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OPTIMA Optimization for Sustainable Water Resources Management Kick-off Meeting, Malta October 2004 Lebanon Partner 8 WP 5 – Land use change: Remote Sensing & GIS data National Center for Remote Sensing M. Khawlie. OPTIMA: Kick-off Meeting/ Malta 2004.

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OPTIMA

Optimization for Sustainable Water Resources Management

Kick-off Meeting, Malta October 2004

Lebanon Partner 8

WP 5 – Land use change: Remote Sensing & GIS data

National Center for Remote Sensing

M. Khawlie


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OPTIMA: Kick-off Meeting/ Malta 2004

  • The capacity for human organisms to alter their environment, including water resources, covers the potential for self destruction.

  • Human existence depends on a multitude of natural resources which in turn can be negatively impacted by human actions.

  • Since Land cover is related to land use; thus, increasing the stresses due to human populations may lead, if not properly managed to an imbalance in water resources.


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OPTIMA: Kick-off Meeting/ Malta 2004

  • Thus, the spatial distribution of land use/ land cover information and its changes is desirable for any planning, management and monitoring programs for water resources.

  • Planning means the assessment of future and making provisions for it.

  • Therefore, to ensure sustainable development with availability of water there is a necessity to monitor ongoing changes in LUC pattern over a period of time.

  • Remote sensing techniques along with GIS play a vital role in building the “desired” LUC change model and relevant water resources needs.


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OPTIMA: Kick-off Meeting/ Malta 2004

Causes of Land use change/degradation

Anthropic

Natural (Climate)

  • Increase in population rate

  • (2%; between 1963 and 1990 )

  • Immigration towards civilized areas

  • (about 80% of population in the coastal, urban region)

  • Neglecting agricultural areas

  • ( from1600km2 to 1030km2 between 1963 and 1990)

  • Excess use of natural resources

  • (e.g. water, soil, raw materials,

  • forests decrease 305 km2 in 35 years)

  • Excess of construction practices

  • (e.g. new settlements, roads, dams, etc.)

  • Use of new technologies

  • (e.g. greenhouses in urban areas, drilling water wells in remote areas, etc.)

  • Decline in precipitation rate

  • (about 950 to 800 mm in 50 years:

  • This led to an increase in dry lands,

  • decrease in irrigated areas, etc.)

  • 2. Increase in temperature extremes

  • (within 3Cº in 30 years: Helps forest fire,

  • droughts, desertification, torrents etc. )

  • 3. Torrential rainfall

  • (Enhances flooding and mass movements, modifying drainage systems, etc.)


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OPTIMA: Kick-off Meeting/ Malta 2004

Both (Land use changeanthropic & natural) are integrated with water resources

Example:


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OPTIMA: Kick-off Meeting/ Malta 2004

Data requirements

  • Time series data: “satellite imageries” of the study area within a period of 15 years would be undertaken through the process of change detection

  • DEM: to ensure the good overlay processing and referencing for different data sets “Ortho-rectification”, morphological distribution, drainage network extraction & sub-catchments identifications

  • Ancillary data: Topographic maps, water management issues, Hydrogeological data climatic data, socio-economical information, demographic developments, etc..

  • Software: Remote sensing, GIS and their related extensions and modules


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OPTIMA: Kick-off Meeting/ Malta 2004

To apply the LUC Model in water resources, data must be

compiled and standardized

Required data via Remote SensingandGIS

Direct :

Indirect :

Through assessing the effect of LUC on water resources modeling (WRM) and river run-off modeling (RRM)

Through CORINE Land-Use classification-

Level 3


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OPTIMA: Kick-off Meeting/ Malta 2004

Direct :

Beirut

Time series data ( available at NCRS)

Mediterranean Sea

- Multispectral Landsat (30m) TM image

-Two time series winter and spring (1988)

Qaroun Lake

Study

Area

Saida


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OPTIMA: Kick-off Meeting/ Malta 2004

Time series data ( available at NCRS)

Mediterranean Sea

  • Multispectral SPOT imageries (20m)

  • April & September images 1994

Study

Area

Saida

Tyr


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OPTIMA: Kick-off Meeting/ Malta 2004

Time series data ( available at NCRS)

  • Two images IRS (5m) and Landsat (30m)

  • Pan-sharpened to have better interpretation


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OPTIMA: Kick-off Meeting/ Malta 2004

DEM ( available at NCRS)

-Elevation Contour lines (10m)

Altimetric accuracy ≈ 3m

  • Utilization:

  • Ortho-rectification

  • Drainage network extractions

  • Sub-catchments delineation

  • Elevation distribution


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CORINE classification

Image classification would be based on the European CORINE ( CoORdination des INformation sur l’Environnement) classification (level 3). Adapted to the Lebanese standards

The CORINE Land cover nomenclature is a physical and physiognomic land cover hierarchical nomenclature, which is strongly related to the process of image interpretation

  • Deductive analysis is required for some classes especially classes of level 3

  • The aggregation of primitive objects required in some cases of spatial organization of landscape elements is a subjective process

The spatial unit in CORINE corresponds both to:

1- an area of homogeneous cover ( water, forest,…)

2- an aggregation of small homogeneous areas representing a land cover structure

Highly related to the extraction level, “level 2”, “level 3”, or even “level 4”


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Plus

OPTIMA: Kick-off Meeting/ Malta 2004

A spatial unit is attributed to a class not only on the basis of the satellite imagery, but also through the use of additional data available for image-interpreter

Ancillary data essentially comprise:

-standard topo maps

-old thematic maps (where available)

-statistical information

-aerial photographs

A set of pre-processed images (for example, using PCA, contrast stretching, filtering, color composition and NDVI) might be produced and integrated for LUC mapping and change detection


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OPTIMA: Kick-off Meeting/ Malta 2004

Indirect data

Deals with changes of land features and their effect on water resources. This is to be used in modeling (WRM) and (RRM)

*Important notes in compiling and standardizing data:

1. The study area should be classified into a number of zones, such as: sub-catchments, clusters, typological zones, etc.

2. Data required will be compiled for each zone separately

3. Data must be presented on time seriesfor future scenarios

4. Emphasis should be concentrated on indicators and scenarios


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OPTIMA: Kick-off Meeting/ Malta 2004

Data required

To be integrated in a uniform framework. This is for easy access to advanced tools of data analysis, simulation modeling and multi-criteria decision support system DSS

Treats

1. River basin objects

2. Meteorological data

3. Hydrological data

4. Water quality and economic data


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OPTIMA: Kick-off Meeting/ Malta 2004

1. River basin objects

RS & GIS

GIS

1. Supply Objects:

River nodes:

Gauging station, major confluences, major diversions, dams, lakes

Springs nodes:

Location of major springs

Man-made nodes:

Water wells, tanks, reservoirs

Aquifers and Sub-catchment:

Areal extent of aquifers and sub-basins

2. Demand Objects:

Cities/villages:

Areal extent of urban settlements

Agricultural areas:

Areal extent of irrigated lands

Industrial areas:

Areal extent of industrial areas


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Water Nodes and Areas in Abou Ali River Basin, Lebanon

OPTIMA: Kick-off Meeting/ Malta 2004

Example


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OPTIMA: Kick-off Meeting/ Malta 2004

Example from

Abou-Ali River basin, Lebanon


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OPTIMA: Kick-off Meeting/ Malta 2004

2. Meteorological and 3. Hydrological Data

Must be on a time-series

  • Hydrological properties of running water in rivers

  • Hydrological properties of issuing water from springs

  • Volume of precipitated and evapotranspirated water

  • Precipitation

  • Evapotranspiration

  • Supplementary climatic data (e.g. temperature, humidity, wind velocity, etc. )

Can be done by RS

Partially achieved by RS


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OPTIMA: Kick-off Meeting/ Malta 2004

Example:

Calculating the volume of water in the form of snow

Mediterranean Sea

Beirut


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OPTIMA: Kick-off Meeting/ Malta 2004

Example:

Delineating catchment areas

Mediterranean Sea


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No RS involvement

OPTIMA: Kick-off Meeting/ Malta 2004

4. Water Quality and Economic data

It deals with water prices & LUC impact, which reflects services, distribution costs and environmental costs.

It deals with water quality, with special emphasis on rivers, springs, aquifers as well as drinking water

Water quality: Cl, SO4--, CO3--, HCO3-, F+++, Cu++, SiO2, TDS, etc..

Economic data: Water price for domestic, agricultural, industrial, tourism, etc…

Water consumption for domestic, agricultural, industrial, etc…


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OPTIMA: Kick-off Meeting/ Malta 2004

The Land use change model

Land use change model is a dynamic model that afford to space, time and system attributes.

  • LUC change model would be based on:

  • A set of space organized into discrete areal units ( land use classes based on CORINE classification)

  • Transition rules which are the real driving forces behind the model dynamics

  • Functions which serve as the algorithms that code real-world behavior into the artificial “raster” world

  • Time or temporal resolution that maintain the uniform application for the transitional rules


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OPTIMA: Kick-off Meeting/ Malta 2004

Variables/ Driving forces

Variables would be introduced in a GIS format to implement the LUC change model

  • Urban growth rate

  • Climatic data

  • Estimates dynamic water budget, supply/demand, reliability of supply

  • Integrated master land use planning

  • National environmental policies, programs & regulations


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OPTIMA: Kick-off Meeting/ Malta 2004

Model Examples

- Transition probability where the rows of the matrix sum up to one and the diagonal cells represent the probability of no change

- Map Algebra ( Rules, Conditions, operator and functions)

If condition AND/OR condition Then P(n,m) Change-Operator Value

Condition: TRUE/FALSE, FRACTION, FREQUENCY & LAST

Operators: REL-DECREASE, REL-INCREASE, ABS….etc

Functions: FRACTION (N,i), FREQUENCY (N,i), LAST (i)

Example:

If FRACTION (1.1,2) > 500 p(1.1) RE-INCREASE 500

If more than half the immediate neighbors in a 5x5 area around a cell are city (1.1), then the probability of transition increase by 50%


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OPTIMA: Kick-off Meeting/ Malta 2004

Documentation / Meta-data

A documentation catalogues that include information about the content, representation, extent ( both geometric and temporal ), spatial reference system, quality and administration of the datasets

Example:

Identification

Title, area covered, themes, restrictions

Data quality

Accuracy, completeness, logical consistency, lineage

Spatial data organization

Vector, raster, type of elements, number

Spatial reference

Projection, grid system, datum, coordinate system

Entity and attribute information

Features, attributes, attribute values

Meta-data reference

Author, date


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OPTIMA: Kick-off Meeting/ Malta 2004

Publication, implementation and user interface (web access)

A- GIS data

1- LUC map for different time series

2- Drainage networks

3- DEM / TIN

4- Anthropic map (showing urban settlements, road network, etc..)

B- RS data

1- Different imageries utilized in the LUC

2- Derived data (NDVI, PCA, etc…)

C- Model implementation

scenario selector that access available cases, compromising the following parts

1- the region (initially, start time initial conditions, time horizon)

2- the development scenario ( transition probabilities and rules)

3- initial conditions and time frame