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This article was downloaded by: [Acadia University] On: 11 May 2013, At: 08:18 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Journal of Remote Sensing Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tres20 Ecoregion classification in the Okavango Delta, Botswana from multitemporal remote sensing J. McCarthy a , T. Gumbricht b & T. S. McCarthy b a Department of Land and Water Resources Engineering, Royal Institute of Technology (KTH), S100 44 Stockholm, Sweden b Department of Geology, University of the Witwatersrand, Private Bag 3, 2050 Wits, South Africa Published online: 22 Feb 2007. To cite this article: J. McCarthy , T. Gumbricht & T. S. McCarthy (2005): Ecoregion classification in the Okavango Delta, Botswana from multitemporal remote sensing, International Journal of Remote Sensing, 26:19, 4339-4357 To link to this article: http://dx.doi.org/10.1080/01431160500113583 PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-and- conditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

Ecoregion classification in the Okavango Delta, Botswana from multitemporal remote sensing

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This article was downloaded by: [Acadia University]On: 11 May 2013, At: 08:18Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

International Journal of RemoteSensingPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tres20

Ecoregion classification in theOkavango Delta, Botswana frommultitemporal remote sensingJ. McCarthy a , T. Gumbricht b & T. S. McCarthy ba Department of Land and Water Resources Engineering, RoyalInstitute of Technology (KTH), S‐100 44 Stockholm, Swedenb Department of Geology, University of the Witwatersrand, PrivateBag 3, 2050 Wits, South AfricaPublished online: 22 Feb 2007.

To cite this article: J. McCarthy , T. Gumbricht & T. S. McCarthy (2005): Ecoregion classification inthe Okavango Delta, Botswana from multitemporal remote sensing, International Journal of RemoteSensing, 26:19, 4339-4357

To link to this article: http://dx.doi.org/10.1080/01431160500113583

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representationthat the contents will be complete or accurate or up to date. The accuracy of anyinstructions, formulae, and drug doses should be independently verified with primarysources. The publisher shall not be liable for any loss, actions, claims, proceedings,demand, or costs or damages whatsoever or howsoever caused arising directly orindirectly in connection with or arising out of the use of this material.

Ecoregion classification in the Okavango Delta, Botswana frommultitemporal remote sensing

J. MCCARTHY{, T. GUMBRICHT{ and T. S. MCCARTHY*{{Department of Land and Water Resources Engineering, Royal Institute of Technology

(KTH), S-100 44 Stockholm, Sweden

{Department of Geology, University of the Witwatersrand, Private Bag 3, 2050 Wits,

South Africa

The Okavango inland Delta in Botswana is characterized by a high spatial and

temporal variation in vegetation patches and flooding. Predicting the effects of

escalating development projects in this pristine wildlife area is hampered by a

lack of accurate maps. Efforts using traditional statistical methods have been

futile. The processes forming this highly dynamic environment, however, give rise

to a well-documented consistency in the land cover pattern at scales ranging from

single island architecture to an overall gradient in wetland, flood plain and island

occurrence. We conducted a classification in a two-step process starting with

statistical methods, and then refining using indices and flooding data. The indices

and flooding data were created and selected to make possible the inferring of

knowledge about the patterns at different scales through declarative IF … THEN

… statements. The initial statistical classification achieved a best result of 46%

accuracy for 10 classes, whereas the rule-based classification achieved an

accuracy of 63%. Application of the derived classification for mapping islands

and topography shows a surprisingly high accuracy.

1. Introduction

The Okavango inland ‘Delta’ in Northern Botswana is one of Africa’s largest, most

pristine wetlands and a refuge for wildlife (McCarthy and Ellery 1998). It is,

however, increasingly under threat from development projects. A land cover map is

needed for portraying this system as such, and for use as a framework dataset for

modelling the hydrological, sedimentological and ecological consequences of

various development activities. This study was carried out in order to derive the

first vegetation or ecoregion (henceforth ecoregion) map of the Okavango. The map

presented herein has hitherto been used in several studies aiming at understanding

the relation between spatial patterns and temporal processes in the Okavango

(Bauer et al. 2002, 2003, Gumbricht et al. 2004a, b, Gumbricht and McCarthy 2003,

Gyllenhammar and Gumbricht 2005).

The Okavango Delta is annually flooded by water from the Angolan highlands

(10 km3 per annum) augmented by direct local rainfall (5 km3 per annum). The area

of inundation varies between 4000 and 13 000 km2, depending on the seasonal and

annual variation in incoming water flow and local precipitation (McCarthy et al.

2003). The Delta is a mosaic of channels and islands embedded in a matrix of

*Corresponding author. Email: [email protected]

International Journal of Remote Sensing

Vol. 26, No. 19, 10 October 2005, 4339–4357

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2005 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160500113583

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wetlands and flood plains. The channels vary in size from trails created by

hippopotamuses to the 100-m-wide Okavango River; islands vary in size from small

ant hills (m2) to forest-covered islands of several hundred square kilometres. The

general spatial architecture of the internal wetland environment is a channel

surrounded by wetlands, followed by flood plains that merge into island-studded

grasslands, with a tree fringe marking the shore of an island (figure 1).

The Okavango system starts with an entry corridor, the ‘Panhandle’, a tectonic

graben that is 100 km long and 10 km wide (figure 2). The Okavango River is well

defined in the Panhandle, but bifurcates (divides) into smaller channels at the apex

of the Delta proper. The Delta forms an almost perfect cone on a regional scale

(Gumbricht et al. 2001), 150 km long and with a total area of around 40 000 km2

(figure 2). The channels on the Delta surface migrate as a result of combined tectonic

and sedimentological processes; major channel positions change over timescales of

centuries (McCarthy and Ellery 1998, Gumbricht et al. 2001). The islands are born

as old channels, and associated flood plains are abandoned (Gumbricht et al. 2004).

Island nuclei are composed of clastic sediments (i.e. sand) but, once formed, grow

through the accumulation of chemical sediments (precipitated salts). Islands grow

around their edges as a riparian tree fringe causes high transpiration and subsurface

salt accumulation. As islands grow, the tree fringe migrates with the island

expansion, and the island interior becomes saline. Mature islands are characterized

by a typical succession of species from the freshwater supplied fringes towards the

saline centres (Ellery et al. 1993). The island genesis and growth processes give rise

to typical spatial island architectures. The birth and growth processes also mean that

most islands are elongated, and parallel to the slope of the Delta surface (Gumbricht

and McCarthy 2003).

Land cover classification of wetlands from remote sensing data is hampered by

several factors, including high costs, difficult ground accessibility, steep ecological

gradients and problems of extrapolating small-scale features over large areas

Figure 1. Photograph of the Okavango Delta, Botswana, showing a channel, its adjacentwetlands and flood plain, surrounding grasslands, and islands and their typical architecture.Note the salt patches in the islands in the upper-left part of the image.

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(Harvey and Hill 2001). The similarity between different vegetation in broadband

multispectral data (cf. Smith and Fuller 2001), and the spatial complexity of the

Okavango, calls for the adoption of very-high-resolution images (Ringrose et al.

2003), hyperspectral imagery (cf. Schmidt and Skidmore 2001) or contextual

classification methods (cf. Lunetta et al. 2003). Ideally, a high-resolution

hyperspectral image could have been used in our study, but as the objective was

to create a consistent land cover map over the entire Okavango area, and, due to

financial restrictions, we had to rely on broadband multispectral Landsat Thematic

Mapper (TM) data, and the use of contextual post-classification.

In general, contextual classifiers rely on a similarity between a pixel and its

neighbours, i.e. that ground cover occurs on scales larger than a single pixel. The

simplest contextual classifier is a moving window with a 363 pixel kernel (see

Stuckens et al. 2000). More advanced methods include Markov random field

modelling (Zhou and Robson 2001) and image segmentation (Groom et al. 1996,

Lunetta et al. 2003). Lunetta et al. (2003). Both of these show convincingly that

contextual classifiers can improve both visual appearance and quantitative accuracy

Figure 2. Study area and Landsat TM 5 scenes used for the classification of ecoregions ofthe Okavango delta, Botswana. The maps are projected to Universal Transverse Mercator,South Zone 34, Cape datum (central meridian: 21; scale factor 0.9996; false easting: 500 000;false northing: 10 000 000). The coordinates are given in kilometres in the main map.

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of complex regions, including wetlands. The high spatial and temporal variability of

the Okavango system, however, violates the assumption of class resemblance

between neighbouring pixels. To overcome this problem, our classification instead

relied on initial supervised maximum likelihood and unsupervised clustering, post-

classification update based on multitemporal low-resolution images of flooding, and

contextual information extracted from different sources including the original

Landsat TM scenes (cf. King 2002).

2. Data

Four radiometric (level 1b) Landsat 5 TM scenes were mosaicked for full coverage

of the Okavango Delta (174/073 and 174/074 from 1 August 1994, 175/073 from 7

July 1994, and 175/074 from 7 July 1989; see figure 2). As vegetation patches in the

Okavango are stable over decades, the use of 5- to 10-year-old imagery as such will

not have any noticeable effects. No atmospheric correction was performed. For

geocorrection (bilinear nearest-neighbour resampling), Botswana topographic maps

printed on orthophotos at 1 : 50 000 scale were used (projected to Universal

Transverse Mercator, South Zone 34, Cape datum). The theoretical precision for

each of the four scenes was within two pixels. Positional accuracy varies, however,

between 0 and 200 m (up to six or seven pixels) when directly overlain on the

photobased maps. The same map series, together with aerial photographs at the

same scale, were also used for identifying exact locations of visited field sites and for

deriving training and evaluation data (see below, figure 3). The main river channel

system was digitized from the Landsat TM scenes, supplemented with scanned maps

at 1 : 250 000 to 1 : 350 000 scales. A series of Local Area Coverage (LAC) NOAA-

AVHRR (Advanced Very High Resolution Radiometer), ERS-ATSR (Along Track

Scanning Radiometer) and lower-spatial-resolution Landsat Multi-Spectral Scanner

(MSS) and Thematic Mapper (TM) images were used to determine the flooding

pattern between 1972 and 2000 (see Gumbricht et al. 2000 and McCarthy et al. 2003,

for details). All data were resampled to SUTM 34, Cape datum (see figure 2).

3. Classification scheme

The land cover classification scheme in this study was determined based on earlier

vegetation surveys (e.g. SMEC 1989, Ellery and Ellery 1997), and on the intended

use of the map in hydrological, sedimentological and ecological studies, for example.

The field surveys were done using species and species community-level data, and

translated to ecoregion classes as a step in the rule-based classification. Based on the

earlier surveys, we intended to identify 12 ecoregions (table 1). Our botanical

knowledge was not sufficient to unambiguously distinguish dry grasslands with and

without occasional flooding, nor salt crusts (or salt pans) with and without

occasional flooding during a single field visit. With additional information derived

from flooding frequency and island/channel juxtaposition, we could however

distinguish these classes in the classification. As these classes have a significant

meaning for deriving other information from the ecoregion map (see below), we

decided to include them despite the ambiguity. Aggregated classifications in 10 and

6 classes were also done, and used for accuracy evaluation. The 10-class version

(column C2 in table 1) represents an unambiguous classification. Distinguishing

between different dry woodland classes (dominated by Acacia, Mopane and

Combretum, respectively), albeit of ecological importance, turned out to be difficult

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as the broadband spectral properties are similar and no supplementary data exists

that could be utilized. In a further simplified version of the ecoregion map, the

woodland and forest classes were joined, as were the two flood-plain classes (column

C3 in table 1).

4. Training and evaluation data

Training and evaluation data for the classification were collected on four different

occasions, in June and September 2000, and January and May 2001. Site data were

collected on foot, from boat, from motor vehicles and from a helicopter survey. Thefield survey was done using handheld GPS with about 10 m accuracy, and sought to

identify areas with homogenous vegetation cover of at least 10 pixels (about 1 ha).

Figure 3. Sub-regions used in the ecoregion classification, and major sites used for collectingtraining and evaluation field data indicated by rectangles. Black rectangles indicate areasscanned from aerial photographs, and grey rectangles indicate areas scanned fromorthophoto maps. Both these datasets were used for assisting in capturing and georeferencingthe surveyed field data (see text). The maximum extent of flooding is indicated.

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Inaccessibility and extremely dangerous conditions (i.e. wildlife) confined field-data

collection to along channels, around camps and along airfields. As penetrating a

back-swamp area or a single island can take a full day, field data had to be sampled

on the basis of accessibility.

A total of 170 sample sites were surveyed and manually delimited to an exact

position in the Landsat TM mosaic. Dominant species were recorded, and transects

of hemispherical photographs taken for the estimation of vegetation cover (Sohlman

2001). The total area of training and evaluation data amounted to about 6.5 km2, of

which one-quarter (1.6 km2) were used for training, and three-quarters were used in

the evaluation. All primary training and evaluation data used were inspected in the

field. Several of the key sites visited were also scanned from orthophoto maps at

1 : 50 000 scale and/or as aerial photographs (figure 3). These maps/aerial photo-

graphs are especially helpful for delimiting the depth of riparian fringe areas (and

other vegetation patches), which is difficult to achieve in the field. Additional

Table 1. Ecoregion classification scheme for the Okavango Delta, Botswana and aggregatedclassifications used in the accuracy evaluation (C1512 classes, C2510 classes, C356 classes),

ecoregion classes, and key species belonging to each class.

C1 C2 C3 Ecoregions/land cover classesExample of key

species/vegetation types

1 1 1 River/madiba/backswamp lakes Nymphaea spp.2 2 2 Permanent swamp communities Cyperus papyrus, Vossia

cuspidata, Phragmites communisL., Typha capensis

3 3 3 Primary flood plain Miscanthus junceus, Phragmitescommunis L., Cyperusarticulatus, Schoenoplectuscorymbosus

4 4 3 Secondary flood plain Panicum repens, Sorgastrumfriesii, Imperata cylindrica

5 5 4 Grassland occasionally flooded Different grasses, sparsethickets, Pechuel-loeschealeubnitziae

6 5 4 Dry grassland/savannah thickets Different grasses, sparsethickets, Pechuel-loeschealeubnitziae

7 6 5 Sparse dry grassland/salt crust Sporobolus spicatus,Pechuel-loeschea leubnitziae,Cynodon dactylon

8 6 5 Sparse dry grassland/salt crustoccasionally flooded

Sporobolus spicatus,Pechuel-loeschea leubnitziae,Cynodon dactylon

9 7 6 Riparian forest Ficus natalensis, F. sycomorus,F. verrucolosa, Diospyrosmespiliformis, Phoenix reclinata,Syzygium cordatum, Garcinialivingstonei

10 8 6 Dry woodland (dominated byAcacia spp.)

Acacia erioloba, A. nigrescens,Combretum spp., Lonchocarpusspp.

11 9 6 Dry woodland (dominated bymopane)

Colophospermum mopane

12 10 6 Dry woodland (dominated byCombretum spp.)

Combretum spp.

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training data used only for the rule structuring included sites visited by collaborative

partners with known local conditions (e.g. McCarthy et al. 1993).

5. Classification

5.1 Statistical classification

Initial categorization was done by maximum likelihood classification (MLC)

covering the whole study area with training data representing species levels (see

table 2). The MLC classification was iterated several times and evaluated against the

training areas themselves. Rather than changing the training areas, the iterated

classifications were based on incorporating a priori likeliness of occurrence for the

species classes concerned (ER Mapper 2003). The two most satisfactory MLC

classifications were evaluated against the independently evaluated field dataset and

then used as input in the subsequent rule-based classification. Unsupervised

classification for the whole area was made in ER Mapper using the ISOCLASS

algorithm (ER Mapper 2003). Supervised (MLC) and unsupervised (ISOCLASS)

classifications were also made separately for the Panhandle region and the Delta

proper (figure 3), using only training data related to the specific environment of each

region (MLC). For the MLC classifications, posterior probability and class

typicality were calculated for each training area and included as information layers

(‘bands’) in the subsequent rule structuring. Typicality of a pixel is a measure of how

confidently a pixel belongs to the class, using a function of the Mahalanobis distance

of a pixel to the class mean. The posterior probability (P) index indicates for each

pixel the relative probabilities of falling into each class, i (sum of Pi51). In the rule-

based classification, the typicality and posterior probability index were used, among

other data, to modify pixels with doubtful classification (Pedroni 2003).

Table 2. Training areas used in the classification (total area of training data51.6 km2).

Species/vegetation type Number of training areas Class relation

Water 4 WaterNymphaea spp. 1 WaterCyperus papyrus 6 Permanent swamp communitiesPhragmites communis L. 9 Permanent swamp communitiesPennisetum glaucocladum 1 Permanent swamp communitiesTypha capensis 1 Permanent swamp communitiesVossia cuspidata 1 Permanent swamp communitiesSchoenoplectus corymbosus 2 Primary flood plainCyperus articulatus 1 Primary flood plainMiscanthus junceus 2 Primary flood plainGrass seasonal flood plain(mixed species)

1 Secondary flood plain

Dry grassland (mixed species) 2 Dry grasslandSporobolus spicatus 1 Dry grasslandPechuel-loeschea leubnitziae 1 Dry grasslandSparse grassland/salt crust 1 Sparse grassland/salt crustRiparian forest (mixed species) 3 Riparian forestAcacia spp. 3 Acacia woodlandColophospermum mopane 2 Mopane woodlandCombretum spp. 1 Combretum woodland

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5.2 Textural indices

From the mosaicked Landsat TM scenes, the Normalized Difference Vegetation

Index (NDVI) was calculated as (Band4 – Band3)/(Band4 + Band3) (Spanner et al.

1990). Wetness and Brightness were derived from tasselled cap calculations (Crist

et al. 1986), and a land-water mask was created from the thresholding of tasselled

cap wetness and band ratioing between TM bands 2 and 5. Because of differences in

aquatic vegetation density over open water, the Panhandle region and the Delta

proper were done separately.

5.3 Inundation and flooding frequency

As a Landsat TM scene only represents a snapshot in time, and the inundated area

of the Delta varies over time, the ecoregion classification was supplemented with a

flooding mask in 1 km resolution derived from time-series data of NOAA-AVHRR,

ERS-ATSR and composites of Landsat MSS/TM data—see Gumbricht et al. (2000)

and McCarthy et al. (2003) for details. In total, 212 images were combined into a

map of flooding frequency in the Delta going back in time as far as 1972 (Landsat

MSS). To achieve a flooding map representative of an average annual situation,

normalization was done by forcing each month to be represented by the same

number of flooding images (by assigning a factor to maps of all months). The

flooding frequency was subdivided into five classes of inundation: (1) permanent

swamps (.80% of time), (2) seasonal swamps (30–80% of time), (3) regularly

flooded areas (10–30% of time), (4) sparsely flooded areas (5–10% of time), and

(5) areas flooded in singular events.

5.4 Contextual information extraction

Keeping in mind the spatial architecture of the Okavango at different scales, we

created intelligible contextual indices to use in the rule-based classification.

Proximity was adopted for calculating distances from digitized rivers, from the

land-water mask (using the land as source and calculating proximity over water, and

vice versa), and from island cores (defined as grassland or salt crust classes confined

to within the maximum area of the Delta). Cost grow functions were adopted for the

same sources using various other initial MLC classes as friction surfaces (table 3,

figure 4). The derived distance indices assisted us in distinguishing inter alia island

and channel fringe vegetation.

A second type of extended contextual (focal) information was created by

calculating the area and perimeter of each cluster of contiguous pixels from the

MLC classification and subsequently from the rule-based classes. Area and

perimeter relations of contiguous groups of pixels were used for identifying spatial

forms of the clusters. Elongated regions were assumed to indicate either a river

channel or an elongated island. The area of the contiguous groups was also used to

discard small, isolated groups of contiguous pixels, using the mode class value as

derived from neighbourhood (filtering) analyses.

5.5 Rule-based classification

The rule-based method used in this study expresses knowledge in the form of IF …

THEN … declarative statements, usually found as part of expert systems (Lein and

Lemons 1997). Such logical rules developed by domain experts have been shown to

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give improved classification results in various landscapes (e.g. Chmiel and

Gumbricht 1996). By creating a multi-band image in ER Mapper including the

datasets described above as individual layers (‘bands’), logical rules declaring

relations of flooding and spatial architecture could be combined and processed

without using a separate inference engine. Data layers with information pertaining

to local, focal and regional scales (see table 3) were combined in a forward-driven

classification that was driven by a combination of our knowledge of the spatial

architecture of the Okavango and the data created. Rules were strictly given based

on knowledge of the patterns and processes, and no random or ‘chance’ rules were

used.

Table 3. Image data, derived indices and ancillary data (information) used in the rule-basedclassification.

Data type Information content Origin

Spectral (local) Maximum-likelihood classes(species wise)

Landsat TM

Class typicality (one pertraining area, all not used)

MLH classification

Class Posterior Probability(one per training area, all notused)

MLH classification

Unsupervised classification Landsat TMNormalized differencevegetation index (NDVI)

Landsat TM

Wetness (tasselled captransformation)

Landsat TM

Brightness (tasselled captransformation)

Landsat TM

Flooding frequency NOAA AVHRR, ATSR, LandsatMSS/TM/ETM

Water/land mask Landsat TM, 1 : 350 000 papermaps

Contextual (focal) Average 363 filter ofclassifications

MLH classification/rule-basediteration

Mode filter of classifications MLH classification/rule-basediteration

High-pass filter for linearfeatures

Landsat TM

Contextual (regional) Distance from open water(including madiba)

MLH classification

Distance from island core MLH classificationDistance from rivers Digitized rivers from paper maps/

MLH classificationCost distance from island core MLH classificationCost distance from rivers Digitized rivers from paper maps/

MLH classificationArea of the land cover classes MLH classification/rule-based

iterationShape (perimeter in relationto area)

MLH classification/rule-basediteration

Digitized Rivers Landsat TM and paper mapsDelta outline (maximumextent)

NOAA AVHRR, ATSR, LandsatMSS/TM/ETM, 1 : 350 000 papermaps

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The rules only refer to a per-pixel relation, with contextual information being

available on a per-pixel basis (table 3, figure 5). The first rule statement was always a

class relation, initially from the MLC classifications, and subsequently from

classifications derived from earlier rules—when one set of logical rules was finished,

the resulting map was saved as a new data layer and used as input for the next set of

rules. In a sense, the whole rule-based classification can thus be seen as an

adjustment of the statistical classifications using physically interpretable data and

related knowledge.

The Landsat TM images used in the classification represent winter conditions

(cloud-free season) when flooding is at its maximum. Hence, the statistical

classification over-represents wet classes. This was adjusted based on the flooding

duration map. The categorization between permanent swamp communities, primary

and secondary flood plains, and dry and occasionally flooded grasslands, was

largely engendered using the flooding information, supplemented with contextual

relations (e.g. cost distance to rivers).

Generalization rules were applied as an interwoven iterative step where a majority

filter together. An area of contingency was used for different classes and area

thresholds. Details were kept for the hydrologically important classes, so that

majority filters did not reduce the sparsely occurring classes of riparian forest and

rivers.

Region masking was used to overcome the problems of satellite scene differences.

The Panhandle and the central delta were classified separately from the rest of the

Figure 4. Illustration of the difference between proximity analysis (Euclidean distance) andcost grow analysis. In both cases the channel was used as the source area. The cost growanalysis used the flood plain as a friction surface and the grassland as a barrier. In thesubsequent rule-based classification, small flood-plain areas with no connection to the rivercould be further analysed and the classification modified accordingly.

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Delta. The final classification was done by merging the three classification regions,

and applying rule structuring for pixels with conflicting classifications in over-

lapping areas. The resulting ecoregion map in 12 categories was finally aggregated

into maps with 10 and 6 classes before final evaluation of the result.

Image processing and rule structuring were done using ER Mapper. Raster GIS

functionality was written in Pascal, using functions from the GIS-software IDRISI

when applicable.

6. Results

Based on 130 independent ground data samples, the best MLC-derived land cover

classification had an estimated accuracy of 46% for 10 classes (table 4). With the

rule-based classifier, an estimated overall accuracy of 63% was achieved for the 10-

class land cover map (table 5) and 74% for the 6-class map (table 6, figure 6). It is

well known that fewer classes give a better accuracy, but also that the Kappa index

(taking into consideration the probability of correct classification due to chance)

improves.

The results from the classifications are illustrated in figure 7, showing a small area

with an oxbow lake as an aerial photograph, from the Landsat TM image, from the

MLC classification and the rule-based classification. On a scale of one to a few

pixels, the MLC classification was highly fragmented. Visual comparison with

scanned aerial photographs revealed that many areas of riparian forests were

identified as wetland classes, and vice versa.

Figure 5. Schematic diagram of data processing.

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Table 4. Error matrix and accuracy for the MLC classification in 10 classes (the columns represent the ground-data image and the rows the classified image).

Wat PSC P FP S FP GL SP RF Aca Mop Com Total ErrorC

Uncl 7 0 0 0 0 0 0 0 0 0 7 1.000 Uncl Unclassified pixelsWat 840 0 8 23 0 0 0 0 0 0 871 0.036 Wat WaterPSC 14 1010 523 73 7 0 419 143 8 3 2200 0.541 PSC Permanent swamp

communitiesP FP 29 134 525 185 2 11 110 76 8 0 1080 0.514 P FP Primary flood plainS FP 0 0 20 36 44 0 4 104 10 0 218 0.835 S FP Secondary flood plainGL 0 0 19 28 145 20 9 260 76 1 558 0.740 GL GrasslandSP 0 0 0 0 54 75 2 58 21 3 213 0.648 SP Sparse grassland/salt

crustRF 0 24 3 34 3 2 170 40 3 15 294 0.422 RF Riparian forestAca 0 1 146 75 25 1 71 145 29 5 498 0.709 Aca Acacia woodlandMop 0 7 102 52 3 3 29 146 79 4 425 0.814 Mop Mopane woodlandCom 0 0 56 26 12 9 12 73 1 0 189 1.000 Com Combretum woodlandTotal 890 1176 1402 532 295 121 826 1045 235 31 6553 ErrorC Error of commissionErrorO 0.056 0.141 0.626 0.932 0.509 0.380 0.794 0.861 0.664 1.000 0.538 ErrorO Error of omission

The abbreviations correspond to the 10 ecoregion classes. Overall accuracy546.2%; Kappa index of agreement50.37.

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Table 5. Error matrix and accuracy for the rule-based classification in 10 classes (the columns represent the ground-data image and the rows the classifiedimage).

Wat PSC P FP S FP GL SP RF Aca Mop Com Total ErrorC

Wat 861 20 21 0 0 0 1 0 0 0 903 0.046 Wat WaterPSC 15 1000 234 21 0 5 106 1 0 0 1382 0.276 PSC Permanent swamp

communitiesP FP 9 135 980 279 27 0 160 2 0 3 1595 0.386 P FP Primary flood plainS FP 1 14 57 149 2 0 78 8 0 1 310 0.519 S FP Secondary flood plainGL 0 0 97 76 191 11 46 328 56 2 807 0.763 GL GrasslandSP 0 0 0 0 71 98 2 73 15 2 261 0.624 SP Sparse grassland/salt

crustRF 4 7 13 5 4 3 382 190 0 20 628 0.392 RF Riparian forestAca 0 0 0 2 0 4 34 362 57 3 462 0.216 Aca Acacia woodlandMop 0 0 0 0 0 0 14 79 106 0 199 0.467 Mop Mopane woodlandCom 0 0 0 0 0 0 3 2 1 0 6 1.000 Com Combretum woodlandTotal 890 1176 1402 532 295 121 826 1045 235 31 6553 ErrorC Error of commissionErrorO 0.033 0.150 0.301 0.720 0.352 0.190 0.538 0.654 0.549 1.000 0.370 ErrorO Error of omission

The abbreviations correspond to the 10 ecoregion classes. Overall accuracy563.0%; Kappa index of agreement50.57.

Table 6. Error matrix and accuracy for the rule-based method in six classes (the columns represent the ground-data image and the rows the classified image).

Wat PSC FP GL SP For Total ErrorC

Wat 861 20 21 0 0 1 903 0.046 Wat WaterPSC 15 1000 255 0 5 107 1382 0.276 PSC Permanent swamp

communitiesFP 10 149 1465 29 0 252 1935 0.231 FP Flood plainGL 0 0 173 191 11 432 807 0.763 GL GrasslandSP 0 0 0 71 98 92 261 0.624 SP Sparse grassland/salt crustFor 4 7 20 4 7 1253 1295 0.032 For ForestTotal 890 1176 1934 295 121 2137 6553 ErrorC Error of commissionErrorO 0.033 0.150 0.242 0.352 0.190 0.414 0.257 ErrorO Error of omission

The abbreviations correspond to the six aggregated ecoregion classes. Overall accuracy574.3%; Kappa index of agreement50.67.

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7. Discussion

The high spatial and temporal variability in the Okavango Delta makes ecoregion

classification from remotely sensed data a non-trivial task. The spatial variation

of channels, wetlands, flood plains and islands prompts the use of high

spatial resolution remotely sensed data, e.g. Landsat (E)TM. We feel that a

grain size of 28.5 m was relevant, and that in lower-resolution data, too much of

the spatial architecture would be lost. The high spatial variability at a scale

corresponding to the grain size of the image data makes the use of multitemporal

high-resolution images almost impossible. Simultaneously, several of the ecoregions

(vegetation communities) that compose the Okavango have very similar spectral

properties in the (broad) bands available from Landsat (E)TM. Not only do

various grasslands and flood plains resemble each other, but papyrus (Cyperus

papyrus) and reed (Phragmites communis L.) have very similar spectral properties

Figure 6. Ecoregions of the Okavango Delta, Botswana, derived from a combined statisticaland contextual rule-based post-classification.

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to various tree species. Statistical (both supervised and unsupervised) classi-

fication hence gave poor results. Other studies (e.g. Schmidt and Skidmore

2001) indicate that a high spatial hyperspectral image should have been more

favourable.

We used inundation metrics from multitemporal remote sensing to separate

classes in dry vs. wet areas at a regional (.1 km) scale. Knowledge about tolerance/

requirement for inundation periods was used to infer rules concerning the

occurrence of primary and secondary flood plains, for example. The coarser

resolution of the flooding data, however, introduces an artificial crisp boundary

between the flood-plain classes, for example.

The internal spatial architecture of the Okavango in general is a drying and

increasing salinity gradient from channel–wetland–flood plain–grassland–island rim

(riparian forest)–island centre (grassland to salt crust). The channel and the island

central salt crust are easily identified and delimited from Landsat (E)TM data.

Hence, proximity and cost grow analysis, and contextual filtering based on those

classes were used to assist in improving the classification. Islands and channels are

born and developed from unidirectional fluvial processes, and hence have typical

forms. Thus, form parameters related to the shape of contiguous clusters of pixels

were also used to modify the classification. Yet another characteristic is that the very

nutrient-poor water can only sustain dense vegetation growth adjacent to the

channels; hence, high-pass filtering could be used to assist in identifying channels of

sub-pixel width.

The applicability of our approach in improving the vegetation classification of the

Okavango using expert rules pertaining to different spatial patterns and temporal

processes is dependent on several factors:

N the internal spatial architecture (channels–wetlands–flood plains–islands) of

the Okavango is consistent throughout the system;

N the vegetation communities are stable and have a memory length for the

flooding regime;

N flooding being variable in area but regular in time; and

Figure 7. Comparison of a detailed area (see figure 3) in the Okavango Delta, Botswana,from (a) aerial photograph, (b) Landsat TM scene, (c) initial MLC classification, and (d ) rule-based post-classification.

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N suitable remote sensors of different spatial and temporal resolution could be

applied for creating indices and metrics relating to both spatial and temporal

variations.

Without these prerequisites, our approach would not have been possible.

Great efforts were made to obtain a correct representation of riparian forests, as

they have a disproportionally large influence on hydrology, sedimentology and

ecology. As many riparian forests are confined to a width of only one pixel

(approximately 30 m), the selection of training and evaluation areas was tedious and

difficult. The final classification of a densely vegetated pixel (i.e. high NDVI) with

typicality/posterior probability representing both a tree and an aquatic grass was

determined based on both the typicality/probability and the pixel juxtaposition vis-

a-vis channels and island centres.

Finally, we must note that the class definitions are ambiguous, even if distinct

species composition is suggested by Ellery and Ellery (1997), for example. The

identification and coverage of various species in a patch in the field is difficult. Part

of the classification error stems from this. Another artificial error is introduced from

spatial generalizations at pixel levels (identified training and evaluation areas are

sometimes so thin/small, that a shift in one pixel introduces an error—the water

class is in reality 100% correct, but because of generalizations, the errors reported in

tables 5 and 6 are introduced). The use of the map created from this study has

however been successfully adopted for deriving maps of islands (Gumbricht et al.

2004) and microtopography (Gumbricht et al. 2004a) of the Okavango Delta, both

evaluated against independent datasets with a surprisingly high accuracy.

8. Conclusions

Ecoregion classification of the Okavango Delta was achieved by supplementing

snapshot high-spatial-resolution Landsat TM images with ancillary GIS data,

flooding data derived from low-resolution NOAA-AVHRR, ERS-ATSR and

Landsat MSS/TM data, and by indices derived from the original Landsat TM

scenes. Initial statistical classification could unambiguously identify a few key

classes (most notably water and salt crust), but largely failed to separate different

vegetation classes. The use of the broadband multispectral Landsat TM sensor was

insufficient for achieving a satisfactory land cover classification in this fragmented

landscape. Although fragmented and dynamic, the pristine Okavango environment

has a logical architecture with channels, wetlands, flood plains and islands being

closely associated with flooding regime and vegetation communities. The system is

well understood and has evolved into a meta-stable system where rivers and

vegetation patches have a memory length extending on decadal scales. Knowledge

about the internal spatial architecture and the flooding regime allowed us to use an

expert system approach for improving the initial statistical classification. This was

achieved by creating indices and metrics representing different features of the

Okavango system. Some misclassifications from the statistical method could be

corrected, resulting in a more accurate classification.

We conclude that for the regional scale, the use of low-resolution multitemporal

images for deriving flooding frequency could be applied as an ancillary data layer

for regional segmentation, albeit it introduced into an artificial crisp boundary

between some classes. Known relations of water needs and confinement on a species

level could be inferred to adjust the classification to more accurate outcomes.

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For the internal mosaic of the Okavango, we created indices and metrics by

combining textural and contextual information derived from the original Landsat

TM Scenes. As indices of vegetation density, brightness, wetness, proximity,

connectedness and form were derived from the same image source as the statistical

classifications, the usual problems with geopositional accuracies were avoided. The

conclusion from this part of the study is that for a landscape composed of a large

number of logical (i.e. natural) juxtapositioned patches, the adoption of knowledge-

based rules is a superior classification technique compared with statistical methods.To be feasible, the knowledge rules must be anchored in intelligible indices for

facilitating expert knowledge to be translated into declarative rules.

Acknowledgements

Data were kindly supplied by Anglo American. The post doc for T.G. was financed

by the Royal Swedish Academy of Sciences and the scholarship for J.M. by The

Swedish Foundation for International Cooperation in Research and HigherEducation (STINT). University of the Witwatersrand supported the participation

of T.M. Support was also given by the Royal Institute of Technology, through

Fredrik Bjorns fund, and from the Swedish International Development Agency

(SIDA). This study was part of the SAFARI2000 Southern African Regional

Science Initiative. The authors thank anonymous reviewers for helping to improve

the manuscript.

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