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# Seagrass mapping and monitoring along the coast of Crete, Greece

Polina Lemenkova

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## To cite this version:

Polina Lemenkova. Seagrass mapping and monitoring along the coast of Crete, Greece. 2011, 10.6084/m9 figshare.7435316.v1. hal-01999637

HAL Id: hal-01999637

https://hal.science/hal-01999637

Submitted on 30 Jan 2019

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Seagrass mapping and monitoring
along the coast of Crete, Greece

By Lemenkova Polina

March, 2011

Supervisors:

Valentijn Venus,

Albertus G.Toxopeus

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ITC

FACULTY OF GEO-INFORMATION SCIENCE AND EARTH OBSERVATION, UNIVERSITY OF TWENTE
ENSCHEDE, THE NETHERLANDS
Introduction

o Seagrasses - a unique group of aquatic plants growing submerged in the sea water

o Seagrasses play vital role in the marine ecosystems of the world Ocean (e.g. basis of the food web)

o Seagrasses create unique, complex, extremely diversified and productive ecosystems in the littoral coastal zones (0-50 meters) all over the world

o Globally, there are 58 recognized and described seagrass species

o In this work we focused on seagrass *P.oceanica*, an endemic species of the Mediterranean Sea

o The purpose of current study was to apply methods of remote sensing for mapping of seagrass *P.oceanica*
Research problem

More than 50 % of the world population lives within one km of the coast, which results in continued anthropogenic pressure on the coastal regions.

Mapping and environmental assessment of coastal resources become increasingly important nowadays and is necessary for monitoring of the shelf zones

This research is a contribution to the development of the methodology of seagrass mapping which aims on the environmental monitoring.

The case study of this work is *P.oceanica* seagrass, an endemic Mediterranean seagrass species, dominating in marine landscapes and ecosystems along the coasts of Crete Island.
# Most important facts about *P.oceanica*

*P.Oceanica* consists of long, 5-12 mm broad, hairy-like leaves, 3-4 mm thick roots and short rhizomes (0.5-2.0 mm).

The leaves of *P.oceanica* have length of 20-40 cm, in some cases they can reach up to 1 m

Growing *P.oceanica* make meadows which consist of smaller patches, called matte

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*P.Oceanica* creates one of the most productive Mediterranean ecosystems, usually serves as a perfect biological indicator for the assessment of quality of water and environment
# Geographic location of *P.oceanica*

*P.oceanica* is...

* Endemic species of the Mediterranean Sea

* Main species in the marine coastal Environment of Greece

* Creates dominant and most productive coastal ecosystem of the Mediterranean Sea

Distribution of *P.oceanica* is limited by the western part of the Mediterranean Sea, where cold Atlantic waters enter Gibraltar (as on picture)

<figure><img src="image_7.png" /></figure>
# Goal & general research objective

The main goal of this study is to analyse optical properties of the seagrass *P.oceanica* and other seafloor types (carbonate sand), and to apply remote sensing techniques for seagrass mapping in the selected locations of northern Crete

**General objectives:**

* Analysing spectral reflectance of the *P.oceanica* and other seafloor cover types by means of radiative transfer model tools (RTMs), using WASI.

* Mapping spatial distribution of the seagrass *P.oceanica* over selected locations along the northern coasts of Crete Island
# Specific objectives

1) To study narrow-band spectral reflectance properties of *P.oceanica* and other seafloor cover types (sand and silt) using WASI water colour simulation software

2) To use methods of the *in-situ* diving observations and underwater videometric measurements by Olympus ST camera in order to receive large-scale imagery of the *P.oceanica* mattes

3) To apply remote sensing data (Google Earth aerial images, Landsat scenes) for the monitoring of the seagrass meadows distribution

4) To perform images classification for the mapping of the *P.oceanica* distribution along the selected locations over the coasts of northern Crete.
Research questions

▸ Is *P.oceanica* spectrally distinct from carbonate sand with varying *in-situ* conditions ?

▸ Do broadband and hyperspectral sensors provide enough radiometric information for spectral discrimination of seagrass, and therefore, can be used for mapping of *P.oceanica*?
Research Approach

General methodological approach for the analysis of spectral reflectances

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# Seagrass global monitoring: history and perspectives

* The methodology of the current work was guided by various reports and guidelines published by scientific organisation focusing on seagrass research, such as following.

* Global-scaled: *Global Seagrass Monitoring Network* and the *World Seagrass Association*;

* The *World Atlas of Seagrasses* is published by the UNEP.

* Australian *Seagrasswatch*

* European: the *Mediterranean association Seagrass-2000*, the *Mediterranean Institute for Advanced Studies* and *Seagrasses.org*

* US American seagrass recovery campaign by the *Seagrassgrow*, *Seagrass Ecosystems Research Laboratory*

* in South Florida, *Seagrass.LI* and *Florida Seagrass* organisation;

* Asian: *UNEP/GEF South China Sea Project*, *Marine Conservation Cambodia* and *Sosmalaysia.org*.
# Application of the remote sensing towards seagrass mapping

Various methods and approaches have been applied towards studies of seagrass *P.oceanica*, based on following data:

* aerial Google Earth photographs,

* iPAQ data and GPS records

* non-destructive SCUBA based fieldwork sampling and seagrass observations

* videometric footage by means of Olympus waterproof camera

* multispectral imagery

Remote sensing techniques offer clear advantages for seagrass monitoring due to their following characteristics:

* weather-independence,

* cost-effectiveness,

* accuracy and

* spatial coverage

which enable periodic monitoring of the seagrass meadows and give access to the distant and unapproachable areas.
Measuring optical properties of
benthic vegetation (continue)

➤ The optical properties of the sea water vary with different environmental conditions

➤ Optical properties (e.g. spectral reflectance, radiance, irradiance) reflect current chemical content and physical specifics of the water

➤ Shallow waters generally contain more dissolved substances and suspended particles

➤ Radiative Transfer Models (RTM) are used for simulation and study of optical properties;

➤ In current work we used WASI Water Colour Simulator
# Measuring optical properties of benthic vegetation

Hyperspectral radiometers are used for measuring optical water and seafloor properties in *in-situ* conditions during the fieldwork

<figure><img src="image_9.png" /><figcaption>Trios-RAMSES-ACC-UV - Hyperspectral UVA/UVB
Irradiance Sensor: 280-500 nm</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Trios-RAMSES-ARC - Hyperspectral UVVIS
Radiance Sensor: 320-950 nm</figcaption></figure>
# In-situ observations of the seagrass

The method of in-situ seagrass sampling has been based on the standard scheme:

1. The seagrass is being sampled on the selected sites using transect lines, quadrant frame, single point markers, SCUBA gear diving equipment.

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2. The geographic coordinates of measurement path are taken by means of GPS and iPAQ, from where data are stored in GIS in laptop.

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3. The seagrass sampling is taken on the regular way to cover the research area

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# Materials

The research data include following materials:

* ✓ **Google Earth aerial images** and scenes from the Landsat TM ETM+.
  The imagery provides information of the recent distribution of *P.oceanica* within the coastal areas

* ✓ Sampling of the **in-situ measurements** of the seagrass distribution.
  Sampling stations were located in northern coasts of Crete (Ligaria beach), as northern Crete is well suitable for the seagrass *P.oceanica* growth due to the favourable climatic (annual mean water t° C) and geological seafloor factors, i.e. substrate conditions and sediments.

* ✓ The **fieldwork** has been carried out during the September-October period 2010

* ✓ The results of the **videographic measurements** are used for the seafloor types detection, because seafloor types can be well distinguished and classified according to their optical characteristics

* ✓ The **optical measurements** of the irradiance and radiance of the sea water and bottom cover types of the seafloor have been received in 2009 by means of the optical sensors *Trios-RAMSES Hyperspectral UVA/UVB Irradiance* and *UV-VIS Radiance Sensors* by Ms. S. Noralez
Materials (continue):
Flowchart for Data Capture

<figure><img src="image_14.png" /><figcaption>WORKFLOW FOR THE DATA AQUISITION</figcaption></figure>
Fieldwork research area

• Crete Island, Greece

• Ligaria Beach
(36°20′N;22°59′E)

• Ligaria Beach -
narrow, sandy
and pebble beach

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Fieldwork data collection

<figure><img src="image_18.png" /><figcaption>Monitoring different seafloor cover types:
matte of *P.oceanica* vs carbonate sand</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>Google Earth aerial imagery grabbing, Heraklion, the
University of Crete</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>Placing the 0.5m circle and depth marker in the matte
of *P.oceanica* for photo capture</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Sticking marker into the sea bottom in matte of
*P.oceanica* for depth measurements</figcaption></figure>
# Fieldwork equipment

1. Three iPAQs

2. Three GPS

3. Waterproof video cameras, Olympus ST 8000

4. Markers and cords for depth measurements

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Fieldwork equipment

▶ Waterproof plastic Otterbox

▶ SCUBA diving equipment

▶ Boat

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# Sampling design

The sampling design of the fieldwork was aimed at surveying of the spatial distribution of the meadows of *P.oceanica* along northern coasts of Crete, at sampling place Ligaria beach

* The fieldwork included several routes of the boat in the Ligaria beach sampling site, in the directions parallel to the coastline, ca 180-200 m long each one

* The transect sampling method enables even and objective selection of sampling sites and covers area of growing seagrass

* The videometric measurements of the seafloor cover types were made using underwater video cameras *Olympus ST 8010*

* Transect sampling method, i.e. photographs were taken along the research path

* Camera were adjusted horizontally by a leveller and mounted under the bottom of the boat
SAMPLING DESIGN

DEPTHS MEASUREMENTS FOR THE SEAGRASS MONITORING

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Scheme of the sampling design during the fieldwork.

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- TRANSECT MARKER (d): 5 cm depth, 30 cm height

Using of markers:

1) Position of transect line

2) Depth of the sea floor

3) Depth of the marker (d)

Measurement important points:

1. Photos of the seagrass meadow

2. Type of the sediment sand: coarse, fine vs middle-sized

3. Name of species (mostly Posidonia)

4. % cover of the seagrass
Locations of the videometric
measurements and GPS tracklogs

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Various seafloor cover types of on
Crete Island

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# Various seafloor cover types (enlarged). Ligaria beach, Crete.

<figure><img src="image_33.png" /><figcaption>Microrelief of the seafloor</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>Seafloor type: rocks</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>Seafloor type: gravel & rocks</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>Seafloor type: seagrass coverage</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>Seafloor type: sand & gravel</figcaption></figure>
Review of the collected data

The collected data consist of the following types:

✓ Optical spectra of *P.oceanica*, carbonate sand, seawater with sediments and seawater measured in aquarium tank, without sediments, at different environmental conditions

✓ Aerial imagery from the *Google Earth*

✓ Satellite images from various open sources
(Landsat)

✓ Results of underwater videometric measurements of the *Olympus ST* cameras made during the ship route
Optical measurements using Trios-
RAMSES: spectral dataset from 2009

In this work we also used materials of previous measurements made by Ms. S. Noralez in 2009 using Trios-RAMSES hyperspectral radiometer

A data collection of visible spectra of two seafloor cover types – *P.oceanica* and carbonate sand – consist of data measured by means of RAMSES

o 700 multiple measurement sets of *P.oceanica*;

o 106 for water without sediments, measured in aquarium tank;

o 27 for seawater with sediments measured in aquarium tank;

o 75 for carbonate sand

☐ The spectrometer Trios-RAMSES was adjusted for automatic measurements mode, with measurements taken as fast as possible.

☐ Trios-RAMSES head was held submerged, and the sampling was controlled by an operator on the surface boat.

☐ The head of the sensor was pointed downward at an angle of 0° (nadir) in order to capture the spatial discernibility in the radiance for the benthic cover types.

☐ The frame was held at 45° angle in order to keep sensor looking down at 0° (nadir view).

☐ A waterproof camera was attached to the platform to assist with the identification of the target object being measured
# Normality testing

Frequency normality test against normal distribution: radiance of the seawater, measured in aquarium tank.

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Frequency normality test against gamma distribution: radiance of the seawater, measured in aquarium tank
# Spectral modelling method: WASI water colour simulator

* WASI is an RTM software used for artificial modelling of the seawater optical properties

* It enables simulation of radiance distribution within a water column and to understanding optical properties of seafloor cover types

* WASI has been chosen among other RTMs due to its

* effectiveness,

* adaptability for the Mediterranean environment,

* open source availability,

* coverage of necessary wavebands

* clear, user friendly interface, enabling to adjust various environmental parameters.

<figure><img src="image_40.png" /><figcaption>Main interface of Water Colour Simulator (WASI) software</figcaption></figure>
# Spectral simulation of aquatic objects

* The main aim of spectral simulation using RTMS is to clarify if the bottom reflectance of two different seafloor types – mattes of seagrass *P.oceanica* and carbonate sand – differ and can be clearly discriminated during images interpretation for further mapping

* The remote sensing reflectance has been compared under the conditions of different water depths and cover fraction of the seafloor, in order to assess spectral signatures of the seagrass and carbonate sand as major seafloor types

* The ecological variables, specific to the field environmental conditions, were factored into the WASI-based simulation models. Through the WASI simulation process, imitating spectral properties of *P.oceanica* and carbonate sand for various broadband and narrowband sensors, models were created that accounted for not only atmospheric conditions (i.e. sun zenith angle), but also height of water column, thus approaching it to the Mediterranean conditions, and chemical content of the seawater
# Assumptions.

## WASI Modelling parameters: depth and bottom cover fraction

* We assumed constant values of the optical properties of the seawater, phytoplankton, total amount of suspended particles and solids, atmospheric conditions, as well as CDOM, which have been set up in modelling part of this work, during WASI simulations of various remote sensors.

* The specific parameters have been chosen for the simulation of the environmental conditions of Mediterranean Sea, endemic for P.oceanica seagrass

* Although seagrass *P.oceanica* can be found until depth limits down to 40 m depth, the most preferable limits of its distribution in the Mediterranean Sea, and most suitable for the research are shallow waters until 4 meters of depth.
Assumptions (continue)

▸ concentration of phytoplankton is accepted at the interval of 0,035 - 0,089mg⁻¹

▸ viewing zenith angle is near to 90°

▸ reflection factor of sky radiance = 0.0201

▸ water temperature lies in the diapason 17-25°C

▸ The anisotropy factor of upwelling radiation or the quality (Q-) factor is taken as 5

▸ concentration of phytoplankton is accepted at the interval of 0,035-0,089mg⁻¹

▸ Reference wavelength for Coloured Dissolved Organic Matter (CDOM) absorption is equal to 440

▸ The backscattering is accepted to be 0,00144m⁻¹

▸ The coefficient of attenuation = 1.0546

▸ The concentration of non-chlorophyll particles (absorption at λ0) and concentration of small suspended particles is equal to zero, so we do not count them in this work

▸ Exponent of CDOM (Gelbstoff) absorption is accepted as 0.0140

▸ The Bidirectional Reflectance Distribution Function (BRDF) of bottom reflectance (sand) is assumed to be 0.318 sr⁻¹
Assumptions (continue)

Model-specific parameters of water colour simulator WASI, adjusted to simulate environmental conditions of the Mediterranean Sea along Crete.

For the spectral analysis we applied forward calculations, i.e. a computing and plotting of series of spectra according to specified parameter settings, with exactly defined depths and cover fraction.

<table><thead><tr><td>Parameter, WASI</td><td>Name and description</td><td>Values</td></tr></thead><tbody><tr><td>C-L</td><td>Concentration of large suspended particles</td><td>8</td></tr><tr><td>C(i), i=0..5</td><td>Concentration of Phytoplankton</td><td>0.035-0.089 µg-l</td></tr><tr><td>bbS</td><td>Specific backscattering for small particles</td><td>0.005 m2g-1</td></tr><tr><td>T-W</td><td>Temperature of water</td><td>17-25 C</td></tr><tr><td>n</td><td>Exponent of Backscattering by small particles</td><td>0.005 m2g-1</td></tr><tr><td>Q</td><td>Anisotropy factor of upwelling radiation ("Q-factor")</td><td>5.00</td></tr><tr><td>sigma-L</td><td>Reflection factor of sky radiance</td><td>0.0201</td></tr><tr><td>bl</td><td>Backscattering coefficient of saline waters</td><td>0.00144 m-1</td></tr><tr><td>0</td><td>Reference Wavelength for Gelbstoff absorption</td><td>440</td></tr><tr><td>sun</td><td>Sun zenith angle</td><td>45.0</td></tr><tr><td>zB</td><td>Bottom depth</td><td>4.00</td></tr><tr><td>f(i), i=0...5</td><td>Areal fraction of bottom surface type number n</td><td>01/10/10</td></tr><tr><td>K0</td><td>Coefficient of Attenuation</td><td>1.0546</td></tr><tr><td>view</td><td>Viewing angle (0 = nadir)</td><td>0</td></tr><tr><td>C-X</td><td>Concentration of non-chlorophyllous particles (absorption at 0)</td><td>0</td></tr><tr><td>S</td><td>Reference wavelength for scattering of small particles</td><td>500</td></tr><tr><td>C-S</td><td>Concentration of small suspended particles</td><td>0</td></tr><tr><td>S</td><td>Exponent of Gelbstoff absorption</td><td>0.0140</td></tr><tr><td>C-Y</td><td>Concentration of Gelbstoff (absorption at 0)</td><td>0.400</td></tr><tr><td>Bn</td><td>BDRF of bottom reflectance (sand)</td><td>0.318 sr-1</td></tr></tbody></table>
Results

The finding of these studies showed that the relationship
between the spectral reflectance of various seafloor cover types
was tied to depth, i.e. water column height.

* The results of the *in-situ* fieldwork measurements revealed that spectral reflectance of *P.oceanica* undergo alterations at depths of 0.5, 2.0 and 3.5m and differ from carbonate sand

* Studies of the broadband and narrowband sensors demonstrate that simulated spectra of the seagrass, made using WASI modeller, have the best results at CZCS scanner, especially devoted to the measurement of ocean color.

* Other remote sensors (MODIS, SeaWiFS) may also be used for the seagrass mapping, because their technical characteristics enable to spectrally discriminate P.oceanica seagrass from other seafloor cover types particularly carbonate sand, as tested in the current work.
# Data pre-processing: raw observations

Right: preliminary statistical analysis of spectral reflectance of *P.oceanica*

<figure><img src="image_41.png" /></figure>

Above: fragment of the spreadsheet with observed data: transposing columns to rows.

<table><thead><tr><th>Wavelength, nm</th><th>mean</th><th>Q1</th><th>min</th><th>Q3</th><th>max</th><th>median</th></tr></thead><tbody><tr><td>318.23354</td><td>0.030467</td><td>0.038907</td><td>0.029764</td><td>0.069762</td><td>0.116100</td><td>0.048984</td></tr><tr><td>338.29900</td><td>0.033560</td><td>0.043030</td><td>0.032847</td><td>0.076252</td><td>0.128290</td><td>0.054334</td></tr><tr><td>358.37932</td><td>0.039897</td><td>0.051320</td><td>0.039131</td><td>0.091143</td><td>0.154409</td><td>0.066354</td></tr><tr><td>378.47247</td><td>0.045786</td><td>0.058849</td><td>0.045039</td><td>0.105993</td><td>0.177150</td><td>0.078339</td></tr><tr><td>398.57640</td><td>0.052346</td><td>0.066859</td><td>0.051625</td><td>0.122328</td><td>0.201038</td><td>0.091905</td></tr><tr><td>418.68905</td><td>0.058942</td><td>0.074842</td><td>0.058240</td><td>0.138984</td><td>0.226028</td><td>0.106207</td></tr><tr><td>438.80838</td><td>0.070690</td><td>0.089761</td><td>0.069999</td><td>0.167825</td><td>0.267712</td><td>0.130580</td></tr><tr><td>458.93235</td><td>0.080107</td><td>0.102089</td><td>0.079392</td><td>0.191424</td><td>0.304933</td><td>0.149762</td></tr><tr><td>479.05891</td><td>0.086278</td><td>0.110348</td><td>0.085546</td><td>0.207940</td><td>0.332934</td><td>0.162881</td></tr><tr><td>499.18600</td><td>0.098498</td><td>0.126592</td><td>0.097689</td><td>0.239161</td><td>0.380603</td><td>0.188192</td></tr><tr><td>519.31159</td><td>0.110222</td><td>0.143976</td><td>0.109022</td><td>0.265948</td><td>0.421370</td><td>0.210237</td></tr><tr><td>539.43363</td><td>0.125694</td><td>0.166156</td><td>0.124310</td><td>0.302678</td><td>0.475842</td><td>0.240492</td></tr><tr><td>559.55006</td><td>0.131844</td><td>0.175865</td><td>0.130257</td><td>0.319385</td><td>0.503907</td><td>0.253726</td></tr><tr><td>579.65885</td><td>0.121915</td><td>0.166084</td><td>0.120186</td><td>0.302078</td><td>0.485661</td><td>0.237873</td></tr><tr><td>599.75794</td><td>0.077236</td><td>0.111113</td><td>0.075669</td><td>0.200557</td><td>0.343876</td><td>0.154016</td></tr><tr><td>619.84529</td><td>0.057038</td><td>0.085386</td><td>0.055588</td><td>0.154781</td><td>0.281737</td><td>0.117003</td></tr><tr><td>639.91885</td><td>0.050841</td><td>0.077438</td><td>0.049432</td><td>0.141810</td><td>0.265181</td><td>0.106188</td></tr><tr><td>659.97657</td><td>0.038118</td><td>0.059971</td><td>0.036997</td><td>0.110774</td><td>0.223366</td><td>0.082411</td></tr><tr><td>680.01641</td><td>0.032892</td><td>0.052265</td><td>0.032012</td><td>0.095789</td><td>0.198389</td><td>0.071686</td></tr><tr><td>700.03633</td><td>0.034756</td><td>0.058426</td><td>0.033643</td><td>0.101504</td><td>0.215413</td><td>0.075789</td></tr><tr><td>720.03426</td><td>0.026287</td><td>0.047084</td><td>0.025162</td><td>0.082940</td><td>0.193535</td><td>0.061184</td></tr><tr><td>740.00817</td><td>0.009740</td><td>0.022355</td><td>0.010145</td><td>0.042861</td><td>0.132244</td><td>0.029414</td></tr><tr><td>759.95601</td><td>0.008651</td><td>0.020234</td><td>0.009191</td><td>0.039371</td><td>0.128165</td><td>0.026558</td></tr><tr><td>779.87573</td><td>0.006019</td><td>0.014157</td><td>0.006397</td><td>0.028320</td><td>0.100314</td><td>0.019322</td></tr><tr><td>799.76528</td><td>0.008607</td><td>0.020030</td><td>0.009050</td><td>0.037323</td><td>0.118123</td><td>0.026535</td></tr><tr><td>819.62263</td><td>0.008777</td><td>0.020295</td><td>0.009288</td><td>0.037598</td><td>0.118055</td><td>0.026799</td></tr><tr><td>839.44571</td><td>0.002562</td><td>0.008010</td><td>0.002864</td><td>0.017472</td><td>0.076515</td><td>0.011432</td></tr><tr><td>859.23249</td><td>0.002653</td><td>0.006921</td><td>0.002897</td><td>0.016121</td><td>0.070377</td><td>0.009889</td></tr><tr><td>878.98091</td><td>0.002327</td><td>0.008080</td><td>0.002821</td><td>0.018625</td><td>0.082462</td><td>0.011617</td></tr><tr><td>898.68893</td><td>0.004077</td><td>0.013098</td><td>0.004709</td><td>0.031217</td><td>0.101146</td><td>0.019767</td></tr><tr><td>918.35451</td><td>0.006215</td><td>0.020162</td><td>0.007229</td><td>0.045213</td><td>0.199711</td><td>0.029950</td></tr><tr><td>937.97559</td><td>0.005783</td><td>0.026132</td><td>0.007307</td><td>0.065475</td><td>0.195072</td><td>0.040848</td></tr></tbody></table>
Statistical analysis of the
observational data

The total amount of measured data was large and
included following datasets made using
hyperspectral radiometer Trios-Ramses in 2009:

350 measurement sets of *P.oceanica* reflectance
for 14th Oct,

400 sets of *P.oceanica* reflectance for 15th Oct,

84 datasets for seawater reflectance with
sediments,

105 datasets for seawater reflectance without
sediments,

87 sets for spectral reflectance of carbonate sand
A statistical approach was used for proper
processing of such amounts of data.
Implementation of statistical
analysis

<figure><img src="image_42.png" /></figure>
# Radiance and irradiance of the seawater with sediments

<figure><img src="image_43.png" /><figcaption>Model 6: LAD, using observations 1:01–8:23 (T = 1
Dependent variable: v16</figcaption></figure>

<table><caption>If seawater, measured in aquarium tank. Smooth Bezier interpolation. Visualization in GNUplot</caption><thead><tr><th></th><th>Coefficient</th><th>Std. Error</th><th>t-ratio</th><th>p</th></tr></thead><tbody><tr><td>const</td><td>0.738911</td><td>0.0862011</td><td>8.5719</td><td>0</td></tr><tr><td>v1</td><td>-0.000775186</td><td>0.000114010</td><td>-6.7993</td><td>0</td></tr><tr><td>Median depend. var</td><td>0.227857</td><td></td><td></td><td></td></tr><tr><td>Sum absolute resid</td><td>30.22342</td><td></td><td></td><td></td></tr><tr><td>Log-likelihood</td><td>28.74727</td><td></td><td></td><td></td></tr><tr><td>Schwarz criterion</td><td>-46.99000</td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_44.png" /></figure>
Spectral reflectance of the
seawater with sediments

<figure><img src="image_45.png" /></figure>

measured in aquarium tank, HCMR, Crete
Spectral reflectance of the
seawater without sediments

<figure><img src="image_46.png" /></figure>
Optical properties of the
seawater with sediments

► The graphs showing optical properties of seawater with and without sediments focused on spectral variability of the water with changed physical and chemical content.

* The alterations in the individual spectral signatures of single measurements reflect individual health properties of leaves: different nitrogen and chlorophyll content causing diverse colour pigmentation and light absorption, water content in leaves and plant physiological conditions, which vary across seagrass meadow, shoot morphology, etc.

* The differences in spectral reflectance values of the measurements taken on various days might have been caused by the impact of atmospheric conditions, such as solar radiation and sun illumination by different zenith angle.
Statistical evaluation of data
(continue): seawater with
sediments measured in aquarium

Exponential moving
average
of spectral reflectance
of seawater with
sediments.
Ligaria Beach, Crete.
Example for
measured variable
V15

<figure><img src="image_47.png" /></figure>
Optical properties of the seawater with sediments, measured in aquarium tank

<figure><img src="image_48.png" /><figcaption>Radiance of the seawater with sediments, watts per steradian per square metre (W*sr-1*m-2)</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>Irradiance of the seawater with sediments, ergs per square centimeter per second (erg*cm-2*s-1).</figcaption></figure>

<figure><img src="image_50.png" /></figure>
Statistical evaluation of spectral
measurements of *P.oceanica* reflectance

<figure><img src="image_51.png" /></figure>

The reflectance spectra of
*P.oceanica* show a values
maxi mum between 450
nm and 600 nm,

1. first, because of the chlorophyll absorption peak at 465 and 665nm

2. secondly, because of
the weakening of
CDOM (or Gelbstoff) in
the blue part of the VIS
spectrum, as it most
strongly absorbs short
wavelength light in blue
to ultraviolet range,

3. and finally, because the
absorption of the
seawater increases in
the red part of the VIS
spectra.
Statistical evaluation of spectral
measurements of *P.oceanica* reflectance

<figure><img src="image_52.png" /></figure>

Absorbance spectra of
chlorophyll in a solvent.
The spectra of chlorophyll
molecules are slightly modified
depending on specific
pigment-protein interactions.
Source of picture above:
Wikipedia.org

Remote sensing reflectance of *P.oceanica*.
Series 1-25. Shown midspread of the
statistical quartiles Q1 and Q3 (vertical
dashes) and mean value within the range

Spectral reflectance of the seawater without sediments. Measurements No 1-25, 15th October.

<figure><img src="image_53.png" /></figure>
Spectral reflectance of P.oceanica

The analysis of spectra
shows that the
appropriate wavebands
for seagrass mapping
lay between 500 and
600 nm, and has also
peaks at around
700 nm, ca between 680
and 710 nm.

The highest values of
the bottom reflectance
are at spectra
of 500-600 nm.

The most appropriate
depths at which the
spectral signatures of
the seagrass could be
discriminated are lesser
than 2.5 meters.

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /></figure>
# Spectral reflectance of carbonate sand

The analysis of the spectral signatures of the seagrass *P.oceanica* and carbonate sand clearly shows that seagrass has spectral reflectance much lesser than that of a carbonate sand, in general not increasing values of 10% reflectance in spectra of 500-600 nm, while carbonate sand has spectral reflectance approaching 63% in its highest values.

<figure><img src="image_58.png" /></figure>

Spectral reflectance of carbonate sand.
A.Pelagia beach. Results of single
measurement set made
by spectroradiometer Trios-RAMSES

<figure><img src="image_59.png" /></figure>
Bottom albedo of carbonate sand
and seagrass *P.oceanica*.
Agia Pelagia, Crete.

<figure><img src="image_60.png" /><figcaption>(a) Sand</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>(b) *P.oceanica*</figcaption></figure>
# Hypothesis

For the Research Question 1 the *Hypothesis Ho* claims: seagrass types are not spectrally distinct from other seafloor types with varying in-situ conditions, which means
*Ho : μ1 = μ2 = μ3 = ... = μn*.

The alternative *Hypothesis Ha* claims the opposite statement:
seagrass is spectrally distinct with varying in-situ conditions, *Ho : μ1 ≠ μ2 ≠ μ3 ≠ ... ≠ μn*.

SPSS statistical analysis and hypothesis testing:

ANOVA one-way analysis: results of the single factor (depth) testing of spectral reflectance of *P.oceanica* at various depths: 0.5, 1.5 and 2.5 m

<table><thead><tr><th>Source of Variation</th><th>SS</th><th>df</th><th>MS</th><th>F</th><th>P value</th><th>F crit</th></tr></thead><tbody><tr><td>Between Groups</td><td>373841.7048</td><td>2</td><td>186920.8524</td><td>407.85359</td><td>1.11677</td><td>3.01153</td></tr><tr><td>Within Groups</td><td>261233.1668</td><td>570</td><td>458.3038014</td><td></td><td></td><td></td></tr><tr><td>Total</td><td>635074.8716</td><td>572</td><td></td><td></td><td></td><td></td></tr></tbody></table>

P more than .05, which means that there is a significant difference in radiance of *P.oceanica* at three different depth (0.5, 1.5 and 2.5).
Answering *Research Question 1:*
comparative analysis of spectral signatures of
*P.oceanica* and carbonate sand

<figure><img src="image_62.png" /><figcaption>Comparison of spectral reflectance of seagrass *P.oceanica* and carbonate sand (evaluated in GRETL)</figcaption></figure>
Answering *Research Question 1:*
comparative analysis of spectral signatures of
*P.oceanica* and carbonate sand

These results indicate that seagrass *P.oceanica*
can be detected and discriminated from other
seafloor cover types (carbonate sand) with
varying environmental conditions, i.e. water
column height, by hyperspectral
spectroradiometers (Trios-RAMSES), which
positively answers the first research question
of this thesis ("Is *P.oceanica* spectrally distinct
from carbonate sand with varying *in-situ* conditions?").
Answering Research Question 1 (continue):
distinguishability of spectral signatures of
*P.oceanica* and carbonate sand

The results of spectral
measurements at
various depths
shown:

*P.oceanica* is spectrally
distinct from other
sea floor types
(carbonate sand),
based on differences
in their spectral
signatures,
with changing
environmental
conditions:
increasing water
column height, i.e.
depths.

<figure><img src="image_63.png" /><figcaption>Simulated remote sensing reflectance of
*P.oceanica* at various depths: 0.5, 2.0 and 3.5 m</figcaption></figure>
Answering Research Question 2

Do broadband and hyperspectral sensors provide enough radiometric information for spectral discrimination of seagrass, and therefore, can be used for mapping of seagrass *P.oceanica*?

Separated plots of
simulated remote
sensing
reflectance of
seagrass
*P.oceanica* at
various sensors:
MODIS,
MERIS,
SeaWiFS and
CZCS,
iterated over three
depths

<figure><img src="image_64.png" /><figcaption>(a) CZCS</figcaption></figure>

<figure><img src="image_65.png" /><figcaption>(b) MERIS</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>(c) MODIS</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>(d) SeaWiFS</figcaption></figure>
Answering Research Question 2
(continue: comparison of various sensors)

Combined plots of
simulated remote
sensing reflectance of
seagrass *P.oceanica*
at various sensors:
MODIS,
MERIS,
SeaWiFS and
CZCS,
iterated over three
depths, as stripes
shown spectral bands
covered by these
sensors

<figure><img src="image_68.png" /></figure>
Answering *Research Question 2*
(continue)

* Studies of simulated spectra of the seagrass, made using WASI modeller, demonstrated best results at CZCS scanner, especially devoted to the measurement of ocean color. The spectrum of *P.oceanica* reflectance, simulated for CZCS, covers the wavelength interval of 400-800 nm, and is distinctive for various depths.

The second research question of this MSc thesis ("Do broadband and hyperspectral sensors provide enough radiometric information for spectral discrimination of seagrass, and therefore, can be used for mapping of *P.oceanica*?") is therefore answered with "yes", and the most suitable sensor is the Coastal Zone Color Scanner CZCS
GIS mapping of seagrass

<figure><img src="image_69.png" /></figure>

► Arc GIS 10.0 has been used for data incorporation, storage, analyses, visualizing and mapping.

► Data integration: the integrated approach used in this research work has high potential as a means to monitor changes in seagrass landscape occurring in shallow waters over Crete area.

► Current work integrated data from various sources: high resolution aerial color *Google Earth* images, spaceborne satellite imagery, assessment of spectral signatures using WASI software and their statistical analysis, image processing by means of Erdas Imagine and Arc GIS based mapping.

► The final mapping has been supported in ArcGIS through the data exporting, conversion and integration in one GIS-project
# Google Earth aerial imagery for the seagrass mapping

## Capturing aerial imagery from the Google Earth

<figure><img src="image_70.png" /></figure>
Image processing using Erdas
Imagine

The *in-situ* field large-scale matte-level level of seafloor monitoring was
then upscaled to airborne *Google Earth* aerial imagery interpretation, to
provide a meadow-level view of seagrass landscapes.

enables to analyze
environmental
changes within
seagrass landscapes
based on data from
various sources:
aerial and satellite
images,
geographically
referenced maps of
Crete island and
results of images
classification
showing areas of
seagrass
distribution.

<figure><img src="image_71.png" /></figure>
# Seafloor classification on Google Earth imagery

Supervised classification is based on training sites of seafloor cover types. Spectral properties were used for classification of seafloor types: carbonate sand, patches and meadows of seagrass *P.oceanica* and rocks.

<figure><img src="image_72.png" /></figure>

<figure><img src="image_73.png" /></figure>

The classification is based on the properties of seafloor cover types, incl. *P.oceanica* seagrass: brightness, colour, texture and structure of the seagrass mattes.
# Image processing using Erdas Imagine

The analysis of the imagery of the Cretan coasts is based on the images classification and is aimed to investigate the distribution of the seagrass *P.oceanica* within the research area.

<figure><img src="image_74.png" /><figcaption>Unsupervised classification</figcaption></figure>

<figure><img src="image_75.png" /><figcaption>Supervised classification</figcaption></figure>
# Accuracy assessment for unsupervised classification

Overall Kappa (k) accuracy is calculated using the formula: $\Sigma A=N$, where A is number of correctly mapped points (172) and N is the total number of points (270). Thus, according to the results the overall accuracy = 172/270 = 0.6370, which is 64%. Overall accuracy for unsupervised classification = 64%.

## Users accuracy
(Reliability of classes) varies between 0.22 and 0.94 depending on class, which proves that supervised classification has better results for seagrass mapping than the unsupervised classification.

Producer accuracy lies in interval between 0.52-0.77, according to class.

<table><thead><tr><th rowspan="2">Correctly classified</th><th colspan="4">Rocky bottom</th><th colspan="4">Seagrass Poceanica</th><th colspan="4">Shelf waters, 0-3m</th><th colspan="4">Seagrass, other > 3m</th><th rowspan="2">Ground</th><th rowspan="2">Buildings (roofs)</th><th rowspan="2">Trees</th><th rowspan="2">Bushes</th><th rowspan="2">Total</th><th rowspan="2">k producer accuracy</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>Rocky bottom</td><td>14</td><td>2</td><td>0</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>2</td><td>0</td><td>1</td><td>1</td><td>24</td><td>0.58</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Shelf, < 3m</td><td>0</td><td>12</td><td>0</td><td>3</td><td>1</td><td>2</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>20</td><td>0.60</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Healthy seagrass</td><td>0</td><td>1</td><td>10</td><td>3</td><td>2</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>19</td><td>0.52</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Seagrass Poceanica</td><td>0</td><td>1</td><td>1</td><td>13</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>2</td><td>0</td><td>0</td><td>1</td><td>21</td><td>0.62</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Carbonate sand</td><td>2</td><td>0</td><td>0</td><td>0</td><td>16</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>20</td><td>0.80</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Shelf waters, 0-3m</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>9</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>14</td><td>0.64</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Shelf waters, 3-7m</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>3</td><td>8</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>15</td><td>0.53</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Deep waters, > 7m</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>2</td><td>3</td><td>16</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>25</td><td>0.64</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fields: corn, greens</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>14</td><td>0</td><td>0</td><td>0</td><td>3</td><td>4</td><td>21</td><td>0.66</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Roads: asphalt+ground</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>5</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9</td><td>0.55</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Seagrass, other > 3m</td><td>0</td><td>0</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>24</td><td>2</td><td>0</td><td>1</td><td>1</td><td>31</td><td>0.77</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ground</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>11</td><td>3</td><td>1</td><td>1</td><td>17</td><td>0.65</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Buildings (roofs)</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9</td><td>2</td><td>3</td><td>16</td><td>0.56</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Trees</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>7</td><td>3</td><td>12</td><td>0.58</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Bushes</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>4</td><td>6</td><td>0.66</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total</td><td>20</td><td>22</td><td>13</td><td>22</td><td>26</td><td>17</td><td>13</td><td>17</td><td>17</td><td>6</td><td>31</td><td>17</td><td>13</td><td>18</td><td>18</td><td>270</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>k user's accuracy</td><td>0.70</td><td>0.55</td><td>0.77</td><td>0.59</td><td>0.62</td><td>0.53</td><td>0.62</td><td>0.94</td><td>0.82</td><td>0.83</td><td>0.77</td><td>0.65</td><td>0.69</td><td>0.39</td><td>0.22</td><td>-</td><td>0.64</td><td></td><td></td><td></td><td></td></tr></tbody></table>
Accuracy assessment for supervised classification

<table><thead><tr><th>Correctly classified</th><th>Seagrass-1</th><th>Roads</th><th>Fields</th><th>Earth</th><th>Forest</th><th>Buildings</th><th>Seagrass-2</th><th>Seagrass-3</th><th>Terrace</th><th>Seagrass-4</th><th>Water</th><th>Total</th><th>κ producer accuracy</th></tr></thead><tbody><tr><td>Seagrass<br>Poceanica</td><td>22</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1</td><td>0</td><td>1</td><td>1</td><td>27</td><td>0.81</td></tr><tr><td>Roads</td><td>0</td><td>18</td><td>0</td><td>3</td><td>1</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>24</td><td>0.75</td></tr><tr><td>Fields</td><td>0</td><td>1</td><td>10</td><td>0</td><td>3</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>15</td><td>0.66</td></tr><tr><td>Earth</td><td>0</td><td>1</td><td>1</td><td>13</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>17</td><td>0.76</td></tr><tr><td>Forest</td><td>0</td><td>1</td><td>1</td><td>1</td><td>9</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>13</td><td>0.69</td></tr><tr><td>Buildings</td><td>0</td><td>0</td><td>2</td><td>0</td><td>1</td><td>24</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>28</td><td>0.85</td></tr><tr><td>Seagrass-2</td><td>3</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>33</td><td>2</td><td>0</td><td>2</td><td>2</td><td>42</td><td>0.78</td></tr><tr><td>Seagrass-3</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>27</td><td>0</td><td>1</td><td>1</td><td>31</td><td>0.87</td></tr><tr><td>Terrace</td><td>0</td><td>2</td><td>3</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>19</td><td>0</td><td>0</td><td>26</td><td>0.73</td></tr><tr><td>Seagrass-4</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>2</td><td>0</td><td>37</td><td>1</td><td>43</td><td>0.86</td></tr><tr><td>Water</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>14</td><td>19</td><td>0.74</td></tr><tr><td>Total</td><td>29</td><td>23</td><td>17</td><td>18</td><td>14</td><td>28</td><td>39</td><td>33</td><td>23</td><td>42</td><td>19</td><td>285</td><td>-</td></tr><tr><td>κ user's accuracy</td><td>0.76</td><td>0.78</td><td>0.59</td><td>0.72</td><td>0.64</td><td>0.86</td><td>0.85</td><td>0.82</td><td>0.83</td><td>0.88</td><td>0.74</td><td>-</td><td>0.72</td></tr></tbody></table>

Overall Kappa (κ) accuracy is calculated using the formula: $Σ A=N$, where A is number of correctly mapped points (226) and N is the total number of points (285).

Overall κ accuracy for supervised classification = 72%.

Users accuracy (Reliability of classes) varies between 0.59 and 0.88 depending on class. Producer accuracy lies in interval between 0.66-0.87 according to class as well.
Discussion

❖ An approach of the seagrass spectral analysis, monitoring and mapping has been taken in this work, which integrates various research techniques and tools, combining remote sensing methods of spectral analysis of the seafloor cover types, and knowledge of the ecology of *P.oceanica*, with the aim to develop a method of seagrass spectral optical discrimination for the seagrass mapping based on the aerial imagery classification

❖ The relationship between the optical properties (spectral reflectance) of the seafloor cover types and hydrological parameters of the environment has been studied in order to analyse limitations and capabilities of broadband and narrowband sensors under the conditions of altering environmental parameters

❖ For further development of the remote sensing based monitoring and mapping of the seagrass and other seafloor cover types it is desirable to consider upscale mapping with concern to bathymetry

❖ Landscape fragmentation and patchiness in seagrass meadows is caused, besides natural reasons, by the anthropogenic disturbances (e.g. ocean trawling).
Remote sensing for seagrass
mapping

Application of the remote sensing towards seagrass mapping in this work is based on the assumption that various types of the seafloor bottom have different characteristics of the reflectivity, which is visually expressed in distinct colours of the objects (seagrass *P.oceanica* and carbonate sand).

* In its turn, reflectivity of the sediments is affected by the water optical properties and content (suspended particles, microalgae, etc)

* Measuring optical properties of the seawater allows to calculate spectra of the objects and to discriminate them on the aerial and satellite images which enables spectral discrimination of submerged vegetation and other seafloor cover types
# Upscale mapping of the seagrass landscapes

<figure><img src="image_76.png" /><figcaption>Seagrass meadows</figcaption></figure>

<figure><img src="image_77.png" /><figcaption>Seagrass mattes</figcaption></figure>

The general principles of the hierarchy within the seagrass landscapes are based upon the quantitative analysis of the spatial patterns, consisted by components and separate elements.

1) Bunches of individual shoots construct **patches** of seagrass

2) Patches are arranged into discrete clumps of **mattes** (at a scale of several *cm-meters*).

3) Mattes create **beds** with 1-100m in diameter.

4) Seagrass beds are arranged into homogeneous, continuous seagrass meadows that may reach in size from tens up to hundreds *meters* and even extend over kilometre-wide areas.

Meadows are sometimes defined as landscapes.⁶⁵
Conclusion

► The goal of this MSc research was to explore the perspectives, advantages and limitations of the narrowband and broadband sensors for the environmental mapping and monitoring of *P.oceanica* seagrass along the coasts of Crete Island.

► The methodology of the spectral discrimination of seafloor cover types is..

○ designed in the frame of this research

o based on the application of the remote sensing RTM techniques,

o uses data from broadband sensors, hyperspectral radiometers
for measurements of optical properties of the seawater,

o applies categorical and continuous statistical analysis for the data processing and

o uses GIS raster based software for images visualization,
classification and analysis
Research outcome

► The results of this work demonstrated that

□ the application of the remote sensing data from the broadband sensors is highly advantageous for the seagrass mapping,

□ the spectral discrimination of *P.oceanica* from other seafloor cover types is possible at diverse and changing environmental conditions (water column height),

□ *P.oceanica* is spectrally distinct from other seafloor types (carbonate sand) at varying environmental conditions, as well as from other seagrass species (*Thalassia testudinum*)

► The RTM software is a powerful means for analyzing spectral signatures of various seafloor types and enabling simulations of data received from broadband and narrowband remote sensors.
# Future Directions & Recommendations

* To extend the research area towards the whole Crete Island

* To extend the temporal period of the imagery coverage, once the data are available

* To apply various classifications methods for the available imagery in order to compare accuracy and precision of results

* To consider various factors determining the effect of the ecology, health and spatial distribution of *P.oceanica* (besides bathymetry and chemical content of seawater)

* In upscaling to the small-scale mapping level further environmental variables need to be considered: health conditions of the seagrass, hydrology, geomorphology

* Other RTM software may be tested and the modelling outcomes compared

* Application of various open source GIS could be very useful for analysis of accuracy of the results
Major Influences for this work
(most important works from Bibliography)

Amoutzopoulou-Schina H. and Haritonidis S. 2005. Distribution and phenology of the marine phanerogam *Posidonia oceanica* in the Pagassitikos Gulf, Greece. Journal of Biological Research 4: 203 211, 2005 J.

Bierwirth P. N., Lee T. J. and Burne R. V. 1993. **Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery.** Photogrammetric Engineering and Remote Sensing, vol. 59, No. 3, March, pp. 331-338.

Duarte, C.M. 2002. The future of seagrass meadows. Environmental Conservation 29 (2), 192206

Hogarth P. 2007. The Biology of Mangroves and Seagrasses. Oxford University Press.

Leoni V., Pasqualini V., Pergent-Martini C., Vela A., Pergent G. 2007. **Physiological responses of Posidonia oceanica to experimental nutrient enrichment of the canopy water.** Journal of Experimental Marine Biology and Ecology 349, 7383

Matarrese R., Acquaro M., Morea A., Khalid T., Chiaradia M.T. 2006 **Applications of remote sensing techniques for mapping Posidonia oceanica meadows.** International Geoscience and Remote Sensing Symposium

McKenzie L.J., Campbell S.J. and Roder C.A. 2003. **Seagrass-Watch: Manual for Mapping and Monitoring Seagrass Resources by Community (citizen) Volunteers.** 2nd Edition. QFS, NFC, Cairns. 100pp

McKenzie L.J., Campbell S.J. and F.Lasi. 2006. **Seagrasses and Mangroves.** In: Green A.P., Lokani W., Atu P., Ramonia P., Thomas P. and Almanmy J. (eds). 2006 Solomon Islands Marine Assessment.

Mount R. 2003. The application of digital aerial photography to shallow water seabed mapping and monitoring how deep can you see? Coastal GIS 2003: and integrated approach to Australian coastal issues, 7th - 8th July 2003. Wollongong, University of Wollongong, pp. 139-156

Pasqualini V., Pergent-Martini C. and Pergent, G. 1998(a). Use of remote sensing for the characterization of the Mediterranean coastal environment the case of *Posidonia oceanica*. Journal of Coastal Conservation 4: 59-66, 1998, EUCC; Opulus Press Uppsala. 24

Pasqualini V., Pergent-Martini C., Clabaut P., Marteel H., and Pergent G. 2001. **Integration of Aerial Remote Sensing, Photogrammetry, and GIS Technologies in Seagrass Mapping.** Photogrammetric Engineering and Remote Sensing Vol. 67, No. 1, January 2001, pp. 99-105. 7

Schmidt K. and Skidmore A.K. 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment. 85:92-108

Schultz S.T. 2008. Seagrass monitoring by underwater videography: Disturbance regimes, sampling design, and statistical power. Aquatic Botany 88 (2008) 228238

Walker, D.I., 1989. Methods for monitoring seagrass habitat. Proceedings Rep.Workshop, 20-22 June 1988, Melbourne, Australia, VIMS Working Paper 18, pp. 1-26.
Thank you for attention.

Questions ?