الفهرس 
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Abstract
Emerging technology in databases and spatial decision support system (SDSS) engineering provides excellent possibilities in sustainable land use and management of new land reclamation project’s areas in arid regions. Sustainable land use and management must sustain biophysical land potentiality and, at the same time, diversify the agricultural soil system, considering all the possible options to increase crop production: (i) expansion of the agricultural land surface; (ii) introduction of improved crop varieties; (iii) use of irrigation techniques; (iv) application of fertilizers and pesticides; (v) rationalization of soil tillage practices; and (vi) minimize environmental impacts.
A SDSS is defined as an interactive, computer-based system designed to support a user or a group of users in achieving a more effective decision by solving a semi-structured spatial problem. The development of spatial decision support for environmental resource management, e.g. forest and agroecosystem management, biodiversity conservation, or hydrological planning, started in the 1980s and was the focus of many research groups in the 1990s. The regional GIS-based modeling of environmental resources, and therefore ecosystems in general, requires setting-up an extensive geodatabases model.
2. Objectives
The major objective of this study is to develop a framework for a SDSS organized in (i) spatial land information system to provide environmental resource information on regional level; (ii) models to analyze the impact of anthropogenic activities and simulate scenarios of different impacts; and (iii) interfaces to ensure the communication between the SDSS components. The desired SDSS will be used to simulate scenarios that assist the Banger Al-Sokar Land Reclamation Project (BSLRP) Council in managing, discovering and planning potential activities in the BSLRP area.
The specific objectives are to:
(i) develop seven sub-information systems:
• base geodata information system (BGDIS),
• climate information system (CIS),
• soil information system (SIS),
• land use information system (LUIS),
• hydrological information system (HIS),
• spatial/temporal biodiversity information system (STBIS),
• forest/agricultural management information system (FAMIS).
And to populate these sub-information system with data for BSLRP.
(ii) develop four models:
• traditional and Courtney DSS model for the reuse of drainage water;
• cut/fill detection model using multi-temporal digital elevation models;
• soil salinity spatial geostatistical ‘best’ model and the estimation of spatial crop’s yield;
• microelement nutrient diagnosis model
And to apply these five models in the BSLRP.
3. Thesis Outline
This thesis is made of five chapters: introduction, review, theoretical, materials and methods, and results and discussion besides a summary and a list of references. Then, it ends with a summary in Arabic language.
Chapter 2 presents detailed reviews on spatial decision support systems (SDSS), decision support systems (DSS) and reviews related to the development of four models. The first one is a reuse of drainage water model in terms of the suitability of drainage water for irrigation, strategies, management, long term effect, principles of sustainability, and wicked problems. The second model is cut/fill detection using multi-temporal digital elevation models derived from topographic maps, GPS measurements, and Shuttle Radar Topographic Mission (SRTM) data. The review related to the third model is dealing with spatial estimation of soil salinity, and estimation of EC values of saturated soil paste extracts using EC values for more diluted extracts. The fourth review is on micronutrient diagnosis modeling using soil testing and GIS.
Chapter 3 deals with the theoretical aspects of SDSS in terms of structure of Spatial Land Information System (SLIS), integration of models in SLIS, and interfaces. In addition, it presents model equations for spatial estimation of soil regionalized variables in terms of variogram modeling and interpolation using ordinary and universal kriging.
Chapter 4 is material and methods. The study area is the Bangar Al-Sucar Land Reclamation Project (BSLRP), NW Egypt. A description for the study area includes location, climate, the soil resource, the ground water resource, and the irrigation and drainage network and water composition. The suggested SDSS framework includes seven information subsystems which are populated with spatial and non-spatial data for the BSLRP. In addition the SDSS includes four models and interfaces. A traditional DSS paradigm is suggested based on a model for the reuse of drainage water for irrigation and extended using the Courtney’s decision making paradigm for wicked problems. Finally, descriptions of soil sampling and analysis are given in terms of soil sampling design, GPS measurements, data normalization, soil salinity and calcium carbonate determinations, Fe, Mn, Zn, and Cu soil testing and particle size analysis.
Chapter 5 presents results and discussions of the proposed SDSS framework applied to the BSLRP. The traditional DSS paradigm is applied to predict crop yield drainage irrigation water under different irrigation scenarios. The Courney’s decision making paradigm is applied for planning and budgeting of the reuse of drainage water infrastructure with citizen, environmental-health issues, ethical abuses, and four principles of sustainability. Results of the cut/fill detection using multi-temporal digital elevation models (MAP, SRTM, and GPS) are presented and discussed in terms of variogram’s parameters, assessment of the vertical accuracy of SRTM data sets, and the cut/fill extent, volume, and directions. Results of the soil salinity spatial models are presented and discussed in terms of statistical distributions, neighboring points differences, variogram model differences, universal versus ordinary kriging, crop salt tolerance, relationship between EC1:3 and ECe, relationship between ECe (root-zone) and ECe (top-layer), and ECe (root-zone) thematic maps and yield potential of salt-tolerance classes crops. Finally, results of the micronutrient diagnosis model are presented and discussed in terms of the descriptive statistics of soil test values, varigram’s parameters, frequency distributions, micronutrient thematic diagnosis maps, and validation based on fertilization experiments.