From satellite imagery to hydrogeological survey maps of Chad

11th Swiss Geoscience Meeting

Lausanne, November 16, 2013

M. Aubert*, A. Kraiem*, Y. Haeberlin*, F. Zwahlen**

*UNOSAT, United Nations Institute for Training and Research (UNITAR) Palais des Nations, CH-1211 Genève

** CHYN Centre d'hydrogéologie,

rue Emile-Argand 11, 2009 Neuchâtel

Actors and objectives

Funding Project management

I. Introduction II. Mapping III. Perspectives

RésEAU project objectives

Partners

1. Water ressources mapping: RésEAU project 2. SIRE data collection 3. SIRE consolidation

1. Develop a Water Resource Information System (SIRE): Improved knowledge of

water resources to strengthen and develop initiatives in the sector

2. Strengthen national capacities in geology, hydrogeology and GIS

= Support Chad water resource management

For a better management of water resources in Chad

Actors and objectives

Funding Project management

I. Introduction II. Mapping III. Perspectives

Partners

1. Water ressources mapping: RésEAU project 2. SIRE data collection 3. SIRE consolidation

RésEAU project objectives

1. Develop a Water Resource Information System (SIRE): Improved knowledge of

water resources to strengthen and develop initiatives in the sector

2. Strengthen national capacities in geology, hydrogeology and GIS

= Support Chad water resource management

For a better management of water resources in Chad

Actors and objectives

Funding Project management

I. Introduction II. Mapping III. Perspectives

Partners

1. Water ressources mapping: RésEAU project 2. SIRE data collection 3. SIRE consolidation

1. Develop a Water Resource Information System (SIRE): Improved knowledge of

water resources to strengthen and develop initiatives in the sector

2. Strengthen national capacities in geology, hydrogeology and GIS

= Support Chad water resource management

For a better management of water resources in Chad

RésEAU project objectives

5

Weaknesses of data collection

SIRE Geodatabase

I. Introduction II. Mapping III. Perspectives

- Information is scattered, actors might be reluctant to share it.

- Data is outdated, spatial coverage is heterogeneous.

= More accurate and exhaustive (coverage) data are necessary

1. Water ressources mapping: RésEAU project 2. SIRE data collection 3. SIRE consolidation

~600 Thematic and

topographics maps

Field data water points, geology,

SITEAU well/borehole database

~400 Water/Geology related articles

~600 Satellite images

SIRE

SIRE Geodatabase

I. Introduction II. Mapping III. Perspectives

1. Water ressources mapping: RésEAU project 2. SIRE data collection 3. SIRE consolidation

~600 Thematic and

topographics maps

Field data water points, geology,

SITEAU well/borehole database

~400 Water/Geology related articles

~600 Satellite images

SIRE

Weaknesses of data collection

- Information is scattered, actors might be reluctant to share it.

- Data is outdated, spatial coverage is heterogeneous.

= More accurate and exhaustive (coverage) data are necessary

SIRE Geodatabase

I. Introduction II. Mapping III. Perspectives

1. Water ressources mapping: RésEAU project 2. SIRE data collection 3. SIRE consolidation

~600 Thematic and

topographics maps

Field data water points, geology,

SITEAU well/borehole database

~400 Water/Geology related articles

~600 Satellite images

SIRE

Weaknesses of data collection

- Information is scattered, actors might be reluctant to share it.

- Data is outdated, spatial coverage is heterogeneous.

= More accurate and exhaustive (coverage) data are necessary

SIRE Geodatabase

I. Introduction II. Mapping III. Perspectives

1. Water ressources mapping: RésEAU project 2. SIRE data collection 3. SIRE consolidation

~600 Thematic and

topographics maps

Field data water points, geology,

SITEAU well/borehole database

~400 Water/Geology related articles

~600 Satellite images

SIRE

Weaknesses of data collection

- Information is scattered, actors might be reluctant to share it.

- Data is outdated, spatial coverage is heterogeneous.

= More accurate and exhaustive (coverage) data are necessary

SIRE consolidation

- New satellite image acquisitions

- Field observations and measurements for validation

= Powerful tools to improve water information over Chad and understand the different aquifers

Remote sensing tools

New data acquisitions

1. Water ressources mapping: RésEAU project 2. SIRE data collection 3. SIRE consolidation

Field work

+ Structure, Moisture, Faults, Morphology

RADAR+ DEM Optical

+ Geology, Landcover

+ True data - Punctual data

I. Introduction II. Mapping III. Perspectives

SWIR VNIR

SIRE consolidation

- New satellite image acquisitions

- Field observations and measurements for validation

= Powerful tools to improve water information over Chad and understand the different aquifers

Remote sensing tools

New data acquisitions

1. Water ressources mapping: RésEAU project 2. SIRE data collection 3. SIRE consolidation

SWIR VNIR

Field work

+ Structure, Moisture, Faults, Morphology

RADAR+ DEM Optical

+ Geology, Landcover

+ True data - Punctual data

I. Introduction II. Mapping III. Perspectives

Tools limited to: Rocks varnish over bare soil or scarse vegetation cover

II. Mapping process 1. SIRE database

2. Process line

I. Introduction II. Mapping III. Perspectives

1. SIRE satellite database 2. Process line

II. Mapping process

I. Introduction II. Mapping III. Perspectives

1. SIRE satellite database 2. Process line

1. SIRE database

2. Process line

II. Mapping process

I. Introduction II. Mapping III. Perspectives

1. SIRE satellite database 2. Process line

1. SIRE database

2. Process line

II. Mapping process

I. Introduction II. Mapping III. Perspectives

1. SIRE satellite database 2. Process line

1. SIRE database

2. Process line

II. Mapping process

I. Introduction II. Mapping III. Perspectives

1. SIRE satellite database 2. Process line

1. SIRE database

2. Process line

400 ASAR

C (~5 GHz)

Resolution: 30 m

Scene : 100x100 km

Free

50 ALOS/ PALSAR

L (~1.5 GHz)

Resolution: 24 m

Scene: 40x70 km

Low cost

> 61 LANDSAT-7

4 VNIR, 2 SWIR, 1 TIR

Resolution: 15 à 120 m

Size: 185 x185 km

Free

150 ASTER

3 VNIR, 5 SWIR, 5 TIR

Resolution: 15 à 90 m

Size: 60x60km

Low cost Google Earth

BING

SRTM

Resolution: 90 m

Free

> 61 LANDSAT-8

4 VNIR, 2 SWIR, 1 TIR

Resolution: 15 à 120 m

Size: 185 x185 km

Free

Image acquisitions

17 17

I. Introduction II. Mapping III. Perspectives

SIRE database potential

Sensor choosen because of their:

- low cost - appropriate spatial resolution for 500’000 or 200’000 mapping - spectral diversity (VNIR, SWIR, TIR, HF)

= Exhaustive database to support hydrogeological mapping

1. SIRE satellite database 2. Process line

Google Earth

BING

Image acquisitions

18 18

I. Introduction II. Mapping III. Perspectives

SIRE database potential

400 ASAR

C (~5 GHz)

Resolution: 30 m

Scene : 100x100 km

Free

50 ALOS/ PASAR

L (~1.5 GHz)

Resolution: 24 m

Scene: 40x70 km

Low cost

> 61 LANDSAT-7

4 VNIR, 2 SWIR, 1 TIR

Resolution: 15 à 120 m

Size: 185 x185 km

Free

150 ASTER

3 VNIR, 5 SWIR, 5 TIR

Resolution: 15 à 90 m

Size: 60x60km

Low cost

SRTM

Resolution: 90 m

Free

> 61 LANDSAT-8

4 VNIR, 2 SWIR, 1 TIR

Resolution: 15 à 120 m

Size: 185 x185 km

Free

1. SIRE satellite database 2. Process line

Sensor choosen because of their:

- low cost - appropriate spatial resolution for 500’000 or 200’000 mapping - spectral diversity (VNIR, SWIR, TIR, HF)

= Exhaustive database to support hydrogeological mapping

First step: Geology

I. Introduction II. Mapping III. Perspectives

STEP 0: Pre-process SIRE database: download, calibrate, analyse

Landsat-7, raw data (RGB: Blue, Green, Red)

Calibrate

Compute product

Image calibrated and mosaiked

Sultan color composite

Raw data

1. SIRE satellite database 2. Process line

First step: Geology

I. Introduction II. Mapping III. Perspectives

STEP 0: Pre-process SIRE database: download, calibrate, analyse

Data Brut (DN) (RVB :b1,b2,b3)

Calibrate

Compute product

Image calibrated and mosaiked

Sultan color composite

Raw data

1. SIRE satellite database 2. Process line

Landsat-7, raw data (RGB: Blue, Green, Red)

First step: Geology

I. Introduction II. Mapping III. Perspectives

STEP 0: Pre-process SIRE database: download, calibrate, analyse

Data Brut (DN) (RVB :b1,b2,b3)

Landsat-7, VNIR colored composition mosaiked

(RGB: Blue, Green, Red)

Calibrate

Compute product

Image calibrated and mosaiked

Sultan color composite

Raw data

1. SIRE satellite database 2. Process line

First step: Geology

I. Introduction II. Mapping III. Perspectives

STEP 0: Pre-process SIRE database: download, calibrate, analyse

Data Brut (DN) (RVB :b1,b2,b3)

Granodiorite intrusion

Crystalline basement

Basalt flows

Landsat-7, VNIR colored composition mosaiked

(RGB: Blue, Green, Red)

Calibrate

Compute product

Image calibrated and mosaiked

Sultan color composite

Raw data

1. SIRE satellite database 2. Process line

First step: Geology

I. Introduction II. Mapping III. Perspectives

STEP 0: Pre-process SIRE database: download, calibrate, analyse

Data Brut (DN) (RVB :b1,b2,b3) Landsat-7, Sultan product (RGB: 5/7, 5/1, 5/4x3/4)

Calibrate

Compute product

Image calibrated and mosaiked

Sultan color composite

Raw data

1. SIRE satellite database 2. Process line

Granodiorite intrusion

Crystalline basement

Basalt flows

Ancient dunes

1. Photo-interpret and digitize at large scale to delimit major geological regions

2. Subdivision at fine scale using additionnal criteria

First step: Geology

STEP I: GEOLOGY

Basalts flows

I. Introduction II. Mapping III. Perspectives

Alluvial deposits (along wadis)

Sandstones

(500k)

1. SIRE satellite database 2. Process line

1. Photo-interpret and digitize at large scale to delimit major geological regions

2. Subdivision at fine scale using additionnal criteria

STEP I: GEOLOGY

Ancient dunes

Basalts flows

Alluvial deposits (along wadis)

Sandstones

(500k)

First step: Geology

I. Introduction II. Mapping III. Perspectives

1. SIRE satellite database 2. Process line

Intrusion

Granodiorite intrusion

1. Split and merge process done by hydrogeologicals experts

2. Productivity classification

Second step: Hydrogeology

STEP II: HYDROGEOLOGY

I. Introduction II. Mapping III. Perspectives

Cross geology with hydraulic data to obtain hydrogeological layer, taking into account:

- Nature, Porosity, Grain size, Permeability

- Rocks condition: layer’s geometry, hydraulic parameters, productivity, the piezometric levels of the wells/boreholes .

1. SIRE satellite database 2. Process line

1. Hydrogeological Split and merge process done by hydrogeologicals experts

2. Productivity classification (potentially high, moderate, low to zero)

Second step: Hydrogeology

STEP II: HYDROGEOLOGY

I. Introduction II. Mapping III. Perspectives

1. SIRE satellite database 2. Process line

Third step: Cartography

STEP III: CARTOGRAPHY

I. Introduction II. Mapping III. Perspectives

Wadi Fira hydrogeological survey map at 500k scale (right: front side of map; left: explanatory note on back side)

1. SIRE satellite database 2. Process line

Extract of SIRE database results on a « sandstone » request

I. Introduction II. Mapping III. Perspectives

1. SIRE satellite database 2. Process line

Sandstone aquifer = high potential of productivity at the basement

SRTM slope map (200k)

Landsat-7 SUltan (200k)

Google earth 3D view Sandstone field and macro views

Sandstone aquifer potential

Current results & publications

Provide up-to-date and relevant water-related informations: SIRE

Hydrogeological maps

Improve the knowledge of different aquifers for a better

management and further drilling programs.

Knowledge transfer : Professionnal Master 1 HydroSIG

UNOSAT training

Further steps (to be planed)

Support Chad in water management, keeping in mind their

sustainable development :

- Phase II: Water quality,

- Phase III: Water Management

I. Introduction II. Mapping III. Perspectives

Dr. Maëlle Aubert

Télédétection et SIG

UNITAR – UNOSAT

maelle.aubert@unitar.org

Amira Kraiem

Hydrogéologie et SIG

UNITAR – UNOSAT

amira.kraiem@unitar.org

Yves Haeberlin,

F. Zwahlen

Dr. Yves Haëberlin

Chef de projet

UNITAR – UNOSAT

yves.haeberlin@unitar.org

Pr. François Zwahlen

Professeur

Université de Neuchâtel

françois.zwalen@unine.ch

http://www.unitar.or

g/unosat/