Using spot landsat images for mapping inventory and monitoring of reefs
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Using SPOT-LANDSAT images for mapping, inventory and monitoring of reefs. - Serge Andréfouët - Remote Sensing/ Biological Oceanography  University of South Florida, St Petersburg, USA Laboratoire de Géosciences Marines et Télédétection Université Française du Pacifique, Tahiti. 5 km.

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Using spot landsat images for mapping inventory and monitoring of reefs
Using SPOT-LANDSAT images for mapping, inventory and monitoring of reefs

- Serge Andréfouët -

Remote Sensing/ Biological Oceanography 

University of South Florida, St Petersburg, USA

Laboratoire de Géosciences Marines et Télédétection

Université Française du Pacifique, Tahiti


Using spot landsat images for mapping inventory and monitoring of reefs

5km

Anaa Atoll (French Polynesia)

SPOT XS-3,2,1

Lagoon

Ocean

Rim


Using spot landsat images for mapping inventory and monitoring of reefs

5km

Marquesas Key (Florida, USA)

LANDSAT 5 TM-3,2,1


Using spot landsat images for mapping inventory and monitoring of reefs

20/30 meters

20/30 meters


Using spot landsat images for mapping inventory and monitoring of reefs

20/30 meters

20/30 meters


Using spot landsat images for mapping inventory and monitoring of reefs

40/60 meters

40/60 meters


Using spot landsat images for mapping inventory and monitoring of reefs

300 meters

300 meters


Using spot landsat images for mapping inventory and monitoring of reefs

Atoll

Lagoon

1500 meters

Ocean

1500 meters

Lagoon


Using spot landsat images for mapping inventory and monitoring of reefs

XS3

XS1

XS2

SPOT IMAGES

Pixel P

XS3

XS3

XS2

XS1

XS2

Spectral space

P

XS1


Remotely sensed information
Remotely sensed information

  • LwXSi= LwXSb + LwXSw (+ Lwa)

    SPOT: XS1, XS2

    LANDSAT: TM1, TM2, TM3

  • LwXSbrelated to the “bottom” features

  • LwXSw related to the water column features



Spectral discrimination
Spectral discrimination

Sensitivity TM1

Sensitivity XS1

Sensitivity XS2


Spectral discrimination1
Spectral discrimination

0 meters depth




Minimum discernable unit mdu
Minimum Discernable Unit (MDU)

Size_MDU = PixelSize.(1+2.ErrorLocation)

if ErrorLocation= 1 pixel (pretty good!!)

SPOT MDU= 60 m x 60 m

LANDSAT MDU = 90 m x 90 m


Using spot landsat images for mapping inventory and monitoring of reefs

Minimum Discernable Unit (MDU)

2 x 2 m : not enough

CASI image:

PixelSize= 1 meter

4 x 4 m : ok for training


Using spot landsat images for mapping inventory and monitoring of reefs

Minimum Discernable Unit (MDU)

MDU= 3 x 3 m

MDU= 60 x 60 m


Remotely sensed information1
Remotely sensed information

  • Lwi= Lwb + Lww (+ Lwa)

  • 2 or 3 known measurements:

    XS1 and XS2

    TM1, TM2 and TM3

  • 2 unknown variables Lwb and Lww



Using spot landsat images for mapping inventory and monitoring of reefs

Computed depth

Real depth

Depth

8km

Bathymetric modeling

(Lww)


Using spot landsat images for mapping inventory and monitoring of reefs

Image of thebottom

Scale

“Radiance” scale



Architecture forms and dimensions

Branching

Massive

Laminar

Foliaceous

Columnar

Encrusting

Free-living

Architecture (forms and dimensions)

Source: Veron (1986)


Hierarchical clustering of the stations

Similarity

Hierarchical clustering of the stations

Reef

Soft Bottom

Hard bottom

Sand/Rubble with

Isolated-Patches

Pure Rubble

Pure Sand

Living

coral

Living

Dead

field stations


Using spot landsat images for mapping inventory and monitoring of reefs

What type of habitat can you map with SPOTwith a good accuracy (70%) ?Depth < 7-8 metersDefinition: coarseMinimum Discernable Unit= 60 meters x metersBoundary analyses


Using spot landsat images for mapping inventory and monitoring of reefs

Spatial structure of a reef system

Transition

Fragmented

Gradient

Abrupt

boundaries

Patches


Using spot landsat images for mapping inventory and monitoring of reefs


Is this membership degree useful
Is this membership degree useful? membership degrees in each of the classes

  • Mapping

  • Habitats boundary analyses

  • Acanthaster planci outbreaks


Using spot landsat images for mapping inventory and monitoring of reefs

Tiahura membership degrees in each of the classes

Ocean

Land


Using spot landsat images for mapping inventory and monitoring of reefs

Ocean membership degrees in each of the classes

Motu

Land

Ocean

Membership degree:

Motu

Land

1

0

Ocean

Motu

Land

Fuzzy

classification

Coral

One map for each class of bottom.

Mapping of the degree of membership.

Heterogeneous

Dead structures


Using spot landsat images for mapping inventory and monitoring of reefs

Tiahura membership degrees in each of the classes

Ocean

Land


Using spot landsat images for mapping inventory and monitoring of reefs

Ocean membership degrees in each of the classes

Motu

Land

Ocean

Membership degree:

Motu

Land

1

0

Ocean

Motu

Land

Fuzzy

classification

Coral

One map for each class of bottom.

Mapping of the degree of membership.

Heterogeneous

Dead structures


Using spot landsat images for mapping inventory and monitoring of reefs

Tiahura membership degrees in each of the classes

Ocean

Land


Using spot landsat images for mapping inventory and monitoring of reefs

Ocean membership degrees in each of the classes

Motu

Land

Ocean

Membership degree:

Motu

Land

1

0

Ocean

Motu

Land

Fuzzy

classification

Coral

One map for each class of bottom.

Mapping of the degree of membership.

Heterogeneous

Dead structures


Is this membership degree useful1
Is this membership degree useful? membership degrees in each of the classes

  • Mapping

  • Habitats boundary analyses

  • Acanthaster planci outbreaks


Using spot landsat images for mapping inventory and monitoring of reefs

Tiahura membership degrees in each of the classes

2.5 km


Using spot landsat images for mapping inventory and monitoring of reefs

Transitions between bottom types membership degrees in each of the classes

Coral

Isolated Patches

Land

Sand

Land

Land

Land

1

0

Possibility

measurement


Is this membership degree useful2
Is this membership degree useful? membership degrees in each of the classes

  • Mapping

  • Habitats boundary analyses

  • Monitoring and sampling designs (Acanthaster planci outbreaks)


Using spot landsat images for mapping inventory and monitoring of reefs

Ocean membership degrees in each of the classes

Land

Location of A. planci infestations in the 80’s (Faure, 1989)


What about change detection
What about change detection ? membership degrees in each of the classes

0 meters depth


Using spot landsat images for mapping inventory and monitoring of reefs

What about change detection ? membership degrees in each of the classes

Histograms of bottom-types in XS1 after bathymetric corrections for 2 atolls


Using spot landsat images for mapping inventory and monitoring of reefs

What about change detection ? membership degrees in each of the classes

  • Problems in calibration and correction of the images:

  • not enough accurate

  • Benthos:

    • Shifts in living communities : ??????

  • Change in sediment cover (hurricanes) : ok


Work in the field
Work in the field membership degrees in each of the classes

Moorea:

20 transects (60m x~1km) for training and control,

6 days,

2 investigators (Yannick Chancerelle, CRIOBE, Moorea),

Semi-quantitative (5%, 15%, 25%, >50%) rapid assessment for 4 variables

Atolls: 20 transects, 2 days, 2 investigators

Caveat: Only assessment of the coarse level of habitat without

hierarchical sampling (if not, time x 10) !!!


Work in the image processing lab
Work in the image processing lab membership degrees in each of the classes

Bathymetric correction

Fuzzy classification to output membership degrees

Mapping of the membership degrees

3days - 1week

Conditions:

- user-friendly software does exist

- good control of the software

- good quality of the data (image and field data)

- skilled analyst (if not, time x 10)


Water parameters
Water parameters membership degrees in each of the classes

Few direct observations.

Potentially interesting for atoll lagoons

(phytoplanctonic biomass or suspended matter)

Many indirect observations

(the water body is not the target)

rivers run-off, pollution, boundary characterization and residence time


Using spot landsat images for mapping inventory and monitoring of reefs

Spatial structure of a reef system membership degrees in each of the classes

and fluxes

Reka-Reka Tepoto Sud Tekokota

Boundary conditions controls:

Nutrients limitations

Residence time of lagoon waters

Recruitment

Community structure


Using spot landsat images for mapping inventory and monitoring of reefs

Atoll rims typology membership degrees in each of the classes

Structure

Wave Exposure Hydrodynamic

aperture

aperture

33 %

aperture

> 70 %

South


Using spot landsat images for mapping inventory and monitoring of reefs

Empirical relationships between flows of oceanic water membership degrees in each of the classes

and wave height for each type of rim

H_Topex (m)

Flows (m2/s)


Using spot landsat images for mapping inventory and monitoring of reefs

Residence time in atoll lagoons membership degrees in each of the classes


Distribution of coral reefs
Distribution of coral reefs membership degrees in each of the classes

Global scale

Estimated 255.000 km2 (Spalding & Grenfell, 1997)


Using spot landsat images for mapping inventory and monitoring of reefs

Similarity membership degrees in each of the classes

Reef

Soft Bottom

Hard bottom

Sand/Rubble with

Isolated-Patches

Pure Rubble

Pure Sand

Living

coral

Living

Dead

field stations


Using spot landsat images for mapping inventory and monitoring of reefs


Using spot landsat images for mapping inventory and monitoring of reefs

Present coverage of reefs by LTAP per day worldwide:

Expected: one cloud-free image per year


Using spot landsat images for mapping inventory and monitoring of reefs

Present coverage of prioritized reefs by LTAP per day worldwide:

(research activities)

Expected: 4-6 cloud-free images per year


Using spot landsat images for mapping inventory and monitoring of reefs

Global coverage per day worldwide:

  • Estimation of global distribution of reefs,

  • without ground-truth,

  • 2 classes (soft and hard-bottom),

  • 80% accuracy

  • A basis for extension of monitoring worldwide,

  • 6 classes (gradient of soft and hard-bottom),

  • with ground-truth,

  • 70% accuracy

  • Interface with monitoring organizations is required to get

  • training data for image processing


Using spot landsat images for mapping inventory and monitoring of reefs

Conclusions per day worldwide:

Using SPOT-LANDSAT images for mapping, inventory and monitoring of reefs?

Pragmatic point of view

Mapping: Yes:

- Coarse habitats with ground truth

Bathymetric and atmospheric corrections required

- Soft/hard bottoms without ground-truth and corrections

- Boundary analyses

Inventory: Yes

% of soft/hard bottoms: global scale (LTAP):

% of coarse habitats:reef-scale


Using spot landsat images for mapping inventory and monitoring of reefs

Conclusions per day worldwide:

Using SPOT-LANDSAT images for mapping, inventory and monitoring of reefs?

Monitoring: Not directly

Change detection generally not possible

Coarse-habitats level not generally a relevant parameter

Water quality generally not directly available

But provide:

Geophysical parameters (exposure, bathymetry, residence time, geomorphology)

Habitat mapping to stratify monitoring and establish new sites

Generalize species indicator at reef scale

Timing: once or variable (catastrophic event)