Remote Sensing of Environment 88 (2003) 128 – 143 www.elsevier.com/locate/rse
Multi-site evaluation of IKONOS data for classification of tropical coral reef environments Serge Andre´foue¨t a,*, Philip Kramer b, Damaris Torres-Pulliza c, Karen E. Joyce d, Eric J. Hochberg e, Rodrigo Garza-Pe´rez f, Peter J. Mumby g, Bernhard Riegl h, Hiroya Yamano i, William H. White j, Mayalen Zubia k, John C. Brock c, Stuart R. Phinn d, Abdulla Naseer l, Bruce G. Hatcher l, Frank E. Muller-Karger a a
Institute for Marine Remote Sensing, College of Marine Science, University of South Florida, 140 7th Avenue S., St. Petersburg, FL 33701, USA b Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA c Center for Coastal and Regional Marine Studies, United States Geological Survey, St. Petersburg, FL, USA d Biophysical Remote Sensing Group, Department of Geographical Sciences and Planning, University of Queensland, St. Lucia, Australia e Hawaii Institute of Marine Biology, University of Hawaii, Honolulu, Kaneohe, USA f Coral Reef Ecosystems Ecology Laboratory, Marine Resources Department, CINVESTAV-I.P.N. Unidad Me´rida, Merida, Mexico g Marine Spatial Ecology Laboratory, University of Exeter, Exeter, UK h Oceanographic Center, National Coral Reef Institute, Nova Southeastern University, Miami, FL, USA i Social and Environmental Systems Division, National Institute for Environmental Studies, Onogawa, Tsukuba, Ibaraki, Japan j Department of Marine Science and Coastal Management, The University of Newcastle, Newcastle upon Tyne, UK k Laboratoire Terre-Oce´ans, Universite´ de la Polyne´sie Francaise, Tahiti, French Polynesia l Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada Received 3 June 2002; received in revised form 5 December 2002; accepted 22 April 2003
Abstract Ten IKONOS images of different coral reef sites distributed around the world were processed to assess the potential of 4-m resolution multispectral data for coral reef habitat mapping. Complexity of reef environments, established by field observation, ranged from 3 to 15 classes of benthic habitats containing various combinations of sediments, carbonate pavement, seagrass, algae, and corals in different geomorphologic zones (forereef, lagoon, patch reef, reef flats). Processing included corrections for sea surface roughness and bathymetry, unsupervised or supervised classification, and accuracy assessment based on ground-truth data. IKONOS classification results were compared with classified Landsat 7 imagery for simple to moderate complexity of reef habitats (5 – 11 classes). For both sensors, overall accuracies of the classifications show a general linear trend of decreasing accuracy with increasing habitat complexity. The IKONOS sensor performed better, with a 15 – 20% improvement in accuracy compared to Landsat. For IKONOS, overall accuracy was 77% for 4 – 5 classes, 71% for 7 – 8 classes, 65% in 9 – 11 classes, and 53% for more than 13 classes. The Landsat classification accuracy was systematically lower, with an average of 56% for 5 – 10 classes. Within this general trend, inter-site comparisons and specificities demonstrate the benefits of different approaches. Pre-segmentation of the different geomorphologic zones and depth correction provided different advantages in different environments. Our results help guide scientists and managers in applying IKONOS-class data for coral reef mapping applications. D 2003 Elsevier Inc. All rights reserved. Keywords: Landsat; Bathymetric correction; Glint; Accuracy; Habitat mapping; Seagrass
1. Introduction Remote sensing provides an effective way to observe and monitor shallow coral reefs worldwide, to characterize
* Corresponding author. Tel.: +1-727-553-3987; fax: +1-727-553-1103. E-mail address:
[email protected] (S. Andre´foue¨t). 0034-4257/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2003.04.005
inter-reef structural differences, and to map intra-reef habitat diversity and zonations, assess bathymetric variations, design survey protocols, conduct biogeochemical budgets, and map beta-diversity (Andre´foue¨t, Claereboudt, Matsakis, Page`s, & Dufour, 2001; Andre´foue¨t, MullerKarger, Hochberg, Hu, & Carder, 2001; Andre´foue¨t & Payri, 2000; Capolsini, Andre´foue¨t, Rion, & Payri, 2003; Hochberg & Atkinson, 2000; Jupp et al., 1985; Liceaga-
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Correa & Euan-Avila, 2002; Mumby, 2001; Mumby & Edwards, 2002; Mumby, Green, Clark, & Edwards, 1998; Palandro, Andre´foue¨t, Dustan, & Muller-Karger, 2003; Purkis, Kenter, Oikonomou, & Robinson, 2002; Roelfsma, Phinn, & Dennisson, 2002). The recent increase of remote sensing applications targeting reef environments (Andre´foue¨t, Muller-Karger, et al., 2001) reflects the growing concern about drastic and negative changes occurring on reefs over the past three decades due to anthropogenic (e.g. pollution, fishing, and coastal development) or natural (e.g. global warming) stresses. The satellite data most commonly used since the mid1980s for direct observation of coral reefs have been medium spatial resolution digital images, i.e. a spatial resolution of 10– 30 m. This includes data delivered by the Indian Remote Sensing Satellite C (IRS-C), Satellite pour l’Observation de la Terre (SPOT) 1 –4 High Resolution Visible (HRV), Landsat 5 Thematic Mapper (TM), and more recently by SPOT 4 – 5, Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors. Conversely, ‘‘high resolution’’ images are those with a spatial resolution greater than 10 m such as those provided by IKONOS or Quickbird (1 –4 m). This study aims to clarify the potential of high spatial resolution IKONOS data for reef mapping worldwide. In their review presenting the use of remote sensing for coastal tropical assessment, Green, Mumby, Edwards, and Clark (1996) pointed out the difficulty of comparing different reef assessments due to lack of consistency in classification schemes, in in situ data collection and image processing methods, and in accuracy assessment protocols. SPOT HRV and Landsat TM data have been used most frequently because of their availability starting in the mid1980s. The work of various independent investigators worldwide has now helped define the potential of medium spatial resolution data for reef applications (e.g. Ahmad & Neil, 1994; Andre´foue¨t, Muller-Karger, et al., 2001; Matsunaga & Kayanne, 1997; Purkis et al., 2002; Yamano & Tamura, in press). It is now clear that for geomorphology and habitat-scale applications, SPOT and Landsat data are adequate for simple complexity mapping (3 –6 classes), but for more complex targets (7 –13 classes) they are limited by their spatial and spectral resolution and likely by their digitization rate (8 bits) (Capolsini et al., 2003; Hochberg & Atkinson, 2003; Mumby, Green, et al., 1998; Mumby & Edwards, 2002). The 1999 launch of the commercial IKONOS satellite, operated by Space Imaging (SI), provides for the first commercial space sensor with 11 bit, high-spatial resolution (4 m), calibrated data in four wide spectral bands that are potentially useful for coral reef studies. The spectral bands closely match the first four bands of the ETM+ sensor (Thome, 2001). Despite the quick attenuation of red radiance in water, the near-infrared (NIR) band is potentially useful for very shallow water targets (Menges, Hill,
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& Ahmad, 1998) and low tide conditions when benthos is exposed. The IKONOS satellite data has generated great interest among coral reef researchers that were previously limited to the use of aerial color photographs (Andre´foue¨t et al., 2002; Palandro et al., 2003) or costly digital airborne multispectral or hyperspectral data (Mumby, Green, Edwards, & Clark, 1999) for very high resolution work. To the best of our knowledge, four independent peerreviewed studies have already addressed the potential of IKONOS for reef habitat mapping and there are probably many more investigations in progress, judging by the number of coral scenes acquired and archived by SI. Mumby and Edwards (2002) compared IKONOS classification results of Caribbean (Turks and Caicos) coastal areas between different spaceborne and airborne sensor data. They concluded that IKONOS data were unable to discriminate habitats in a complex (13 classes) classification scheme, but that they were adequate for moderate to simple complexity mapping (9 – 5 classes). They found IKONOS provides an acceptable accuracy (64 – 74% overall accuracy), although similar to that obtained with a medium resolution sensor like the Landsat TM. Capolsini et al. (2003) applied a similar multi-sensor comparative approach for South Pacific (Tahiti) reefs and reached similar conclusions (66 –86% overall accuracy for 7 – 3 classes). Maeder et al. (2002) also mapped a Caribbean reef (Roatan Island, Honduras) with good results (>85% accuracy) using a simple (five classes, including a deep water class) classification scheme. Finally, assuming linear mixing and using in situ spectral reflectance measurements, Hochberg and Atkinson (2003) simulated IKONOS classification for various sea floors made of different proportions of algae, coral, and sand. Without actually processing any IKONOS data, they suggested that coral dominated habitat could not be accurately separated from algae dominated habitat using IKONOS data. Using simulated IKONOS data, coral cover was overestimated and algae cover underestimated on their test-site of Kanehoe Bay, HI. Capolsini et al. confirmed this prediction using real images of Tahiti reefs, where true coral habitats (coral>60%) were poorly assessed (9.54% users’ accuracy) in a simple classification scheme (ruble, sand, algae, coral). The number of IKONOS evaluations for reefs is already quite impressive and, more importantly, each seems to provide results consistent with the others. However, because of the variety of sites and methods considered, we felt that some of the Green et al. (1996) remarks would still be valid if many independent studies were conducted without some coordination to prevent too much heterogeneity in terms of classification scheme (habitat description), classification algorithms, and accuracy assessment protocols. Therefore, beginning in 2000, the University of South Florida (USF) worked with the NASA Scientific Data Purchase (SDP) program (Stennis Space Center) to task IKONOS for acquisition of scenes of representative
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reefs around the world. We worked with international remote sensing and coral biology/ecology/geology scientists to process and evaluate the images on their most intensively studied research sites where ground-truth data, local reef expertise, and other satellite or airborne remote sensing data were available. Most of these investigators agreed to contribute to the present study, making this effort certainly the largest international cooperation for remote sensing of reefs. This paper presents a synthesis of the results in terms of classification and mapping of coral reef habitats using IKONOS data, as well as a comparison with ETM+ performances for selected sites. Each investigator that contributed to this study will likely publish their own detailed results in the future on different subjects since each of them has a specific interest in the IKONOS data (e.g. change detection). Despite the initial wish to have consistent methodology rigorously applied throughout the mapping exercise, local specificities and expertise, and cost of field work lead to slightly different means of data processing, ground-truthing strategy, or evaluation of results. Nevertheless, the bulk of work provides clear trends and lessons discussed hereafter.
2. Material and methods 2.1. Reef-sites and image data-set Ten different sites were considered for this comparative study (Fig. 1). The sites represent the primary biogeographic coral regions of the world according to Veron (1995), and includes bank reefs, fringing reefs, barrier reefs, and atolls. Table 1 summarizes the IKONOS and Landsat 7 data used for this study. We avoided images with obvious water quality issues (e.g. suspended sediments). Only scenes with clear waters were used, thus minimizing the effects of variable water optical properties for this comparative study. Until early 2001, the IKONOS images delivered to NASA SDP were systematically resampled with cubic convolution (CC), but analysis of the same image delivered with CC and nearest neighbor (NN) resampling showed that the textural information was significantly degraded using CC in different coral reef shallow floors (SA, unpublished data). Thus, we systematically requested NN resampling after April 2001. IKONOS data were geocorrected in UTM WGS-84, as Master Standard (MS) or Original Standard (OS) products (Dial, Bowen, Gerlach, Grodecki, & Oleszczuk, in press), but never as Precision products since no ground control points were provided to Space Imaging for our study sites. Landsat 7 ETM+ data (Table 1) were ordered through the Eros Data Center in either Geotiff or HDF format, with NN resampling and UTM WGS-84. The Landsat image of Shiraho Reef, Japan was provided by
the National Space Development Agency of Japan, in Fast-L7 format, NN resampling. 2.2. Habitats: similarities and differences between sites One of the challenges of this comparative study is to reconcile different habitat classification schemes. Indeed, the 10 sites are characterized by different bottom features, and the methods used by investigators in each region addressed their problems in particular ways. The various investigators provided classification schemes ranging from simple (4– 5 classes for Shiraho and Glovers), to moderately complex (7– 8 classes for Arue, Addu, Biscayne Dubai, and Andros), and then to very complex (13 –15 classes for Mayotte, Heron, and Boca Paila) (Table 2). The very complex sites were also described in less detail by hierarchical simplification. Heron Reef has been described using 13, 7, and 5 habitats classes. Boca Paila has been described using 15 and 8 habitats. Mayotte has been split from 1 general site with 14 general classes into 2 sub-sites with 10 specific classes each, providing better thematic description for each sub-site. To compare classification results throughout this range of habitat complexity, it is desirable to put the various schemes into the same hierarchical framework. For the four Atlantic/ Caribbean sites (Biscayne in USA, Andros in Bahamas, Glovers in Belize, and Boca Paila in Mexico), we can refer to the scheme provided by Mumby and Harborne (1999) (Fig. 2). This is a multi-level hierarchical model with geomorphologic and benthic components. This scheme was applied to each site but it was necessary to adjust the thresholds in benthic cover and depth (Fig. 3). For instance, Andros coral cover was high (5 – 15%) in most of the geomorphological strata. Thus, most of the Andros reef should be classified as ‘‘coral’’ since according to Mumby and Harborne (1999) ‘‘coral’’ classes are characterized by a cover >1%. However, a coral label for the entire Andros reef would be misleading. Similarly, the notion of ‘‘deep’’ or ‘‘shallow’’ lagoon floors differs between sites (Figs. 2 and 3). Extrapolation of the Mumby and Harborne (1999) geomorphology/benthic hierarchical framework to non-Caribbean sites is possible in theory. For Shiraho (Japan) and Dubai, the geomorphology of the site is simple: shallow (0 – 3 m) lagoon for Shiraho and gentle slope for Dubai. Therefore, since there is only one geomorphologic unit, there is no specific reference to geomorphology in the classification scheme and only benthic features were used (Table 2). Conversely, for Addu, Arue, Heron, and especially Mayotte, reference to geomorphological strata is required (Table 2). Mayotte is by far the most complex site. Quod, Bigot, Dutrieux, Maggiorani, and Savelli. (1995) pointed out the richness of this site, characterized by two contrasted barrier-reefs separated by the deep ( f 70 m) Longogori pass. The northern barrier reef (Pamandzi Reef) is characterized by extensive seagrass and algal beds and internal spur and grooves systems, while the southern reef (Ajangoua Reef) is free of seagrass
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Fig. 1. Location of the study sites. The map indicates the main coral biogeographic areas according to Veron (1995). Our sites represent the Arabian Gulf, Indian Ocean, Indo – Pacific, Pacific, and Caribbean biogeographic zones. Each site is presented using a RGB color composite based on the red, green, and blue bands of the IKONOS sensor. Size (in km) of the image showing the most characteristic zone is indicated, though the actual processed area may be wider. Includes material Space Imagingn.
and richer in small coral heads, with presence of enclosed lagoons. Our own in situ survey of Mayotte Reef in December 2000 highlighted nearly 30 different habitats, the main difference with Quod et al. was the extensive
areas of dead corals, consequences of a 1998 coral bleaching event. For mapping purposes, we considered as a starting point only 14 broader classes for Mayotte (Table 2). Arue reef in Tahiti Island (French Polynesia) is repre-
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Table 1 Site information and characteristics of the IKONOS and Landsat images Latitudeb
Longitudeb
IKONOS
Landsat 7 ETM +
Acquisition date
Product
Resampling
Path/row
Acquisition date
Format
Ground-truth
Depth (m)
References
Stoddart (1964) Kramer, Kramer, and Ginsburg (1998) Frouin and Hutchings (2001) Jaap (1984) Kramer and Kramer (2000)c Riegl (1999) McClanahan and Muthiga (1998) Rogers (1997), Smith et al. (1998)
Addu AN, SA, BGH Andros PK
0.6149 24.5833
73.1201 77.7833
14 March 2000 12 March 2001
MS OS
CC NN
145/60 13/43
20 December 2000 26 March 2000
HDF EDC-Geotif
March 2002 2000/2001
0 – 15 0 – 30
Arue SA, MZ Biscayne DTP, JB, SA Boca Paila RG Dubai BR Glovers PJM, WHW
17.5750 25.3500 19.9166 24.9400 16.8166
149.6000 80.2166 87.5000 54.8900 87.8000
11 March 2000 18 March 2001 20 July 2000 2 May 2001 12 April 2001
OS OS MS MS OS
CC CC CC NN NN
53/72 15/42 19/46 160/42 18/48
6 June 2000 5 February 2000 24 July 1999 N/A 8 November 2000
Earthsat-Fast EDC-HDF EDC-Geotif N/A EDC-Geotif
June 2000 2001/2002 1999/2000 Fall 1995 1999 – 2001
0 – 12 2 – 12 0 – 30 0–9 0 – 18
Heron SA, KEJ, SRP
23.5258
151.8900
7 May 2001
MS
NN
90/77
14 November 1999
EDC-HDF
2001/2002
0 – 15
Mayotte SA Shiraho HY
12.8933 24.3166
45.2180 124.2333
31 August 2000 28 March 2002
OS OS
NN NN
91/77 161/69 115/43
18 September 1999 30 August 2000 23 February 2002
EDC-HDF EDC-Geotif Fast-L7A EROSd
Dec. 2000 1999
0 – 15 0–3
OS: Original Standard, MS: Master Standard, CC: cubic convolution, NN: nearest neighbor convolution, N/A: not available. a Investigators: initials from author list. b Lower left corner of the IKONOS scene. Negative latitude for South, negative longitude for West. c Describe Yucatan reefs. d Provided by National Space Development Agency of Japan.
Quod et al. (1995) Kayanne et al. (2002), Harii and Kayanne (submitted for publication)
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Site and investigatora
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Table 2 Classification scheme for the 10 sites (Boca Paila 15 classes and Mayotte 14 classes are not shown) Site
Class label
Site
Class label
Addu Classes: 8
Sand/rubble, backreef Sand, backreef Sand, lagoon floor Coral/algae, patch reef Algae, crest Coral, forereef Seagrass, backreef Coral, backreef Dense brown algae (>80%), crest High density coral heads on heterogeneous pavement Low density coral heads on sandy floor Dense brown algae (>50%) on reef flat, heterogeneous floor Moderate brown algae (15 – 25%) on reef flat, heterogeneous floor Sparse brown algae ( < 5%) on reef flat, heterogenous floor Sand and ruble (>90%) Deep (>10 m) lagoon floor Dense coral (>25%) Moderate coral ( < 25%) Seagrass/algae Heterogeneous lagoon floor Pavement Shallow sand, lagoon floor Deep sand, forereef
Andros Classes: 8
Coral (Acropora sp.) crest Dense coral (Montastraea sp.) on forereef Gorgonian plain on forereef Dense gorgonian on escarpment Algae on low relief spur and grooves Pavement on forereef Deep (>12 m) sand on forereef Shallow sand/ruble on forereef Dense patches Diffuse patches Deep (>3 m) dense seagrass on lagoon floor Deep (>3 m) sparse seagrass on lagoon floor Shallow ( < 3 m) dense seagrass on lagoon floor
Arue Classes: 8
Boca Paila Classes: 7
Glovers Classes: 5
Heron Classes: 13
Forereef Brown algae Seagrass/Lobophora sp. Seagrass Sand Branching corals (>75%) on forereef and crests Multi-growth forms, dense corals (>50%) on reef flat Multi-growth forms, moderate corals (25 – 50%) on reef flat Coral pavement (>75%) on crest and reef flats Coral head, forereef Heterogeneous reef flat (coral < 10%, sand, rocks, fleshy algae, coralline) Dead coral (>80%) coated by encrusting coralline Dead coral (>80%) covered by fleshy algae Sand with rocks ( < 15%) covered by fleshy algae Sand with scattered dead coral heads ( < 15%), coated by encrusting coralline Sand with dense dead coral heads (>50%), covered by fleshy algae Coral sand and mud (>90%) Pavement and ruble (>90%)
Shiraho Classes: 4
Biscayne Classes: 8
Dubai Classes: 8
Heron Classes: 5
Mayotte Classes: 14
Shallow ( < 3 m) moderate seagrass on lagoon floor Shallow ( < 3 m) sparse seagrass on lagoon floor Deep (>3 m) sand on lagoon floor Dense coral Sparse coral Seagrass Shallow algae Deep algae Pavement Shallow sand Deep sand Branching and massive corals on forereef Multi-growth forms corals (>25%) on reef flat Heterogeneous (coral < 25%) reef flat Heterogeneous reef flat, sand bottom (10% < rock – algae – coral < 25%) Sand (>90%) Forereef Dense coral margins on spur-and-grooves, enclosed lagoon and pass Dense coral heads (>25%) on heterogeneous floor, reef flat Sparse coral heads ( < 10%) on sand floor, reef flat Dense Thalassodendron sp. seagrass and Padina sp. algae (>80%) Dense Thalassodendron sp. seagrass (>80%) Diffuse seagrass ( < 25%) on sand Mixed seagrass and algae Mixed seagrass beds, with corals, rocks, ruble, algae Brown algae on reef flat and crests Coralline mounts Pavement and ruble Deep sand channels on forereef Shallow sand on reef flat
Coral Seagrass/algae Pavement Sand
sentative of the Tahitian reefs under moderate influence of the harbor and industrial activities (Frouin & Hutchings, 2001). This is a morphologically complex reef, with a fringing reef, deep channels, a large barrier reef and three large patch reefs (Fig. 2). Two wide passes connect the channels with the ocean on each side of the reef complex.
Heron Reef is a large reef platform of the southern Great Barrier Reef (Australia) and one of the most studied reef sites in the world, including using remote sensing. Here, we considered only the western side (Fig. 2) where most of the ground-truth data were collected in 2001 and 2002. It is also the most heterogeneous area since the eastern
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Fig. 2. Top: Habitat classification scheme as proposed by Mumby and Harborne (1999) for mapping Caribbean coral reefs. Both geomorphologic and benthic keys may define a habitat. For instance, three habitats encountered on dense patch reefs are highlighted: a coral zone with presence of massive colonies of Montastraea sp., an algal zone dominated by the brown algae Lobophora sp., and a bare substrate zone densely covered by gorgonians. Depending on the precision of the classification scheme, a practitioner can provide details only at geomorphological level, or at benthic levels, or a combination of both. Bottom: for Glovers Reef, the final classification scheme is a simple five-class scheme, with a forereef (geomorphology) class and four generic benthic classes (brown algae, seagrass, mixed seagrass, and sand) not related to a specific geomorphologic zone (e.g. sand includes sandy areas in any geomorphological zones).
side of the bank is dominated by a large shallow sandy lagoon (Smith, Frankel, & Jell, 1998). Table 2 summarizes the different classes, without details on the main benthic species, which are available in the references provided for most of the sites (Table 1). These references describe in detail the benthic community structure present on each site. Species-level description is the final level of the hierarchy of habitats. Clearly, dominant coral, algae, or seagrass species are generally different from one region (or one site) to another. For instance, Mayotte has extensive Thalassodendron sp. seagrass beds, while Thalassia sp. and Syringodium sp. dominate Biscayne and Halophila sp. and Halodule sp. are frequent in Dubai. Andros and Boca Paila coral crests are dominated by Acropora palmata, but behind Shiraho’s crest Montipora spp. and Heliopora coerulea are dominant. Brown algae Sargassum sp. and
Turbinaria sp. are dominant on Arue patch reef, while Lobophora sp. extensively colonized Glovers patch reefs. 2.3. IKONOS image processing IKONOS data were processed differently depending on image quality and characteristics of the site. Main stages of processing included surface roughness correction, depth correction, classification, and accuracy assessment. 2.3.1. Surface roughness correction A large fraction of the images acquired via NASA SDP suffer from sea surface effects due to wind-generated wave patterns and associated sun glint. As of May 2002, nearly 50% of the 40 images (not all of them processed here) delivered to USF suffered significantly from this problem
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Fig. 3. Example of contrasted habitat classification schemes for two Caribbean sites: Biscayne Bay, FL (USA) and Andros island (Bahamas). Coloured sections highlight the zones present on each reef. For Biscayne, the study area includes lagoon floor and patch reefs. For Andros, the area of interest includes the oceanic side of the coral reef system with the forereef, crests, and spur-and-grooves. In both sites, benthic classes are related to a geomorphologic zone, highlighted with similar color. For instance, for Biscayne, a class of dense seagrass has been defined both for the shallow lagoon floor (blue) and the deep lagoon (green). Sand is related on the lagoon floor (yellow), without separation between deep and shallow. Diffuse gorgonians are related to diffuse patch reefs (purple). For Andros, the same bi-component geomorphology-benthos hierarchy occur, e.g. dense gorgonians occur only on high relief escarpment (purple); forereef (green) is split into four classes: sand (yellow), diffuse gorgonian (light purple), coral Montastraea (dark green), and bare bedrock (grey). Andros and Biscayne both comprise eight classes of habitats, but with minimum overlap between the two schemes. This illustrates the variety of habitats classification scheme that can occur in the same region (see also Fig. 2 for Glovers).
(Fig. 4). For this study, a sea surface roughness correction was required for Heron and Biscayne. Dubai and Glovers images were also corrected, but the wave patterns were not that significant and the original images were used. Details of the algorithm for sea surface correction are provided in Hochberg, Andre´foue¨t, and Tyler (2003). Briefly, the process consists of analyzing the sea surface assuming that the water is virtually opaque in the NIR band (Siegel, Wang, Maritorena, & Robinson, 2000) and that the relative amount of radiance reflected upward at the sea surface is solely a function of geometry, independent of wavelength.
This means that pixels with glint contribution in NIR bands also have similar glint contribution in total upward radiance in visible bands. Identifying the pixels with maximum and minimum radiances in the NIR enables estimation of the percentage of glint contribution in each pixel, which is then corrected to absolute radiance in the visible bands. 2.3.2. Depth correction or depth-invariant indices Lyzenga (1981) proposed a method to eliminate the effects of water column attenuation on bottom radiances
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Fig. 4. Pre-processing of IKONOS images. Top: Example of surface correction aimed at removing wave and glint effects that occur on almost 50% of the 40 images provided by NASA SDP to USF. The level of details available in deep areas is drastically improved after correction. Bottom: An example of false color composite made with three depth-invariant bottom indices, one from each pair of IKONOS bands 1 – 3. The backreef appears in pink dividing the outer forereef from the lagoon. This technique limits misclassification due to depth between shallow dark objects and deep bright objects. Surface correction and depthinvariant indices were not systematically applied, depending on image quality and site characteristics (see Table 3).
(or reflectances). Basically, Lyzenga showed that pixels of the same bottom-type located at various unknown depths appear along a line in the bidimensional histogram of two log-transformed visible bands. The slope of this line is the ratio of diffuse attenuation of the two bands. Repeating this for different bottom types at variable depth results in a series of parallel lines, one for each bottom type. Projection of these lines onto an axis perpendicular to their common direction results in an unitless depth-invariant bottom-index where all pixels from a given bottom-type receive the same index-value regardless of its depth. Two visible bands (or one 2D histogram) provide one index. Three bands can provide three depth-invariant indices by permutation. The main drawback of this method is that index values cannot be related to radiance or reflectance measurements. Also, in some cases, application of Lyzenga’s method is problematic because the same bottom type may not occur over a wide range of depths, thus biasing the accurate estimation of the ratio of diffuse attenuation (Maritorena, 1996). This is the case for Biscayne and Arue in this project. Nevertheless, it has been proven that applying similar empirical techniques and classifying the
resulting index images instead of the initial images could significantly increase the accuracies of the maps (Mumby, Clark, Green, & Edwards, 1998). Here, the technique has been applied to Glovers (Fig. 4), Heron, Andros, and Boca Paila for two reasons: (1) sites presented a significant depth range and (2) deep sand channels and shallow lagoon sand pools were adequate to train and apply the method. 2.3.3. Unsupervised and supervised classification The regional investigators applied two strategies. For Glovers and Shiraho, they first conducted an unsupervised classification and then assigned the different segments to a given benthic category according to expert knowledge and ground-truth data. For Andros, Heron, Mayotte, Dubai, Arue, Biscayne, Boca Paila, ground-truthed polygons in each class were used to train a supervised maximum likelihood classifier. For Mayotte, the two contrasted barrier-reefs were processed simultaneously and then separately. For Glovers only, a contextual decision rule was applied. It re-classified any lagoonal pixel classified spectrally into ‘‘forereef’’ to the correct
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bleaching event. Dubai reef areas that may have changed after 1998 and that could bias the accuracy assessment were removed. The assessment was systematic, along a grid placed over the image. Observed and computed classes at each grid-point were used to build the confusion matrix, for a total of 1086 points. The accuracy metric that we have considered in this study is the overall accuracy (Stehman, 1997), i.e. the proportion of control points correctly classified.
category. Depending on the depth of the site or the tide conditions, three or four IKONOS spectral bands were used (Table 3). 2.3.4. Accuracy assessment It was not possible to survey any of the reefs simultaneously with the acquisition of the images (Table 1) even though some sites were visited several times in consecutive years (Table 1). The time gap between ground observations and image acquisition is generally between a few months and a year. The exceptions were Dubai, where large-scale video tracks (Riegl, Korrubel, & Martin, 2001) were collected in 1995 and the image acquired in 2001, and Shiraho, with a 3-year gap (Kayanne, Harii, Ide, & Akimoto, 2002). Mumby and Edwards (2002) also reported a gap of 5 years. They stated that habitat delineation was unlikely to evolve significantly during this time frame. This may actually be questionable depending on the history of the site and the type of perturbations that may have occurred (hurricanes, coral bleaching, etc.), but for our data set we have considered this statement valid, except for Dubai. Formal accuracy assessments were summarized in confusion matrices for each site with generally more than 100 independent control points for the total partition, with the exception of Mayotte (1230 points along 25 transects of variable length) and Heron (5 transects provided 93 points, in addition to 72 isolated independent points reef-wide) (Fig. 5). The Dubai classification has been compared with continuous shipborne video surveys that highlight the rate of change on the reefs after the 1998
2.4. Landsat 7 image processing Where both IKONOS and Landsat 7/ETM+ data were available (Table 1), ETM+ data were processed similarly to the IKONOS data, with the exception of sea surface correction that was never applied. We did not try to classify ETM+ data for high complexity classification (>10 classes), but only for moderate complexity (5– 10 classes). The same ground-truth point/transects were used for accuracy assessments for both IKONOS and Landsat classifications. Of particular interest is the tandem IKONOS/ETM+ for Mayotte, acquired only 1 day apart in August 2000 in similar low-tide conditions. For Heron, we considered two ETM+ images, one each in high and low tide condition, and we used three and four bands, respectively. For Andros, we do not provide an overall accuracy because the area considered for Landsat mapping included the shallow lagoon and forereef, whereas for IKONOS, only the forereef was processed. Moreover, Landsat accuracy assessment was not done independently but was based on original training polygons. Further, we
Table 3 Image processing parameters and classification results Site
Surface correction
Depth correction
IKONOS bands
Nb control points
Classes
Overall accuracy IKONOS (%)
Overall accuracy Landsat 7 (%)
Addu Andros Arue Biscayne Boca Paila
N N N Y N
N Y N N Y
4 3 4 3 3
400 150 200 123 150
8 8 8 8 15
66 74 70 84 45
56 N/A 52 56 N/A
Dubai Glovers
N N
N Y
3 3
N/A 150
Heron
Y
Y
3
165a
Mayotte
N
N
4
1230c
Shiraho
N
N
4
104
7 8 11 5 13 7 5 14 10d 10e 4
74 71 51 77 42 61 78 61 73 68 81
53 N/A 42 71 N/A N/A 66/61b N/A 56 50 63
N/A: Not computed. a Partly independent point, partly clusters (see text for details). b Using image from 18/09/99 (low tide) and 14/11/99 (high tide), respectively. c Clusters only (see text for details). d Ajangoua barrier reef (no seagrass). e Pamandzi barrier reef (seagrass).
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Fig. 5. Example of validation of IKONOS classification using in situ large scale transects on Heron Reef. The classifications of three sites (Dubai, Mayotte, and Heron Reef) have been controlled using this technique. Here, on the south of Heron, the transition between five benthic classes can be compared with in situ observation where the percent cover of different substrates has been estimated visually in 20 20 m units. Despite the difference in resolution (4 vs. 20 m), we note the good agreement between in situ data and classification, with accurate definition of transition zones, homogeneous zones, and heterogeneous patchy zones (data from Joyce, Phinn, Roelfsema, Neil, & Dennison, 2002).
compare IKONOS and Landsat only in qualitative terms for this site.
3. Results and discussion Examples of classification accuracy and final maps are provided in Figs. 5 and 6. Classification accuracies for each site obtained with IKONOS and Landsat are provided in Table 3. Fig. 7 presents the pooled overall accuracies vs. habitat complexity achieved with IKONOS. We also added the results available for Roatan (Maeder et al., 2002), Turks and Caicos (Mumby & Edwards, 2002), and Punaauia (Capolsini et al., 2003). For Dubai, image classification in eight classes (Table 2) identified as algae several areas previously dominated by corals in 1995. Generally, the four different classes of dense corals identified by video were accurately recognized but only as a broad coral class (Table 2). A posteriori ground-truthing showed that the coral areas apparently misclassified as algae really changed after the 1998 bleaching event (Riegl,
1999), suggesting that IKONOS could be used for monitoring changes in benthic communities for similar sites if a reference had been available. This is confirmed by Palandro et al. (2003) who combined aerial photographs and one IKONOS image of Carysfort Reef (Florida) to quantify the rate of coral loss in the last 20 years. The general trend in Fig. 7 is a linear decrease of accuracy with increasing complexity. Accuracies range from an average of 77% for 4 –5 classes to 71% for 7– 8 classes, 65% in 9 –11 classes, and 53% for more than 13 classes. There is no obvious bias that could be explained by the skills of the investigators. For instance, in 13– 14 classes, both best and worst results are provided by the same investigator (SA). At 8– 10 classes, all results are very consistent. The variations are the natural consequences of the nature of the site and the way that the images have been processed (Table 3). We did not compute the variance of each accuracy because the sampling schemes for accuracy assessment were not the same and in some cases the rigorous conditions of application would likely have been violated (Foody, 2002; Stehman, 1997; Steh-
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Fig. 6. Examples of classification maps for Arue, Andros, Biscayne, and Glovers. For the three first sites overall accuracy was >70%, while for Glovers the 11-class scheme led to a poor 50% accuracy, prompting the implementation of a more simple five-class scheme (Fig. 2).
man, 1999). For reference, Mumby and Edwards (2002) or Capolsini et al. (2003) computed the 95% confidence interval for different accuracy metrics (Tau and overall accuracy). Confidence intervals were in the range F 5 – 10% for several hundred independent control points, depending on the classification schemes (3– 13 classes) and the number of control points per class. Our initial intention was to apply more systematic processing using the same classification schemes, but this proved to be quite difficult or impossible because of the many local specificities and constraints. The linear decreasing trend in Fig. 7 could be used to estimate a priori the accuracy to be expected for a given site using the methods described here. Fig. 7 compares the results achieved by IKONOS and Landsat. Landsat also provides a linear trend, but at a level 15– 20% lower than IKONOS throughout the range of habitat complexity.
Depending on the range of depth, type of geomorphology and habitats, and water clarity at the time of acquisition, the final accuracy will be modulated in the range highlighted here. For instance, for IKONOS, for simple four-class mapping, 68– 81% overall accuracy can be expected according to Turks and Caicos (T&C), Shiraho, and Punaauia. Highest accuracies are obviously obtained for the shallow fringing and barrier reefs (Shiraho and Punaauia), while T&C comprised habitats between 0 and 25 m depth. Fig. 7 also can aid in deciding the level to which habitat complexity can be addressed for a given level of accuracy for both sensors. Using the data presented in Fig. 7, two relations may be derived: overall accuracy ( Y) vs. number of classes (X) was Y = 3.90X + 86.38 (r2 = 0.63) for Landsat and Y = 2.78X + 91.69 for IKONOS (r2 = 0.82). If an 80% accuracy is required for scientific or management applications, only four to five classes can be used using IKONOS.
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90
Overall accuracy (%)
80 70 60 50 40 30 20 2
3
4
5
6
7
8
9
10
11
12
13
14
15
Roatan Punaauia Turks and Caicos Heron Andros Arue Biscayne Glovers Mayotte Shiraho Boca Paila Addu Dubai
Number of habitat classes 90 Arue Biscayne Mayotte (Ajangoua) Mayotte (Pamandzi) Shiraho Boca Paila Addu Heron (3 bandes) Heron (4 bandes) Punaauia Turks and Caicos Glovers
Overall accuracy (%)
80 70 60 50 40 30 20 2
3
4
5
6
7
8
9
10
11
12
13
14
15
Number of habitat classes Fig. 7. Relation between habitat classification scheme complexity (number of classes) and overall accuracy for the IKONOS sensor (top) and Landsat ETM+ sensor (bottom). Roatan data are from Maeder et al. (2002). Punaauia data are from Capolsini et al. (2003). Turks and Caicos data are from Mumby and Edwards (2002), using textural information for IKONOS processing and Landsat 5 TM data.
Around the 70% threshold, most of sites with less than 10 classes are included, but there are exceptions. The accuracies reported here are for the overall partition. Individual habitats that may be of importance for particular applications do not necessarily have the same accuracy, for better or worse. For most of the sites, we do not have enough control points to discuss accuracy within each individual class (Congalton & Green, 1999), but some observations are worth mentioning. For instance, sand is always very well classified (e.g. 90 –92% user’s accuracy for deep and shallow sand in Dubai). Conversely, with the exception of Dubai (59% user’s accuracy), true coral areas are generally poorly classified when algaedominated habitats also occur on the same site at the same depth in a patchy fashion, confirming the predictions of Hochberg and Atkinson (2003) with 43.8% user’s accuracy. High accuracy for coral zones seems possible on several sites (e.g. Andros and Heron) but this is because the image processing has been stratified by geomorphologic zone (e.g. Andros with 97% user’s accuracy for forereef-high relief Montastraea annularis zones) or be-
cause there is no real competition with other classes (e.g. Heron does not have shallow dense seagrass beds spectrally similar to deep corals). High accuracy also occurs for forereef geomorphologic zones, which are implicitly coralrich (95% user’s accuracy in Glovers). Comparison of the accuracy of IKONOS and Landsat assessments confirms previously published results obtained with moderate resolution sensors (Landsat TM, SPOTHRV, ASTER). An exception was Chauvaud, Bouchon, and Manie`re (1998) and Chauvaud, Bouchon, and Manie`re (2001), whose accuracies are certainly optimistically biased (>90% for more than 25 classes using SPOT-HRV). Landsat accuracies also decreased linearly with increasing habitat complexity. It is adequate for simple habitat complexity mapping, and the results for seven to eight classes are consistent, within a low range of accuracy (50 –56%) (Table 3). For Heron, lower complexity (five classes) provided acceptable accuracy (>60%), especially with the image acquired at low tide since this resolved the shallow heterogeneous hard-bottom and coral zones along the reef rim (Table 3). For the Andros forereef where ETM+
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accuracy could not be computed without optimistic bias, IKONOS better discriminated shallow water ( < 5 m) habitats which are often small and patchy. The texture of the habitat seems exploitable using the IKONOS sensor at 4-m resolution for this site (see below further discussion on texture). In deeper (>5 m) forereef zones, habitats are more spatially continuous (e.g. high relief Montastraea zone visible in Fig. 6) and both sensors provide similar patterns. Narrow geomorphological features such as terraces and steps are more evident on IKONOS. The decision to use IKONOS or Landsat (or any other medium resolution sensor) is constrained generally by consideration of cost-effectiveness (Mumby et al., 1999). IKONOS is clearly not the best option for covering large areas (Mumby & Edwards, 2002). For small areas of high interest (research sites or marine protected areas), our results show the level of improvement in mapping accuracy that can be expected for science or management applications. This also demonstrates the potential of conducting multi-sensor reef mapping by coupling Landsat broad (but accurate) 4/5-class mapping for large reef stretch (e.g. Florida Keys, Bahamas, Great Barrier Reef, etc.) with more precise (and still accurate) IKONOS mapping for specific (patchy) areas. Higher accuracies could be potentially achieved using textural measurements as suggested by Mumby and Edwards (2002). For coarse habitat mapping (four classes) on Turks and Caicos, overall accuracy increased from 68% to 75% when textural neo-channels (3 3 window variance) were combined with depth-invariant indices in the classification process. However, this is likely to be site (patchiness, depth) and image quality dependent. Indeed, tests on Mayotte show that despite contrasted textural signatures for different bottom-types, the texture did not improve the overall accuracy. For Mayotte, the fact that we could also use the NIR bands throughout most of the reef system likely explains this lack of improvement. Mumby and Edwards also noted that texture did not improve classification results when using 10-band multispectral airborne data. Thus, the extra spectral information seems more important than texture. We suggest that more systematic tests are required to fully quantify and qualify the benefits of using textural signatures in various conditions. Another potential way to improve accuracy is to use contextual knowledge to modify the classifications a posteriori (Mumby, Clark, et al., 1998). The benefit of contextual knowledge can also be inferred from our results. Indeed, higher accuracies were achieved when the reefs were a priori segmented into main geomorphologic or contrasted zones. Glovers was processed as a whole (including forereef, lagoon, and rim) and reached 77% accuracy in five very broad classes (Table 2), even after some contextual editing. Conversely, Andros reached 74% in eight very specific classes (Table 2), but was initially pre-segmented to process only the forereef, thus avoiding confusion with classes present on the backreef (e.g. seagrass, small patch
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reefs). The backreef processed alone would likely provide results similar to Biscayne (lagoon floor with seagrass/sand, patch reefs), where eight classes were mapped with 84% accuracy. Finally, splitting the Mayotte reef system in 2 barrier reefs with 10 very specific habitats each yielded better results than a global classification in 14 thematically broader classes. This strongly suggests that the practitioner should carefully pre-segment the image and then process by zones to optimize results. This can be performed empirically, by visual interpretation, or in a formal way. Andre´foue¨t, Roux, Chancerelle, and Bonneville (2000) formalized with a fuzzy membership function the ‘‘distance to the shore’’ factor for the classification of SPOT images to avoid misclassification between fringing mud and coral crests. Suzuki, Matsakis, Andre´foue¨t, and Desachy (2001) integrated directions (‘‘perpendicular’’, ‘‘parallel’’) when classifying reef flats in atolls using Landsat imagery. However, these are complex processes and for simplicity, in most of the cases, pre-segmentation is made relatively easy by the high resolution of the IKONOS images where fronts and boundaries can be accurately delineated by simple visual interpretation (e.g. Andros). Eventually, similar habitats in different zones can be merged depending on the desired classification scheme (e.g. algae on forereef merged with algae on patch reefs). It has been suggested that the depth correction technique should be systematically applied (Green, Mumby, Edwards, & Clark, 2000) because of its simplicity and potential benefit. Here, we had difficulties to do so at each site. Biscayne, for instance, does not have the same habitats at different depths and the Lyzenga (1981) method cannot be applied. In this case, slightly different techniques (e.g. ratios, Maritorena, 1996; Paredes and Spero, 1983; Polcyn, Brown, & Sattinger, 1970) or ancillary field data (bathymetry) are required to overcome the bathymetric challenge. The patterns of misclassifications (patches classified as dense seagrass) on Biscayne suggest that a depth correction could be useful, even though without bathymetric correction, the overall accuracy appears quite good (84%). For Andros, tests showed that the maximum distance between habitats was achieved using two depthinvariant indices (using bands 1 – 2 and bands 2– 3) and the original band 3. A similar conclusion arose for Boca Paila where only the depth-invariant index based on bands 1 and 2 was useful, in conjunction with other unprocessed bands. In general, there is no doubt that bathymetric correction at the scale of habitats should enhance overall accuracy and avoid misclassifications. However, many of these misclassifications may happen simply because very different zones are considered simultaneously in the classification process. In a Caribbean reef, considering lagoon and back reef communities (e.g. seagrass) with forereef coral communities in the same process typically leads to problems. Therefore, we suggest that future work should examine the relative benefits of contextual knowledge and depth compensation to achieve high accuracy. Indeed, this is proba-
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bly site-dependent. Mumby, Clark, et al. (1998) showed that depth correction is more critical than contextual editing for a Turks and Caicos site, but in this case, the morphology of the site was simple.
Hooten, for their support for travel and field work in Heron Isl. Finally, thanks to Adam Lewis (Great Barrier Reef Marine Park Authority) who provided the Landsat images of Heron Reef. This is IMaRS contribution 052.
4. Conclusion
References
This is the first international coordinated effort to assess the potential of a high resolution spaceborne sensor for coral reef studies. This is also the first consistent compilation of coral reef applications from commercially available data, made available via the NASA SDP program. This work informs scientists and managers on the type of accuracy they can expect using IKONOS and Landsat 7 ETM+ data for a coral reef mapping applications. However, our objectives were partially met. We failed to adequately conduct similar processing on a variety of sites as we initially planned. We quickly realized that this is inherent to the diversity of sites we have selected and to the different image qualities dependent on environmental conditions (wind, tide, water quality). Nevertheless, the processing of 10 sites, completed by 3 independent studies, clearly highlight the potential of IKONOS for coral reef habitat mapping in general, using standard methods for reef habitat mapping. Results can be improved for each site since more sophisticated techniques may work better for a given site and for a given image. For instance, to achieve high accuracy, we recommend to pre-segment a reef into different zones if possible. The comparative approach also suggests which algorithms need to be improved or developed in the future (bathymetric correction, contextual edition, and texture) for a given type of reefs.
Acknowledgements Obviously, this study would not have been possible without the spirit and crew of the NASA Scientific Data Purchase program at Stennis Space Center under the successive responsibility of Fritz Pollicelli and Troy Frisbie. We greatly acknowledge their help in the tasking requests and their support of our research since 1999. Andrew Mattee and Michael Satter were our contacts at Space Imaging. This research was supported by NASA grants NAG5-10908 to SA and NAG-3446 to FMK and Kendall Carder. Many individuals helped gather field data on the various sites. We are indebted to Chris Roelfsema, Bill Dennison, Fabienne Bourdelin, Claude Payri, Bernard Thomassin, Michel Pichon, Hajime Kayanne, Saki Harii, Yoshiyuki Tanaka, Ernesto Arias-Gonzales, Don Hickey, Nancy Dewitt, Tonya Clayton, David Palandro, Chuanmin Hu, and the staffs of Heron Island Research Station, Biscayne National Park, and Service des Peˆches et de l’Environment Marin de Mayotte. We are grateful to GISLAGMAY (Mayotte) and the World Bank, via Andy
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