Transcript Slide 1

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
 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.
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.
OPTIMA: Kick-off Meeting/ Malta 2004
Causes of Land use
change/degradation
Anthropic
1.
Increase in population rate
(2%; between 1963 and 1990 )
2.
Immigration towards civilized areas
(about 80% of population in the coastal, urban
region)
3.
Neglecting agricultural areas
Natural (Climate)
1.
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 extreme
( from1600km2 to 1030km2 between 1963 and 1990) (within 3Cº in 30 years: Helps forest fire,
4.
Excess use of natural resources
(e.g. water, soil, raw materials,
forests decrease 305 km2 in 35 years)
5.
Excess of construction practices
(e.g. new settlements, roads, dams, etc.)
6.
droughts, desertification, torrents etc. )
3. Torrential rainfall
(Enhances flooding and mass movements,
modifying drainage systems, etc.)
Use of new technologies
(e.g. greenhouses in urban areas, drilling water wells
in remote areas, etc.)
OPTIMA: Kick-off Meeting/ Malta 2004
Both (Land use change anthropic & natural) are integrated with
water resources
Example:
OPTIMA: Kick-off Meeting/ Malta 2004
Data requirements
1. Time series data: “satellite imageries” of the study area
within a period of 15 years would be undertaken through
the process of change detection
2. DEM: to ensure the good overlay processing and
referencing for different data sets “Ortho-rectification”,
morphological distribution, drainage network extraction &
sub-catchments identifications
3. Ancillary data: Topographic maps, water management
issues, Hydrogeological data climatic data, socioeconomical information, demographic developments, etc..
4. Software:
Remote sensing, GIS and their related
extensions and modules
OPTIMA: Kick-off Meeting/ Malta 2004
To apply the LUC Model in water resources, data must be
compiled and standardized
Required data via Remote Sensing and GIS
Direct :
Through CORINE
Land-Use
classificationLevel 3
Indirect :
Through assessing the effect
of LUC on water resources
modeling (WRM) and river
run-off modeling (RRM)
OPTIMA: Kick-off Meeting/ Malta 2004
Direct :
Beirut
Time series data ( available at NCRS)
- Multispectral Landsat (30m)
TM image
Qaroun
Lake
-Two time series winter and
spring (1988)
Saida
Study
Area
OPTIMA: Kick-off Meeting/ Malta 2004
Time series data ( available at NCRS)
-Multispectral SPOT imageries
(20m)
Saida
- April & September images 1994
Tyr
Study
Area
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
OPTIMA: Kick-off Meeting/ Malta 2004
DEM ( available at NCRS)
-Elevation Contour lines (10m)
Altimetric accuracy ≈ 3m
Utilization:
1. Ortho-rectification
2. Drainage network extractions
3. Sub-catchments delineation
4. Elevation distribution
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”
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
Plus
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
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 series for future scenarios
4. Emphasis should be concentrated on indicators and scenarios
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
1. River basin objects
2. Meteorological data
3. Hydrological data
4. Water quality and economic data
OPTIMA: Kick-off Meeting/ Malta 2004
1. River basin objects
RS & GIS
1. Supply Objects:
GIS
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:
Industrial areas:
Areal extent of irrigated lands
Areal extent of industrial areas
OPTIMA: Kick-off Meeting/ Malta 2004
Water Nodes and Areas in Abou Ali River Basin, Lebanon
Example
Node
Gauging
stations
Major
confluenc
es
Definition
Abbreviatio
n
Local name
Control
nodes along
the river
network,
used for
calibration
G1
Abou Samra
G2
Rachaeen
G3
Daraya-Kafer
Zeghab
G4
Kousba
G5
Houeit-Marh
C1
Tahoun EdDeir
C2
El-Mikhada
C3
Ain Stanboul
Intermittent streams
C4
Mazra’at EnNaher
Permanent water course
Conjunction
between a
tributary and
the primary
water course
Description
Liminographs with weekly
measures. Rehabilitated in
1998
Non-operational station
(Liminograph)
Liminographs with weekly
measures. Rehabilitated in
1998
Permanent water courses
OPTIMA: Kick-off Meeting/ Malta 2004
OPTIMA: Kick-off Meeting/ Malta 2004
2. Meteorological and 3. Hydrological Data
Must be on a time-series
-Precipitation
-Evapotranspiration
-Hydrological properties
of running water in rivers
-Hydrological properties
of issuing water from
springs
-Supplementary
climatic data (e.g.
temperature, humidity, -Volume of precipitated
wind velocity, etc. )
and evapotranspirated
water
OPTIMA: Kick-off Meeting/ Malta 2004
Example: Calculating the volume of water in the form of snow
Beirut
OPTIMA: Kick-off Meeting/ Malta 2004
Example: Delineating catchment areas
1
1b
2
1a
2b
3b
3
4b
Tripoli
2a
5b
4
7b
Se
a
8b
6b
5a 4a
6a
7a
3a
5
6
9b
8a
an
ea
n
9a
10a
10b
7
11a
Me
d it
err
11b
12a
13a
12b
Jbiel
14a
13b
15a
8
14b
Ba'albak
15b
16a
16b
17a
9
17b
19a
21b
10
20a
22b
21a
11
Sy
r ia
23b
22a
24b
23a
25b
26b
24a
25a
27b
26a
28b
29b
Saida
30b
27a
31b
32b
33b
13
28a
29a
Be
ka
aV
18a
19b
20b
all
ey
18b
Beirut
12
No RS involvement
OPTIMA: Kick-off Meeting/ Malta 2004
4. Water Quality and Economic data
It deals with water quality,
with special emphasis on
rivers, springs, aquifers as
well as drinking water
It deals with water prices
& LUC impact, which
reflects
services,
distribution costs and
environmental costs.
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…
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 realworld behavior into the artificial “raster” world
 Time
or temporal resolution that maintain the uniform
application for the transitional rules
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
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%
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
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
Thank you …