GIS and Optimization Modelling in this Study

The GIS and Optimization modelling system intended for this research theoretically and fundamentally is divided into four main key constituents or subsystems (Figure 3.2). The different factors are:

3.1.2.1 Geo-Database Management System: which stores the required data in the

necessary format for management and manipulation of data.

3.1.2.2 Land Suitability Analysis and Assessment Component: Land suitability is an

important link form between land use resources appraisal and evaluation of the decision procedure in land-use management and planning. This element plays an important role in land-use management and planning. Land evaluation and suitability assessment for each specified use is the key work of this element.

3.1.2.3 Scenarios Development and analysis (SDA): Scenarios are generated and

implemented based on future climate change and a range of possible policies.

3.1.2.4 Development of the linear modelling (LM) approach and calculation of Carbon

under different scenarios: This will be used to find the optimal spatial distributions of land-use based on the results of scenarios and data analysis.

The role of this system is to provide a flexible mechanism for communicating between data, models, and knowledge rules in the quantification of carbon and other GHG, energy storage and emissions respectively under possible future land use change. In this study some assumptions are made such as no change in the amount of soil carbon over time; land evaluation process takes into account to control on distribution of land cover. Also, it has been assumed that all agricultural and forestry lands are fulfilled in accordance to the principles of the best management practise and carbon saving intention, presuming suitable and adequate use of inputs, policies and methods.

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Figure 3.2: Conceptual Framework of Methodology Employed in the Research.

Footprint:

= Process, = Input Data, = Model Outputs

Farm Income

Optimization Results

- Objective Functions

- Land Use Allocations

- Output for Scenarios

Analysis Result Presentation

-Tables and Reports -Figures and Maps

Current Carbon Density in TV Land Evaluation and Land

Suitability Analysis Geo-DatabaseManagement

System

Scenario Development and Analysis Resource Base, Current Land Uses, Problems and Policy issues

Definition of Activities and Inputs

Land uses Constrain: Land Allocation Definition of Objective Functions

Linear Programming Model

Policy Issue Land Cover Soil Carbon Biomass Emissions DEM

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3.1.2.1 Geo-Database Management System

In land-use management spatial and non-spatial data with different formats and structures are needed. In order to develop the methodological system, there is a need to use a geo-database management system to create the potentialities that are the objectives of this research. The first important capacity, in relation to this requirement, is the integrated abilities and potentials of the system, which means that the different models and modules should combine and work together in an appropriate way and transfer data to each other. The synergistic relationship of the policies and economics in different parts of the system also is another key capacity. The geo-database management system can provide an appropriate and proper data model for managing different data types needed for land-use management and planning, as stated above, and allows the ability and potential for fundamental interaction with different sub systems and models.

The processes of developing the geo-database management system in this research are explained as follows:

a) Identifying and categorizing of data which are required in the land-use scenario generation procedure, as part of the set-up and construction of the system.

b) Evaluating and assessing the purposes of the system which has fundamental

interactions with databases in order to evaluate the system for data storage and management.

a) Evaluating and assessing the existing software for an expanded geo-database management system and selecting the one which meets the assessed needs.

b) Designing, aiming and implementing the data model and organisation of the geo- database management system.

c) Generating datasets (Table 3.1) required for the analysis, including details of the types of data available to the geo-database.

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Table 3.1: Required datasets for the geo-database in this site study (Met-office, British

Geological Survey, 1985, LCM 2000, Soils and their use in Southwest England., 1984, Soils in the British Isles, 1990).

Data Type Geometry Resolution Source

Land cover /Land use Raster 25m LCM2000

Soils Vector 1:25,000 Soil survey of England and

Wales

Elevation Raster 1:25,000 Digital elevation model

(DEM)

Aspect Raster 1:25,000 Digital elevation model

(DEM)

Slope Vector 1:25,000 Digital elevation model

(DEM)

Soil Depth Vector 1:25,000 Soil survey of England and

Wales

Soil Bulk Density Vector 1:25,000 Soil survey of England and Wales

Soil Organic matter Vector 1:25,000 Soil survey of England and Wales

3.1.2.2 Land Suitability Analysis and Assessment Component (LSA)

This phase of the project will identify criteria and constraints for each specified land-use type and develop spatial models to assess suitability and appropriateness of the study area for different land cover types. These models will be developed and expanded based on Multi-Criteria Analysis/ Evaluation (MCA/E) and the FAO’s land elevation (Food and Agricultural Organization) technique and classification, which involves finding out the factors, constraints, limitations and alternatives for each land use. There are two key stages in developing the LSA model:

a) Identification of the environmental, policy and socio-economical factors, which explain current land cover and land use in the study area.

b) Implementing and using the MCA/E suitability models, such as Linear Combination (LC), to produce and generate a final suitability value for each land area type.

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3.1.2.3 Scenario Development and Analysis (SDA)

The three main key conceptions of this stage are scenario building, land-use future studies and forecasting of the land-use changes, including future hindrances and uncertainties.

This stage was initiated through a literature review on scenario typologies, evaluation, analysis, the methods of scenarios to handle uncertainty and existing work on land cover change scenarios (Refer to chapter 2). The results of this stage are to identify what types of scenarios can be created for land-use management and how these are applied and to choose the most suitable and appropriate methodological approach. Also to evolve a methodology and implement this for scenario generation, development and scenarios for land cover changes in the Tamar Valley catchment.

The second action is to recognise and define the driving powers for future land cover change and spot the critical policy objectives, ideas and consider the landscape-scale storage and emissions of GHG and energy under the range of different scenarios for different periods in the 21st century for the Tamar Valley catchment area. This methodology will be based on computer implementation to make possible the relation and fundamental interaction of policies and climate change in scenario generation and analysis (refer to Chapter 5).

3.1.2.3 Development of the LM (Linear Modeling) approach and calculation of

Carbon under different scenarios

In this section of the methodology approach, the key components are defined as: definition of activities, land assessment and evaluation, and finding of inputs of the activities (refer to Figure 3.2 in this Chapter). The land use activities have been defined on the basis of environmental and available economic-socio information, taking into consideration the current land use situation and crisis recognised.

The linear programming (LP) model (addressed in Chapter 5) employs the linear purposes to connect the land use activities, constraints and objective functions. A complex and adaptable process is used that permits the model to find out the carbon value, GHG and energy emissions, and farm business income. The constraints and limitations of the model are split into land use (resource) constraints. These constraints include the available land allocation. The objective functions are categorised based on land and GHG National Inventory policies

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and problems defined that could be maximized and minimized in this research. Each of the objective functions is activated as a limit and constraint or non-connecting objective when has not optimized. The scenarios are adjusted and generated by different objective functions with different concern places (see Chapter 5). The objective functions are calculated independently (separately) with the model, and the scenarios in a repetitive process. Firstly, each of the picked objective functions is optimized independently with enforcing constraints under the range of different scenarios, and is then assessed. With considering this optimization model, in relation with the farm income, the results have been produced (Chapter 5 and 6). In the end, the total outputs are presented in different tables, maps, and figures.