Author version: Geo-Mar. Lett., vol.30(6); 2010; 617-626
Image analysis of seafloor photographs for estimation of deep-sea minerals
Rahul Sharma, S. Jai Sankar, Sudeshna Samanta, A.A. Sardar. D. Gracious
R. Sharma (corresponding author) (e-mail: [email protected], Fax: +91-832-2450609),
S.J. Sankar, S. Samanta, A.A. Sardar, D. Gracious
National Institute of Oceanography, Council of Scientific and Industrial Research, Dona Paula, Goa
403004, India
Abstract
Factors such as non-uniform illumination of seafloor photographs and partial burial of polymetallic
nodules and crusts under sediments have prevented the development of a fully automatic system for
evaluating the distribution characteristics of these minerals, necessitating the involvement of a user
input. A method has been developed whereby spectral signatures of different features are identified
using a software ‘trained’ by a user, and the images are digitized for coverage estimation of nodules
and crusts. Analysis of >20,000 seafloor photographs was carried out along five camera transects
covering a total distance of 450 km at 5,100–5,300 m water depth in the Central Indian Ocean. The
good positive correlation (R2>0.98) recorded between visual and computed estimates shows that both
methods of estimation are highly reliable. The digitally computed estimates were ~10% higher than
the visual estimates of the same photographs; the latter have a conservative operator error, implying
that computed estimates would more accurately predict a relatively high resource potential. The fact
that nodules were present in grab samples from some locations where photographs had nil nodule
coverage emphasises that nodules may not always be exposed on the seafloor and that buried nodules
will also have to be accounted for during resource evaluation. When coupled with accurate
positioning/depth data and grab sampling, photographic estimates can provide detailed information
on the spatial distribution of the mineral deposits, the associated substrates, and the topographic
features that control their occurrences. Such information is critical for resource modelling, the
selection of mine sites, the designing of mining systems and the planning of mining operations.
Electronic supplementary material The online version of this article (doi:10.1007/s00367-010...contains supplementary material, which is available to authorized users.
1
Introduction
Just as remote sensing finds its application for evaluating different landforms as well as
oceanographic conditions through series of satellite images, underwater photography is a remote
sensing technique for evaluating seafloor features such as microtopography, mineral resources, rock
outcrops, organisms, and manmade structures. It developed in the 1940s (e.g. Ewing 1946) and
became an effective tool for observing the seafloor environment, including its benthic organisms and
bottom currents (e.g. Shipek 1960; La Fond 1962; Edgerton 1967; Heezen and Hollister 1971;
Borowski 2001; Grizzle et al. 2008).
Notably, large deposits of deep-sea minerals—e.g. polymetallic nodules and ferromanganese
crusts—that occur on abyssal plains (>4,000 m deep) are considered as key future resources for
various metals such as Mn, Fe, Co, Cu and Ni (Cronan 2000). In the Indian Ocean, the Central
Indian Basin represents one of the key regions for polymetallic nodules, in which their distribution
and other characteristics have been studied in considerable detail using conventional techniques such
as freefall grabs, corers and dredges (Jauhari, 1989; Banerjee et al., 1991; Banerjee and
Mukhopadhyay, 1991; Banerjee and Miura, 2001; Jauhari et al., 2001; Jauhari and Iyer, 2008;
Vineesh et al, 2009); the associated bathymetry is studied with sonar systems (Kodagali, 1988;
Kodagali and Sudhakar, 1993); and the seafloor features with photography (Sharma, 1989a; Sharma,
1993).
The application of photographic techniques for studying these deposits has steadily gained
significance and photography is now established as a reliable method for evaluating deep-sea
deposits (e.g. Glasby and Singleton 1967; Fewkes et al. 1979; Cronan 1980; Jung et al. 2001),
enabling rapid surveying and, more importantly, providing unambiguous visualization of the seafloor
(e.g. Scrutton and Talwani 1982; Edwards et al. 2003). However, non-uniform illumination of
seafloor photographs, varying altitude of the camera system due to continuous motion, and partial
burial of minerals such as nodules and crusts under sediments hinder the formulation of standard
analytical procedures for evaluating seafloor photographs and stress the need for user-defined
parameters while analysing each photograph.
Some methods initially developed for the estimation of nodules from photographs include point
counting (Anonymous 1982), using an electronic planimeter (Fewkes et al. 1979), projecting
cartoons of photographs of known nodule coverage (Frazer et al. 1978), and image processing
techniques (e.g. Park et al. 1997). Several researchers focused on photography-based mineral
resource estimations of the seafloor (e.g. Felix 1980; Handa and Tsurusaki 1981; Lenoble 1982;
2
Sharma 1993). However, many of these methods were either tedious or lacked the human input
required to differentiate between specific features in the photographs.
Within the context presented above, this study aims to develop a user-interfaced image analysis
method for estimating deep-sea mineral resources such as polymetallic nodules and ferromanganese
crusts based on seafloor photographs obtained from a deep-towed photography system deployed in
the Central Indian Basin. The method and its applications would help in the rapid mapping of
seafloor mineral resources in terms of their two-dimensional distribution as well as relation with
other substrates and topography. Archiving of data and its application for evaluating the distribution
characteristics of the deposits are also described.
Materials and methods
Deep-towed photography system
The deep-towed photography system consists of a tow body, a depressor and a deck unit (Fig. 1).
The tow body weighs about 600 kg and comprises two still cameras, one video camera, an altimeter,
light source and battery packs. The depressor weighs about 1.5 tons and is used to dampen the effect
of ship movement on the tow body. The photography system is towed behind the ship at 1–2 knots
along a pre-determined track and generally operated at an altitude of 4–5 m, the depressor being
positioned 40–50 m above the seafloor. Data and power transmission from the ship is achieved
through a coaxial cable between the deck unit, depressor and tow body.
Raw data types
In the period August–December 1994, deep-towed camera surveys were carried out in the Central
Indian Ocean Basin onboard R/V A.A. Sidorenko, during which >50,000 seafloor photographs were
taken at a prefixed interval of 30 s on long spools of 35-mm black/white (B/W) photographic films.
Ship navigation data were recorded every 1 min using a global positioning system throughout the
surveys. Depth information was obtained from the ship’s echosounder. The various datasets were
later integrated into a single database (Sharma et al. 1995; see also below) for easy retrieval using
interactive software (Sankar and Sharma 2005).
Raw data processing
For this study, 20,500 photographs were analysed along five selected deep-tow transects covering a
total distance of 450 km at 5,100–5,300 m water depth (Table 1, Fig. 2). In these photographs,
nodules appear as small, black spherical objects scattered on the seafloor, crusts as large angular
3
outcrops, and the surrounding sediments as light-coloured matrix (Fig. 3). The negative of each
photograph was scanned individually by means of a UMAX PowerLook 180 (uniform resolution of
450 dpi) combined with SilverFast Ai NegaFix software for conversion to the positive of the image,
which was saved in 8-bit nonlinear grey-scale format for further processing.
In these images, nodule and crust coverages (percent area covered) were evaluated by means of the
GIS-compatible ERDAS Imagine software (version 8.5; ERDAS 1991). This user-friendly image
processing software uses advanced image classification methods enabling the operator to assign a
range of grey shades to different features, depending on his/her expertise.
Data archive
A database was created in MS Access, consisting of (1) basic data such as photo identity number,
ship name, cruise number and line/transect number; (2) navigation data such as the date, time,
latitude, longitude, distance and water depth at each photo location; (3) operation data such as photo
number, altitude, length, breadth and area of seafloor photographed; (4) seafloor data such as visual
coverage estimates of nodules (pn), crusts (pc) and sediments (ps), as well as ERDAS computed
estimates of nodules (cnp), crusts (ccp) and sediments (csp). A detailed description of this database is
provided in Table 1 in the electronic supplementary material for this article, available online to
authorized users.
Data presentation
The percentage of visual versus computed estimates of nodule coverage (pn/cnp*100), and the
percentage difference between these (difference %) were calculated for all the photographs. This
enabled data presentation in terms of frequency distributions of nodule coverage (cf. computed
estimates), and correlation plots of visual versus computed estimates for all the transects. The spatial
distributions of nodules and crusts are presented in the form of XY plots relating position to depth,
using Grapher software.
Seafloor photograph analysis
The human eye can inherently distinguish between nodules and crust from a seafloor B/W
photograph. Depending on the prevailing light conditions and sediment cover, however, the grey
shades associated with nodules and crusts can vary. Nevertheless, a trained operator can easily
recognise specific signatures on the basis of additional information on nodule/crust shape, size and
other morphological characteristics. Analogously, it is necessary to ‘teach’ any software what a
nodule, a crust or simply sediment are. An artificial neural network (ANN) uses a mathematical or
4
computational
model
for
information
processing
based
on
a
connectionist
approach
(www.wikipedia.org). ANN can be used to model complex relationships to identify patterns in data,
and a similar approach has been followed in ERDAS. This entails sorting the pixels into different
classes by assigning different shades of grey to each pixel representing different features in an image.
(Note that images judged as having nil nodule/crust coverage by visual estimation were not used in
this case.) ERDAS offers an unsupervised and a supervised digital image classification method.
Unsupervised classification
In unsupervised classification, the software automatically assigns grey shades to each pixel based on
the number of classes defined for the image. In this multispectral classification, each pixel is
assigned a class based on similar spectral signatures. The classified image produces an attribute table
showing the number of pixels for different grey shades assigned to each class. An example is
provided in Fig. 1 in the online electronic supplementary material for this article. In the present case,
the various photographs had different levels of illumination, making it difficult to identify a cut-off
between different grey shades. Therefore, this classification was not used to estimate
nodule/crust/sediment coverage per se. Nevertheless, it yielded images of clarity superior to that of
the original images; these were selected for further processing via supervised classification.
Supervised classification
Compared to unsupervised classification, supervised classification is more user-friendly. User input
plays a key role to identify the spectral characteristics of each photograph for a particular class by
careful selection of ‘training sites’ (polygons), as part of the following steps:
-
Defining the training sites (polygons) for each feature and creating different classes
-
Evaluating and editing the classes
-
Merging closely related classes into a single class for particular features
-
Classifying the image
-
Evaluating the classification.
Training sites or polygons were created around the features (nodules and/or crusts and sediments)
that needed to be classified. For this, a range of grey shades was assigned to each feature and then
the classes were merged into a single class for that particular feature (nodule/crust), which was then
used for processing the image under supervised classification. The classified output image produces
an attribute table in which the numbers of classes prescribed by the user are shown together with
their pixel values. Examples are provided in Figs. 2, 3 and 4 in the online electronic supplementary
material for this article. The total number of pixels for nodules and/or crusts and sediments was
5
calculated. The percent cover of each feature was then estimated by dividing the individual number
of pixels for nodule and/or crusts and sediments by this sum. It should be noted that, in order to
minimise error, care was taken in identifying the training sites in images containing nodules and
crusts, and also in merging the classes. An overview of the procedure adopted for unsupervised /
supervised classification in ERDAS is shown in Fig. 4.
Results
Of the 20,550 photographs taken along the five selected deep-tow photography transects covering a
total distance of 450 km, image processing could be carried out for all but 34 photographs (0.16%)
which were of insufficient quality (Table 1). With an optimum operating altitude of 3–5 m above the
seafloor, the average seafloor area covered by each photograph varied between 5 and 16 m2, with an
average distance of 22 m between any two photographs.
Visual versus computed estimates
Comparison of average values for nodule estimates shows that the visual estimates (pn) are always
lower than the computed estimates (cnp) for all the photographs, the former ranging from 2.1–12.9%,
the latter from 2.5–15.3% (Table 2). Differences between the two types of estimates vary from 4.5–
16% for all transects combined (overall difference of 12.4%), representing an underestimation by
visual estimates. The difference is low for transects 4-4 and 12-1 (4.5 and 8.2%), and high for the
other three transects (14.9, 16.0 and 16.0%). Considering only the photographs that have at least
some nodule coverage (i.e. excluding those with 0% nodule coverage), the difference in estimates is
low (4.6–8.4%) for all transects, except transect 9-1 (difference of 16.4%), with an overall difference
of 10.0% for all transects combined.
For the photographs classified in intervals of 10% (0, 10, 20, 30, ... 90%), the correlation graphs in
Fig. 5 show the range of computed values (cnp) corresponding to every 10% class range of visual
values (pn) for the different transects. In all cases, there is positive correlation between the visual and
computed estimates, with high coefficients of determination (R2>0.98). This shows that both
methods of estimation have good reliability and that the computed values are sufficiently accurate.
Frequency distribution of nodules
The frequency distribution of nodule coverage from visual and computed estimates (Table 3) shows
that almost two-thirds (65%) of the photographs have nil nodule coverage (i.e. no exposed nodules);
most (17–21%) of the remaining photographs have low nodule coverage (1–20% coverage), some
6
(~11%) have moderate nodule coverage (20–50% coverage) and very few have high nodule coverage
(50–80% coverage).
Frequency distribution of crusts
Ferromanganese crusts or rock outcrops were observed in only 1% of the photographs (203 of
20,550). The frequency distribution of computed estimates shows that, of these photographs, about
half (48%) have low (<20%) coverage, one third (34%) have moderate (20–50%) coverage and the
remainder (17%) have high (50–90%) coverage on the seafloor (Table 4).
Spatial distribution of nodules and crusts
Plotting the photographic data in terms of geographic locations (latitude, longitude) reveals any
spatial distribution trends for nodules and crusts on the seafloor. As illustrated in Fig. 6a for a
selected section of transect 4-4 (cf. Fig. 2), distinct zones of occurrence of crust outcrops and nodule
fields can be mapped. Analogously, plotting nodule and crust occurrence relative to water depth (Y
axis) and distance along a specific transect (X axis) reveals spatial variation controlled by, for
example, local topography. Along the same section of transect 4-4, crust outcrops are exposed
preferentially near the top of an abyssal hill (Fig. 6b); nodules are exposed largely on the lower
slopes, where sediment accumulation would be less than in the flat abyssal plains or valleys that have
higher sediment accumulation.
Discussion and conclusions
Application of image processing software for the evaluation of seafloor photographs in assessments
of deep-sea minerals has always been limited due to the inability of the software to distinguish
between different seafloor features of similar grey shades. Hence, human expertise is required in
order to ‘teach’ the software how to differentiate between features before quantifying them. In this
study, a procedure for estimating the coverage of polymetallic nodules and ferromanganese crusts
has been developed that combines the capability of ERDAS image processing software with a userdefined, supervised classification for identifying these features. Based on an analysis of more than
20,000 seafloor photographs, this method substantially helped in image enhancement and accurate
computation of their coverages. As the photographs were taken at an average interval of only 22 m,
the photographic surveys served the important purpose of filling the gaps in spatial nodule coverage
associated with earlier campaigns involving coarser-grid grab sampling at intervals of at least ~6 km
(e.g. Jauhari et al. 2001).
7
The finding that the computed estimates were higher (by ~10%) than the visual estimates—i.e. there
was an underestimation by visual estimates—is not surprising. The photographs were generally
classified in intervals of 10% (0, 10, ... 90%), and an operator making these estimates would tend to
conservatively assign to a lower class and not overestimate the resource; by contrast, the computed
estimates account for intermediate values as well. By implication, the computations yield a more
accurate, higher potential of the deposit. This is an important development that could serve to
improve earlier empirical formulae for the estimation of nodule abundance from photographic data in
the Central Indian Basin (Handa and Tsurusaki 1981; Sharma 1989a, 1989b).
The finding that the coverage of exposed nodules can be extremely variable (0–80%) on the seafloor,
and evidence of spatial segregation of crusts and nodules on a hill top–flank gradient are consistent
with earlier work showing that site-specific topographic (e.g. slope) and sediment characteristics are
critical factors in explaining distribution patterns of nodules on the seafloor (e.g. Kodagali 1988;
Banerjee et al. 1991; Yamazaki et al. 1994; Yamazaki and Sharma 1998; Banerjee and Miura 2001).
Moreover, the fact that nodules have been collected in grab samples at many locations where no
nodules were visible in the photographs confirms the occurrence of buried nodules under the
sediment–water interface layer (Sharma 1989a; Banerjee et al. 1991). This burial and exposure of
nodules is known to depend on sediment accumulation, which in turn is controlled by seafloor
topography (Sharma and Kodagali 1993). This buried reservoir needs to be duly considered during
resource evaluation of nodule deposits. Indeed, one promising approach involves the use of
backscatter data from multibeam surveys which can be used for demarcating potential nodules
bearing areas on the seafloor (Chakraborty and Kodagali, 2004).
Although, crust/rock outcrops were observed at very few locations in the photographic surveys, these
need detailed mapping as possibly unfavourable areas where mining operations may be difficult and
risk equipment damage due to interference by hard substrates. Again, any burial of nodules under
sediments would imply that the design of the mining device needs to take this factor onto
consideration, as well in order to mine these nodules along with the exposed ones. Those
photographs in which nodules and crusts co-occur indicate transition zones between the outcrops and
nodule fields, mapping of which would be very useful in deciphering the margins of potential nodule
mining areas. This applies not only to regional- but also to latitudinal-scale assessments (e.g. Iyer
and Sharma 1990; Sharma and Kodagali 1993).
In conclusion, the findings of the present study strengthen the need for both small- (site-specific,
regional) and large-scale (latitudinal) photographic surveys complementing generally wider-spaced
and more time-consuming sampling campaigns to evaluate mineral resources. When combined with
the usage of suitable software such as the GIS-compatible ERDAS Imagine under operator-defined,
8
supervised classification, photographic surveys can provide valuable data for the accurate
quantification of mineral deposits and their associated substrates. Our findings also highlight the
need for further technological development enabling meaningful evaluation and recovery of buried
resources. Such information is critical for resource modelling, the selection of mine sites, the
designing of the mining systems and the planning of mining operations.
Acknowledgements
This study was carried out under the Polymetallic nodules program funded by the Ministry of Earth
Sciences, Govt. of India. This paper is NIO contribution no. .____.
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11
Fig. 1 Schematic diagram of deep-tow photographic system
Fig. 2 Locations of deep-tow photographic transects in the Central Indian Ocean. The five transects
reported in more detail in the present study are highlighted in bold (transects 4-4, 9-1, 12-1, 12-2 and
12-3)
Fig. 3 Photographs showing different seafloor features: a dense nodules, b crusts, c nodules and
crusts and d nodules and animals
Fig. 4 Procedure adopted for classification of seafloor photographs
Fig. 5 Correlation between visual (pn) and computed (cnp) nodule estimates along the five selected
transects: a transect 4-4, b transect 9-1, c transect 12-1, d transect 12-2, e transect 12-3
Fig. 6 a Distribution of nodules and crusts along a section of transect 4-4. b Distance vs. depth
distribution of nodules and crusts along a section of transect 4-4
12
Table 1 Details of deep-tow transects analysed
Series
Transect
Av.
Distance
No. of
No. of poor-quality
no.
no.
depth
covered
photos
photos rejected (with %
(m)
(km)
taken
age)
No. of photos analysed
1
4-4
5,284
77.6
3,700
5 (0.135)
3,695
2
9-1
5,074
140.0
6,259
16 (0.255)
6,243
3
12-1
5,103
66.8
3,146
13 (0.413)
3,133
4
12-2
5,142
124.2
5,445
0
5,445
5
12-3
5,122
42.0
2,000
0
2,000
Total
5
-
450.6
20,550
34 (0.165)
20,516
13
Table 2 Comparison of visual (pn) and computed (cnp) nodule estimates for all transects
Series no.
Transect
Av.
Average (%) values for all photos (including nodule
Average (%) values for all photos (excluding
no.
photo
coverage=0%)
nodule coverage=0%)
area
(m2)
Photos
pn
cnp
pn/cnp*100
Difference
Photos (n)
pn/cnp*100
Difference
(n)
1.
4-4
7.7
3,695
10.95
11.47
95.46
4.54
1,563
95.39
4.61
2.
9-1
11.4
6,243
12.88
15.34
84.00
16.00
2,512
83.56
16.43
3.
12-1
11.8
3,133
2.13
2.32
91.81
8.19
525
95.42
4.58
4.
12-2
13.7
5,445
5.21
6.12
85.13
14.87
2,168
91.61
8.39
5.
12-3
9.5
2,000
2.10
2.50
84.00
16.00
457
92.40
7.60
Overall differencea
12.40
10.00
a
Overall difference computed by dividing total difference by all photos analysed
14
Table 3 Frequency (%) distribution of nodule coverages from visual (pn) and computed (cnp) estimates for all transects
Transect 0%
<10%
10–20%
20–30%
30–40%
40–50%
50–60%
60–70%
70–80%
80–90%
pn
pn
pn
pn
cnp
pn
cnp
pn
cnp
pn
cnp
no.
pn
Cnp
pn
cnp
pn
cnp
cnp
cnp
cnp
4-4
57.6
57.6
19.0
15.0
3.3 4.2
2.7 2.4
5.6 9.6
5.6 4.4
5.8 1.0
0.1
5.7
0.0
0.1
0.0
0.0
9-1
59.7
59.7
9.8
5.3
5.7 4.1
6.1 5.1
7.6 6.2
4.8 7.6
4.4 5.2
1.5
4.5
0.1
2.0
0.0
0.0
12-1
83.2
83.2
12.3
11.2
1.3 1.2
0.9 1.2
1.0 0.5
0.5 1.5
0.1 0.3
0.2
0.3
0.6
0.6
0.0
0.0
12-2
60.0
60.0
28.5
22.7
2.6 5.9
3.3 2.3
1.9 3.1
3.1 1.9
0.2 3.5
0.0
0.2
0.0
0.0
0.0
0.0
12-3
77.0
77.0
17.8
14.2
1.7 3.9
2.1 1.4
1.1 2.2
0.0 1.1
0.0 0.0
0.0
0.0
0.0
0.0
0.0
0.0
Overall
64.8
64.7
17.6
13.4
3.4 4.1
3.6 2.9
4.1 4.7
3.4 4.0
2.5 2.8
0.5
2.5
0.1
0.7
0.0
0.0
av.
15
Table 4 Frequency (%) distribution of computed crust coverages (ccp) for all transects
Transect no.
<10%
10–20%
20–30%
30–40%
40–50%
50–60%
60–70%
70–80%
80–90%
4-4 (n=53)
24.53
5.66
22.64
22.64
11.32
7.55
3.77
0.00
1.90
9-1 (n=62)
25.80
11.30
3.23
11.30
17.75
4.84
12.90
8.06
4.84
12-1 (n=22)
36.36
22.73
4.55
9.09
4.55
0.00
13.64
9.09
0.00
12-2 (n=42)
19.05
33.33
19.05
9.52
9.52
4.77
2.38
2.38
0.00
12-3 (n=24)
100.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Overall av.
34.00
14.28
11.33
12.31
10.84
4.43
6.90
3.94
1.97
(n=203)
16
DEEP-TOW SYSTEM : SCHEMATIC
A. SHIP
Power supply
Controls
Video recorder
SBP, SSS recorder
Pinger depth recorder
Position data
B. DEPRESSOR
Pinger (20 kHz)
SBP (4 kHz)
SSS (100 kHz)
Electronics
5000 m
C. TOW BODY
Still cameras (5 - 18 sq. m)
Video camera (1 sq. m)
Light source
Altimeter
Stabilising fin
Electronics
50 m
4m
1.5 km
50 m
Fig. 1: Schematic of deeptow system
17
-10
6-1
7-1
6-2
-11
7-2
11.1
8-1
8-2
1-2
1-3
-12
3-1
Latitude (˚S)
12-1
4-3
4-4
9-1
12-2
-13
5-1
12-3
10-1
-14
10-2
-15
INDIA
-16
100 km
Scale
73
74
75
76
77
Longitude (˚E)
Fig. 2 Locations of deep-tow photographic transects in the Central Indian Basin. The five
transects reported in more detail in the present study are highlighted (transects 4-4,
9-1, 12-1, 12-2 and12-3)
18
Fig. 3 Photographs showing different seafloor features: a dense nodules, b crusts, c nodules
and crusts and d nodules and animals
19
Classification of seafloor photographs
Unsupervised classification
No. of classified images
Supervised classification
Assigning training sites to the
features in the image
Closest resembling image
Merging classes for the same feature
Command for supervised classification
Classified image is generated
Attribute table giving pixel values for
different features
Calculate percentages of
nodules, crusts and sediments
Input to main database
Fig. 4 Procedure adopted for classification of seafloor photographs
20
Correlation (4-4)
80
Correlation (9-1)
y = 0.9967x + 0.5463
70
R2 = 0.9587
cnp
60
50
40
30
20
10
0
0
20
40
60
100
90
80
70
60
50
40
30
20
10
0
80
y = 1.1388x + 0.6479
R2 = 0.9856
0
20
40
pn
Correlation (12-1)
90
100
80
R2 = 0.9772
y = 1.149x + 0.1368
70
70
R2 = 0.9872
60
60
50
50
cnp
cnp
80
Correlation (12-2)
y = 1.0166x + 0.1458
80
60
pn
40
40
30
30
20
20
10
10
0
0
0
20
40
60
80
100
0
10
20
pn
30
40
50
60
70
pn
Correlation (12-3)
60
y = 1.1854x + 0.0213
50
R2 = 0.9838
cnp
40
30
20
10
0
0
10
20
30
40
50
pn
Fig. 5 Correlation between visual (pn) and computed (cnp) nodule estimates along the five selected
transects: a transect 4-4, b transect 9-1, c transect 12-1, d transect 12-2, e transect 12-3
21
-12.720
a
Crust locations
-12.721
Nodule locations
-12.722
-12.723
Latitude (°S)
-12.724
-12.725
-12.726
-12.727
-12.728
-12.729
-12.730
75.670
75.671
75.672
75.673
75.674
75.675
75.676
Longitude (°E)
75.677
75.678
75.679
75.680
Fig. 6a:
22
2.80
5240
2.90
3.00
3.10
3.20
3.30
3.40
3.50
5240
b
Crust
Nodule
Hill top
5260
Depth (m)
5260
5280
5300
2.80
5280
Flank
2.90
3.00
3.10
3.20
Distance (km)
3.30
3.40
5300
3.50
Fig. 6b:
23