GIS and Remote Sensing Phycological Applications

2.1. GEOREFERENCING SPECIMENS

Acquiring GPS coordinates has become self-evident, with handheld GPS devices nowadays fitting within any budget, provided that accuracy requirements are not smaller than 10-15 m. Devices capable of handling publicly available differential correction signals like Wide Area Augmentation System (WAAS, covering North America), European Geostationary Navigation Overlay Service (EGNOS, covering Europe), and equivalent systems in Japan and India are slightly more expensive but offer accuracies between 1 and 10 m. However, accuracies are almost always found to be better in practice, especially in phycological field studies where the device would mostly be used in areas free from trees and mountains. However, field workers attempting to log shallow dives and snorkel tracks using GPS should make sure to mount the device well clear from the water, as even a single splashing wave can hamper signal reception. Accuracies within 1 m can be obtained with commercial differential GPS systems, although this increases the cost and reduces mobility of field workers as a large portable station needs to be carried along, hence restricting use on water to larger boats. However, logging GPS coordinates does not eliminate the need for textual location information, preferably using official names or transcriptions as featured on maps, and using a hierarchical format going from more to less inclusive entities (cf. GenBank locality information; NCBI, 2008). This is vital to allow for error checking (see further). Several authors have recently independently and unambiguously stated that a lack of geographic coordinates linked to each recently and future sampled specimen can no longer be excused (Nature Editorial, 2008; Kidd and Ritchie, 2006; Kozak et al., 2008). Moreover, recommendations were made to require a standardized and publicly available deposition of spatial meta-information on all used samples accompanying each publication, including nonspatially oriented studies. This idea is analogous to most journals requiring gene sequences to be deposited in GenBank, whenever they are mentioned in a publication (Nature Editorial, 2008). For instance, the Barcode of Life project, aiming at the collection and use of short, standardized gene regions in species identifications, already requires specimen coordinates to be deposited for each sequence in its online workbench (Ratnasingham and Hebert, 2007).

Adding coordinates to the existing collection databases can be a lot more challenging and time-consuming. At best, a locality description string in a certain format is already provided. In that case, gazetteers can be used to retrieve geographic coordinates. However, many coastal collections are made on remote localities without specific names, such as a series of small bays between two distant cities. Efforts have been made to develop software (e.g., GEOLocate; Rios and Bart, 1997) combining the use of gazetteers and civilian GPS databases to cope with information such as road names and distances from cities. Unfortunately, most of the existing automation efforts are specifically designed for terrestrial collection databases, lacking proper maritime names, boundaries, and functions.

For instance, the software should allow specimens to be located at a certain distance from the shoreline. For relatively small collections, coordinates can also be manually obtained by identifying landmarks described in the locality fields or known by experienced field workers using Google Earth, a free GIS visualization tool with high to very high resolution satellite coverage of the entire globe (available online at http://earth.google.com). However, manually adding specimen coordinates to database records does increase the chance of errors in the coordinates when compared with automatically retrieving and adding coordinates.

Quality control of specimen coordinates is crucial. GIS allow for overlaying collection data with administrative boundary maps such as Exclusive Economic Zone (EEZ) boundaries, and comparing respective attribute tables to check for implausible locations. A common error, for instance, involves an erroneous positive or negative sign to a coordinate pair, resulting in locations on the wrong hemisphere, on land, or in open ocean. Additionally, when used in niche modeling studies (see Section 2.3), sample localities should be overlaid with raster environmental variable maps, to check if samples are not located on masked-out land due to the often coarse raster resolution.

2.2. REMOTE SENSING

In documenting the consequences of global change, it is crucial to repeatedly and automatically obtain baseline thematic and change detection maps of (commercially or ecologically critical) seaweed beds. It has long been acknowledged that remote sensing is an ideal technique to overcome numerous problems in mapping and monitoring seaweed assemblages (Belsher et al., 1985). Accessibility of seaweed-dominated areas can be an issue if the location is remote, and the exploration of rocky intertidal shores can be hard or even hazardous. More importantly, most benthic marine macroalgal assemblages are permanently submerged, restricting their exploration to SCUBA techniques. Thus, mapping and monitoring extensive stretches on a regular basis is very time- and resource-consuming when using in situ techniques only. This section provides an overview of different remote sensing approaches, without providing procedural information. For hands-on information on image processing techniques, see Green et al. (2000).

From a technical point of view, airborne remote sensing would seem most appropriate for seaweed mapping (Theriault et al., 2006; Gagnon et al., 2008). Light fixed-wing aircrafts are relatively easy to deploy, and sensors mounted on a light aircraft flying at low to moderate altitudes (1,000-4,000 m) will typically yield data sets with a very high spatial and spectral resolution. For instance, the Compact Airborne Spectrographic Imager can resolve features measuring only 0.25 x 0.25 m in up to 288 bands programmable between 400 and 1,050 nm in the visible and near-infrared (VNIR) light depending on the study object characteristics. Additionally, the low acquisition altitude can result in a negligible atmospheric influence. However, light aircraft are generally not equipped with advanced autopilot capabilities and are sensitive to winds and turbulence. It takes considerable time and effort to geometrically correct images acquired from such an unstable platform. Altitude differences combined with roll and pitch (aircraft rotations around its two horizontal axes) all result in different ground pixel dimensions. Moreover, low altitude acquisitions result in a limited swath, increasing both acquisition time (and hence expense) through the use of multiple flight transects and processing time to geometrically correct and concatenate the different scenes. Alternatively, a more advanced (and hence more expensive) and stable aircraft can acquire imagery at higher altitudes covering larger areas, but this is at the cost of spatial resolution and atmospheric influence.

Overall, atmospheric and weather conditions play an important role in aerial seaweed studies, as the aircraft and the airborne and ground crew must be financed over an entire standby period in areas with unstable weather conditions (quite typical for coastal areas), as the weather conditions at the exact moment of acquisition cannot be forecasted long enough in advance during the planning stage of the campaign.

In contrast, satellites are more stable platforms that can cover much larger areas in one scene daily to biweekly, making these ideal monitoring resources (Tables 1 and 2). However, satellite-based studies of seaweed assemblages were suffering from a lack of spatial resolution until the late 1990s. Typically, seaweed assemblages are very heterogeneous due to the morphology of rocky substrates, characterized by many differences in exposure to light, temperature fluctuations, waves, grazers, and nutrients on a small area. These differences result in many microclimates and niches, creating patchy assemblages in the scale of several meters to less than a meter, while no satellite sensor resolved features less than 15 m until 2000. From that year onwards, very high resolution sensors were developed and made commercially available (Table 1), allowing for detailed subtidal seaweed mapping and quantification studies in clear coastal waters (e.g., Andréfouet et al., 2004).

With the availability of more advanced sensors in the twenty-first century, a trade-off between spatial and spectral resolution became apparent (Fig. 2) - an issue of particular relevance to seaweed studies. The trade-off situation evolved because of computer processing power and data storage capacity limitations at the time of sensor development - often 5 years prior to launch followed by another 5 years of operation. This is a long time in terms of Moore's law (Moore, 1965), describing the pace at which computer processing power doubles. These historical limitations dictated a choice between a high spatial resolution and a high spectral resolution in current sensors, but not both, whereas seaweed studies would arguably benefit from both. While the main macroalgal classes (red, green, and brown seaweeds) are theoretically spectrally separable from each other as well as from coral and seagrass in three bands, this is not the case on a generic level. Additionally, information from seaweeds at below 5-10 m depth can only be retrieved from blue and green bands owing to attenuation of red and NIR in the water column. Hence, several blue and green bands can increase thematic resolution and the resulting classification accuracies, and this is of particular value in turbid waters, characteristic of many coastal stretches. By contrast, the absence of a blue band combined with only one green band (see several sensors in Tables 1 and 2) prevents spectral o to

Table 1. Current and future space-borne remote sensors apt for seaweed mapping and monitoring: technical features.

Sensor

Platform

Scene (km)

Spatial Res.

Spectral Char.

Temp. Res.

Availability

Cost

ETM+

Landsat 7

183x170

30 m (60 m TIR,

0.45-12.5 jim, 7 bands + 1 pan

16 days

1999-...

Free

15m pan)

ASTER

TERRA

60x60

15m (30 m SWIR,

0.52-11.65 jim, 14 bands

16 days

2000-...

$

90 m TIR)

-

IKONOS

11.3x 11.3

4 m (0.8 m pan)

0.45-0.9 jim, 4 bands + 1 pan

3-5 days

2000-...

$$$

off-nadir

ALI

EO-1

37x37

30 m (10 m pan)

0.433-2.35 jim, 9 bands + 1 pan

2000-...

$$

Hyperion

EO-1

7.5 x 100

30 m

0.4-2.5 jim, 220 bands

2000-...

$$

-

Quickbird

16.5x 16.5

2.4 m (0.6 m pan)

0.45-0.9 jim, 4 bands + 1 pan

1-3.5 days

2001-...

$$$

off-nadir

CHRIS

PROBA

14x 14

18 m (36 m)

0.40-1.05 jim, 18 bands (63

7 days

2001-...

Free

bands), programmable

HRG

SPOT 5

60x60

10 m (2.5 m pan)

0.5-1.75 jim, 4 bands + 1 pan

1-3 days

2002-...

$$

LISS 3-4

IRS-P6 (Re source Sat-1)

23.9x23.9

5.8 m (23.5 SWIR)

0.52-1.7, 4 bands

5 days

2003-...

-

FORMOSAT-2

24x24

8 m (2 m pan)

0.45-0.9 jim, 4 bands + 1 pan

1 day

2004-...

$$$

-

KOMPSAT-2 (=Arirang-2)

15x 15

4 m (1 m pan)

0.45-0.9 jim, 4 bands + 1 pan

3 days off-nadir

2006-...

$$$

AVNIR-2

ALOS

70x70

10 m (2.5 m pan)

0.42-0.89, 4 bands + 1 pan

2 days

2006-...

-

WorldView-1

17.6x17.6

0.5 m pan

1 pan

1.7-5.4 days

2007-...

$$$

-

WorldView-2

16.4x16.4

1.84 m (0.46 m pan)

8 bands + 1 pan

1.1-3.7 days

2009-...

$$$

-

PLEIADES-HR1-2

20x20

2.8 m (0.6 m pan)

0.43-0.95 jim, 4 bands + 1 pan

1 day off-nadir

1: 2009-...

using HR1-2

2: 2010-...

OLI

LDCM

185x 185?

30 m (15 m pan)

0.43-2.3 jim, 8 bands + 1 pan

16 days?

2011-2021

Table 2. Current and future space-borne remote sensors apt for seaweed mapping and monitoring: operational and quality remarks.

Sensor

Platform

Remarks

ETM+

ASTER

Hyperion

CHRIS HRG LISS 3-4

AVNIR-2

Landsat 7 TERRA

IKONOS

EO1 EO1

Quickbird PROBA SPOT 5

IRS-P6 (ResourceSat-1)

FORMOSAT-2

KOMPSAT-2 (= Arirang-2) ALOS

WorldView-1 WorldView-2 PLEIADES-HR1-2 LDCM

Highest quality earth observation data: calibration within 5%; Scenes flawed with 25% gaps since 2003 failure

Lack of blue band limits the use to intertidal and surfacing/floating seaweeds; VNIR cross track 24° off-nadir and NIR backward looking capability for stereo 3D imaging

Cross track 60° and along-track off-nadir capability for stereo 3D imaging

ALI is a technology verification instrument. EO-1 follows same orbit as Landsat 7 by about 1 min to benefit from Landsat 7's high quality calibration. EO-1 has cross-track off-nadir capability Cross and along-track 30° off-nadir capability for stereo 3D imaging

Technology verification instrument; Along track ±55° off-nadir capability for stereo 3D imaging Lack of blue band limits the use to intertidal and surfacing/floating seaweeds

Lack of blue band cf. SPOT 5; 26° off-nadir capability for stereo 3D imaging

Cross and along-track 45° off-nadir capability for stereo 3D imaging

Cross-track 30° off-nadir capability

44° off-nadir capability; Panchromatic stereo 3D

imaging

Cross-track 45° off-nadir capability; lack of multi-spectral information limits use to texture analysis Successor for WV-1; Cross-track 40° off-nadir capability

Planned successor in SPOT series; capable of steering 30° off-track and viewing 43° off-nadir Planned successor in Landsat series discrimination of submerged seaweeds altogether and confined early remote sensing studies on seaweeds to the intertidal range (Guillaumont et al., 1993). Besides the intertidal, NIR bands are useful (in combination with red) to discriminate surfacing or floating seaweeds, and allow one to discern decomposing macroalgae, as NIR reflection decreases with decreasing chlorophyll densities (Guillaumont et al., 1997).

From Fig. 2, it should be noted that two high spatial resolution spectral imaging sensors have been developed recently, Hyperion (onboard EO-1) and CHRIS (onboard PROBA), with a spectral resolution approaching that of airborne sensors, hence forming an exception on the historical trade-off. Ongoing

LOG spsctral res. (nr. of bands)

Hyperion

-100

CHRIS A

MODIS ♦

FY-3A

MERIS^

ASTER ♦

-10 "—

LDCM ♦ALI WorldView-2

1

HRC EROS-B World View-1 . ♦ . ♦ ♦

100 50

1 0.1 Lnfi VNIR «r n*n«n«fttf"l wwtial r°« 'm1

100 50

1 0.1 Lnfi VNIR «r n*n«n«fttf"l wwtial r°« 'm1

Figure 2. Trade-off between Log spectral resolution plotted against Log VNIR or pan-sharpened (where available) spatial resolution in current and future satellite sensors. All sensors are space-borne, except for the airborne CASI sensor, shown here for comparison. We consider sensors featuring a spatial resolution between 0 and 50 m and a spectral resolution above 50 bands in the visible and NIR spectrum of high value for seaweed mapping and monitoring (upper right quadrant). We therefore recommend future satellite sensor developments toward the CASI position, but note the position of the planned earth observation missions LDCM, Worldview-2, and Pleiades along the current trade-off situation (see Sections on 2.2 and 3.3). Current sensors; future sensors; current sensors forming an exception to the general trade-off situation between spectral and spatial resolution in satellite sensors (line).

research by the first author of this chapter suggests that CHRIS imagery can be used to map and monitor benthic communities in turbid waters at the south coast of Oman (Arabian Sea). Intertidal green, brown, and red seaweeds as well as submerged mixed seaweed beds, coral, and drifting decomposing seaweeds were discerned with reasonable accuracy during both monsoon seasons.

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