International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856

Sugar Cane Modelling Using GIS And Remote Sensing For

T.Subramani1, K.Sukumar2, S.Priyanka3

1Professor & Dean, Department of Civil Engineering, VMKV Engineering College, Vinayaka Missions University, Salem, India

2PG Student Of Environmental Engineering, Department of Civil Engineering, VMKV Engg. College, Vinayaka Missions University, Salem, India

3UG Student, , Department of Civil Engineering, VMKV Engineering College, Vinayaka Missions University, Salem, India

Abstract either before or after the rainy season and can be harvested This study addresses land evaluation for sugar cane suitability, around 10 to 12 months after cultivation. A large number and demonstrates the usefulness of integrating both legacy of farmers grow sugarcane on the basis of marketing price cartographic and contemporary data to help solve assessment rather than the highly potential soils. Lands inherently problems. Land evaluation techniques have proved useful for unsuitable and depleted are used to plant sugarcane, supporting rational management of land resources and resulting low productivity. As a consequence, the farmers sustainable development across many sectors. A Geographical suffered from increasing debt. The allocation of sugar-cane Information System (GIS) and Remote Sensing (RS) were used to suitable land is needed to enhance the productivity. The to identify suitable lands for growing sugar cane at 2 sites in perambalur sugar mills district.The basic FAO land evaluation land suitability, based on integration of land qualities is framework was adopted, using readily available data including widely accepted. FAO guideline on the land evaluation is terrain and soil. Satellite data were utilised to derive several well known worldwide for land suitability evaluation thematic maps to help identify areas with the required method. In addition a number of reports provide potentials. A GIS-based suitability analysis was conducted using methodologies on the application of GIS to the land the ESRI ArcGIS software, and the input datasets reclassified to evaluation. Important in this process was recording the assign categories that could be integrated in one model. A quality of the data and how this data could be utilized weighted overlay method was used, along with a traditional within the GIS environment for the proposed analyses. The boolean raster method, to allow comparison of results from following are the data sources used for this research: each method. The weighted overlay method areas demarked more land as ‘suitable’ than did the traditional boolean method. This could derive from the assignment of differing weightings  Historic legacy data for soil was retrieved (scanned in the weighted overlay, making it a more flexible operation and digitised) from the World Soil Archive and when compared to the strict “true or false” assessment of the catalogue (WOSSAC) available at Cranfield boolean method. Across the selected study area, an estimated University. 75% of the land was classified as being ‘moderately suitable’  The Shuttle Radar Topography Mission 90m Digital for sugar cane. One future means to fully differentiate these Elevation Model; areas would be the introduction of precision farming techniques to enable continuous management of the crop and to obtain  Landsat 8 satellite imageries by the United States improved yield production. Geological Survey covering the study areas and;  Soil properties extracted from the Harmonised World Keywords: Land suitability analysis, weighted overlay, Soil Database. sugar cane, legacy data It was considered of great importance to integrate the 1.INTRODUCTION process of land evaluation such that the approaches be Agriculture is one of the world’s most important activities applicable for any given purpose, delineating soil supporting human life. From the beginning of the constraints, severity and similarity of soil as a means to civilization man has used the land resources to satisfy his assist land managers and farmers to plan for better needs. The land resources regeneration is very slow while agricultural production. the population growth is very fast, leading to an unbalance. On a global scale, agriculture has the proven potential to 1.1 Aim increase food supplies faster than the growth of the To investigation into the development of a crop suitability population. Lack of wise and suitable agricultural practices geo database and modelling system for Sugar Cane in results the degradation of natural habitats, ecosystems and Perambalur sugar mills district, drawing on both agricultural lands round the globe. The average sugarcane contemporary environmental data and legacy thematic yield in the North-East was estimated to be 47 ton/ha. All information. sugarcane produced in the North-East are supplied to sugar factories. There are 15 sugar factories in the North-East, distributing in 9 provinces. Sugarcane is usually planted

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856 1.2 Objectives  Identification of study area;  Adoption of an applied case study-based approach  Assessment of suitability modelling technique; identifying suitability for Sugar Cane at two land  Data sourcing; sites in Perambalur sugar mills district;  Data preparation and analyses, and;  Compilation for selected study sites of sources of  Crop suitability model implementation. contemporary environmental data, including satellite imagery, together with appropriate legacy, 2.2 The History Of Study Area historical cartographic and report-based information from previous survey activities; 2.2.1 About Eraiyur  Development of land use suitability modelling Eraiyur is a Village in in Perambalur framework for sugar cane drawing on these District of State, India. It is located 21 KM available data; towards North from District head quarters Perambalur. 17  Application of model to selected case study areas KM from Veppanthattai. 274 KM from State capital and review of appropriateness of approach. Chennai. Eraiyur Pin code is 621133 and postal head office is Eraiyur (Perambalur).Thevaiyur North ( 3 KM ) , 2. STUDY AREA Ponnagaram ( 3 KM ) , Namaiyur ( 4 KM ) , Ranjankudi ( 4 KM ) , Thirumandurai ( 5 KM ) are the nearby Villages 2.1 Materials And Methods to Eraiyur. Eraiyur is surrounded by Mangalur Taluk In order to accomplish the objectives for this project, it was towards North, Veppanthattai Taluk towards west , necessary to source all relevant and available data, and towards west , Alathur Taluk towards then establish how best to incorporate these within a GIS South . Tittakudi , Perambalur , Virudhachalam , Thuraiyur environment for the analyses. This section outlines the data are the nearby Cities to Eraiyur. collection and methodology used to determine land suitability for sugar cane in the study areas. fig describes 2.2.2 Demographics Of Eraiyur the procedure adopted for this research. The sequences of Tamil is the Local Language here. tasks undertaken to achieve the goal of this project are outlined below: 2.2.3 Suitability Modelling Technique Various methods for land suitability have been trialled, each having its own flaws. An appropriate suitability method was adopted based on what data is available and the area of interest. Some techniques were identified during this research after which the weighted overlay method was chosen for this study as this was readily available and allows for a multi criteria assessment, accepts data in different resolutions and analyses these thematic layers based on a user defined weighting which can be useful to determine the importance of each parameter used. The weighted overlay approach was assessed along with the traditional Boolean method for comparison.

2.3 Data Sourcing Land suitability analyses in this research seek to use GIS and remote sensing to perform a multi criteria analyses, requiring several data inputs. Due to the nature of this research, only freely available data were used.

Figure 1 Study Area Volume 6, Issue 3, May – June 2017 Page 209

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856 3.METHODOLOGY 3.3 Intended Land Use  A major kind of land use, which is a major subdivision Figure. 2 shows the Methodology adopted in the study of rural land use such as rainfed or irrigated agriculture. This type of land use is usually considered in qualitative land evaluation studies at the reconnaissance level.  A land utilisation type, which is a kind of land use defined in a degree of detail greater than.  It is usually used in quantitative land evaluation studies and are described in much detail and precision as the purpose of study requires. It consists of a list of technical specifications in a given physical, social and economic setting. It takes into consideration if the current environment would be modified, e.g. by irrigation schemes or is intended to remain in the present condition. Examples of land utilisation types includes, market orientation (either commercial or small-scale production), labour intensity and infrastructure requirements.

3.4 Generation Of Thematic Maps Thematic maps were generated for each of the soil physical and chemical parameters using IDW interpolation provided in Arc GIS 9.3 software. Inverse Distance Weighted (IDW) interpolation determines cell values using a linearly weighted combination of a set of sample points. The weight is a function of inverse distance. IDW lets the user control the significance of known points on the interpolated values, based on their distance from the output point.

3.5 Integration With GIS Once the standardized thematic layers and their weights or weightages were obtained for each crop, the weighted sum

Figure. 2 Methodology overlay within Arc GIS 9 was applied to produce the crop suitability map. Two multi crops suitability maps for rabi 3.1 Analysis Of Land Suitability and kharif crops were generated using the same procedure. The process of evaluating the land in the Northeast is based on the guidelines for land evaluation. This study 4.ABOUT SOFTWARE implemented a synergistic approach, creating land unit as a result of land quality combination related to crop 4.1 Geographic Information Systems requirement. The land qualities or thematic layers were Geographic information systems (GIS) is a technological digitally encoded in GIS database and eventually framework that enables analysis and manipulation of performed the overlay of the thematic layers. With defined spatial data. It can provide information on relationships model for the sugarcane the output layer was classified into and trends between spatial features in a geographic area. 4 classes: highly suitable (S1), moderately suitable (S2), GIS is therefore defined as a “computer based system for marginally suitable(S3) and not suitable. the capture, storage, retrieval, analysis and display of spatial data” Its spatial analytical capabilities makes it a 3.2 Extraction Of The Study Area And Agricultural convenient tool for land suitability analysis, presenting Land results in the form of maps and reports which can be The study area was extracted from the whole image meaningful to a local user.GIS proves useful in meeting through on screen digitization of the area of interest (AOI) the objectives of a land suitability assessment, such as and masking out using subset module of ENVI software constructing geographical databases for land suitability, (ver.4.7). The Normalized difference vegetation index assessing land suitability as well as the selection of new (NDVI), being a potential indicator for crop growth and areas for crop plantations. For these reasons several land vigor was used for identifying the agricultural area. suitability studies have employed GIS as the main data Incorporated (NDVI) with decision tree classifier (DTC), processing and analytical tool. agricultural land successfully delineated and used for further analysis.

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856 4.2 Remote Sensing in the overlay process to create the spatial layer of NAI. Remote sensing is collectively referred to as the collection The sub-layers (N, P, K, and pH) were assigned the values and interpretation of information about an object or area of rating factor as given in the table 1. The values of rating without being in physical contact with that object. This of the NAI are also given in the table 1. involves the use of platforms such as aircrafts and satellites utilising different portions of the electromagnetic (EM) 4.4.3 Particle Size (PS) spectrum to gather information about the natural The "PS" includes soil texture and coarse surface materials environment. The physical basis underlying remote sensing on which is important edaphic constraint for the sugar- is concerned with the EM spectrum and the way in which cane. The PS is defined as class of the particle size. The emitted illumination interacts with the surface of objects. values of rating factor of the particle size were given in the Therefore, the central hypothesis of remote sensing is that table. radiation reflected from a surface of an object carries information about that object and the state of its surface. 4.4.4 Rooting Condition (R) The "R" land quality layer was determined using the soil 4.3 Data Limitations depth. Available soil map was used to assign this factor rating for the evaluation. 4.3.1 Railroads It is assumed in this study, that railroads within each site could be dedicated to loading agricultural produce and 4.4.5 Topography (TOPO) processed material (including ethanol). Specifically, trains The topography layer is a matrix of slope gradient and may stop at designated points at a distance from the landform. The map of the slope and landform combination agricultural area to load sugarcane and other by-products. was digitally established and values assigned were given as Use of the rail line could be shared between transporting in sub-table. Each of the defined land qualities with their passengers or for other uses perhaps once or twice a week associated attribute was digitally encoded in GIS database passing through the selected areas, with the remainder of to create five thematic layers. the week dedicated to transporting sugarcane and its by- products. 4.4.6 Land Suitability The evaluation model for sugar-cane was given using the 4.3.2 Existing Sugarcane Farms values of the factor rating as follows: The precise geographic locations of existing sugarcane farms in the study area are not known. The factor of Suitability = W x NAI x PS x R x TOPO climate change has not been explicitly addressed in this study, thus it is assumed that the areas identified as suitable These thematic layers were integrated by spatially for sugarcane cultivation pertain to current climatic overlaying each with the suitability model of the defined 5 scenarios. As it was beyond the scope of the project, land layers. The output layer yields 4 classes: S1=highly tenure and land reform have not been addressed. suitable, S2=moderately suitable, S3=marginally suitable and N=unsuitable. The validation of the model was made, 4.3.3 Land Mines based on the field investigation of the crop yield.(Figure.3) During the civil war landmines restricted agriculture in certain areas. They impose a constraint on the expansion of cropped area. Areas with landmines have not been addressed in this study and thus, it is assumed that landmines are not an issue in the selected areas.

4.4 GIS Layer Establishment

4.4.1 Water Availability (W) Rainfall data of 30 years (1976-2005) recorded by the Metheorological Department was used for the establishment of the "W". Spatial interpolation of mean annual rainfall for the entire North-East Thailand was undertaken with kriging method of the rainfall data to yield "W" spatial map. The spatial "W" layer was then divided into 4 classes.

4.4.2 Nutrient Availability Index (NAI) The "NAI", is based on the method developed by Radcliffe et al (1982) and is given by NAI = NxPxKxpH. The soil map of Land Development Department (LDD) provides information of N, P, K and pH, those of which were used Figure. 3 The process of studying land suitability for sugarcane Volume 6, Issue 3, May – June 2017 Page 211

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856 4.4.7 Land Cover Sustainable land management requires an assessment of 5.DATA PREPARATION AND ANALYSES the current land cover of the area of interest and as such In order to analyse the available data for sugar cane land cover information becomes an essential tool for requirements, it was necessary to first assemble the data development planning and management of the territory It and organize them in a geospatial database for proper gives an indication of the current coverage of a particular management. The data were derived and classified to meet area, and in addition to the types of vegetation that may the suggested requirements for sugar cane as outlined cover a certain area, land cover types such as cities, water below. bodies and built up areas are also included. Sustainable land management is made possible by land cover The datasets assembled for the selected classification are: information by avoiding the cultivation or development in 1. Soil data; areas which are already under use for a specific purpose or II. Normalised Difference Vegetation Index (NDVI); are of biological and ecological significance. III. Landforms; IV. Slope. 4.5 Satellite Image Analysis 6. ANALYSIS RESULTS 4.5.1 Satellite Image Selection And Processing A high resolution satellite image was obtained from the For Perambalur District Different maps prepared by using Quickbird satellite. The Quickbird Bundle Standard GIS and shown in Figure.4,5,6,7,8,9,10,11,12,13, 14,15,16, Orthoready Geotiff DVD format was chosen among 17,18 & 19 available products, with 8K tiling and a 60 cm spatial resolution for the panchromatic image and a 2.44m resolution for the, multispectral image. Standard imagery is radiometrically corrected, sensor corrected and mapped to a cartographic projection (WGS84 UTM).The ortho ready format was chosen so that ground control points could be applied for geo rectification. Ground control points (GCPs) were taken in the field at the corners of each study parcel as well as at easily identifiable points near the edge of the image capture region. The “Georeferencing” module of IDRISI Andes software was used to georeference the original image using ground control points; the resulting spectral response of the adjusted image varied considerably in the NDVI values produced from the raw image, due to the “rubber sheeting” process used to geo rectify the image.“Rubber sheeting” causes the satellite image pixels to be recalculated, thus no longer providing a true representation of the spectral response. Upon examination of the geo rectified satellite image in comparison to the raw image, it was decided to accept the original image with basic geo rectification; this, in order to save accuracy of the spectral response. Regardless, overlaying of the original satellite image with the digital boundaries of parcels created using aerial photography, showed excellent visual agreement. Discrepancies between the spatial accuracy of the original satellite image when overlaid with the digitized parcel boundaries were on the order of less than 1m and hence no additional georectification was deemed acceptable.

4.5.2 NDVI Spectral analysis of the remotely sensed imagery obtained from the Quickbird satellite was carried out in the IDRISI Andes Edition software. In order to create NDVI coverages from the raw multispectral bands provided, the “Raster Calculator” module of IDRISI was used. The software calculates the value for each pixel of a new raster using the algebraic combination of the red (Band 3) and near Figure. 4 Location Map infrared (Band 4) which corresponds to the formula for NDVI which was input into the raster calculator.

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856

Figure.8 Geomorphology Map

Figure. 5 Drainage

Figure. 9 Road Network Map

Figure. 6 FCC

Figure.7 Geology Map Figure. 10 Soil Order Map

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856

Figure 14 Slope Reclass

Figure. 11 DEM Map

Figure. 12 Slope Degree

Figure. 15 Road Buffer

Figure. 16 River Density Figure. 13 NDVI Map

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856 parameters used in the modelling process. The results from the weighted overlay shows for the most part, the area as being moderately suitable while no portion of the land is actually classified as unsuitable - based on the datasets used for this project. Conversely, the boolean method identifies most of the land as being marginally suitable. This is assumed to be as a result of the rigid form of assessment (true or false) inherent in the traditional boolean method.

Figure. 17 Ndvi Reclass

Figure. 20 Area distribution of Perambalur Study area based on the suitability classes

Figure. 20 Showing area distribution of Perambalur Study area based on the suitability classes Due to the flexibility of the weighted overlay, several suitability maps were derived using different percentage weighting on the input parameters utilized. This was able to help in prioritizing some data themes over others. However, regardless of the weightings, it was understood that most of the area still Figure. 18 Site Suitability ranges between marginally to moderately suitable with little to no highly suitable areas for growing sugar cane. Shows the different outputs from the weighted overlay and boolean method and shows the area distribution of the suitability classes from both methods used.

7.2 Associated Challenges This research was conducted as a rapid ‘desk-based’ assessment with all data being remotely acquired, with only the soil data being is a historic map collected from a field survey. Therefore some challenges were encountered during this research which added to knowledge.

7.2.1 Collecting Soil Data The WOSSAC archive at Cranfield University (www.wossac.com) holds a vast amount of global historic Figure. 19 Sugarmill Route data which can be very useful to integrate in a contemporary analyses like this however, some challenges 7. RESULTS AND DISCUSSION also comes with such data as this has been collected a very long time ago with probably no access to the original 7.1 Model Outputs author (Hallett et al., 2011; 2006). The problems at this The weighted overlay and boolean “true or false” methods point were the soils are mapped as associations and not were undertaken to help assess the study areas. This series with a scale of 1:100,000 therefore having a broad produced an output of thematic layers showing the information as to the soil texture and other characteristics suitability classes arising from the interaction of the within the landforms though personal contacts with soil

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856 experts (pers comm. Dr. Ian Baillie; Mr. Brian Kerr) were by ESA. Although this data was intended to serve as one of made to help identify missing information in the historic the parameters in the land evaluation analyses, due to the data and this was made possible by the aid of visual coarse resolution at which this data was derived in (global interpretation from aerial photographs of the area and their scale at 27km2 grid size), it was deemed inadmissible for field experiences. the purpose of this research as the study areas are covered in just one pixel as seen. and thereby having one value 7.2.2 Collecting Digital Elevation Model across the study area. The digital elevation model proved highly applicable for this project in helping distinguish between landforms and 7.2.5 Collecting Rainfall Data to characterize the topography. The data’s resolution is One of sugar cane’s major requirement is adequate water expressed on a 96x96m grid and mapping detailed supply. The project therefore sought to source rainfall data information was not ideal, although it was found useful as to help in the assessment. Unfortunately, most of these data a first step in guiding future surveys within other farm also do not have a suitable spatial resolution for the study sites. sites in this project (about 27km2 grid sizes) as the whole or half of the areas are covered in just one pixel thereby 7.2.3 Deriving Landforms From The Dem having just one value of rainfall which cannot help in Data availability for key land characteristics was limited discriminating rainfall distribution. The study areas are for the study sites. To better evaluate the land, expert’s small and therefore would have same amount of rainfall advice was sought as a means of establishing a way of across. Generalised rainfall data was therefore considered understanding the morphology of the land and what the as insufficient. land characteristics might be and its formation. As a result, the Soil and Terrain Database (SOTER) method was 7.3 Methods Adopted adopted (ISRIC, 2014) to help determine the During this research a range of GIS and Remote geomorphology of the study areas thereby delineating techniques, outlined below, were attempted to help between the derived landforms (e.g. river plains, highlands evaluate the study areas, some of these proved useful. etc.). To do this, the DEM was manipulated in GIS to Mostly issues arose due to the limited area of the study derive 4 thematic layers (Slope, Relief intensity, sites. The following section outlines the analyses that were Hypsometry and Potential drainage density) using a conducted but that were ultimately excluded in the final “SOTER-like” methodology as the full SOTER method assessment. could not be adopted in the study area to discriminate features as it is designed for a global scale (1:1million) 7.3.1 Solar Irradiance Map while these study areas are 30 kilometres across. The Solar irradiance was initially intended to form part of the “SOTER like” method used for this project was found very analyses for determining suitable lands. However, after the helpful in discriminating between landforms and this, with results were derived it was realised that the sun hour the aid of visual interpretation from Google earth and duration per day was broadly similar across the study area Landsat imageries, was combined with expert knowledge (with just few minutes between the highest and lowest (pers comm. Dr. Ian Baillie) to determine the major soil areas) as shown in (Figure 13.). This was deemed types of the land could be (e.g. FTS or Vertisols), as seen insufficient for discriminating between suitable lands. It is in .This was included in the model to help determine however a requirement for sugar cane and this method can potential sugar cane plantations. very well be adopted for larger geographical areas which will have variations is the daily amount of sun hours and so It should be borne in mind that this methodology requires further analyses can be made.Solar irradiance was created some local knowledge of the study area in terms of the from the digital elevation model using the area solar labelling of outputs as the topographic characteristics radiation tool in ArcGIS. This was calibrated for the local might not mean the same thing in different places. This sun angle over one year to produce an accurate figure for was experienced in whereby a “River plain” in Perambalur solar radiation which was given in wh/m2.In order to Tau area was not replicated in the second site in Hadeija, convert this to represent duration of sun hours per day, being a much drier area. The methodology therefore can be conversions were made to the derived solar radiation. The described as a semi-automated ‘guided’ approach – standard unit conversion adopted was 1kwh/m2 being however, this approach is pragmatic where substantive equal to 1 peak hour of sun wh/m2, it was divided by 1,000 local datasets are not available, this often being the case in to get kwh/m2 and then divided by 365 days which then African studies gives a daily sun hours received by the whole study area per square meter. 7.2.4 Soil Moisture Data Soil moisture can be useful for land evaluation. One source 7.3.2 NDVI VS. EVI of this data is from microwave remote sensing. Some vegetation indices were derived from Landsat data Appropriate data was obtained freely at (www.esa- which helped in differentiating between the greenness of soilmoisyture-cci.org). The global soil moisture data was vegetation and un-vegetated areas (bare soil or built up downloaded in the Net CDF format which was converted areas), both indices were calculated and had a minimal to a raster grid file using the BEAM application provided difference in the index values (Figure 14.), during this

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856 research it was noted that NDVI can easily become Research and Applications, Vol. 4, Issue 6( Version saturated in its reflectance and therefore cannot easily 2), pp.274-282, 2014. distinguish patches of bare soils between vegetation. [7] T.Subramani., P.Someswari, “Identification And Analysis Of Pollution In Thirumani Muthar River 8.CONCLUSION Using Remote Sensing”, International Journal of Based on the proposed maps on methodology generated Engineering Research and Applications, Vol. 4, Issue and concluded our project has sought to analyse one of the 6( Version 2), pp.198-207, 2014. approaches in the literature within the variety of land [8] T.Subramani., S.Krishnan., C.Kathirvel. S.K.Bharathi evaluation techniques, with the numerous methods Devi., “National Highway Alignment from Namakkal applicable to land suitability analyses it was however to Erode Using GIS” , International Journal of possible to imitate a feasible method within the given time Engineering Research and Applications ,Vol. 4, Issue of this research. Data assembling was possible using GIS 8( Version 6), pp.79-89, 2014. to build a spatial database holding several datasets [9] T.Subramani., A.Subramanian.,C.Kathirvel.,S.K. including soil, contemporary and historic data with Bharathi Devi., “ Analysis and Site Suitability attribute tables in order to identify potential sugar cane Evaluation for Textile Sewage Water Treatment Plant sites for sustainable production. A GIS based traditional in Salem Corporation, Tamilnadu Using Remote boolean and weighted overlay method was applied to the Sensing Techniques” , International Journal of produced thematic layers which helped in the process of Engineering Research and Applications , Vol. 4, Issue segmenting the land based on suitability classes for sugar 8( Version 6), pp.90-102, 2014. ” cane. In this research, a GIS weighted overlay method [10] T.Subramani. C.T.Sivakumar., C.Kathirvel., S.Sekar., proved more advantageous over the traditional boolean Identification Of Ground Water Potential Zones In method in combining several data to help in a multi-criteria Tamil Nadu By Remote Sensing And GIS Technique” decision analyses with potential of it being extended to International Journal of Engineering Research and other areas. This project therefore hopes to serve as an Applications , Vol. 4 , Issue 12(Version 3), pp.127- initial approach to land suitability analyses and guide 138, 2014. towards field survey activities in order to effectively make [11] T.Subramani., S.Sekar., C.Kathirvel. C.T. Sivakumar, decisions and how further land management can be made. “Geomatics Based Landslide Vulnerability Zonation Through our results drainage map, geology map, soil order, Mapping - Parts Of Nilgiri District, Tamil Nadu, slope map, geomorphology map will be explored. For India”, International Journal of Engineering Research sugaercane site suitability will be find out based on above and Applications, Vol. 4, Issue 12(Version 3), pp.139- maps. Also the results will catagorize exact suitable map, 149, 2014. not suitable, highly suitable areas. [12] T.Subramani., S.Sekar., C.Kathirvel. C.T. Sivakumar, ”Identification Of Soil Erosion Prone Zones Using References Geomatics Technology In Parts Of North Arcot And [1] T.Subramani, and R. Elangovan, “Planning Of A Ring Dharmapuri District”, International Journal of Road Formation For Salem Corporation Using GIS”, Engineering Research and Applications, Vol. 4, Issue International Journal of Engineering Research And 12(Version 3), pp.150-159, 2014 Industrial Applications, Vol.5, No.II, pp 109-120, [13] T.Subramani, R.Vasantha Kumar, C.Krishnan “Air 2012 Quality Monitoring In Palladam Taluk Using Geo [2] T.Subramani,, S.Krishnan. and P.K.Kumaresan.., Spatial Data”, International Journal of Applied “Study of Ground Water Quality with GIS Application Engineering Research (IJAER),Volume 10, Number for Coonur Taluk In Nilgiri District.”, International 32, Special Issues pp.24026-24031,2015 Journal of Modern Engineering Research,Vol.2, No.3, [14] T.Subramani,”Identification Of Ground Water pp 586-592, 2012. Potential Zone By Using GIS”, International Journal [3] T.Subramani, and S.Nandakumar,, “National Highway of Applied Engineering Research (IJAER), Volume Alignment Using Gis” International Journal of 10, Number 38, Special Issues, pp.28134-28138, 2015 Engineering Research and Applications, Vol.2, [15] T.Subramani, M.Sivagnanam , " Suburban Changes In Issue.4, pp 427-436, 2012. Salem By Using Remote Sensing Data" , International [4] T.Subramani, and P.Malaisamy,“Design of Ring Road Journal of Application or Innovation in Engineering & For Erode District Using GIS”, International Journal Management (IJAIEM) , Volume 4, Issue 5, May 2015 of Modern Engineering Research,Vol.2, No.4, pp 1914 , pp. 178-187 , ISSN 2319 - 4847. 2015 - 1919,2012. [16] T.Subramani, P.Malathi , " Drainage And Irrigation [5] T.Subramani., P.Krishnamurthi., “Geostatical Management System For Salem Dist Tamilnadu Using Modelling For Ground Water Pollution in Salem by GIS" , International Journal of Application or Using GIS”, International Journal of Engineering Innovation in Engineering & Management (IJAIEM) , Research and Applications ,Vol. 4, Issue 6( Version Volume 4, Issue 5, pp. 199-210 , 2015 2), pp.165-172, 2014. [17] T.Subramani, P.Malathi , " Land Slides Hazardous [6] T.Subramani., T.Manikandan., “Analysis Of Urban Zones By Using Remote Sensing And GIS" , Growth And Its Impact On Groundwater Tanneries By International Journal of Application or Innovation in Using Gis”, International Journal of Engineering

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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected] Volume 6, Issue 3, May- June 2017 ISSN 2278-6856 Engineering & Management (IJAIEM) , Volume 4, AUTHOR Issue 5, pp. 211-222 , 2015 [18] T.Subramani, D.Pari, “Highway Alignment Using Prof. Dr.T.Subramani Working Geographical Information System” , IOSR Journal of as a Professor and Dean of Civil Engineering, Volume 5 ~ Issue 5 ,Version 3, pp 32-42, Engineering in VMKV Engineering 2015 College, Vinayaka Missions [19] T.Subramani, G.Raghu Prakash , " Rice Based University, Salem, TamilNadu, Irrigated Agriculture Using GIS" , International India. Having more than 27 years Journal of Emerging Trends & Technology in of Teaching experience in Various Computer Science (IJETTCS) , Volume 5, Issue 3, pp. Engineering Colleges. He is a 114-124 , 2016. Chartered Civil Engineer and [20] T.Subramani, E.S.M.Tamil Bharath , " Remote Approved Valuer for many banks. Chairman and Member Sensing Based Irrigation And Drainage Management in Board of Studies of Civil Engineering branch. Question System For Namakkal District" , International Journal paper setter and Valuer for UG and PG Courses of Civil of Emerging Trends & Technology in Computer Engineering in number of Universities. Life Fellow in Science (IJETTCS) , Volume 5, Issue 3, pp. 071-080 , Institution of Engineers (India) and Institution of Valuers. 2016. Life member in number of Technical Societies and [21] T.Subramani, A.Janaki , " Identification Of Aquifer Educational bodies. Guided more than 400 students in UG And Its Management Of Ground Water Resource projects and 300 students in PG projects. He is a reviewer Using GIS In Karur" , International Journal of for number of International Journals and published 174 Emerging Trends & Technology in Computer Science International Journal Publications and presented more than (IJETTCS) , Volume 5, Issue 3, pp. 081-092 , 2016. 25 papers in International Conferences. [22] T.Subramani, C.Kathirvel , " Water Shed Management For Erode District Using Gis " , International Journal K.Sukumar received his B.Tech. of Emerging Trends & Technology in Computer Degree in the branch of Chemical Science (IJETTCS) , Volume 5, Issue 3, pp. 093-103 , Engineering in Ahhiyaman 2016. Engineering College, Hosur. [23] T.Subramani, A.Kumaravel , " Analysis Of Polymer VMKV.Engineering College, Fibre Reinforced Concrete Pavements By Using Vinayaka Missions University, ANSYS" , International Journal of Application or Salem, TamilNadu, India.. Now, he is Innovation in Engineering & Management (IJAIEM) , working as an Assistant Professor in Volume 5, Issue 5, pp. 132-139 , 2016 . Sri Nandhanam College of Engineering and Technology in [24] T.Subramani, S.Sounder , " A Case Study And Trupatur. Currently he is doing his ME Degree in the Analysis Of Noise Pollution For Chennai Using GIS" , branch of Environmental Engineering in the division of International Journal of Emerging Trends & Civil Engineering in VMKV Engineering College, Salem. Technology in Computer Science (IJETTCS) , Volume 5, Issue 3, pp. 125-134 , 2016. [25] T.Subramani, K.M.Vijaya , " Planning And Design Of S.Priyanka is persuing B.E. Degree Irrigation System For A Farm In Tanjavur By Using in the branch of Civil Engineering in Remote Sensing" , International Journal of Emerging V.M.K.V. Engineering College , Trends & Technology in Computer Science Vinayaka Missions University, (IJETTCS) , Volume 5, Issue 3, pp. 135-146, 2016. Salem. She has illustrious career in [26] T.Subramani, G.Kaliappan , " Water Table Contour her intermediate and matriculation For Salem District Tamilnadu using GIS" , exams, her hobby is cooking and International Journal of Emerging Trends & surfing internet. Technology in Computer Science (IJETTCS) , Volume 5, Issue 3, pp. 147-158 , 2016. [27] T.Subramani, K.Kalpana , " Ground Water Augmentation Of Kannankuruchi Lake, Salem, TamilNadu Using GIS – A Case Study " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 5, Issue 3, pp. 210-221 , 2016.

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