3. Methodology

To implement the SDSS and support decision-making, we have to first look into the decision-making process. The steps of basic decision-making include:

1. Define the alternatives.


This is the process used to identify the problems and the goals to be accomplished through decision-making. As for the reclamation planning, the problem is that there are too many abandoned sites and limited resources or funding. The goal is to select the most urgent sites and reclaim them using limited resources.

2. List comparison criteria.


Comparison criteria consists of the available variables that might affect or relate to the problem and need to be taken into consideration. For simple decision making, the criteria can be listed in a table. For example, a customer wants to make a decision on choosing a computer from different brands. The decision-maker needs to do: first decide what components or criteria such as memory, speed, size and hard drive are considered, and then acquire the information on, and then lists them in a table to make comparison. But reclamation is a far more complex process which involves spatial aspect, criteria can range from physical, social to political data, therefore the data have to be organized in a more systematic manner, a simple table is not sufficient for this kind of problem. Since GIS is the best tool to organize spatial data, it definitely improves the process to access to spatially distributed data.

3. Weigh the importance of each criterion.


Not every criterion is created equal. The importance of each criterion can be identified by its weight. Another use of weight is to calculate. For qualitative data, there is no way to calculate and compare this type of data, and therefore the advantage of weighing is more significant. There are three ways to assign weight to a criterion. One way is to give a particular number to each criterion. Another way is to set the total weight of all the criteria as 100, and assign a percentage to each criterion according to its relative importance in the whole criteria set. The other way is a little more complicated and needs some algorithms to generate the final list of weights for each criterion. Pairwise comparison belongs to this category. In a pairwise comparison, each one of a pair of the criteria is given a relevant weight and the algorithm is applied to calculate the weight set for all the criteria.

4. Rate each alternative on each criterion.


Within each criterion, there is a range of values if it is quantitative or a distinct set of categories if it is qualitative to describe the data. Each alternative on a criterion is rated by assigning a value or a score according to its original value or category depending on the data type. There are several approaches to rate a range of values. Linear method can be used for normally distributed data. Another approach is to classify the range of values into several categories and assign each category with a value. All the alternatives belonging to a category get the same value.

5. Perform the comparison.

Decision supporting systems (DSS) provide the environment or tools for the decision-makers to go through all the steps without bothering to understand the system. Decision-makers do not have to code the programs and what they need to do is just to select criteria, weigh and rate using the functions that DSS provide. The DSS can take care of the rest such as storing the data, organizing the data and models, calculating the scores and presenting the result in a more reasonable way. In a spatial decision support system for reclamation, a GIS database is used to store and manipulate the data, a model base is used to manage the models for calculation, and multiple criteria analysis is used to form the decision matrix and produce the result eventually.

Developing a GIS Database

The GIS database stores all raw data, processed data, and models. The ability to capture, retrieve, and manipulate complex spatial data can be the key to successful decision making. Each data set of any particular variable or criterion forms a data layer in the database. Since data must be collected from various sources, however, before the layers can be overlaid, the data must be referenced to a common geographic coordinate system. The GIS database in this study used a Universal Transform Mercator (UTM) projection system as its coordinate system. There is not much difference in appearance and calculation between different projection systems for small area like South Fork. The reason that UTM was chosen is that most of the data were originally projected to UTM such as TM images, streams and mine sites, and therefore no more efforts are needed if they are kept in UTM. Also unnecessary distortion and loss of information from projection transformation were avoided. The common map unit for UTM projection is meter. The data were stored either in the vector or in the raster format depending on the particular layer's properties.

Vector vs. Raster

The vector-based data represents geographic features similar to the way maps do. Points represent geographic features too small to be depicted as lines or areas; lines represent geographic features too narrow to depict as areas; and areas represent homogeneous geographic features. A x, y (Cartesian) coordinate system references real-world locations. In a vector-based data model, each location is recorded as a single x, y coordinate. Points are recorded as a single coordinate. Lines are recorded as a series of ordered x, y coordinates. Areas are recorded as a series of x, y coordinates defining line segments that enclose an area, hence the term polygon, meaning ‘many-sided figure’.

There are two file formats to store vector-based data in ESRI standards, shape file and ArcInfo coverage. Shapefile is a simple, non-topological format for storing the geometric location and attribute information of geographic features. It is one of the spatial data formats that can be used in ArcView. The shapefile format defines the geometry and attributes of geographically referenced features in as many as five files with specific file extensions that should be stored in the same project workspace. They are:

Raster-based systems, like vector-based systems, also store geographic data, but they view and store surfaces differently. Vector systems define an object and proceed to define its characteristics and attributes. One of these characteristics is the x, y coordinate location. The raster-based data model is more like a photograph than a map and works in a similar way as photograph; it is a regular grid of dots (called cells, or pixels) filled with values. In fact, when a picture is stored in a computer, the raster data model is used (ESRI, 1997).

Raster-based systems divide the world into discrete uniform units called cells. Every cell represents a certain specified portion of the earth, such as a square kilometer, hectare or square meter. Each cell is given a value to correspond to the feature or characteristic that is located at or describes the site, such as a drainage basin, soil type, or residential classification. Location is not defined as an attribute but is inherent in the storage structure.

The cell is the primary spatial entity within a grid. Each cell is square, has the same size as other cells in the grid and contains a numeric value representing the spatial variable at that location. Cell values can be 32-bit integer or real (floating-point) numbers.

The uniform cells are organized into a Cartesian matrix consisting of rows and columns. A row identifies all cells equidistant from the top or bottom boundary of a grid. Columns identify all cells equidistant from the left or right boundary of the grid. Each Cartesian matrix is called a grid. Every cell in a grid has a unique row and column identifier.

Each grid represents a spatial variable. While vector features are stored as a series of x, y coordinates and topological relationships, grid cells are stored as rows and columns.

The data needed for the implementation of SDSS for coal mine reclamation include soils, streams and lakes, mine-sites, land cover, topographic maps, as well as Digital Elevation Models (DEM).

The soil layer was digitized from county soil survey maps. The physical, chemical and mineralogical soil properties were correlated to the geographic locations. The attributes included here are soil series, texture, organic matter content, structure, permeability and pH. Since soil distributes as areas, this layer was stored in vector format. Soil properties were the main source of input for RUSLE model, and also they were used to generate acidity map.
 
 

The stream layer was provided by the Indiana Geological Survey (IGS). It includes the streams and lakes in the area. Since the stream layer is used primarily for reference of location, no attributes are needed. This layer was stored in vector format.

The mine site layer was provided by IGS also. The attribute associated with mines are mining period and mining methods. This layer, too, was stored in vector format.

The land cover layer was generated from interpreting remote sensing data and serves as an input to the RUSLE model. Two date sets of Landsat Thematic Mapper (TM) data acquired in the study area in 1993 are the primary source for classification. Traditionally, there are two major approaches to extract cluster information from remotely sensed data: unsupervised and supervised classification.

Unsupervised classification

Supervised classification

Accuracy assessment

Error matrix

DEM was acquired from U.S. Geological Survey (USGS) 7.5 minute DEM data and clipped by the study area boundary. The 7.5 minutes DEM data files are digital representations of cartographic information in a raster form. DEMs consist of a sampled array of elevations for a number of ground positions at regularly spaced intervals. Each 7.5 minute DEM is based on 30 by 30 meter data spacing with the Universal Transverse Mercator UTM projection. It provides the same coverage as the standard USGS 7.5 minute map series.

Topographic maps were the digital version of USGS 7.5- minute topographic maps. These maps had no role in the spatial decision support system. They were not criteria for analysis, neither input for any criteria calculations. While serving as back draft for other layers, topographic maps could provide field data for better land use/ land cover classification, and also visualization of the location information of all the other thematic layers. This layer was stored as TIFF image file with a world file attached to it to identify the location.

Considering the property of the whole set of layers that include both raster and vector data. All spatial analysis was carried out in a raster format. The resolution was set at 30 by 30 meter to match the resolution of both TM and USGS DEM data. Since this study deals with regional environmental management, this resolution is reasonably appropriate. All the vector data therein were converted to a raster format for analysis.

Soil Erosion Rate Model

The erosion rate for a given site results from the combination of many physical and management variables. The (RUSLE) is an erosion model designed to predict the longtime average annual soil loss carried away by runoff from specific field slopes in specified cropping and management areas. Widespread use of this equation has substantiated its usefulness and validity for this purpose. It works well too for nonagricultural conditions (Renard et al. 1997).

Along with the RUSLE model, USDA also developed a computer program and three databases for the calculation of the six factors. The CITY Database contains information on climate, the CROP Database holds the parameters defining the characteristics of vegetative growth and residue, and the OPERATIONS Database defines the effects of field operations on the soil, crop, and residues. Some of the values can be derived directly from the database, while others have to be calculated using the data from the GIS database.

Multi-criteria decision-making analysis

In this study, MCDM methods were integrated with a GIS to provide a means to place reclamation proceedings in priority order based upon a variety of different choice criteria and on the importance (weight) a decision-maker attaches to these criteria. The decision model selected for this ordering of reclamation proceedings is a combination of weighted summations (Voogd, 1983).

Implementation of the SDSS

The reclamation priorities of coal mine sites are primarily determined by pollution intensity, soil erosion rates, soil properties, and a coal mine site’s proximity to streams. To build a SDSS, all the data must be placed into distinct decision making categories. In this SDSS, three criteria are selected: soil erosion rate, soil acidity, and the proximity of a site to streams.

It is not an easy task to develop a weighting scheme for decision making in mine reclamation. As the priority of reclamation is decided by the attractiveness of the sites according to their values on each criterion, aggregation of the values could generate the rankings, therefore the linear method is adopted in this study. The value of each criteria layer is normalized to a value from 0 to 10 (VC_i) depending on the statistics of the original data set or the decision-makers’ preference. Then each layer is given a weight (W_i) according to its importance. The output value for each site is the sum of each criteria value multiplied by its weight.

For site j, the output value is

RP_j = S ( VC_ij * W_ij ) (i = 0..2) [2]

The erosion rate priority is positively related to the reclamation priorities. This means a high erosion rate gets high priority value. High erosion rate soil has a great potential to be carried away into a stream or channel by running water and, therefore, to degrade the water quality and cause sediment pollution.

Soil acidity can be another major source of pollution. Highly acidic soil may cause severe problems for environmental management and for re-vegetation because of its low nutrient content.

Proximity to streams is the most important factor for reclamation as pollutants are usually carried by water, so it should be weighted the most highly in the weighting scheme. This factor is followed by the soil erosion rate factor, and soil acidity factor. The closer the site is to streams, the higher is the risk that it will spread pollutants from the abandoned sites.

User Interface

The user interface is designed to allow the decision-maker to step through a process that will result in the calculation and display of a map of weighted reclamation priorities. To use this system, the user has to have a knowledge or preference for each factor. After the user input the weight of each variable, the system will calculate the final result and generate maps according to the user’s preference. In addition, the user can add new data sets to the system, in order to add a new set of criteria, such as land owner preference, in the multiple criteria list. Modification of the weighting scheme is also possible by interacting with the interface.

It was quite clear that with the task at hands which was originally dealing with spatial data, GIS functions were crucial in building the application system. The implementation of the interface had combine four major parts in its structure, main frame or programming platform, GIS functions, visualization of analysis results, and communications among them.

Borland Delphi by Inprise is a popular software like Microsoft Visual Basic and Visual C++. Compared to its peers, Delphi has the best performance/input ratio. Visual Basic, which is predominant programming platform, uses very simple syntax in coding and is easy to get hands on. But the compiled Visual Basic program is usually large in file size and runs slow. Visual C++, on the other hand, is more complicated in concepts and syntax, though the compiled file runs faster. Delphi is in between Visual Basic and Visual C++ with the respect of speed, size and programmer’s effort. Another advantage of using Delphi for this research is that several applications have been built with MapObjects and it definitely helps to get on building application directly without spending time to get familiar with the programming platform.

GIS functions have to be extracted from GIS software. With a couple of GIS packages and component at hand, a developing environment had to be chosen among the available options. The available GIS software included ArcInfo by ESRI, ArcView by ESRI and MapObjects by ESRI.

ArcInfo is by far the most powerful GIS software in the GIS world concerning the functions it provides for both raster and vector analyses. There is no doubt that ArcInfo could fulfil the task for building a spatial decision support system. The shortcomings of using ArcInfo to develop a SDSS are:

1. AML (ArcInfo Macro Language) has to be used which might limit the future modification from other parties who are not familiar with AMLs.
2. Interface is quite simple and not in windows program standard that might cause confusion for some users those who are used to traditional windows interface.
3. Display of maps is a little bit more complicated in ArcInfo environment than using GIS components. It takes some commands or functions to set up a single display window.
4. The AML program running in ArcInfo environment requires a quite large amounts of memory, which calls a lot internal commands or functions that cause the application to run slowly.

ArcView is yet another available GIS package by ESRI. It is a much easier system for the users compared to ArcInfo because of the Windows interface. The functions for vector analysis and process in ArcView are well developed. The advantage of ArcView is that it allows multiple windows displaying different themes or overlays. This makes it very easy for the users to conduct any comparison side by side. However, it lacks the capabilities to do raster analysis and grid modeling. To implement the spatial decision support system for this research that mostly required grid analysis, ArcView only could not fulfil it.

MapObjects is a new product by ESRI using GIS componentware technology. The portability and flexibility of MapObjects for using with other industrial standard packages distinguish itself from ArcInfo and ArcView packages. Wrapping most popularly used GIS functionalities it provides means for those users who are outside of GIS field to display and analyze geographic information within the framework of familiar programming environments. In implementing the spatial decision support system for this research, MapObjects was chosen to display multiple windows by using the "Map" object provided by MapObjects. Though MapObjects has the capability to display raster or image layers, by far it is not able to support operations on raster data.

As Delphi was selected to be the programming platform, ArcInfo functions to be the main GIS function source, and MapObjects to be the visualization tool, the only step left was to choose a method to connect these three parts. There was no difficulty in embedding MapObjects in Delphi program because MapObjects was just an Active X control. The remaining question was how to get access to ArcInfo functions within Delphi environment. The Open Development Environment (ODE) by ESRI provides such environment for standard programming languages to call ArcInfo functions through Active X controls. The main ArcInfo components such as ArcEdit, ArcPlot and Grid are encapsulated in the controls. Embedding the controls in the program, the application is able to access the commands and functions.

The Open Development Environment (ODE) by ESRI was used as the primary means to develop a user interface to access ArcInfo GIS functions. This was accomplished through the use of Rapid Application Development (RAD) tools to take advantage of the flexible programming environment, which has easy access to any ArcInfo functions. Since there is a limitation of accessing the ArcInfo through ODE controls designed for Windows NT, MapObjects, which is a GIS component developed by ESRI, was used for displaying multiple windows. There were three major steps to implement the fully functioned graphic user interface. They are interface design, function calls and the interaction of interface and function.

• Interface design

• Function calls

• Interaction