Supervised classifications of Landsat TM band ratio images and Landsat TM band ratio image with radar for geological interpretations of central Madagascar

Journal of African Earth Sciences 37 (2003) 59–72

www.elsevier.com/locate/jafrearsci

Supervised classifications of Landsat TM band ratio imagesand Landsat TM band ratio image with radar forgeological interpretations of central Madagascar

Jennifer Inzana a, Tim Kusky b,*, Gary Higgs c, Robert Tucker d

a Center for Remote Sensing, Boston University, Boston, MA 02215, USAb Department of Earth and Atmospheric Sciences, St. Louis University, 3507 Laclede Ave., St. Louis, MO 63103, USA

c College of Public Service, St. Louis University, St. Louis, MO 63103, USAd Department of Earth and Planetary Science, Washington University, St. Louis, MO 63130, USA

Received 27 February 2003; accepted 8 July 2003

Abstract

Landsat TM and radar JERS-1 SAR (L-Band) imagery of the Itremo area, central Madagascar, were processed to emphasize

structural geology features including folded quartzite ridges and plutons. TM band ratios 5/7, 5/1, 5/4*3/4 were assigned to RGB.

Band 5/7 highlights pelitic schist, band 5/1 emphasizes mafic igneous rocks, and 5/4*3/4 distinguishes mafic from non-mafic rocks. In

a second technique, band 5/7 was replaced with registered L-band radar imagery because radar is useful for differentiating between

granite, granodiorite, diorite and serpentinite. The last technique evaluated in this study used the spectral information from the

radar image as well as the 5/7, 5/1, 5/4*3/4 band ratio bands. Supervised classification training sites were selected using nine classes

(clouds, quartzite, schist, gneiss, gabbro and basalt, granite, vegetation, water, and cloud shadows). The band ratio classification

results are fairly accurate (a confusion matrix shows an accuracy of 89.346) and correspond well with geologic maps of the area

showing complexly refolded nappes of quartzite, carbonate, schist, gneiss and gabbro, intruded by late granites. The radar, 5/1,

5/4*3/4 classification (accuracy of 89.04) shows significant differences from the band ratio classification, with fewer schist pixels

displayed in the radar, 5/1, 5/4*3/4 classification, but with greater resolution of structural features including faults, fold nappes, and

foliations. More pixels are displayed as mafic gneiss, and fewer quartzites appear in the radar classification. Some areas classified as

quartzite in the first classification (and on the geologic maps) were classified as clouds in the radar/band ratio classification. This

indicates that the 5/7 band contains significant spectral information that the radar band does not contain, which aided in mapping

quartzite. This comparison illustrates that combined use of TM band ratioing merged with radar imagery can emphasize both

spectral and textural features that aid geologic mapping using supervised classifications. A third technique was examined where a

supervised classification was performed on an image containing the 5/7, 5/1, 5/4*3/4, and radar bands. The confusion matrix for this

classification produced an accuracy of 91.23 which was better than either the 5/7, 5/1, 5/4*3/4 or the radar, 5/1, 5/4*3/4. It is

preferable to keep all band ratio bands and the radar band to produce the most complete supervised classification image for

geological feature discrimination.

� 2003 Elsevier Ltd. All rights reserved.

1. Introduction

Madagascar is the world’s fourth largest island,

consisting of 627,000 km2 of area, little of which is un-

derstood in detail (Ashwal and Tucker, 1999; Windley

et al., 1994; de Wit, 2003). Unlocking the secrets to the

geology of this region could reveal information about

* Corresponding author. Fax: +1-314-977-3350.

E-mail address: [email protected] (T. Kusky).

0899-5362/$ - see front matter � 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/S0899-5362(03)00071-X

the formation, break-up, and dispersal of several su-

percontinents including Rodinia, Gondwana and Pan-

gea. French colonial geologists pioneered by Moine

(1968) mapped and described the principal geologic

elements of Madagascar. Numerous investigations have

been undertaken since then, including several by the

authors (Tucker et al., 2001, in press).This paper aims to enhance and differentiate geologic

units and structures and advance the understanding and

definition of specific remote sensing data combinations

useful for structure discrimination in Madagascar and

mail to: [email protected]

60 J. Inzana et al. / Journal of African Earth Sciences 37 (2003) 59–72

similar terrains. Traditional remote sensing techniquesincluding band ratioing, image classification and various

data set fusions are applied and evaluated. Band ratio-

ing is a technique where the DN value of one band is

divided by the DN value of another band. Band ratios

can be useful for highlighting certain features or ma-

terials that cannot be seen in the raw bands. Image

classifications are performed in order to categorize the

pixels of an image into different classes or themes inorder to produce a thematic map. The supervised image

classification process is composed of three main stages;

the training stage, the classification stage, and the out-

put stage. In the training stage, the analyst identifies

representative training areas and develops a numerical

description or class of the spectral attributes of each

land cover type of interest in the scene. In the classifi-

cation stage, each pixel in the image data set is catego-rized into the land cover class it most closely resembles.

After the entire data set has been categorized, the results

are presented in the output stage (Lillesand and Kiefer,

1994).

A standard band ratioed image (5/7, 5/1, 5/4*3/4) is

produced and a supervised classification of this band

ratioed image is subsequently generated. In a separate

image, the first band ratio band is replaced with a radardata set and another supervised classification is gener-

ated from this radar fused data set and compared to the

first. This combination was selected because radar is

useful for differentiating between granitic, granodiortic,

diortic, and serpentinite rocks. It is, however, not as

effective at differentiating mafic and felsic volcanic

rocks, metasediments or serpentinite from other rocks

as is the traditional TM 5/1 and 5/4*3/4 (Kusky andRamadan, 2002). Finally, a classification is performed

on the three band ratio images and the radar band and

compared to the other image data set products. These

band ratio images and classification products are eval-

uated in terms of their ability to discriminate the geo-

logic units and structure of Madagascar as known in

certain standard field references and geologic maps

produced by the authors. The purpose of this paper is,therefore to determine which spectral data sets and

products best distinguish geologic information.

The remote sensing findings presented here are linked

with on-going field based structural and geochronolog-

ical studies (Tucker et al., in press) that have three broad

objectives in mind. The first is to understand better the

timing of Gondwana’s amalgamation, a topic that re-

lates to the debate connecting global-scale tectonics withbiologic and climatic change (i.e. Knoll, 1992; Grotzin-

ger et al., 1995; Kaufman et al., 1997; Narbonne, 1998;

Kusky et al., 2003). The second is to constrain the geo-

metry, kinematics, and rate of Neoproterozoic plate

motions, and mechanisms by which Gondwana formed

(Meert et al., 1993; Gurnis and Torsvik, 1994; Kirs-

chvink et al., 1997; Meert and Van der Voo, 1997;

Torsvik et al., 1998). The third concerns determiningfundamental characteristics of the East African Orogen

(EAO), the youngest collision zone between East and

West Gondwana. These characteristics include identi-

fying continental and oceanic constituents and when

they formed, and estimates of the geometry of major

collision zones that bound accreted terranes. These are

important questions that can be best addressed by

experts in different disciplines working together in acollaborative investigation. The remote sensing and

structural studies reported here help establish the

structural geometry and major rock units present within

one of the major collision zones of the EAO. This col-

lision or suture zone cuts through the island of Mada-

gascar, and its understanding is necessary to evaluate

the timing of terrane accretion and continental con-

figuration during the formation of Gondwana (Kuskyet al., 2003).

In a specific sense, this paper focuses on remote

sensing analysis and field studies to identify and gain

insight into categories and properties of particular ob-

servable features relating to the evolution and archi-

tecture of the East African Orogen, a Neoproterozoic

collisional zone that transects East Africa, India, Mad-

agascar, Sri Lanka and Antarctica, in essence joiningthe elements of West and East Gondwana (Stern, 1994).

The EAO comprises the Arabian–Nubian shield (to the

north) and the Mozambique belt (to the south), and it is

widely believed to contain the vestiges of the former

Mozambique Ocean.

The first category of features, the existence and lo-

cation of Paleoproterozoic cratons, reworked gneissic

terranes, juvenile island-arcs, and arc-modified conti-nental elements within the EAO will help to delineate

the position of important sutures. Moreover, parts of

the eastern EAO contain some of the youngest meta-

morphic rocks within Gondwana and thus improved

understanding of regional map patterns within the EAO

will constrain the age of terminal suturing in Gond-

wana, and possibly test the hypothesis of two super-

continents in the late Neoproterozoic (Powell, 1995;Dalziel, 1977). The second category, the kinematics and

geometry of the collision zone in the eastern part of the

EAO is poorly understood. Basic identification, map-

ping, and interpretation will provide insight into forces

as represented in the structural relations. Central Mada-

gascar is underlain by a superbly exposed sequence

of quartzite, schist, and marble (the QSC or Itremo

Group), that forms a key marker unit useful for re-solving the structure of this complex collision zone.

These rocks are readily mappable on TM imagery,

making them useful for regional correlations. We out-

line below the basic geological elements of Madagascar

and why it is a superb place to study the geometry and

chronology of key collision zones within the East Afri-

can Orogen and greater Gondwana.

J. Inzana et al. / Journal of African Earth Sciences 37 (2003) 59–72 61

2. Geology of Madagascar

Precambrian rocks underlie the eastern two-thirds of

Madagascar, and the western third of the island is un-

derlain by sedimentary and minor volcanic rocks that

preserve a near-complete record of sedimentation from

the Devonian to recent (Fig. 1; Besarie, 1964, 1967,

1971, 1973; Hottin, 1976; Caen-Vachette, 1977, 1979;

Moine, 1965, 1974). The Ranotsara shear zone (RSZ)divides the Precambrian bedrock of Madagascar into

two geologically different parts. The northern part is

underlain by Middle and Late Archean orthogneisses,

variably reworked in the Early and Late Neoproterozoic

(Tucker et al., 1999, 1997; Kr€ooner et al., 1999) whereasthe southern part consists dominantly of graphite-

bearing paragneisses, bounded by N–S-trending shear

zones (Pili et al., 1997) that separate belts with promi-nent fold-interference patterns (Martelet et al., 1997;

Fig. 3). All rocks south of the Ranotsara fault zone have

been strongly reworked and metamorphosed to granu-

lite conditions in the latest Neoproterozoic (Ackermand

et al., 1989; Nicollet, 1990; Andriamarofahatra et al.,

1990; Paquette et al., 1994; Kr€ooner et al., 1996; Ashwal

Fig. 1. Map of the Itremo region, showing major geological units and location

Tucker et al. (in press).

et al., 1999, 2000; Lardeaux et al., 1997; de Wit et al.,1998, 2001; Tucker et al., in press).

The stratified Precambrian rocks of west Madagascar

crop out north of the Ranotsara shear zone to approx-

imately 18� S latitude. From east to west, they comprise

the Itremo, Amborompotsy, and Malakialina Groups

(Fig. 1). The metamorphic grade of these rocks increases

from greenschist facies in the east to amphibolite facies

in the west. Sediments of the Itremo Group were de-posited in the interval between �1750 and �800 Ma

(Handke et al., 1999; Cox et al., 2000).

The Itremo Group, also known as the S�eeries Quartzo–

Schisto–Calcaire or QSC consists of a thick sequence

of Mesoproterozoic stratified rocks comprising, from

presumed bottom to top, quartzite, pelite, and marble

(Moine, 1966, 1967, 1974). Although strongly deformed

in latest Neoproterozoic time (�570–540 Ma), the QSCis presumed to rest unconformably on the Archean

gneisses of central Madagascar. In the Mahaize area on

the northern margin of the QSC belt both Archean

basement and QSC quartzites are complexly folded to-

gether as an early basement/cover contact in later nap-

pes. Both the QSC and its basement are intruded by

of images. Inset shows general geology of Madagascar. Modified after


Early Neoproterozoic (�800 Ma) granitoids (Handkeet al., 1999; Tucker et al., in press) and no intervening

period of tectonism is recognized (Fig. 1). The minimum

depositional age of the QSC is �800 Ma and its maxi-

mum age of�1850 Ma is defined by U–Pb detrital zircon

geochronology (Cox et al., 1998). The QSC has been

variably metamorphosed (�570–540 Ma; greenschist

grade in the east; amphibolite grade in the west) and

repeatedly folded and faulted (Moine, 1967, but originalsedimentary structures and facing-directions are well

preserved. Quartzite displays features indicative of

shallow subaqueous deposition, such as flat lamination,

wave ripples, current ripple cross-lamination, and dune

cross-bedding, and carbonate rocks preserve domal and

pseudo-columnar stromatolites (Trottereau, 1969).

Fig. 2. True color composite of TM image data.

3. Remote sensing methods

Landsat TM and JERS-1 radar data of the Itremo

region have been acquired and processed in several waysto enhance the geological units and structure of the area.

This particular combination of remotely sensed image

data sets was employed to provide a unique selection of

alternative options for evaluating which spectral asso-

ciations products best indicate the geologic structures

and rock types and surface materials (Drury, 1986;

Kusky et al., 1993) as indicted by established maps

(Moine, 1967; Fig. 6) and subsequent field work. Thus,once specific rock types and structures on the ground are

determined from comparison with published maps and

are linked with their remotely sensed spectral signatures,

then these observations can be extended to broad re-

gions and mapped.

The Landsat data used in this project was acquired on

January 29, 1996. Although seven bands were available

only bands 1–5 were used in this study. The radar dataused in this study was collected between 19–25 of Jan-

uary 1997 by the JERS-1 SAR satellite. The JERS-1

satellite was launched by Japan in February 1982. It

includes an L-Band SAR, HH polarization and a 38.5�incidence angle.

Currently, there is a substantial body of research

concerned with identifying what data are best suited

for a particular application. A general consensus ofappropriate spectral tools concerned with establishing a

relation between surface material type and spectral

properties is emerging. This consensus, while not a

completely resolved and settled issue, has its beginnings

with such foundational research as the NASA calibra-

tion studies and continues to the present in the form of

work such as the American Society of Photogrammetry

and Remote Sensing (ASPRS) Primary Data Acquisi-tion (PDA) Image Standards Initiative, (http://gis.slu.

edu/nasa). On the general foundation of these ongoing

efforts, many specific studies have refined the under-

standing of data set spectral application optimization asin the case of Abrams et al. (1983), Sultan et al. (1986),

and Kusky and Ramadan (2002) which have shown that

certain traditional band ratios are particularly suited to

distinguish specific geologic features, such as the TM

band ratio combination 5/7, 5/1, 5/4*3/4. In the instance

of these combinations for example, band 5/7 brings out

argillites, serpentinites, and alteration zones in arid and

semi-arid environments, and band 5/1 distinguishesmafic igneous rocks, while the ratio 5/4*3/4 successfully

discriminates mafic from non-mafic rocks.

The selected TM image data sets were processed be-

ginning with a gaussian stretch on each individual band

in IPW. A True color composite of this image (3, 2, 1)

was created using IV (Fig. 2). All stretched images were

imported into PCIWORKS. The band ratios were per-

formed in PCIWORKS with the ARI function. Asharpening filter was applied to each of the ratioed

bands in order to enhance the edges of the geology. The

end-product of this process is a TM band ratio image

(5/7, 5/1, 5/4*3/4) (Fig. 3).

The radar data component selected for inclusion and

evaluation in this project consists of portions of three

SAR radar images, which were not continuous and

therefore required spatial manipulation. An empty filewas created and the three radar data sets were geo-

positioned into their correct tiled footprints (Fig. 4).

Since a necessary bi-product of this remote sensing

work is a verification and comparison of the accuracies

and spectral geologic feature discriminations of various

band combinations, the radar image had to be registered

to the Landsat band ratio image. The registration pro-

cess consisted of establishing 17 points for adjustmentand positing and had a RMSE of 1.0934 (see Table 1).

The radar data was warped to the Landsat data and the

http://gis.slu.edu/nasa

http://gis.slu.edu/nasa

Fig. 3. Traditional TM band ratioed image (5/7, 5/1, 5/4*3/4). Fig. 4. Geopositioned SAR radar image.


pixel size was resampled using the nearest neighbor

option.

A fused image incorporating the radar data set was

created based on the traditional TM band ratio image

(Fig. 3) by replacing the 5/7 band with the radar band so

that the resulting product was the radar data, TM bands

5/1, and 5/4*3/4 (radar, 5/1, 5/4*3/4) (Fig. 5). This

combination was selected because radar is useful fordifferentiating between granitic, granodiortic, diortic,

and serpentinite rocks (Sultan et al., 1986; Kusky and

Ramadan, 2002). It is, however, not as effective at dif-

ferentiating between mafic and felsic volcanic rocks,

metasediments or serpentinite from other rocks as is the

traditional TM 5/1 and 5/4*3/4. The comparative eva-

luation and interpretation of the traditional TM 5/7, 5/1,

Table 1

Radar image adjustment and positing points

Point # x y Predict x Pred

1 1685.000 2042.000 1684.086 2041

2 2005.000 2060.000 2004.423 2059

3 2432.000 2364.000 2432.836 2363

4 1991.000 1502.000 1991.734 1503

5 1863.000 1492.000 1863.898 1492

6 1676.000 1457.000 1675.513 1457

7 1664.000 1265.000 1665.440 1265

8 2199.000 1127.000 2199.508 1126

9 2898.000 1674.000 2898.378 1674

10 2297.000 1449.000 2295.777 1449

11 2475.000 805.000 2474.603 803

12 2489.000 1795.000 2487.994 1794

13 2389.000 1068.000 2388.158 1067

14 2345.000 1872.000 2344.412 1871

15 2466.000 2247.000 2465.931 2247

16 2798.000 2289.000 2798.547 2289

17 2694.000 1462.000 2694.762 1462

x ¼ þ1:62175eþ 03x0y0 � 4:75216e� 02x0y1 þ 3:03757e� 01x1y0.y ¼ þ3:77829eþ 02x0y0 þ 2:99396e� 01x0y1 þ 4:80210e� 02x1y0.

5/4*3/4 and the fused radar, 5/1, 5/4*3/4 enabled a

generalization concerning the criterion of which data set

combinations best reveal specific geologic information.

In addition, a third band ratio image was produced with

the band combination 5/7, 5/1, 5/4*3/4, radar. This

spectral combination includes both the 5/7 as well as the

radar band. An evaluation of this sequence of band

combinations is useful to see if inclusion of both the 5/7band and the radar band would lead to a more complete

image classification.

To facilitate this comparative generalization between

various image data sets, supervised classifications were

performed on the traditional TM band ratio image data

products and the radar-TM fused image data products.

The traditional TM ratio image was displayed (Fig. 3)

ict y x resid y resid Dist

.002 )0.914 )0.998 1.3536

.112 )0.577 )0.888 1.0594

.466 0.836 )0.534 0.9917

.442 0.734 1.442 1.6184

.747 0.898 0.747 1.1682

.441 )0.487 0.441 0.6567

.872 1.440 0.872 1.6836

.259 0.508 )0.741 0.8984

.702 0.378 0.702 0.7972

.615 )1.223 0.615 1.3687

.606 )0.397 )1.394 1.4491

.000 )1.006 )0.723 1.2386

.693 )0.842 )0.307 0.8960

.016 )0.588 )0.984 1.1460

.162 )0.069 0.162 0.1764

.924 0.547 0.924 1.0741

.664 0.762 0.664 1.0109

Fig. 5. Fused radar and TM data (radar, 5/1, 5/4*3/4).


and a region map with 22 classification training sites was

created with the aid of a geologic map produced byMoine (1968), as modified by Tucker et al. (in press)

(Figs. 6 and 7). These 22 sites were combined into nine

classes, such that each class type is derived from more

than one training site. Spectral statistics were generated

for the training sites and the TM band ratio image.

These statistics were used as input into bayes.crs, which

is the maximum likelihood classifier in IPW. A threshold

value of 0 was used for all of the classifications in thisstudy, therefore every pixel in the image was classified,

so as to produce image maps that completely cover the

study area. Similarly, because multiple classifications

using the same training data were to be compared to one

another, the consistency and integrity of the input values

could be best preserved by maintaining a uniform pro-

cess. The classified TM band ratio image is shown as

Fig. 8. This classification technique was then performedon the fused radar, 5/1, 5/4*3/4 image data set and the

fused 5/7, 5/1, 5/4*3/4, radar image data set respectively.

The resultant image products are shown in Figs. 9 and

10. All classifications produced nine classes: clouds,

quartzite, schist rocks, gneiss, gabbro and basalt rocks,

granites, vegetation, water, and cloud shadows.

4. Comparison with field observations

We distinguish seven principal bedrock map units

that underlie the Itremo region (Fig. 6): (1) Late Arch-

ean to Paleoproterozoic gneisses that form the crystal-

line basement to younger rocks; (2–4) Highly deformed

and variably metamorphosed Mesoproterozoic quartz-ite, mica schist, and carbonate of the Itremo (QSC)

Group, perhaps deposited unconformably upon the

Archean basement; (5, 6) Strongly to weakly deformed

intrusive igneous rocks of calc-alkaline chemistry thatcrop out as large, semi-concordant sheets. These were

emplaced from approximately �1000 to �720 Ma

(Tucker et al., in press), and; (7) A distinctly younger

suite of alkali-feldspar-rich granitoid plutons emplaced

as discordant, cross-cutting dikes and stocks. These

rocks have yielded isotopic ages of �570–530 Ma

(Tucker et al., 1997, 1999, in press; Kr€ooner et al., 2000)and they clearly post-date the principal episode of re-gional deformation. Farther north and east, granites of

this generation occur as concordant sheet-like masses

(the so-called stratoid granites) and were emplaced at a

somewhat earlier time (�630–550 Ma; N�eed�eelec et al.,

1994, 1995; N�eed�eelec and Paquette, 1997; Paquette and

N�eed�eelec, 1998). All rocks described above are overlain

unconformably by Mesozoic and younger sedimentary

and volcanic rocks (Alsac, 1963; Besarie, 1964). Belowwe offer brief descriptions of each main unit, for com-

parison with the spectral and backscattered images

of the same units.

4.1. Late Archean gneiss

Late Archean–Paleoproterozoic gneisses underlie all

of Madagascar north of the Ranotsara shear zone (Fig.

1) to at least as far north as 15� S latitude (Tucker et al.,

1997, 1999, in press; Kr€ooner et al., 2000). Because of

pervasive Neoproterozoic metamorphism and structural

overprinting, Archean rocks are difficult to distinguish

in outcrop from younger, Proterozoic gneisses. In gen-

eral, Archean rocks consist of amphibolite-facies or-thogneiss, paragneiss, and migmatite that range in

composition from gabbro to megacrystic granite, and

include abundant tonalite, amphibolite, mafic schist,

and even possible iron formation (Fig. 7A). Gneisses of

Proterozoic age tend to be somewhat less deformed,

commonly not migmatized, and generally orthogneissic

in character with gabbroic, dioritic, and granitic com-

positions most abundant. Based on current data, theircrystallization ages range between �2520 and �2495 Ma

(Tucker et al., 1999; Kr€ooner et al., 2000). The Late

Archean gneisses appear as dark purple to brown units

on the True color TM image, and as speckled orange–

red–green units on the band ratio image (compare Figs.

2, 3 and 6).

4.2. The Itremo Group (QSC)

The Itremo Group is a metamorphosed sequence of

shallow-water sedimentary rocks consisting of quartzite

(Fig. 7B and C), mica schist, and marble (Fig. 7D). Thin

layers of amphibolite and metabasalt comprise a minor

rock type and, where present, they are generally re-stricted to the mica schist unit (Moine, 1974; Cox et al.,

1998). Where strain and metamorphic grade are low,

pelitic units are dominated by finely laminated siltstone,

Fig. 6. Geologic map of the Itremo region (modified after Tucker et al., in press; Moine, 1968). This map shows the geology of the eastern part of the

area shown in the imagery.


shale, and thinly bedded (1–4 m) fine-grained sandstone

that contains planar and cross-lamination; rare desic-

cation cracks are also present in the mud rocks. The

pelites appear as dark brown bands on the True color

TM image, and as red strips on the band ratio image.

The marble is broadly divisible into two units: a lower,

calcite-rich white marble, and an upper, buff-colored

dolomitic marble that commonly has 2–5 cm dark bands

of siliceous dolomite. In regions of low strain, the cal-

cite-rich white marble preserves domal and pseudo-

columnar stromatolites (Trottereau, 1969; Fig. 7E). The

carbonates appear as greenish-brown units on the True

color image, and as yellow colored units on the band

ratio image. The most distinctive unit in the Itremo

Fig. 7. Field photographs of the different rock types (training sites) in the area. (A) Late Archean gneiss, (B) ridge of quartzite, (C) blocky surface

(radar rough) of ridge of quartzite, (D) beds of carbonate, (E) stromatolites in carbonate, (F) surface of 800 Ma mafic intrusive, (G) river outcrop of

800 Ma mafic intrusive, (H) 550 Ma late cross-cutting granite.


Group is quartzite that crops out in bold white ledges

underlying the higher hills of the map area (Fig. 7B and

C). The quartzite consists of variably metamorphosed

orthoquartzite, quartz arenite, micaceous quartzite, and

rare quartz conglomerate that is commonly found

within the upper beds of quartzite near its contact with

mica schist. Where strain is low, the quartzite preserves

sedimentary structures including wave ripples, fluvial

Fig. 8. Classified TM band ratio (5/7, 5/1, 5/4*3/4) image.

Fig. 9. Fused radar, 5/1, 5/4*3/4 image data set products.

Fig. 10. Fused 5/7, 5/1, 5/4*3/4, radar image data set products.


cross-bedding, dune cross-bedding, and flat lamination.

These features, as well as its distinctive lithology, make

the quartzite the most useful unit for deciphering the

stratigraphy and structure of the Itremo region (Tucker

et al., in press). The quartzite forms prominent light-

brown ridges on the True color TM image, and forms

bright blue units on the band ratio image.

4.3. Circa 1000 Ma intrusives

A suite of foliated intrusive igneous rocks, with U/Pb

ages of 1000–750 Ma (Tucker et al., in press) intrudes

the Itremo Group, and has generally concordant con-tacts (Fig. 6). These are strongly- to weakly-foliated

plutonic rocks and orthogneisses of general calc-alkaline

chemistry that include a variety of rock types including

anorthosite, granodiorite, diorite, and quartz monzo-

nite. Mafic rocks of this suite are dominated by gab-

bronorite and hornblende gabbro, and granitoid

varieties include quartz monzonite and granite (Fig. 7F

and G). Tucker et al. (in press) mapped separatelygabbro and granitoid varieties in parts of Fig. 6.

These rocks are generally not clearly distinguished on

the True color TM image, but form green–brown areas

on the image, with the exception of the pluton north of

the Itsindro, which shows up clearly. On the band ratio

image, these plutons are likewise not clearly distin-

guishable from surrounding rocks.

Based on geochemical characteristics and radiogenicisotope signatures, Handke et al. (1999, and unpublished

thesis data) proposed a continental-arc origin for these

rocks. Kr€ooner et al. (2000) support this view although

details of their interpretations differ. All workers agree

that arc magmatism occurred from �820 to �720 Ma

(Tucker et al., 1997, 2001; Handke et al., 1999; Kr€ooneret al., 2000). Tucker et al. (in press) present new U–Pb

zircon ages on these rocks, suggesting that arc magma-tism began significantly earlier, lasting from �1013 to

�720 Ma.


4.4. Weakly-foliated, intrusive igneous rocks (570–539

Ma) with discordant contacts

Weakly foliated igneous rocks crop out as small,

semi-circular granite plutons that were clearly emplaced

as regionally discordant stocks (Fig. 6). The Vohitra-

kidahy pluton (�35 km2) is a weakly foliated, coarse- to

medium-grained hornblende-biotite granite and biotite

granite that crops out in the southern part of the area(Fig. 6). It was emplaced into stratified rocks of the

Itremo Group, the Archean gneisses near Ambatoma-

rina, and a mafic pluton of probable early Neoprote-

rozoic (�800 Ma) age. The Vohimavo pluton (�79 km2)

consists of coarse- to medium-grained hornblende-bio-

tite and biotite granite (Fig. 7H). Near its northern and

northwestern margin it contains xenoliths of mica schist

and quartzite, and Thematic Mapper images (Tuckeret al., in press) and existing maps (Moine, 1974) clearly

demonstrate that it was emplaced into already folded

rocks of the Itremo Group (Fig. 6).

The Tomy (�12 km2) and Faliarivo (�14 km2) plu-

tons are considerably smaller in size (Fig. 6). We have

not examined the Tomy stock but it is described by

Moine (1974) as similar to the Faliarivo pluton which is a

medium- to coarse-grained biotite granite with a weaklydeveloped foliation and lineation. Like the Vohimavo

granite, the Faliarivo granite was emplaced as a struc-

turally-discordant stock, with obvious rafts of quartzite

and mica schist encased as roof and wall pendants.

Several other small cross-cutting bodies of biotite

granite and syenite, some of them weakly foliated, have

also been identified. These include a small body of

quartz syenite east of Ifasina, a granite underlying thehills of Andringitra, and a number of small granite

masses north and south of the Ibity massif and east and

south of the Andohamaho massif (Fig. 6). Like the

larger plutons, the Andringitra granite contains rafts of

quartzite and mica schist.

Comparison of the geological map with the imagery

shows that these late granites appear as discordant,

dull green bodies on the True color TM image, and asyellow-speckled areas on the band ratio image.

5. Interpretation analysis

Given the results of this classification the TM band

ratio data set products appear to fairly accurately rep-

resent the surface geologic character as indicated in the

geologic map (Tucker et al., in press, modified after

Moine, 1968) and in the original band ratio image. In

the TM band ratio classified image many pixels are

displayed as (red) schist. In contrast the results of thespectrally fused radar, 5/1, 5/4*3/4 image classification

product appears to have some significant differences

from the traditional TM band ratio image classification

product. For example, there are significantly fewer pix-els classified as schist in the fused radar, 5/1, 5/4*3/4

image data product. More pixels are being categorized

as mafic gneiss, and there are also fewer quartzites in

this latter image product classification. The fact that

more mafic gneiss rocks are indicated than actually exist

may be due to pixel misclassification in the radar band

ratio image and may in effect be due to defects in the

instruction set of the training sites caused by not in-corporating a thorough overlay of the geologic maps

into the classification maps. The spectral classification

error of the radar fused classification product as re-

vealed in the fact that there are significantly fewer

quartzite pixels distinguished, suggests that this type of

fused data set is not well suited to geologic surface

mapping. This is further indicated by the fact that some

of the areas that were classified as quartzite in the tra-ditional TM data set classification product, and that

were distinguished as quartzite in the geologic map, were

classified as clouds in the radar fused data set classifi-

cation product (radar, 5/1, 5/4*3/4). This indicates that

the TM 5/7 band contained some significant spectral

information that enabled the discrimination of quartz-

ite, which was lacking in the radar.

To further explore the comparative discriminationproperties of these data sets a classification was also

performed on the fused 5/7, 5/1, 5/4*3/4, radar image.

The derived results of this fused data set more closely

match those of the traditional TM band ratio classifi-

cation. However, there are more pixels classified as

quartzite in this data set classification product than in

that of the traditional TM 5/7, 5/1, 5/4*3/4 data product.

On the basis of these observations it is possible togeneralize that removing the 5/7 band significantly al-

tered the classification map, and that although the fu-

sion of the radar data was supposed to contribute to the

discrimination of the granite it appears to have failed to

fully distinguish granite material pixels because the

number of such pixels has decreased slightly. Further it

appears that the information that was lost in the fused

radar, 5/1, 5/4*3/4 data set classification product hasbeen regained and the result of including both the radar

and the 5/7 band is a more complete classification.

6. Accuracy assessment

A confusion matrix was generated in order to deter-

mine how well the maximum likelihood classifier clas-

sified the training site. The matrices have been included

in Tables 2–4. Table 2 is the confusion matrix for the

traditional TM band ratio image training sites. The

user’s accuracy and the producer’s accuracy have beencalculated. The user’s accuracy measures the probability

that a pixel classified on the map/image actually repre-

sents that pixel on the ground. The producer’s accuracy

Table 3

Confusion matrix for the radar, 5/1, 5/4*3/4 image

Site Accuracy for radar, 5/1, 5/4*3/4 image

b0/b1 1 2 3 4 5 6 7 8 9 Total

Clouds 1 416 60 2 4 0 3 0 7 4 496

Quartzite 2 18 485 0 2 14 1 0 0 0 520

Schist 3 12 0 243 0 0 6 6 1 7 275

Gneiss 4 16 0 0 128 0 11 0 1 1 157

Gabbro and

basalt

5 1 7 1 0 98 0 1 0 0 108

Granites 6 2 0 0 0 0 354 0 0 1 357

Vegetation 7 0 0 35 0 0 0 111 0 0 146

Water 8 3 0 0 0 0 0 1 147 30 181

Cloud shadows 9 0 0 4 0 0 0 0 23 333 360

Total 468 552 285 134 112 375 119 179 376

Trace¼ 2315 (89.0385)

Site # User’s accuracy Producer’s accuracy

Clouds 1 0.8387 0.8889

Quartzite 2 0.9327 0.8786

Schist 3 0.8836 0.8526

Gneiss 4 0.8153 0.9552

Gabbro and

basalt

5 0.9074 0.8750

Granites 6 0.9916 0.9440

Vegetation 7 0.7603 0.9328

Water 8 0.8122 0.8212

Cloud shadows 9 0.9250 0.8856

Table 2

Confusion matrix for band ratio image

Site Accuracy for band ratio image

b0/b1 1 2 3 4 5 6 7 8 9 Total

Clouds 1 447 68 0 0 0 0 0 0 0 515

Quartzite 2 19 457 0 1 3 0 0 2 0 482

Schist 3 1 3 210 1 0 0 2 7 12 236

Gneiss 4 0 0 0 131 0 0 0 0 0 131

Gabbro and

basalt

5 0 24 2 1 109 0 1 0 0 137

Granites 6 0 0 0 0 0 367 0 0 1 368

Vegetation 7 0 0 69 0 0 8 115 0 0 192

Water 8 1 0 0 0 0 0 1 147 23 172

Cloud shadows 9 0 0 4 0 0 0 0 23 340 367

Total 468 552 285 134 112 375 119 179 376

Trace¼ 2323 (89.3462)


Clouds 1 0.8680 0.9551

Quartzite 2 0.9481 0.8279

Schist 3 0.8898 0.7368

Gneiss 4 1.0000 0.9776

Gabbro and

basalt

5 0.7956 0.9732

Granites 6 0.9973 0.9787

Vegetation 7 0.5990 0.9664

Water 8 0.8547 0.8212



indicates the probability of a reference pixel being cor-rectly classified.

Table 2 (the accuracy assessment for the band ratioimage) shows that the overall accuracy of the classified

Table 4

Confusion matrix for the radar, 5/7, 5/1, 5/4*3/4 image

Site Accuracy for 5/7, 5/1, 5/4*3/4, radar image

b0/b1 1 2 3 4 5 6 7 8 9 Total

Clouds 1 454 56 0 0 0 0 0 0 0 510

Quartzite 2 12 468 0 1 3 0 0 2 0 486

Schist 3 0 3 245 1 0 6 6 7 12 280

Gneiss 4 0 0 0 131 0 0 0 0 0 131

Gabbro &

basalt

5 0 25 2 1 109 0 1 0 0 138

Granites 6 0 0 0 0 0 367 0 0 1 368

Vegetation 7 0 0 34 0 0 2 111 0 0 147

Water 8 2 0 0 0 0 0 1 150 26 179

Cloud shadows 9 0 0 4 0 0 0 0 20 337 361

Total 468 552 285 134 112 375 119 179 376

Trace¼ 2372 (91.2308)


Clouds 1 0.8902 0.9701

Quartzite 2 0.9630 0.8478

Schist 3 0.8750 0.8596

Gneiss 4 1.0000 0.9776

Gabbro &

basalt

5 0.7899 0.9732

Granites 6 0.9973 0.9787

Vegetation 7 0.7551 0.9328

Water 8 0.8380 0.8380



training sites is 89.346. The user’s accuracy shows that

site 4 (gneiss) should perfectly represent the ground

truth data. Most of the user’s accuracy scores were 0.85

or greater which are fairly good measures, however, site

5 (gabbro and basalt) would only be correct 79% of the

time and site 7 (vegetation) would only be correct 58%

of the time when compared with ground truth data. The

producer’s accuracy results are mostly above 0.90. Sites2 (quartzite) and 8 (water) had values of 0.82 and site 3

(schist) has a value of 0.73, which is slightly low. Thus,

most of these sites have a strong probability of being

correctly classified.

Table 3 shows that the classification of these training

sites on the radar fused radar, 5/1, 5/4*3/4 image had an

overall accuracy of 89.04. This value is very similar,

although slightly lower, to the overall accuracy achievedfrom the traditional TM band ratio data set image. The

user’s accuracy shows that most sites have values of 0.88

or higher. This means that the probability that these

pixels are actually on the ground when compared to

ground truth data are relatively high. The highest user’s

accuracy was for site 6 (granites). Sites 4 (gneiss), 7

(vegetation) and 8 (water) had low user’s accuracy val-

ues (0.82, 0.76, and 0.81) respectively. The low values forgneiss in the radar fused data set are in marked contrast

to the values that were generated in the user’s accuracy

of the traditional TM band ratio classification (data set

product Table 2). Site 7 (vegetation) has the lowest

user’s accuracy in both this and the traditional TM

classifications. Perhaps more training data was needed

for this class. The producer’s accuracy for the radar/

band ratio training sites yielded results with accuracies

mostly above 0.85. Only site 8 (water) had an accuracy

of 0.82. This value is in contrast to the results from

Table 2 for the band ratio training sites, which shows

site 3 (schist) as the lowest value.

Table 4 shows the overall accuracy of the 5/7, 5/1, 5/4*3/4, radar image to be 91.23. This value is better than

the accuracies of the traditional TM and radar fused

image products. Most of the user’s accuracy sites in this

classification have values above 0.89. The highest user’s

accuracy is, as in the case of the previous product, as-

sociated with site 4 gneiss rocks and the lowest user’s

accuracy is again, as seen in both previous data sets,

associated with site 7 (vegetation). All values derived forthe producer’s accuracy were above 0.85. Granites (site

6) had the highest producer’s accuracy with a value of

0.98 while site 2 (quartzite) had the lowest producer’s

accuracy (0.85). A comparison of Tables 2–4 clearly

confirms the interpretation analysis that it is preferable

to keep all band ratio bands and the radar band to

produce the most complete supervised classification

image for geological feature discrimination.

7. Conclusion

When coupled with field observations, remote sensing

techniques including band combinations and supervised


image classification can be employed to aid geologic andstructural mapping of large regions, even if the terrain is

heavily vegetated. Based on the methods that were used

(supervised classification with a threshold value of 0) the

fused 5/7, 5/1, 5/4*3/4, radar image produced the most

thorough and accurate classification of the geology of the

Itremo region of Madagascar. These results may have

varied if the radar/band ratio image was used to develop

the training sites, or if the radar data was taken intoconsideration while choosing the training sites. The 5/7,

5/1, 5/4*3/4 classification results were better than those of

the radar, 5/1, 5/4*3/4 classification, however this may be

due to the fact that the radar data was not considered in

the selection of the classification training sites.

Further investigation should include incorporating

radar data in the training site selection process, as well as

field and/or airborne GPS control data to confirm thatactual points on the classification map correspond to

their field observed counter points. Extension of this

spectral fusion validation approach to other high reso-

lution surfaces would also be useful, as well as more

classifications, in which radar data are incorporated in

conjunction with increased spectral training on vegeta-

tion sites.

Acknowledgements

This work has benefited from the expert field guid-

ance of Gilles Gauthier, Les Lezard de Tana, and from

thoughtful reviews by Tsilavo Raharimahefa, Brian

Windley, and an anonymous reviewer. The work was

funded by NSF grants EAR 02-21567 and EAR 02-07997, awarded to T. Kusky.

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Documents

Supervised classifications of Landsat TM band ratio images and Landsat TM band ratio image with radar for geological interpretations of central Madagascar