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

2.  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

3.  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

4. Causes of Land use change/degradation AnthropicNatural (Climate) 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 ( from1600km 2 to 1030km 2 between 1963 and 1990) 4.Excess use of natural resources (e.g. water, soil, raw materials, forests decrease 305 km 2 in 35 years) 5.Excess of construction practices (e.g. new settlements, roads, dams, etc.) 6.Use of new technologies (e.g. greenhouses in urban areas, drilling water wells in remote areas, etc.) 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 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.)

5. OPTIMA: Kick-off Meeting/ Malta 2004 Both (Land use change anthropic & natural) are integrated with water resources Example:

6. 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, socio- economical information, demographic developments, etc.. 4.Software: Remote sensing, GIS and their related extensions and modules OPTIMA: Kick-off Meeting/ Malta 2004

7. Required data via Remote Sensing and GIS Direct : Indirect : To apply the LUC Model in water resources, data must be compiled and standardized Through CORINE Land-Use classification- Level 3 Through assessing the effect of LUC on water resources modeling (WRM) and river run-off modeling (RRM)

8. Mediterranean Sea Beirut Saida Qaroun Lake Study Area Time series data ( available at NCRS) - Multispectral Landsat (30m) TM image -Two time series winter and spring (1988) OPTIMA: Kick-off Meeting/ Malta 2004 Direct :

9. Time series data ( available at NCRS) Mediterranean Sea Tyr Saida Study Area -Multispectral SPOT imageries (20m) - April & September images 1994 OPTIMA: Kick-off Meeting/ Malta 2004

10. 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

11. 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 OPTIMA: Kick-off Meeting/ Malta 2004

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

13. 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 photographsPlus LUC mapping and change detection 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

14. Indirect data Deals with changes of land features and their effect on water resources. This is to be used in modeling (WRM) and (RRM) 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 *Important notes in compiling and standardizing data: 4. Emphasis should be concentrated on indicators and scenarios

15. OPTIMA: Kick-off Meeting/ Malta 2004 Data required Treats 1. River basin objects 2. Meteorological data 3. Hydrological data 4. Water quality and economic data 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

16. 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 1. River basin objects 1. Supply Objects: 2. Demand Objects: Cities/villages: Agricultural areas: Industrial areas: Areal extent of urban settlements Areal extent of irrigated lands Areal extent of industrial areas OPTIMA: Kick-off Meeting/ Malta 2004 RS & GISGIS

17. Water Nodes and Areas in Abou Ali River Basin, Lebanon NodeDefinitionAbbreviatio n Local nameDescription Gauging stations Control nodes along the river network, used for calibration G1 Abou Samra Liminographs with weekly measures. Rehabilitated in 1998 G2 Rachaeen G3 Daraya-Kafer Zeghab Non-operational station (Liminograph) G4 Kousba Liminographs with weekly measures. Rehabilitated in 1998 G5 Houeit-Marh Major confluenc es Conjunction between a tributary and the primary water course C1 Tahoun Ed- Deir Permanent water courses C2 El-Mikhada C3 Ain Stanboul Intermittent streams C4 Mazra’at En- Naher Permanent water course OPTIMA: Kick-off Meeting/ Malta 2004 Example

18. OPTIMA: Kick-off Meeting/ Malta 2004 Example from Abou-Ali River basin, Lebanon

19. 2. Meteorological and 3. Hydrological Data -Precipitation -Evapotranspiration -Supplementary climatic data (e.g. temperature, humidity, wind velocity, etc. ) -Hydrological properties of running water in rivers -Hydrological properties of issuing water from springs -Volume of precipitated and evapotranspirated water OPTIMA: Kick-off Meeting/ Malta 2004 Partially achieved by RS Can be done by RS Must be on a time-series

20. Calculating the volume of water in the form of snow Example: OPTIMA: Kick-off Meeting/ Malta 2004 Mediterranean Sea Beirut

21. Mediterranean Sea OPTIMA: Kick-off Meeting/ Malta 2004 Example: Delineating catchment areas

22. 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, SO 4 --, CO 3 --, HCO 3 -, F +++, Cu ++, SiO 2, TDS, etc.. Economic data: Water price for domestic, agricultural, industrial, tourism, etc… Water consumption for domestic, agricultural, industrial, etc… No RS involvement

23. 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 OPTIMA: Kick-off Meeting/ Malta 2004

24. 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

25. 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

26. 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

27. Publication, implementation and user interface (web access) 1- LUC map for different time series 2- Drainage networks 3- DEM / TIN 4- Anthropic map (showing urban settlements, road network, etc..) A- GIS data B- RS data 1- Different imageries utilized in the LUC 2- Derived data (NDVI, PCA, etc…) 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 C- Model implementation OPTIMA: Kick-off Meeting/ Malta 2004

28. Thank you …