Hierarchy Of Agropolitan Area

Published Date: 02 Nov 2017

The purpose of Agropolitan is to build an area which has more interactions between people in urban and rural. One of the main parts of developing Agropolitan area is the choosing the location for Agropolitan area like will be done in this research. Also in this research not only the choosing of location of Agropolitan area but also identifying other locations that support the Agropolitan program. There are some methods to be used to support in developing an Agropolitan, one of these with is an AHP and GIS approach. Therefore AHP and GIS become as supporting tools for decision makers to decide the policies for developing Agropolitan project.

In the process of Agropolitan modeling, broadly two types of database are generated using various methods, which are discussed in the following sections. These two types of database are spatial database and non-spatial/ attribute database. The spatial data comprises of all the thematic and topographic maps viz., land use/land cover, geomorphology, drainage, physiography, base details, slope, geology, soil etc. The non-spatial or attribute data is composed of population details, crop and industrials details and also soil quality data. In this chapter, the steps involved in deriving all these data products, the sources of data acquisition and the ways of transforming these data products suitable for GIS software are described.

3.2 Type and Data Source

Data were collected in several methods: site surveys, interview and from existing data. Sources of data were collected from some government institutions such as AP Irrigation Departments and district Agricultural office, Indian Meteorological Department (IMD), APPCB (Andhra Pradesh Pollution Control Board) and district Industrial Centre, Bureau of Economics and Statistics (BES), Hyderabad Forest Department, Revenue office, Mandal office and Village panchait offices, NRSC (National Remote Sensing Centre), Central Survey office, Hyderabad, Farmers, Consultant, Academics and Agricultural Institution. There are two types of data:

1. Spatial Data: Satellite imagery data, aerial photography, administrative map, Topographic data.

2. Non Spatial Data: Demography, agriculture, building, mining, industry, transportation, financial, education, drainage, social, economy, hydrology, climate and Infrastructure.

3.3 Method of Research

The method is formulated as in Figure 3.1, below, describe combines Analytical Hierarchy Process (AHP) and Geography Information System (GIS) to achieve the goal of this research. The precursor stage is preparation/collecting all data to support the process of this study. The data must support all criteria that establish to GIS and AHP process, with these data the interview model to implement an AHP stage with experts can be composed

Presentation33.jpgFigure 3.1 Map Showing the Methodology to Develop Agropolitan Model

3.4 AHP Stage

3.4.1 Building Hierarchy

Building the hierarchy of the goal, objectives and sub criteria are used to make recommendations. A hierarchy is a treelike structure that was used to decompose a decision problem. It has a top-down flow, moving from general categories (objectives) to more specific ones (sub-objectives and sub-sub-objectives).

3.4.2 Hierarchy of Agropolitan Area

The hierarchy structure of problems which were studied is choosing Agropolitan area based on four factors (commodity, infrastructure, human resources, and natural aspect). Requiring of these four factors relies on classification development conditions of an area of Agropolitan.

1. Commodity Factor

Primary commodities of Prakasham are paddy, Ground nut, Sunflower, Chilies, Cotton, Tobacco and Redgram. The primary commodity can be distinguished by the highest production of commodities in the area.

2. Infrastructure Factor

Transportation infrastructure linking urban markets and rural areas is important linking factors that determines whether rural areas can be competitive in producing agro-industrial products. The marketing system supports are trade transaction between farmer and costumer in the area. Other factors include: Agriculture equipment, Electric network, Banking facility, Process industry.

3. Human Resources Factor

Agricultural activities in an Agropolitan area can be indicated with some activities of the farmer as human resources. In this area, the ability of the rural society is only how to make their land produce many commodities for increasing their level of income.

4. Natural Factor

Natural factor is required for supporting the agricultural activities of the area. Without good natural factor, Agropolitan area cannot produce the maximal production of commodities.

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Figure 3.2 Hierarchy of Agropolitan Area

Figure 3.2 shows the structure of goal, factors and alternatives in a hierarchy. In this hierarchy, the goal to choose Agropolitan area is in the level one. Level 2 shows factors having an effect in choosing the area, and level 3 shows the alternative location in that area.

Connecting lines of boxes between levels indicate relationship which is needed to be measured with a pairwise comparison to the higher level. Level 1 represents the purpose of research which is to choose an area of Agropolitan in Prakasham district, among 4 existing areas (districts) as shown on level 3. Factors at level 2 are measured with pair comparison of aim to level 1. For example, in choosing the Agropolitan area, which one more important commodity factor or infrastructure factor, which one more important commodity or human resource, and which one is more important infrastructure or human resource also natural aspect and so on.

3.4.3 Building Matrix

One of the major strengths of AHP is the use of pairwise comparisons to receive perfect ratio scale priorities, instead of using traditional approaches of ‘assigning’ weights. This process compares the relative importance, performance or likelihood of two elements with respect to another element in the level above. A judgment is made based on its importance and by how much (Badri, 1999). In this study the expert consists of farmers, government, planners, academics, and consultant. Considering with measured factors relatively between one and another, scale relative measurement 1 until 9, as described in Table 3.1. The table consists of all supporting factors, similar to the hierarchy above.

Table 3.1 Pair Comparison Matrix

3.4.4 Expert Judgment View

To derive the weights and calculate the recommendation results, the entire model must be judged and to judge the model, pairwise comparisons is made at each level of hierarchy. Once all the alternative pairwise judgments have been made with respect to each of the lower level criteria, the recommendation is recommended.

3.4.5 The Consistency

After doing with the pairwise comparisons, the consistency is determined by using the Eigen value, λmax, to calculate the consistency index, CI as follows: CI = (λmax – n) /(n – 1), where "n" is the matrix size. Judgment consistency can be checked by taking the consistency ratio (CR) of CI with the appropriate value in Table 2.5. The CR is acceptable, if it does not exceed 0.10. If it is more, the judgment matrix is Inconsistent. To get a consistent matrix, all judgments should be reviewed and improved.

3.5 GIS Stage

GIS will be used based on a set of criteria derived from the spatial aspects, environment, social, agricultural and infrastructure aspect. With GIS capabilities, spatial analysis techniques will be done to determine the objective of this research. Each of the input themes is assigned to weight influence based on its importance, then the result successively multiplying. The first step that was taken in this analysis was to collect all of the data that would be needed to meet all of the criteria. Criteria were selected by concerning the potential location of Agropolitan areas. This criterion also must be identified and selected for Agropolitan view and the master plan of Prakasham district, and then the GIS overlay process can be used to combine all factors in the form of a weighting overlay process.

This study performs a GIS Spatial analysis using ArcGIS model builder. In the model building process the convert these themes to grid themes using the raster conversion process. Models are represented as sets of spatial process (overlay techniques). Each of the input themes is assigned a weight influence based on its importance, and then the GIS overlay process can be used to combine the factors in the form of a weighting overlay process. The result of GIS will display on a map of the Agropolitanarea as final recommendation.

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Figure 3.3. Model builder of AgropolitanSuitability

3.6 GIS DATA SOURCES

A heightened awareness of urban sprawl and environmental problems has developed over the past several decades and this has spurred a need for reliable geospatial data to enable better understanding of environmental processes and their impacts. Environmental and socioeconomic models have also undergone changes and these have created new requirements for geospatial data. In view of critical role, data plays in any kind of spatial modeling; emphasis is given to new information gathering initiations for remotely sensed data and to advancements in integrating data from different sources with GIS. GIS is a powerful tool for environmental data analysis and socioeconomic planning. It stores spatial information (data) in a digital mapping environment. A digital base map can be overlaid with data or other layers of information onto a map in order to view spatial information and relationships. GIS allows better viewing and understanding of physical features and the relationships that influence in a given critical environmental condition. GIS and socioeconomic and also environmental models function with a broad spectrum of geospatial data that are used for spatial analysis and modeling of environmental related problems at different scales. These data generally come in different formats and from various sources and measurements. The examination and organization of data into a useful form produces information content, which is compatible with GIS, which enables appropriate analysis and modeling of rural-urban environment. GIS is an information management system capable of providing spatial analysis tools for storing, retrieving, and manipulating georeferenced computerized maps. In the present study three different sources are used to collect the required data. The three sources are remote sensing satellite data from National Remote Sensing Centre (NRSC), Survey of India (SOI) toposheets and related Government and private agencies for existing data products.

A GIS stores an illustration of the world in the form of layers connected by a general geographical frame of reference. Each of the features on a layer has a distinctive identifier which distinguishes it from the rest of the features on the layer and allows us to relate it to applicable information stored in external databases, etc. This easy controlling mode of abstraction, GIS allows us to capture only those elements of the world that are of interest to us. Different views and data about the world e.g., Transport, forest, vegetation, etc. can be captured and stored in the GIS over time to accommodate the requirements of diverse users and to reveal changes in the landscape over time. Every graphical feature on the earth can be represented by only three identities: line, polygon and Point. The layers of information are stored in the GIS using one of two different data models: Raster and Vector.

In the raster model, a feature is defined as a set of cells on a grid. All the cells on the grid are in the similar shape, size and each one is recognized by a coordinate position and a value which acts as its identifier (Digital Number). The raster model is mainly useful for working with uninterrupted forms of features such as type of soils, vegetation etc.

In vector, a feature is represented as a set of begin and end points used to describe a collection of points, polygons or lines, which give details of the outline and dimension of the feature. The vector model is mainly useful for representing highly discrete data types such as roads, boundaries, buildings etc. Development of Agropolitanmodel needs baseline data, thematic data, topographic data and collateral data. All such data products are derived and extracted through various sources which are given in the following table.

Table 3.2 Data Types and Sources of Acquisition

TYPE OF DATA

SOURCE OF DATA

Toposheets (1:25,000 Scale)

SOI (Survey of India), Hyderabad

Satellite Data (LISS IV-MX)

NRSC (National Remote Sensing Centre)

Village Infrastructure and Utilities Information

Revenue office, Mandal office and Village panchait offices.

Forest Boundaries

Forest department

Census data

Bureau of Economics and Statistics (BES), Hyderabad

Industries Information

APPCB (Andhra Pradesh Pollution Control Board) and district Industrial Centre

Meteorological Data

Indian Meteorological Department (IMD)

Agricultural and Water Resources data

AP Irrigation Departments and district Agricultural office

Land use / Land cover map

Satellite data analysis

Other data like Administrative maps (Districts maps, mandal maps, forest maps)

Concerned Departments of State Government and Central Government

3.7 Satellite Data

Digital remote sensing data of RESOURCESAT-1 (also known as IRS-P6) are an advanced satellite built by the Indian Space Research Organization. The tenth satellite of ISRO in IRS series, RESOURCESAT-1 is intended to not only continue the remote sensing data services provided by IRS-1C and IRS-1D, both of which have far outlived their designed mission lives, but also vastly enhance the data quality. The 1360 kg RESOURCESAT-1 was launched into an 817 km high polar sun synchronous orbit from the eighth flight of India's Polar Satellite Launch Vehicle (PSLV-C5).

Resourcesat-1 carries three cameras similar to those of IRS-1C and IRS-1D but with vastly improved spatial resolutions. A high resolution Linear Imaging Self Scanner (LISS-4) operating in three spectral bands in the visible and Near Infrared Region (VNIR) with 5.8 meter spatial resolution and steerable up to 26 degrees across track to obtain stereoscopic imagery and achieve five day revisit capability.

Resourcesat-1 also carries a solid recorder with a capacity of 120 Giga Bits to store the images taken by its cameras which can be read out later to the ground stations. Table 3.3 gives orbital parameters information of Resourcesat-1.

Table 3.3 the orbit parameters of Resourcesat-1

Cycle /Orbits

341

Major axis

7195.11 km

Height

817 km

Inclination

98.69 deg

Eccentricity

0.001

Orbital period

101.35 minutes

Number of orbits/day

14

Repetivity

24 days

Distance between adjacent paths

117.5 km

The distance between successive ground tracks

2820 km

Ground traces velocity

6.65 km/Sec

Equatorial crossing time

10.30 A.M (at descending node_

3.7.1 LISS-IV Camera

The LISS-IV camera is a multi spectral high resolution camera with a spatial resolution of 5.8Mt at nadir. The sensor consists of three linear odd-even pairs of CCD arrays, each with 12000 pixels. The odd and even pixel rows are separated by 35 microns, which correspond to five scan lines. Also the placement of the three CCDs in the focal plane is such that their imaging strips on the ground are separated by 14.25 km in the along-track direction. This camera can be operated in two modes: Mono and Multi-spectral. In the Multi-spectral mode, data are collected in three spectral bands:

0.52 to 0.59 microns (Green -Band 2)

0.62 to 0.68 microns (Red -Band 3)

0.76 to 0.86 microns (NIR -Band 4)

In the multi spectral mode, the sensor provides data corresponding to pre-selected 4096 contiguous pixels, corresponding to 23.9km swath. The 4K strip can be selected anywhere within the 12K pixels by commanding the start pixel number using electronic scanning scheme.

In Mono mode, the data of full 12K pixels of any one selected band, corresponding to a swath of 70km, can be transmitted. Nominally, Band-3 data are transmitted in this mode. The LISS-IV camera has the additional feature of off-nadir viewing capability by tilting the camera by +/- 26deg. This way it can provide a revisit of 5 days in any given ground area.

3.8 GIS DATA TYPES

Basically all the GIS data used in this study are classified as

Topographical data

Thematic data

Collateral data

The topographical and thematic data are classified as spatial data and the collateral data as attribute data. The details of these types of data products are discussed below.

3.9 Spatial Data

The spatial data consists of topographic, thematic and other derivative maps derived from satellite sensing system and SOI toposheets. The satellite data used is IRS P6 LISS IV MX (5.8 Mt resolution), SOI toposheet series in 1:25,000 scale. The step-by-step procedure for preparing the spatial data for the entire study area discussed below:

Step 1: Satellite data processing using image-processing software like ERDAS (Earth Resource Development Application System) and hardcopy generation.

Step 2: Generation of thematic maps viz., land use / land cover and geomorphology etc. by visual interpretation of satellite imagery and SOI toposheets.

Step 3: Generation of topographical maps showing physical characteristics of the study area. The topographical maps extracted from SOI toposheets are base, road network, drainage, watershed, slope and physiography.

Step 4: Generation of maps derived from other thematic and topographic maps.

Step 5: Scanning and digitization of the thematic, topographic and other derivative maps.

Step 6: Digital spatial database generation using ARC/INFO and Arc View GIS software.

3.10 Geo-coding and Geo-referencing

The following standard techniques employing ERDAS Image Processing software have been adopted for Geo-referencing of IRSP6 LISSIV MX (5.8 Mt resolution) data covering the study area. Toposheets covering the entire study area on 1:25,000 scales are scanned and raster file is created. These are further Geo-referenced based on the longitudinal & latitudinal coordinates. After Geo-referencing all the maps are edge-matched and a digital mosaic is prepared which depicts the continuation of the study area.

The satellite data obtained from NRSC is processed for initial corrections like drop outs, stripping and earth rotations. Ground control points are selected on the toposheet and corresponding imagery. Care is taken to satisfy the condition on the density of GCPs for image registration. Geo-referencing is carried out using ERDAS image processing software. The geo-referenced image is further mosaicked and then feature matching is carried out. At the end of this process the digital data free from all distortions is available for digital image enhancement and thematic map preparation with the help of visual image analysis techniques.

3.11 Digital Image Enhancement of LISS IV data

Image enhancement procedures are applied to image data in order to more effectively display or record the data for subsequent visual interpretation. Image enhancement deals with the techniques for increasing the visual distinctions between features in a scene (Lillesand and Keifer, 2000). The goal of spectral enhancement is to make certain features more visible in an image by bringing out more contrast. The initial display of IRSP6 LISSIV MX data through ERDAS software revealed that the features like minor roads, streams and river are not clear / visible as the contrast of the image is very dull because the raw data values fall within a narrow range. Therefore, an attempt is made to apply linear contrast stretch technique in order to improve the contrast of the image, which can be capable of expanding the dynamic range of radiometric resolution of satellite data.

3.12 Generation of Thematic Maps

The thematic maps namely, land use/land cover, geomorphology, geology, prospects map are generated from hardcopy of the satellite digital data. The standard basic elements and key elements of visual interpretation are applied to the satellite hardcopy image so as to extract the entropy or information content in accordance with the above thematic maps. At the end of the interpretation process, the above paper based thematic maps are ready for subsequent scanning and automated digitization for creation of a digital database for GIS data analysis and modeling.

3.13 Generation of Topographic Maps

Creating a GIS spatial database is a complex operation, which involves data capture, verification and structuring processes. Raw geographical data are available in many different analogue and digital forms such as toposheets, aerial photographs, satellite imageries and tables. Out of all these sources, the source of toposheet is of much concern to natural resource scientist and an environmentalist. In the present study, the base layers generated from toposheet are:

(i) Base map

(ii) Drainage map

(iii) Transportation Network map

(iv) Watershed map

(v) Slope map

(vi) Physiography map

These paper-based maps are then converted to digital mode using scanning and automated digitization process. These maps are prepared to a certain scale and show the attributes of entities by different symbols or coloring. The location of entities on the earth’s surface is then specified by means of an agreed co-ordinate system. It is mandatory that all spatial data in GIS are located with respect to a frame of reference. For most GIS, the common frame of reference co-ordinate system is that of a plane, orthogonal Cartesian co-ordinates oriented conventionally North-South and East-West. This entire process is called Geo-referencing. The same procedure is also applied on remote sensing data before it is used to prepare thematic maps from satellite data.

3.14 Derived Thematic Maps

Although using remote sensing satellite data and survey of India toposheets for making thematic maps as well as topographical maps is very attractive, serious attention is paid to develop maps showing:

(i) Agriculture suitability

(ii) Infrastructure suitability

(iii) Human resource suitability

(iv) Agropolitan suitability

3.15 AGRICULTURE SUITABILITY

The agriculture suitability is mainly based on the inherent soil characteristics, external land features and environmental factors. The agriculture suitability classes were arrived at as per the guidelines in the Soil Survey Manual. The criteria used for agriculture suitability classification are presented in Appendix I.

3.16 IDENTIFICATION OF CONSTRAINTS

Soil constraints for crop production were identified based on the laboratory and field analysis of the soil.

3.17 LAND CAPABILITY EVALUATION

3.17.1 Land capability suitability

Soil suitability for major crop growing was evaluated based on an FAO framework (1976) for land evaluation. It involved the formulation of climatic and soil requirements of the crop and ratings of these parameters viz., highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and unsuitable (N) for agriculture. Soil-site suitability for some of the major crops was evaluated based on the criteria suggested by Sehgal (1996) and Sys et al. (1991). Soil-site suitability characteristics for crops are presented in Appendix II.

3.17.2 Simple and maximum limitation method for land evaluation

In this method land characteristics (or qualities) area compared with the crop requirements and the land class is attributed according to the less favorable characteristics or quality. The methodology suggests an evaluation of the climatic characteristics in the first place with an ultimate aim of one class level to be introduced in the total evaluation. The relationship between land classes or suitability classes and limitations are given below.

Table 3.4 relationship between suitability classes and limitations

Limitations

Suitability class (land class)

0

NO

S1

Highly suitable

1

Slight

S2

Moderately suitable

2

Moderate

S3

Marginally suitable 3

3

Severe

N1

Temporarily Unsuitable

4

Very severe

N2

Permanently unsuitable

Land-capability.jpg

Figure 3.4 Showing the Methodology to Agriculture Suitability

3.18 Infrastructure Suitability

Through integration of remotely sensed, GIS and field data, infrastructure suitability are being developed. The main purpose of the Infrastructure Planning Study is to identify suitability for large scale industrial development, markets, banking facilities and financial center, education, services section, and process industry, also road and infrastructure connections. Based on the density of these factors, areas of developable infrastructure are identified, which is divided into four areas include: high suitable, moderate suitable, marginal suitable and not suitable.

3.19 Human Resources Suitability

Human resources are the set of individuals who make up the workforce of an organization, business sector or an economy. based on Agropolitan definition, human resources are included three factors: A) total population, B) cultivators and C) agro-labour. Based on these factors, areas of developable human resources are identified through more density in population, which is divided into four areas included: high suitable, moderate suitable, marginal suitable and not suitable.

3.20 DEVELOPMENT OF AGROPOLITANMODEL

The attribute and collateral data obtained and collected during fieldwork and the spatial data prepared during the study (maps obtained) are together related in an Improving Agropolitan linkage and also enhancing the economic condition in Prakasam District. Agropolitan model can develop the rural economy by benefiting from economies of scale that is subject to high accessibility, concentration of forward linkages and the development of urban amenities that meets the needs of the local people. The flow of application to develop the Agropolitan model is explained in the followed pages. The flowchart 3.5 showing the overall methodology and the step-by-step procedure for developing the socioeconomic factors and linkage in Agropolitan areas and finally develop the Agropolitan model.

PresentationAA.jpg

Figure 3.5 Showing the Methodology to develop Agropolitan model