks of
East Africa. Collins, London.
Woodwell, G.M., Hobbie, J.E., Houghton, R.A., Melillo,
J.M., Moore, B., Park, A.B., Peterson, B.J. and Shaver,
G.R. 1984. Measurement of changes in the vegetation of the
earth by satellite imagery. In The Role of Terrestrial Vegeta-
tion in the Global Carbon Cycle: Measurement by Remote
Sensing. pp. 221-240, Scope 23. Edited by G.M. Woodwell.
Wiley, New York.
World Resources Institute 1985. Tropical Forests: A Call for
Action. Center for Policy Research, Washington, D.C.
Wrigley, R., Acevedo, W., Alexander, D., Buis, J. and Card, D.
1983. The effect of spatial, spectral, and radiometric factors
on classification accuracy using Thematic Mapper data. Intl.
Geoscience Remote Sens. Symp Digest 1: 93-100.