Classifying the Neotropical savannas of Belize using remote

Journal of Biogeography (J. Biogeogr.) (2006) 33, 476–490
SPECIAL
ISSUE
Classifying the Neotropical savannas of
Belize using remote sensing and ground
survey
Neil Stuart1*, Timothy Barratt2 and Christopher Place1
1
Institute of Geography, School of Geosciences,
University of Edinburgh, Edinburgh, UK
and 2Tweed Forum, South Court, Drygrange
Steading, Melrose, Roxburghshire, UK
ABSTRACT
Aim This paper evaluates a method of combining data from GPS ground survey
with classifications of medium spatial resolution LANDSAT imagery to
distinguish variations within Neotropical savannas and to characterize the
boundaries between savanna areas and the associated gallery forests, seasonally
dry forests and wetland communities.
Location Rio Bravo Conservation Area, Orange Walk District, Belize, Central
America.
Methods Dry season LANDSAT data for 10 April 1993 and 9 March 2001
covering a conservation area of 240,000 acres (97,459 ha), were rectified to subpixel accuracy using ground control points positioned by GPS ground survey.
The 1993 image was used to assess the accuracy with which the boundaries
between the savanna matrix and gallery forests, high forests, wetlands and water
bodies could be discriminated. The image was classified by a maximum likelihood
(ML) classifier and the shapes and areas of forest and wetland classes were
compared with an interpretation of these land cover types from 1 : 24,000 aerial
photography, mapped at 1 : 50,000 scale in 1993. The 2001 image was used to
assess whether different subtypes of savanna could be distinguished from
LANDSAT data. This required the creation of a reference (‘ground truth’) data set
for testing classifications of the image. One hundred and sixty sample patches
(650 ha, distributed over an area of 7000 ha) of ten sub-types of savanna
vegetation and associates identified using a physiognomic classification scheme,
were delineated on the ground by GPS and divided into two subsets for training
and testing. Continuous classifications of LANDSAT data covering the savannas
were developed that estimated potential contributions from up to five sub-types
of land cover (grassland, wetland, pine woodland, gallery forest and palmetto).
The accuracy of each classification was assessed by comparison against ground
data. An ML classification was also produced for the 2001 image using the same
areas for training. This allowed a comparison of the relative accuracy of both
continuous and Boolean ML methods for classifying savanna areas.
*Correspondence: Neil Stuart, Institute of
Geography, School of Geosciences, University of
Edinburgh, Edinburgh, EH8 9XP, UK.
E-mail: [email protected]
476
Results The boundary between savannas and evergreen forests, gallery forests
and open water in the study region could be delineated by the ML classifier to
within 2 pixels (60 m) using LANDSAT imagery. However, the constituent subtypes within the savanna were poorly discriminated. Whilst the shape and extent
of closed canopy forest, gallery forest, wetlands and water bodies agreed closely
with the distributions interpreted from aerial photography, classes such as ‘open
pine savanna’ or ‘grassland’ were only 45–65% accurate when tested against
ground data. A continuous classification, estimating the proportions of three
savanna vegetation subtypes (grassland, marshland and woodland) present in
each pixel, correctly classified more of the ground data for these cover types than
doi:10.1111/j.1365-2699.2005.01436.x
ª 2006 Blackwell Publishing Ltd www.blackwellpublishing.com/jbi
Classifying Neotropical savannas
the comparable ML result. Proportional mixtures of the land cover estimated by
the continuous classifier also compared realistically with the vegetation
formations observed along ground transects.
Main conclusions By using GPS, a ground survey of vegetation cover was
accurately matched to remotely sensed imagery and the accuracy of delineating
boundaries and classifying areas of savanna was assessed directly. This showed
that ML classification techniques can reliably delineate the boundaries of
savannas, but continuous classifiers more accurately and realistically represent the
distribution of the subtypes comprising savanna land cover. By combining these
ground survey and image classification methods, medium spatial resolution
satellite sensor data can provide an affordable means for land managers to assess
the nature, extent and distribution of savanna formations. Over time, using the
archives of LANDSAT (and SPOT) data together with marker sites surveyed in
the field, quantitative changes in the extents and boundaries of savannas in
response to both natural (e.g. fire, hurricane and drought) and anthropogenic
(e.g. cutting and disturbance) factors can be assessed.
Keywords
Belize, continuous classification, GPS survey, land cover classification, LANDSAT, remote sensing, savanna.
INTRODUCTION
The last 15–20 years has witnessed a resurgence of interest in
the biogeography and ecology of savannas. The greater
awareness of savanna biodiversity, the increasing number of
endangered and endemic species being identified within
savannas and an emerging consensus that savannas worldwide may represent a larger sink for carbon than Boreal forests
(Scurlock & Hall, 1998) are among the factors raising
awareness that this globally extensive land cover type may
have been undervalued in the past and afforded until now a
low priority for protection or conservation (Ratter et al.,
1997). With recent studies identifying the potential for
significant biomass to be stored within savanna woodlands,
there is a growing need to map reliably the extent of savanna
land cover, including all its component sub-types and associates, even (and perhaps, especially) where some of these are
dominated by trees (Furley, 2004).
Although the savannas of Central America have fewer
woody species than the cerrado of South America, a phytogeographic analysis by Lenthall et al. (1999) of over forty
savanna sites from southern USA to subtropical South America
identified the savannas of Belize, the Yucatán and the Petén as
a unique subgroup. The woody component of these savannas is
defined by Pinus caribaea Morelet and in some locations,
Quercus oleoides Schltdl and Cham. (Oak), as well as the
characteristic shrubs Byrsonimia crassifolia (L. Kunth) (craboo), Curatella americana (L.) (Yaha) and the palmetto palm
Aceolorraphe wrightii H.Wendl which is prevalent in frequently
inundated savannas. Laughlin (2002), citing Furley (1999),
asserts that ‘these pine savannas are unique amid the variety of
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
neotropical savannas’, whilst Bridgewater et al. (2002) emphasize the phytogeographical and ecological importance of this
most northerly instance in Central America of humid, tropical
lowland savanna. The study of related tree species by Brewer
et al. (2003) also emphasized the biogeographical significance
of this area, challenging the conventional view that the flora of
Belize is simply derived from emigrant species from South
America at the time of the Great American Interchange. They
suggest that the hyperseasonality (Sarmiento, 1984) of the
Belizean savannas and the shallow, oligotrophic soils give rise
to a unique diversity of species.
Thus, previous work has demonstrated a distinctive character for the Central American savannas, which may result
from their distance from the richer, core areas in the
Neotropics, from hyperseasonality and a distinctive flora
adapted to the substrates and ground water conditions. The
problem of delineating subtypes within the savanna biome
remains as acute in Central as for South American counterparts, however, and this is critical in attempts to quantify the
changes occurring to savanna associations at regional and
global scales as a result of both natural and anthropogenic
factors.
Mapping savannas by remote sensing
As savanna areas are fragmented by land development, their
biodiversity and capacity to function as a sink for atmospheric carbon is reduced (Alho & Martins, 1995). Yet the
extent to which Neotropical savanna areas are being cleared
in different countries and regions is often unmeasured.
Comprehensive mapping of savanna areas and their
477
N. Stuart, T. Barratt and C. Place
constituent subtypes is therefore needed to evaluate their
present condition and distribution and to assist in identifying
priority areas for active management and conservation
(Dougill & Trodd, 1999; M.D. Bitencourt et al., unpublished).
The widespread distribution of Neotropical savannas, covering
over 2 million km2 of South and Central America (Mistry,
2000) and their predominance in developing countries has
meant that mapping efforts have generally been undertaken
using remote sensing. Two recently completed projects are
the Central American Ecosystems Maps funded mainly by the
World Bank [in Belize using LANDSAT Thematic Mapper
(TM) to delineate land cover at a map scale of 1 : 250,000;
Meerman & Sabido, 2001] and the 1-km resolution Land
Cover of North (and Central) America published in 2004 by
the Canadian Center for Remote Sensing (CCRS) and the
United States Geological Survey (USGS) as part of the Global
Land Cover Mapping Project (Latifovic et al., 2002). Both
projects used medium spatial resolution remote sensing
[LANDSAT TM or SPOT VEGETATION (VGT)] to map
land cover and monitor change at regional to continental
scales in a cost-effective manner.
In addition to these continental-scale mapping activities,
some studies have explored the use of medium spatial
resolution remote sensing to map the overall extent of savanna
at more regional scales. In Brazil, Valeriano & Bitencourt
(1988) used the Normalized Difference Vegetation Index
(NDVI) to differentiate savanna areas based on their lower
above-ground biomass than closed forest areas. A savanna land
cover inventory was produced from an interpretation of 66
LANDSAT TM images, to identify changes in the areal extents
and to assess the viability of savanna fragments remaining in
Sao Paulo state (Kronka et al., 1997). This provided a useful
quantification of overall loss of savanna, but as it did not
differentiate between subtypes, it was not possible to ascertain
from remote sensing whether clearance was greater for certain
types of savanna (e.g. areas dominated by grassland or
woodland). Also in Brazil, Almeida-Filho & Shimabukuro
(2002) used the now substantial archives of LANDSAT sensor
products to undertake a time-series analysis from 1987 to 1999
to monitor the gross changes in cerrado vegetation within
Roraima state.
Monitoring of the extent and nature of the savannas in the
Neotropics has borrowed ideas from the approaches of
workers in other parts of the world, such as Cahoon et al.
(1992) who showed the potential of medium spatial resolution
imagery to map the seasonal distribution of fires across parts of
the African savanna. Trigg & Flasse (2000) developed means
for wide-area monitoring of recently burnt savanna throughout Namibia using the SPOT VEGETATION (VGT) sensor
and the Moderate Resolution Imaging Spectroradiometer
(MODIS), while Stroppiana et al. (2003) showed how a
time-series of SPOT VGT images could be used to detect
recently burned areas in the savannas of the Australian Kakadu
National Park.
More comprehensive and current data about savanna land
cover from remote sensing creates the potential for ecological
478
studies using digital methods of analysis at a variety of scales.
As well as the possibility to monitor changes in the forest–
savanna boundary over time, the associations theorized by
biogeographers and ecologists such as Cole (1986), Solbrig
(1991), Archer (1995), Kellman & Meave (1997) and Ludwig
et al. (1999) between the internal savanna mosaics and factors
such as topography, water availability, drainage, herbivory and
fire history could, for example, be investigated more rigorously
over time and space using a sequence of satellite sensor images,
a digital elevation model (DEM) and other layers of data, such
as the history of land management for an area that may be
stored in a geographic information system (GIS).
A pre-requisite for such mapping and monitoring is a
reliable assessment of the accuracy with which the boundaries
between and within savanna areas can be determined from
remotely sensed imagery and in particular, from medium
spatial resolution optical data, which is presently the most
affordable for developing nations, where the majority of
savannas are found. King (1994) has argued from experience of
land cover mapping in the humid Neotropics, that when using
LANDSAT data, only the broadest land cover types can often
be discriminated accurately by conventional maximum likelihood (ML) classifiers and only when the optical imagery is
sufficiently free from cloud and haze. Whilst for some
investigations, detailed structural information about the composition of savannas is needed, which is unlikely to be
extracted from medium spatial resolution sensor data (see, for
example, Brown et al. (2004), we believe that recent improvements in GPS and in techniques of digital image classification
offer the potential to extract further information from imagery
such as LANDSAT about the characteristics of savanna
boundaries and about the variations within savannas.
Given the familiarity of land managers with LANDSAT data,
the relative consistency of the Thematic Mapper sensor data
and the continuity of global coverage since 1984, any method
that improves the extraction of information about vegetation
boundaries from these sensor data has significant practical
value for the monitoring and reporting of present conditions
and past changes. In this paper, we evaluate the potential for
extracting information about both boundaries and internal
conditions of savannas, choosing a test site in a developing
nation where the local agencies have an urgent need to report
on the distribution, extent and condition of the savanna under
their conservation.
LOCATION AND CONTEXT
Belize lies approximately between 1552¢ and 1830¢ N and
between 8728¢ to 8913¢ W. The land area of Belize is
estimated by Meerman & Sabido (2001) at 22,963 km2,
including some 1000 offshore cays. The climate is subtropical
to tropical, with mean monthly minimum/maximum temperature ranging from 16/28 C in winter to 24/33 C in
summer. There is a marked rainfall gradient from an annual
average of 1200 mm in the north to 4000 mm in the south.
The climate of Northern Belize is ‘tropical wet-dry’, following
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
Classifying Neotropical savannas
km
Figure 1 Extent of savanna based on the
Ecosystems Map for Belize (Meerman &
Sabido, 2001) and the location of the
Hillbank (HB) and Booth River (BR) tracts
within the Rio Bravo Conservation and
Management Area.
km
Koeppen’s climatic classification, and a marked dry season
occurs between February and May (Wright et al., 1959).
Two main groups of savannas can be identified in Belize. These
are the upland savannas of Mountain Pine Ridge and the
lowland savannas of the northern and coastal plains. Both
groups are found in oligotrophic conditions over coarse
textured soils of low fertility, but the upland savannas are
nearly always well-drained, whereas the lowland savannas
frequently suffer from alternate seasonal flooding and desiccation, leading them to be classified as ‘hyperseasonal’
savannas (Sarmiento, 1984).
Study site
The study was conducted in the Rio Bravo Conservation and
Management Area (RBCMA) in Orange Walk District of
north-west Belize. This conservation area of 97,549 ha is
almost entirely covered with natural forest, savanna and
marsh. The RBCMA is one of the largest conservation areas in
the country, accounting for over 4% of the land area and
containing the largest lowland savanna under protection.
Brokaw and Mallory estimated that lowland savanna covered
2.8% of the Rio Bravo (c. 2300 ha) in 1993. For management
purposes, savanna areas in the eastern part of the reserve
provide a ‘buffer zone’ that protects natural forests in the more
inaccessible western part close to the Guatemalan border, from
the increasing pressures outside the conservation area to clear
woodland for subsistence agriculture and for cash-crop
production (Programme for Belize, 2000).
Figure 1 shows that, in Belize, savannas occur mostly on the
gently rolling Northern and Coastal Plains. This area is
geologically part of the Yucatán limestone platform which,
during the Pliocene (2–13 Myr BP), was a shallow sea into
which white marls, reddish clays, sands and gravels eroded
from the granitic rocks of the Maya Mountains were deposited
and accumulated to form the present low-lying (< 20 m) plain
(Standley & Record, 1936). The lowland savannas are mostly
found on lobes of these outwashed sands and gravels that are
often nutrient poor and very well drained in the dry season but
subject to varying durations of inundation during the wet
season. This is illustrated by Fig. 2, based on observations by
Wright et al. (1959) and re-drawn from Brokaw (2001). On
these outwash deposits, undulating relief gives rise to locally
varying drainage conditions and a repeating sequence of
savanna subtypes is often observed, ranging from the savanna
orchards in the lowest topographic positions that experience
the longest periods of seasonal inundation, to the freely
drained sandy ridges that are dominated by pine and oak. As
Figure 2 Position of lowland savannas in
Belize according to topography and substrate
(after Brokaw, 2001).
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
479
N. Stuart, T. Barratt and C. Place
suggested by Fig. 2, the savannas are often bounded by
wetlands and water bodies at low elevations, and at higher
elevations by denser evergreen forests that develop on
outcropping limestone parent material. The savannas may
also be dissected internally by linear gallery forests that follow
the lines of local water courses that cross these outwash
deposits.
The two main areas of savanna within the RBCMA are
located to the east of the Booth River marsh (marked BR in
Fig. 1) and to the east of the New River Lagoon between the
old logging camp of Hillbank and the East Gate of the
conservation area (marked HB). Whilst the remote sensing
imagery we analysed had been geometrically rectified to allow
mapping for the entire Rio Bravo area, in this paper we focus
our analysis on the Hillbank savanna (nominal centre
1734¢ N, 8841¢ W) as the greatest amount of data has been
collected for the vegetation of this area from a series of
botanical and geographical surveys over the last 10 years.
Previous mapping of Belizean savannas
Although Lundell (1934, cited in Standley & Record, 1936)
devised the first classification scheme for the regional
vegetation of the Yucatán, the natural vegetation of Belize
was mapped in detail for the first time by Wright et al. (1959).
Wright’s classification interpreted soils, drainage and geomorphology to map the agricultural potential of the land, rather
than the actual land use at the time. As such the 262,000 ha
attributed to savanna and related pine forests (c. 11.4% land
area) probably provides an upper estimate of upland and
lowland savanna in the country. A new vegetation classification
and mapping of vegetation cover in Belize was produced by
Iremonger & Brokaw (1995); this was based on Wright’s
classification but greater detail was provided by a visual
interpretation of satellite sensor data. Intended for conservation planning, this map showed actual vegetation cover
interpreted from SPOT panchromatic imagery from 1993 at
a scale of 1 : 250,000. A total of 51 vegetation types were
identified in this national vegetation classification, including a
class for savanna. Field checking by the author revealed,
however, that many savanna woodlands were classified within
‘lowland needle-leaf moist open forests over poor soils’. At the
other end of the ecotone, seasonally inundated savanna
orchards were included within ‘non-saline seasonally waterlogged scrubs’. This underscores the difficulty in estimating
changes in savanna extents based on previous mapping, when
the land cover type has either not been accorded a dedicated
class, or the full extent of savanna is likely to have been
underestimated.
In 2001, the Ecosytems Map for Belize was completed as part
of the Central American Ecosystems Map series. This map is in
fact a revision of the Iremonger & Brokaw (1995) map, with
the nomenclature adapted to conform to the UNESCO
classification adopted for all Central American ecosystems
maps and with the class definitions extended to include an
altitudinal component (Meerman & Sabido, 2001). The revised
480
map depicts 78 terrestrial ecosystems at a scale of 1 : 250,000.
The altitudinal component of the classification allowed
lowland savanna to be identified as a major land cover type.
Two subtypes were defined; both were types of short-grass land
cover, one dominated by trees (V.A.2.a), and one by shrubs
(V.A.2.b). Despite a re-interpretation of the original LANDSAT imagery to resolve greater spatial detail and further
ground checking, the classification scheme still attributed
denser areas of pine and oak woodland to a class (I.A.2.a) for
tropical evergreen seasonal forest, although such formations
are increasingly being included as ‘savanna woodlands’ in
classifications that focus on savannas (e.g. Furley, 1999).
The mapping of savanna land cover by remote sensing
therefore needs to address a series of challenges concerning,
among others, the definition of the land cover that will be
considered and mapped as sub-types of savanna, the recognition of these classes in the field and the identification of their
reflectance spectra in a satellite image. With such high levels of
both endemism and variety of species in savannas in different
regions, there is an emerging consensus that savanna land
cover classes should be identified primarily on the basis of
physiognomy (vegetation structures that can be recognized in
the field) rather than their floristics (Eiten, 1982; Cole, 1986).
Recent mapping of savannas as part of the Central American
Ecosystems Maps and the Global Land Cover Mapping Project
followed this approach, adopting the UNESCO system for
physiognomic–ecological classification of plant formations,
first drafted by Mueller-Dombois & Ellenberg (1974) and the
FAO Land Cover Classification System (Di Gregorio & Jansen,
2000), respectively.
The clarity with which land cover classes can be identified
both in the field and at corresponding locations on a satellite
sensor image is partly an issue of locational accuracy and of
sensor resolution. Mismatch between the image and the
ground survey data can be caused by uncertainty in locating
ground control points (GCPs). This mismatch is a source of
error that causes uncertainty in both the training and testing of
land cover classifications from remote sensing imagery. When
LANDSAT TM data had been corrected using GCPs obtained
from topographic maps, Combs et al. (1996) found the maps
derived from the imagery contained significant errors when
locations, areas and distances were measured. Cook & Pinder
(1996) improved the locational accuracy of mapping from
remote sensing by using GPS to supply control points directly
by ground survey. Stuart & Moss (2000) used GPS to supply
the ground training and testing data, as well as the GCPs for
detailed mapping of the Rio Bravo reserve from LANDSAT
data in 2002. Once the imagery has been corrected using these
higher accuracy GCPs and because selective availability was
removed in 2000, field scientists can use single frequency handheld GPS receivers to position field sites accurately for
re-visiting on maps derived from remote sensing imagery
(McCormick, 1999).
Two further difficulties that were reported when undertaking the ecosystems mapping project in Belize were the limited
availability of LANDSAT imagery between 1996 and 1998 that
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
Classifying Neotropical savannas
was sufficiently free of cloud to allow ground control points to
be selected for image rectification, and problems of classifying
the short grass savanna subtypes from the satellite sensor data
(Meerman & Sabido, 2001). This illustrates the continuing
difficulty of devising land cover classifications of the subtypes
of savanna vegetation that can be used with a spectral
classification from remotely sensed imagery.
Using the classes of the revised Ecosystems map, Meerman
& Sabido (2001) estimated that 8.8% of Belize (c. 200,000 ha)
was covered by lowland savanna at the time of remote sensing
data acquisition in 1993. The relatively small difference
between this and Wright’s estimate in 1959 of 11.4% covered
by upland and lowland savannas supports the general view that
wholesale conversion of savanna to other land uses has not yet
occurred in Belize and reinforces the opportunity for land
management to conserve substantial, unfragmented areas of
savanna.
NGOs such as the Programme for Belize, which manages the
RBCMA, are investing in technology such as geographic
information systems (GIS) to aid in the management of their
land area. Classifications from imagery such as LANDSAT TM
are considered a vital and often the only source of contemporary, comprehensive information about the state of the land
resources that can be obtained at an affordable price. The
accurate reporting of land cover types, extents and condition
can be a requirement placed on NGOs before they may claim
grants from conservation agencies for the upkeep of conservation areas. In the RBCMA, for example, quantitative
assessment and regular reporting of the extent of pine
woodland savanna is required to secure continued funding
for a carbon sequestration pilot project and for various bird
conservation projects in this habitat (Programme for Belize,
2000).
In sum, this growing need at both an international and local
level to analyse the distribution of savannas using digital
methods makes it timely to assess the quality of data that can
be collected about the boundaries and internal conditions of
savannas using affordable, medium spatial resolution remote
sensing imagery. The extent to which the different savanna
classes can be recognized from each other and from associated
vegetation such as evergreen and deciduous forests, gallery
forests and wetlands using LANDSAT TM data is the main
question addressed in the remainder of this paper.
DATA AND METHODS
Physiognomic classification of savanna subtypes from
vegetation survey
The subtypes of savanna to be classified by remote sensing
were defined from a survey of savanna vegetation in Belize by
Bridgewater et al. (2002). Whilst the primary output of that
survey was a detailed floristic inventory to assist local planning
for savanna conservation, a physiognomic classification of
savanna vegetation subtypes was developed as a by-product
from the botanical data. The classification allowed the
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
distribution of different savanna subtypes to be studied in
relation to variations in edaphic and drainage conditions
across a much more extensive area of the RBCMA by using
remote sensing (Furley et al., 2001).
From botanical collecting conducted in the savanna and
associated wetlands and gallery forests of the RBCMA from
July to September 1996, with supplementary visits in March
2000 and March 2001, a total of 258 savanna species were
recorded. Of these, 148 were characteristic of the drier savanna
ecosystem, 47 were associated with seasonally inundated areas
and 57 were woody species, of which 15 could be classified as
trees (Bridgewater et al., 2002). For the purposes of land cover
mapping, broad descriptions were then made of all the subtypes of savanna vegetation encountered. The dominant or
defining species and general physiognomy of each subtype
were recorded. Where possible, sub-types were defined to
conform to land cover classes previously established by Brokaw
& Mallory (1993) for the trees of the RBCMA and the
generalized land cover types of the national vegetation
classification by Iremonger & Brokaw (1995). When further
subdivision was required, new subtypes of savanna were
defined that complemented terminology already used locally,
were easily distinguishable on the ground and had significance
for management purposes. The vegetation was grouped
broadly into three main categories of ‘savanna’, ‘wetland’
and ‘savanna–forest transition’, reflecting the geographic and
topographic position of the savannas between forests and
wetlands in this area. The savanna–forest transition class
included all situations where savanna graded into forest. This
included both denser pockets of forest within the savanna
matrix and also gallery forests that fringed water courses
within savanna areas. Because of the management priority for
information about savanna areas, Bridgewater et al. (2002) did
not subdivide the savanna–forest transition class, which had
been previously studied in Belize by Wright & Baillie (1995),
but focused on six subtypes of savanna and five differing types
of associated wetland communities (Table 1).
Remote sensing data
A dry season LANDSAT 5 TM image of north-west Belize
(Path 19, Row 48, nominal centre 1720¢ N, 8840¢ W)
acquired on 10 April 1993 was used to investigate how reliably
Table 1 Savanna and wetland vegetation classification for the
RBCMA (Bridgewater et al., 2002)
1.0 Savanna
2.0 Wetland
1.1 Grassland and
scrub grassland
1.2 Pine–palmetto savanna
1.3 Palmetto thicket
1.4 Savanna orchard
1.5 Woodland and Pine Ridge
1.6 Oak thicket
2.1 Fringing riverine red mangrove
2.2
2.3
2.4
2.5
Cutting grass marsh
Marl flat
Sedge marshland
Eleocharis–calabash marsh
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N. Stuart, T. Barratt and C. Place
savanna boundaries could be delineated using the sensor data.
A maximum likelihood classification of the 1993 image was
undertaken using ERDAS Imagine (Leica Geosystems, Norcross, GA, USA) and the location of boundaries between
savanna areas and gallery forests, open water and seasonally
inundated marshlands was compared to a delineation of these
three land cover types as interpreted from 1 : 24,000 aerial
photography and mapped on the 1 : 50,000 topographic
mapping of Belize. As this photo-interpretation was undertaken as a training exercise by the UK Military Survey, the
topographic maps contained an unusual richness of detail
about land cover types, with areas of high forest, marshland
and open water all delineated. By accepting the boundaries and
shape of the land cover types on this mapping as an
independent source of higher accuracy, the relative accuracy
with which savanna boundaries could be discriminated from
LANDSAT data was assessed by extensive visual comparison.
Although the topographic mapping provided a high quality
source of ground truth for testing the classification of the land
cover types forming boundaries with savanna in the 1993
LANDSAT data, the mapping could not be used to assess the
accuracy of classification by LANDSAT within the savanna
areas because subtypes of savanna had not been interpreted
from the aerial photography. It was therefore necessary to
collect independent reference data by a ground survey of
vegetation types within the savanna and to obtain an image
that was contemporaneous with the completion of this ground
survey in Spring 2001. A relatively cloud-free LANDSAT TM
image was acquired dated 9 March 2001. A sub-scene was
extracted covering an area of c. 96 km east-west and 68 km
north-south, including the RBCMA together with areas
outside this reserve where there were clearly defined features
(bridges, road junctions, airstrips, etc.) that could be used for
GCPs.
GPS survey of ground control and vegetation patches
Coarse acquistion (C/A) code GPS receivers with postprocessed differential correction, were used to position the
GCPs for rectifying the two satellite images. For the 1993
image, 48 control points were used to rectify the image into
NAD27/UTM(16) projection datum and map projection for
Belize. The accuracy of the rectification had an RMSE of
0.6 pixels (20 m). For the 2001 image the RMS error was 0.37
pixels (11 m). In both cases, further visual assessment was used
to confirm that sub-pixel accuracy had been achieved in the
image rectification. This was necessary to ensure correct
matching between the locations of the field reference data and
the corresponding pixels in the satellite image.
The main sub-types of savanna vegetation and associates to
be mapped were then defined. Several examples of each type
were visited under the supervision of field botanists and land
managers to ensure correct identification on the ground.
Representative patches of each savanna subtype were then
surveyed using a GPS receiver mounted on an all-terrain
vehicle. Since this ground reference data would be matched to
482
image pixels of a minimum size of 30 m, vegetation patches
were chosen that were each as homogenous as possible and
typically of 1–2 km2 in extent. Remaining areas of vegetation
that could not be encircled in this way were encircled on foot
with a GPS receiver. Using this approach, 160 patches of
vegetation, covering a total of 631 ha were encircled with c. 15
person-days of fieldwork. These areas of reference vegetation
data were then divided into two subsets, with one used to train
and the other to test classifications of the satellite data. Further
details of the field survey methods, software and equipment
used are described in Moss et al. (2003).
Classifying savanna vegetation by remote sensing
Given the difficulties anticipated in mapping a land cover type
that is, by definition, an ecotone between the forest and
grassland biomes (Longman & Jenik, 1992), we explored a
two-tiered approach to classification of savanna land cover
from remotely sensed imagery.
The first stage used a conventional Boolean maximumlikelihood (ML) classifier to distinguish areas of high forest,
gallery forest, wetlands and open water. The areas remaining to
be classified within the study region were then primarily the
savanna tracts. The savanna areas were then classified by linear
spectral mixture analysis (LSMA) (Settle & Drake, 1993). An
ML classification of the savanna areas was also undertaken to
allow a comparison with this more widely used technique.
Both classification methods made use of the same ground
reference data to assign classes to image pixels and to evaluate
the accuracy of the resulting classification.
The main characteristic of the ML technique pertinent to
this study is that the resulting classification is Boolean,
meaning that each pixel is allocated to one and only one class
– that to which it is estimated to have the greatest probability
of membership. In contrast, LSMA creates a continuous
classification in which each pixel has partial membership to
many land cover classes. In the same way that the texture of a
soil can be represented as its constituent proportions of sand,
silt and clay on a triangular diagram, so mixture modelling
seeks to ascertain the proportional contributions of certain
vegetation ‘end-members’ into the reflectance observed for a
given pixel in a remote sensing image. The rationale for using
LSMA to ‘unmix’ the constituents of savanna land cover rests
upon an acceptance that a continuous classification is more
able to represent the range and diversity of vegetation subtypes that collectively create savanna formations. For example,
several workers including Furley (1999) have identified at least
five sub-formations to the savannas (cerrados) of Brazil,
ranging from open grasslands (campo limpo) through to dense
savanna woodlands (cerradão), which can contain trees of up
to 20 m in height. An inclusive mapping of savannas should
encompass all these subtypes.
Compared to conventional Boolean classification, a continuous classification is less likely to misallocate pixels that
inherently exhibit a mixed response arbitrarily into one or
another discrete land cover classes. Continuous classification
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
Classifying Neotropical savannas
may be more suitable to a range of natural landscapes for
which the medium spatial resolution of the LANDSAT TM
sensor may not be high enough to resolve natural spatial
variability fully (Adams et al., 1995; Zhang & Stuart, 2001).
Evidence from using ML techniques to classify the savannas of
Belize suggests this may be the case (Waltham et al., 2002).
In this study, ML classification was applied to the 1993
image primarily for the purpose of discriminating the high
forest, gallery forest, wetlands and water bodies that border the
savanna areas. A total of 97 vegetation patches surveyed in the
field were used as training data. Characteristic spectra were
generated for each of 10 land cover types for which training
data were collected. These 10 cover types mostly accord
directly with those listed in Table 1, although a larger amount
of training data for the pine woodlands allowed two sub-types
to be defined according to tree density. Training data were not
collected for areas of inundated marl and mangrove as these
areas could not be accessed on the ground. Separability of each
of the class spectra was evaluated using the Jefferies–Matusita
(JM) index of divergence, using various combinations of
spectral bands (Waltham et al., 2002). Despite evidence that
some of the savanna vegetation sub-types had overlapping
spectral properties in the image, a classification by ML of the
entire area was initially undertaken and the accuracy of the
resulting map was evaluated using the remaining groundsurveyed patches of vegetation as independent test data.
Following the ideas of Settle & Drake (1993), a series of
LSMA models were then formulated to unmix the extract of
the 2001 LANDSAT imagery of the savanna tract south of the
Hillbank station (Fig. 1). Contributions from between three to
five end-members were modelled. Five is the maximum
number that may be defined when using a LANDSAT image
with six spectral bands. The end-members defined were grass,
marsh, woodland, palmetto and evergreen forest. This definition was based on the main physiognomic types of savanna
and associated vegetation identified and for which ground
training areas had already been surveyed. Other end-members
could have been defined, such as photosynthetic, non-photosynthetic vegetation, bare ground or cloud shadow. Such
generic end-members might have had more separable individual spectra and allowed a greater understanding of the
contributions from different surface types to overall scene
reflectance. End-members based on the savanna subtypes were
used in this study because we wished to explore if the
proportional contributions estimated by LSMA showed any
agreement with the vegetation formations identified by the
eco-botanical survey as comprising the savanna in this region.
We also wished to use the same ground data for developing the
training spectra for both the ML and continuous classifiers, to
allow a direct comparison between the two techniques. A
preliminary assessment of the image extract showed that cloud,
shadow and bare ground accounted for only a small number of
pixels. By overlaying the polygonal areas of vegetation types
digitized from the field survey onto the LANDSAT image,
pixels falling within these polygons were selected and used to
compute the mean reflectance of each end-member in each of
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
six spectral bands. These mean reflectance values were then
entered into the MIXMOD module of the public-domain mips
software of Mather (1999).
Spectral mixture analyses were conducted using different
combinations of the savanna end-members. Each analysis
produced a set of gridded images, one for each end-member
used, giving the proportional contribution of that end-member
(vegetation type) to each image pixel. For ease of interpretation, the proportions were constrained to sum to 100%. The
accuracy of these estimated proportions was evaluated by
inspecting the pixel values in areas where the actual land cover
was known from the field survey to be dominated by a single
vegetation type. These ‘purer’ patches of land cover should
be estimated by LSMA to have a high proportion of only one
end-member. The LSMA using the three end-members (grass,
marsh and wood) was found to reproduce the degree of
homogeneity of land cover observed in the tested areas most
accurately. As well as testing this classification in areas where
the land cover was known to be dominated by one vegetation
type (e.g. an area of predominantly woodland), we also
investigated if the variations in the end-member contributions
estimated by LSMA agreed with observations of changes in
the sub-types of savanna vegetation made along transects
surveyed in the field. This was assessed by overlaying the
co-ordinates of the transects onto the image and comparing
whether changes in the proportions of grass, marsh and forest
estimated for the image pixels conformed with any observed
changes in savanna subtypes recorded along the ground.
RESULTS AND DISCUSSION
Classification of savanna boundaries and subtypes by
maximum likelihood methods
The MLC technique reliably discriminated the gallery forest,
forest transition areas and standing water bodies found in the
study area. This was confirmed by a systematic comparison of
the results for extracts of the classified 1993 LANDSAT image
(such as Fig. 3a) against reference data for the high forest, gallery
forest and marsh land cover types interpreted from 1 : 24,000
aerial photography from 1993 and mapped at 1 : 50,000 (such as
Fig. 3b). The location and shape of the boundaries of the gallery
forests, forest thickets and open water bodies classified in the
satellite sensor image were found in most cases to agree to within
1 or 2 pixels (30–60 m) with the boundaries interpreted from
the aerial photography. To illustrate, comparison of Fig. 3(a)
and 3(b) shows close agreement as to the shape of a distinct
boundary between the savanna and the high evergreen forest in
the south-east of this extract. The gallery forest following Big
Pond Creek is clearly resolved by LANDSAT data and many
smaller pockets of forest that were delineated from the photography as outliers within the savanna matrix are also correctly
classified as isolated forest patches in the imagery. Many of the
smaller seasonal ponds that appear on the aerial photography are
also classified correctly by the LANDSAT data in Fig. 3(a). In
contrast to the sharp boundary with evergreen forest, the
483
N. Stuart, T. Barratt and C. Place
Figure 3 Part of the study area, showing (a) a Maximum Likelihood (ML) classification of the land cover and (b) topographic map of the
same area as (a), interpreted from 1 : 24,000 scale air photography. Crown copyright 1993. Crown copyright map reproduced with the
permission of the Controller of Her Majesty’s Stationery Office.
boundary between savanna and wetlands is much less distinct in
this study area. Whilst the extents of the Marsh and Savanna
Orchard classes from LANDSAT correspond in general to the
land interpreted from the photography as subject to inundation
(blue stipple on the western side of Fig. 3b), the satellite sensor
also detects thickets of at least 20 pixels (> 2 ha) within these
seasonally inundated areas where there is a definite woody
response (brown hues interspersed within the light blue areas on
Fig. 3a).
484
Whilst the sharper savanna boundaries could clearly be
discriminated from LANDSAT data using the MLC technique,
classification of the sub-types of vegetation occurring within
the savanna tracts was generally less successful. An assessment
of the accuracy with which this area could be completely
classified into Bridgewater et al.’s (2002) savanna vegetation
subtypes and associates by maximum likelihood methods is
reported in Table 2. The producer’s accuracy statistic indicates
the percentage of pixels covering areas of known reference
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
Classifying Neotropical savannas
Table 2 Accuracy assessment for classification of the savanna areas corresponding to
Bridgewater’s classes by maximum likelihood
method (after Waltham et al., 2002)
Class name
Unclassified (incl. 2.1, 2.3)
Cutting grass marsh (2.2)
Eleocharis marsh (2.5)
Grassland (1.1)
Oak–pine woodland (1.6)
Palmetto thicket (1.3)
Pine savanna (1.2)
Pine wood – dense(1.5)
Pine wood – open (1.5)
Savanna orchard (1.4)
Sedge marsh (2.4)
Totals
Reference
Totals
Classified
Totals
0
45
54
188
164
43
248
299
26
276
37
1380
178
42
67
241
157
140
196
177
12
152
18
1380
Number Producers
Users
Correct Accuracy (%) Accuracy (%)
0
0
1
124
91
2
16
91
12
30
0
367
–
0
2
66
55
5
6
30
46
11
0
–
0
2
51
58
2
8
51
100
20
0
Overall classification accuracy ¼ 26.6%.
vegetation type that were correctly allocated to that class.
Whilst grasslands and some of the oak and pine woodlands
were classified with 45–65% accuracy, the wetland, savanna
orchard and palmetto classes were poorly separated by the
sensor and as a result these classes were poorly distinguished.
The conclusions are that, whilst a classification of LANDSAT
data by conventional MLC techniques is suitable for delineating the overall extents of savannas and their boundaries with
associated vegetation types, it does not reliably map the
distribution of the component vegetation formations within
savanna areas. With many conservation agencies using
LANDSAT data because of its relative affordability and its
long archive of data, these findings demonstrate the potential
to use these data to monitor even relatively localized changes
in some forest–savanna boundaries over the last 20 years.
However, mapping and monitoring conditions within savanna
areas will require either different sensor data, or a different way
of classifying LANDSAT data.
Continuous classification of savanna boundaries and
subtypes
Four classifications of the land cover within the savanna areas
were produced using LSMA. Each classification used different
numbers of end-members and estimated their contributions to
the reflectance of each pixel. Table 3 reports the accuracy of
these continuous classifications of savanna land cover. To
illustrate, the classification using the three end-members
(grass, marsh and wood) was tested against 281 pixels where
the land cover was known from the GPS field survey to be
predominantly open grassland. The reflectance of 191 pixels
was estimated by LSMA to be constituted at least 50% by the
grass end-member (i.e. the response was more like grassland
than anything else). The accuracy for classifying pixels
constituting > 50% grass is therefore 191/281 ¼ 68%. Since
the classification estimates the proportional membership of a
pixel to each of the land cover classes on a continuous scale
(from 0% to 100%), it is necessary to specify the proportional
membership at which the accuracy of a class is to be assessed.
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
For example, using the most stringent criteria that > 80% of a
pixel response should be constituted from the grass endmember, only 98/281 pixels meet this condition, yielding a
class accuracy for ‘>80% grass’ of 35%. For the same 281 test
pixels, the number correctly classified as ‘grassland’ by the ML
classification was 158 (56%).
Adding further end-members into the LSMA did not
improve the accuracy with which the test areas were
represented in the resulting classifications. Model 4a, which
included a fourth end-member for high forest, led to a slightly
improved classification of marshlands but then underestimated
the extent of woodlands. Model 4b included a fourth endmember for palmetto palm but achieved only 35% accuracy in
discriminating palmetto and reduced the areas correctly
classified as wood or marshland. Model 5, which included all
the above sub-types, had the lowest classification accuracy for
all classes, suggesting that LANDSAT data do not have this
much dimensionality of information. This supports the
conclusion reported by others, including King (1994), that
intercorrelation of the visible red, blue and green bands of
LANDSAT data effectively reduces the dimensionality to 3 or
4. Based on this assessment, the three end-member LSMA was
accepted as offering the more accurate continuous classification. The accuracy of this classification was then investigated in
greater detail.
Table 3 shows that, by choosing a modest level of pixel
purity, such as 40%, the LSMA using the three end-members
correctly classified 77% of grasslands, 73% of marshlands and
64% of woodlands. The percentage of pixels allocated to their
correct class declines gradually from 70% to 30% for the
grassland and woodland classes as the required proportion of
the one cover type contributing to the pixel is increased to an
upper limit of 80%. Areas dominated by grassland were the
most reliably distinguished of all savanna subtypes by LANDSAT data. This illustrates how the continuous classification
contains information about both the extents and the relative
certainty with which pixels have been allocated into a given
class, providing an indication of the robustness of the
classification. The final column of Table 3 presents comparable
485
N. Stuart, T. Barratt and C. Place
Table 3 Accuracy of four linear spectral mixture analyses using
different combinations of end-members, and a maximum-likelihood (ML) classification of the same test areas
Table 4 Average (± 1 SE) proportions of each of the three endmembers estimated by LSMA for pixels in areas where the land
cover is known from ground survey to be predominantly the
ground cover type in the first column
Percentage of test pixels classified
by each set of end-members
End-member
proportion (%)
Grass
> 40
> 50
> 60
> 70
> 80
Marsh
> 40
> 50
> 60
> 70
> 80
Wood
> 40
> 50
> 60
> 70
> 80
Forest
> 40
> 50
> 60
> 70
> 80
Palmetto
> 40
> 50
> 60
> 70
> 80
Pixels in
test areas
3
4a
4b
5
ML
281
77
68
63
52
35
49
27
20
15
9
86
78
67
52
34
26
15
11
7
4
56
486
73
21
16
12
11
99
28
22
16
12
51
23
18
14
9
55
27
21
15
11
42
1206
64
44
38
31
22
19
11
8
7
5
41
31
26
21
15
12
6
5
4
3
48
29
22
20
17
13
N/A
25
11
10
8
4
26
638
178
51
44
39
31
22
35
11
8
4
3
End-member combinations were – 3: grass, marsh, woodland; 4a:
grass, marsh, woodland, evergreen forest; 4b: grass, marsh, woodland,
palmetto; 5: grass, marsh, woodland, evergreen forest, palmetto. ML is
the percentage of pixels in the test areas that were assigned to the
correct ground class by the maximum-likelihood classifier.
accuracies for an ML classification based on the same 10-class
classification used by Waltham et al. (2002). With class
accuracy evaluated using the same test pixels, the figures were
56%, 42% and 48% for grass, marsh and woodland, respectively. The output of the ML classifier does not give any
indication, however, about the probability level at which a
pixel was assigned to a given class. It is quite possible, for
example, in areas where it is difficult to discriminate between
classes, for even the most likely class to have a probability < 0.5
(50%).
Whilst the classification of grass and woodlands appears
robust, only 21% of pixel values in Table 3 for known
wetland areas had more than a 50% contribution from the
marsh end-member. This suggests that many wetlands in the
486
Mean (1 ± SE) proportion of cover type estimated by LSMA
Ground cover
Wood (%)
Grass (%)
Marsh (%)
Wood
Grass
Marsh
84.0 (1.8)
17.0 (1.0)
23.8 (0.9)
13.5 (1.5)
75.8 (0.9)
24.0 (0.7)
2.4 (0.5)
6.8 (0.3)
53.9 (0.9)
area have a response that includes a significant, minor contribution from grass and woody vegetation. Further investigation
of individual class accuracy is provided in Table 4. For all
the test pixels where the ground cover was observed to be
mainly a single type, we present the mean proportion of
that same cover type as estimated by the LSMA. The results
suggest that classification accuracy is high for the woodland
and grassland categories, with the mean proportion estimated for these test pixels being 84% and 76%, respectively.
Marshland areas were estimated to contain, on average, 24%
contributions from both woody and grass components. This
prediction is consistent with observations made by the field
botanists working in this area, who found some significant
woody elements even in the seasonally inundated areas. For
example, extensive ‘orchards’ of Crescentia cujete (calabash),
Haematoxylon campechianum (logwood) and Bucida buceras
(bullet tree) were found with understoreys of grass and sedges
in several of the savanna areas that have been observed to
be inundated for several months during the wet season
(Bridgewater et al., 2002).
A significant ‘woody’ response was also interpreted from the
MLC of the LANDSAT data in the areas identified as subject to
inundation from the aerial photography (Fig. 3a). Savanna
orchards that are subject to prolonged seasonal inundation do
not necessarily have fewer woody species than more welldrained savanna woodlands and the two subtypes may not
therefore be simply differentiated when mapping from remote
sensing by assuming a consistently lower biomass (lower NDVI
values) throughout the orchard subtype.
Figure 4 presents the results of the LSMA of the Hillbank
savanna as a map. Variations throughout the savanna in the
estimated proportions of the wood, grass and marsh vegetation
subtypes are shown by the degree of red, green and blue,
respectively, in each part of the map. A circular legend (in the
style of a colour wheel) is used to assist with interpreting the
contributions from each of the three primary colours (the
three subtypes) at each location. Areas of land outside the
savanna tracts were classified separately using the ML classification reported earlier in the paper. This was to meet a
practical requirement of the land managers for the land cover
to be mapped over the whole study area. Figure 4 therefore
illustrates the two-stage approach to classifying land cover,
where a conventional MLC technique is used for mapping the
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
Classifying Neotropical savannas
Figure 4 Mapping of results from the three
end-member mixture analysis. Variations
across the savanna in the estimated proportions of wood, grass and marsh vegetation
subtypes are shown by differing contributions to the image of red, green and blue,
respectively. Note that the legend is circular.
For example, a turquoise pixel represents a
high proportion of marsh (blue) and grass
(green) and a low proportion of wood (red)
estimated to be contributing to the reflectance at that location. Points mark locations
along a transect where the vegetation subtype
was recorded by field survey. Refer to Fig. 1
for the general location of the Hillbank
savanna.
Table 5 Savanna vegetation types observed
along transect line (marked on Fig. 4) and
the proportions of each of the three endmembers estimated in the image at the corresponding location
Pixel proportions from LSMA
Transect point no.
Field description
Grass (%)
Marsh (%)
Wood (%)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Cutting grass marsh by bridge
Cutting grass marsh
Cutting grass marsh
Seasonally dry marshland
Seasonally wet Calabash orchard
Bullet tree/Calabash orchard
Calabash orchard
Grassland with scrub bushes
Open grassland
Open grassland
Open grassland
Start of pine ridge
End of pine fi open grassland
Grassland (open, few trees)
Scrubland
Marshy depression in grassland
Pine, palmetto savanna
Grassland
13
53
47
48
5
0
20
45
93
100
100
8
82
71
76
78
60
87
55
36
43
52
35
16
23
23
7
0
0
0
1
0
0
22
0
13
32
11
10
0
60
84
57
32
0
0
0
92
17
29
24
0
40
0
widespread high forest biome, and the continuous classification based on LSMA is applied only for mapping within the
savanna ecotone.
Table 5 summarizes a series of observations of vegetation
cover made at points along one of the field transects, which
passes through a range of savanna ecotones. The transect is
marked on Fig. 4 for reference. The contributions estimated by
LSMA for grass, wood and marsh vegetation types were
compared with field observations about the dominant vegetation at each location. Points 1–3, for example, are in low-lying
wetlands; the continuous classification of the LANDSAT
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
sensor image at these locations estimates high proportions of
marsh and grassland. Points 4–7 pass through savanna
orchards where the duration of seasonal inundation reduces
with the gradually increasing elevation; for these pixels in the
image, the contribution estimated from marshland reduces
and is replaced by a more woody response. Points 8–11
traverse open grasslands with few trees, leading up to a freely
drained sandy ridge of Pinus Caribaea at point 12. In the
image, a grassland response at points 10 and 11 is replaced by a
strong woodland response at point 12. Points 13–18 pass
through a more heterogeneous area of grass savanna with
487
N. Stuart, T. Barratt and C. Place
scattered pine, palmetto and bushes. In the image, the response
is dominated by the signal for grassland, with an intermittent
smaller contribution from woodland.
At locations where two or more vegetation subtypes are both
estimated to make a significant contribution to the pixel
response, the continuous classification allows the relative
proportions to be examined. For example, point 17 is
described in field notes as an area of ‘pine/palmetto savanna’.
Conventional Boolean classifiers such as ML would allocate
this to the grassland class, without retaining information about
the significant (40%) woody component to the vegetation
estimated by the sensor at this location. Comparison of
Table 5 and Fig. 4 leads to the conclusion that the proportions
estimated by the continuous classification generally agree with
field observations of ground cover type.
CONCLUDING REMARKS
Previous attempts to create resource mapping from medium
spatial resolution LANDSAT and SPOT sensor data have
typically been at map scales of 1 : 250,000. At this scale,
savannas have often been mapped as a single land cover type or
their land area has been divided amongst the classes for
evergreen forests and scrub grasslands. It was generally not
expected that forest–savanna boundaries could be accurately
delimited, or any of the subtypes within the savanna matrix
resolved, using medium-resolution satellite data (King et al.,
1992). Consequently there has been little work specifically
analysing savanna vegetation boundaries and subtypes with
LANDSAT sensor data, even though it is the most affordable source of remote sensing data for many developing
nations.
By using GPS to provide greater accuracy and flexibility in
collecting ground control and vegetation data for training and
testing the classifications, the method reduced mismatch
between image data and ground survey observations. With
sub-pixel accuracy of image rectification, we have shown that,
in the study area, many of the savanna boundaries with
wetland, gallery forest and closed canopy evergreen forests
could be clearly delineated to within 60 m using LANDSAT
TM data. Isolated forest thickets that are similar in composition to evergreen forests could also be discriminated from the
savanna areas surrounding them. These findings suggest that
with improved ground control from GPS, LANDSAT TM data
might be more usable than previously suggested for quantifying changes in forest–savanna boundaries and the growth or
decay of forest patches within savanna areas.
Whilst maximum-likelihood (ML) techniques are commonly available in software for processing LANDSAT data, we
have shown that even using a physiognomic classification
developed locally for the land cover in Belize, many of the
savanna subtypes exhibited subtle variations that led to poor
classification accuracy by the ML method. A similar difficulty
was reported by Meerman & Sabido (2001) for classifications
of savanna subtypes using LANDSAT data. The finding that
several of the savanna cover types have similar spectral
488
properties and cannot consistently be separated, supports the
premise that savanna areas produce a mixed response in
satellite imagery depending upon factors such as the amounts
of woody canopy, grass understorey, degree of seasonal
inundation and soil characteristics. This led us to develop a
continuous classification of the savanna that estimated
contributions to each pixel from three vegetation types (grass,
marsh and woodland) and produced a more accurate classification of the test areas than an MLC using similar classes and
training data. Interestingly for ecological and botanical investigations, a continuous classification of the LANDSAT image
estimated spatial variations in the proportions of woody,
herbaceous and wetland vegetation across the study area in a
manner that agreed well with field observations made at
corresponding locations along ground transect lines. The study
has therefore provided some evidence that further information
about the nature and condition of savannas may be extracted
from LANDSAT data by using more novel types of image
classification.
The reducing cost of medium resolution satellite data and
GPS receivers now enables biogeographers and land managers
to test the applicability of this method for classifying savannas
using remote sensing and ground survey in many locations
world-wide. The accuracy of the ground reference data
collected with this method is sufficient to rectify and test
classifications of savanna with higher spatial resolution optical
imagery, such as 4-m multispectral bands of IKONOS imagery
(Jiao et al., 2001) and with airborne or spaceborne radar data,
when these become more affordable. With the loss of savanna
areas only being recognized relatively recently as a cause of
concern (Ratter et al., 1997), and with relatively few remote
sensing studies having focused on savannas, there appears to be
considerable scope for both a retrospective analysis of changes
in savanna areas, and for their future monitoring using these
methods.
ACKNOWLEDGEMENTS
The physiognomic classification of savanna subtypes of the
RBCMA was developed by Sam Bridgewater and colleagues
at the Royal Botanical Gardens, Edinburgh. We thank the
British Ecological Society for funding additional field survey
and collecting work by Dr Bridgewater during 2000 to refine
the classification for use with remote sensing data. We
thank Duncan Moss for his work in collecting the ground
control and ground reference data used to rectify and test the
classifications of the LANDSAT images. The support of the
Royal Society of London and the RICS Education Trust in
funding the collection of field data is gratefully acknowledged.
We also thank Duncan Moss and Amanda Waltham, whose
earlier classifications of the Belizean savannas laid the foundation for this study. The Programme for Belize provided
access to the RBCMA and the logistical support of their
Ranger Service during the fieldwork was much appreciated.
We would also like to acknowledge the support and encouragement of Prof. Peter Furley who has inspired so many of us
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd
Classifying Neotropical savannas
to undertake research in Belize. Iain Cameron assisted with
the re-drawing of some figures. We are also indebted to two
referees whose constructive criticisms clearly improved the
original manuscript.
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BIOSKETCHES
Neil Stuart lectures on the Masters programme in Geographical Information Science (GIS) in the Institute of
Geography at Edinburgh University. Since leading components of an ecological survey of the savannas of northern Belize
in 1996, he has continued to investigate the extent to which
savannas can be mapped using remotely sensed imagery that is
affordable in developing countries.
Tim Barratt specialized in image processing and linear
unmixing of LANDSAT TM data for his Masters thesis,
working with imagery and field data collected from previous
field seasons in Belize. Tim now works for the Tweed Forum as
a field ecologist, mapping and controlling the spread of
invasive species.
Chris Place is a computing officer in the Institute of
Geography and has twenty years of experience in specialist
processing of earth observation imagery. His work includes
remote sensing studies of the savannas of Belize, Brazil and
Ethiopia, and land-cover mapping projects in southern
Mexico and along the mangrove coastline of Belize.
Editors: Peter Furley, John Grace and Patrick Meir
This paper is part of the Special Issue, Tropical savannas and
seasonally dry forests: vegetation and environment, which owes
its origins to a meeting held at the Royal Botanic Garden,
Edinburgh, in September 2003.
Journal of Biogeography 33, 476–490, ª 2006 Blackwell Publishing Ltd