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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

Volume 6, Issue 3, May – June 2017 Page 208

Abstract This study addresses land evaluation for sugar cane suitability, and demonstrates the usefulness of integrating both legacy cartographic and contemporary data to help solve assessment problems. Land evaluation techniques have proved useful for supporting rational management of land resources and sustainable development across many sectors. A Geographical Information System (GIS) and Remote Sensing (RS) were used to identify suitable lands for growing sugar cane at 2 sites in perambalur sugar mills district.The basic FAO land evaluation framework was adopted, using readily available data including terrain and soil. Satellite data were utilised to derive several thematic maps to help identify areas with the required potentials. A GIS-based suitability analysis was conducted using the ESRI ArcGIS software, and the input datasets reclassified to assign categories that could be integrated in one model. A weighted overlay method was used, along with a traditional boolean raster method, to allow comparison of results from 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 in the weighted overlay, making it a more flexible operation when compared to the strict “true or false” assessment of the boolean method. Across the selected study area, an estimated 75% of the land was classified as being ‘moderately suitable’ for sugar cane. One future means to fully differentiate these areas would be the introduction of precision farming techniques to enable continuous management of the crop and to obtain improved yield production. Keywords: Land suitability analysis, weighted overlay, sugar cane, legacy data 1.INTRODUCTION Agriculture is one of the world’s most important activities supporting human life. From the beginning of the civilization man has used the land resources to satisfy his needs. The land resources regeneration is very slow while the population growth is very fast, leading to an unbalance. On a global scale, agriculture has the proven potential to increase food supplies faster than the growth of the population. Lack of wise and suitable agricultural practices results the degradation of natural habitats, ecosystems and agricultural lands round the globe. The average sugarcane yield in the North-East was estimated to be 47 ton/ha. All 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

either before or after the rainy season and can be harvested around 10 to 12 months after cultivation. A large number of farmers grow sugarcane on the basis of marketing price rather than the highly potential soils. Lands inherently unsuitable and depleted are used to plant sugarcane, resulting low productivity. As a consequence, the farmers suffered from increasing debt. The allocation of sugar-cane to suitable land is needed to enhance the productivity. The land suitability, based on integration of land qualities is widely accepted. FAO guideline on the land evaluation is well known worldwide for land suitability evaluation method. In addition a number of reports provide methodologies on the application of GIS to the land evaluation. Important in this process was recording the quality of the data and how this data could be utilized within the GIS environment for the proposed analyses. The following are the data sources used for this research: Historic legacy data for soil was retrieved (scanned

and digitised) from the World Soil Archive and catalogue (WOSSAC) available at Cranfield University.

The Shuttle Radar Topography Mission 90m Digital Elevation Model;

Landsat 8 satellite imageries by the United States Geological Survey covering the study areas and;

Soil properties extracted from the Harmonised World Soil Database.

It was considered of great importance to integrate the process of land evaluation such that the approaches be applicable for any given purpose, delineating soil constraints, severity and similarity of soil as a means to assist land managers and farmers to plan for better agricultural production. 1.1 Aim To investigation into the development of a crop suitability geo database and modelling system for Sugar Cane in Perambalur sugar mills district, drawing on both contemporary environmental data and legacy thematic information.

Sugar Cane Modelling Using GIS And Remote Sensing For Perambalur District

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



Volume 6, Issue 3, May – June 2017 09

1.2 Objectives Adoption of an applied case study-based approach

identifying suitability for Sugar Cane at two land sites in Perambalur sugar mills district;

Compilation for selected study sites of sources of contemporary environmental data, including satellite imagery, together with appropriate legacy, historical cartographic and report-based information from previous survey activities;

Development of land use suitability modelling framework for sugar cane drawing on these available data;

Application of model to selected case study areas and review of appropriateness of approach.

2. STUDY AREA 2.1 Materials And Methods In order to accomplish the objectives for this project, it was necessary to source all relevant and available data, and then establish how best to incorporate these within a GIS environment for the analyses. This section outlines the data collection and methodology used to determine land suitability for sugar cane in the study areas. fig describes the procedure adopted for this research. The sequences of tasks undertaken to achieve the goal of this project are outlined below:

Figure 1 Study Area

Identification of study area; Assessment of suitability modelling technique; Data sourcing; Data preparation and analyses, and; Crop suitability model implementation.

2.2 The History Of Study Area 2.2.1 About Eraiyur Eraiyur is a Village in Veppanthattai Taluk in Perambalur District of Tamil Nadu State, India. It is located 21 KM towards North from District head quarters Perambalur. 17 KM from Veppanthattai. 274 KM from State capital Chennai. Eraiyur Pin code is 621133 and postal head office is Eraiyur (Perambalur).Thevaiyur North ( 3 KM ) , Ponnagaram ( 3 KM ) , Namaiyur ( 4 KM ) , Ranjankudi ( 4 KM ) , Thirumandurai ( 5 KM ) are the nearby Villages to Eraiyur. Eraiyur is surrounded by Mangalur Taluk towards North, Veppanthattai Taluk towards west , Perambalur Taluk towards west , Alathur Taluk towards South . Tittakudi , Perambalur , Virudhachalam , Thuraiyur are the nearby Cities to Eraiyur. 2.2.2 Demographics Of Eraiyur Tamil is the Local Language here. 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.




3.METHODOLOGY Figure. 2 shows the Methodology adopted in the study

Figure. 2 Methodology

3.1 Analysis Of Land Suitability The process of evaluating the land in the Northeast is based on the guidelines for land evaluation. This study implemented a synergistic approach, creating land unit as a result of land quality combination related to crop requirement. The land qualities or thematic layers were digitally encoded in GIS database and eventually performed the overlay of the thematic layers. With defined model for the sugarcane the output layer was classified into 4 classes: highly suitable (S1), moderately suitable (S2), marginally suitable(S3) and not suitable. 3.2 Extraction Of The Study Area And Agricultural Land The study area was extracted from the whole image through on screen digitization of the area of interest (AOI) and masking out using subset module of ENVI software (ver.4.7). The Normalized difference vegetation index (NDVI), being a potential indicator for crop growth and vigor was used for identifying the agricultural area. Incorporated (NDVI) with decision tree classifier (DTC), agricultural land successfully delineated and used for further analysis.

3.3 Intended Land Use A major kind of land use, which is a major subdivision

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 overlay within Arc GIS 9 was applied to produce the crop suitability map. Two multi crops suitability maps for rabi and kharif crops were generated using the same procedure.

4.ABOUT SOFTWARE 4.1 Geographic Information Systems Geographic information systems (GIS) is a technological framework that enables analysis and manipulation of spatial data. It can provide information on relationships and trends between spatial features in a geographic area. GIS is therefore defined as a “computer based system for the capture, storage, retrieval, analysis and display of spatial data” Its spatial analytical capabilities makes it a convenient tool for land suitability analysis, presenting results in the form of maps and reports which can be meaningful to a local user.GIS proves useful in meeting the objectives of a land suitability assessment, such as constructing geographical databases for land suitability, assessing land suitability as well as the selection of new areas for crop plantations. For these reasons several land suitability studies have employed GIS as the main data processing and analytical tool.




4.2 Remote Sensing Remote sensing is collectively referred to as the collection and interpretation of information about an object or area without being in physical contact with that object. This involves the use of platforms such as aircrafts and satellites utilising different portions of the electromagnetic (EM) spectrum to gather information about the natural environment. The physical basis underlying remote sensing is concerned with the EM spectrum and the way in which emitted illumination interacts with the surface of objects. Therefore, the central hypothesis of remote sensing is that radiation reflected from a surface of an object carries information about that object and the state of its surface. 4.3 Data Limitations 4.3.1 Railroads It is assumed in this study, that railroads within each site could be dedicated to loading agricultural produce and processed material (including ethanol). Specifically, trains may stop at designated points at a distance from the agricultural area to load sugarcane and other by-products. Use of the rail line could be shared between transporting passengers or for other uses perhaps once or twice a week passing through the selected areas, with the remainder of the week dedicated to transporting sugarcane and its by-products. 4.3.2 Existing Sugarcane Farms The precise geographic locations of existing sugarcane farms in the study area are not known. The factor of climate change has not been explicitly addressed in this study, thus it is assumed that the areas identified as suitable for sugarcane cultivation pertain to current climatic scenarios. As it was beyond the scope of the project, land tenure and land reform have not been addressed. 4.3.3 Land Mines 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

in the overlay process to create the spatial layer of NAI. The sub-layers (N, P, K, and pH) were assigned the values of rating factor as given in the table 1. The values of rating of the NAI are also given in the table 1. 4.4.3 Particle Size (PS) The "PS" includes soil texture and coarse surface materials on which is important edaphic constraint for the sugar-cane. The PS is defined as class of the particle size. The values of rating factor of the particle size were given in the table. 4.4.4 Rooting Condition (R) The "R" land quality layer was determined using the soil depth. Available soil map was used to assign this factor rating for the evaluation. 4.4.5 Topography (TOPO) The topography layer is a matrix of slope gradient and landform. The map of the slope and landform combination was digitally established and values assigned were given as in sub-table. Each of the defined land qualities with their associated attribute was digitally encoded in GIS database to create five thematic layers. 4.4.6 Land Suitability The evaluation model for sugar-cane was given using the values of the factor rating as follows:

Suitability = W x NAI x PS x R x TOPO

These thematic layers were integrated by spatially overlaying each with the suitability model of the defined 5 layers. The output layer yields 4 classes: S1=highly suitable, S2=moderately suitable, S3=marginally suitable and N=unsuitable. The validation of the model was made, based on the field investigation of the crop yield.(Figure.3)

Figure. 3 The process of studying land suitability for

sugarcane




4.4.7 Land Cover Sustainable land management requires an assessment of the current land cover of the area of interest and as such land cover information becomes an essential tool for development planning and management of the territory It gives an indication of the current coverage of a particular area, and in addition to the types of vegetation that may cover a certain area, land cover types such as cities, water bodies and built up areas are also included. Sustainable land management is made possible by land cover information by avoiding the cultivation or development in areas which are already under use for a specific purpose or are of biological and ecological significance. 4.5 Satellite Image Analysis 4.5.1 Satellite Image Selection And Processing A high resolution satellite image was obtained from the Quickbird satellite. The Quickbird Bundle Standard Orthoready Geotiff DVD format was chosen among 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 infrared (Band 4) which corresponds to the formula for NDVI which was input into the raster calculator.

5.DATA PREPARATION AND ANALYSES In order to analyse the available data for sugar cane requirements, it was necessary to first assemble the data and organize them in a geospatial database for proper management. The data were derived and classified to meet the suggested requirements for sugar cane as outlined below. The datasets assembled for the selected classification are: 1. Soil data; II. Normalised Difference Vegetation Index (NDVI); III. Landforms; IV. Slope. 6. ANALYSIS RESULTS For Perambalur District Different maps prepared by using GIS and shown in Figure.4,5,6,7,8,9,10,11,12,13, 14,15,16, 17,18 & 19

Figure. 4 Location Map




Figure. 5 Drainage

Figure. 6 FCC

Figure.7 Geology Map

Figure.8 Geomorphology Map

Figure. 9 Road Network Map

Figure. 10 Soil Order Map




Figure. 11 DEM Map

Figure. 12 Slope Degree

Figure. 13 NDVI Map

Figure 14 Slope Reclass

Figure. 15 Road Buffer

Figure. 16 River Density




Figure. 17 Ndvi Reclass

Figure. 18 Site Suitability

Figure. 19 Sugarmill Route

7. RESULTS AND DISCUSSION 7.1 Model Outputs The weighted overlay and boolean “true or false” methods were undertaken to help assess the study areas. This produced an output of thematic layers showing the suitability classes arising from the interaction of the

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. 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 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 data which can be very useful to integrate in a contemporary analyses like this however, some challenges also comes with such data as this has been collected a very long time ago with probably no access to the original author (Hallett et al., 2011; 2006). The problems at this point were the soils are mapped as associations and not series with a scale of 1:100,000 therefore having a broad information as to the soil texture and other characteristics within the landforms though personal contacts with soil




experts (pers comm. Dr. Ian Baillie; Mr. Brian Kerr) were made to help identify missing information in the historic data and this was made possible by the aid of visual interpretation from aerial photographs of the area and their field experiences. 7.2.2 Collecting Digital Elevation Model The digital elevation model proved highly applicable for this project in helping distinguish between landforms and to characterize the topography. The data’s resolution is expressed on a 96x96m grid and mapping detailed information was not ideal, although it was found useful as a first step in guiding future surveys within other farm sites. 7.2.3 Deriving Landforms From The Dem Data availability for key land characteristics was limited for the study sites. To better evaluate the land, expert’s advice was sought as a means of establishing a way of understanding the morphology of the land and what the land characteristics might be and its formation. As a result, the Soil and Terrain Database (SOTER) method was adopted (ISRIC, 2014) to help determine the geomorphology of the study areas thereby delineating between the derived landforms (e.g. river plains, highlands etc.). To do this, the DEM was manipulated in GIS to derive 4 thematic layers (Slope, Relief intensity, Hypsometry and Potential drainage density) using a “SOTER-like” methodology as the full SOTER method could not be adopted in the study area to discriminate features as it is designed for a global scale (1:1million) while these study areas are 30 kilometres across. The “SOTER like” method used for this project was found very helpful in discriminating between landforms and this, with the aid of visual interpretation from Google earth and Landsat imageries, was combined with expert knowledge (pers comm. Dr. Ian Baillie) to determine the major soil types of the land could be (e.g. FTS or Vertisols), as seen in .This was included in the model to help determine potential sugar cane plantations. It should be borne in mind that this methodology requires some local knowledge of the study area in terms of the labelling of outputs as the topographic characteristics might not mean the same thing in different places. This was experienced in whereby a “River plain” in Perambalur Tau area was not replicated in the second site in Hadeija, being a much drier area. The methodology therefore can be described as a semi-automated ‘guided’ approach – however, this approach is pragmatic where substantive local datasets are not available, this often being the case in African studies 7.2.4 Soil Moisture Data Soil moisture can be useful for land evaluation. One source of this data is from microwave remote sensing. Appropriate data was obtained freely at (www.esa-soilmoisyture-cci.org). The global soil moisture data was downloaded in the Net CDF format which was converted to a raster grid file using the BEAM application provided

by ESA. Although this data was intended to serve as one of the parameters in the land evaluation analyses, due to the coarse resolution at which this data was derived in (global scale at 27km2 grid size), it was deemed inadmissible for the purpose of this research as the study areas are covered in just one pixel as seen. and thereby having one value across the study area. 7.2.5 Collecting Rainfall Data One of sugar cane’s major requirement is adequate water supply. The project therefore sought to source rainfall data to help in the assessment. Unfortunately, most of these data also do not have a suitable spatial resolution for the study sites in this project (about 27km2 grid sizes) as the whole or half of the areas are covered in just one pixel thereby having just one value of rainfall which cannot help in discriminating rainfall distribution. The study areas are small and therefore would have same amount of rainfall across. Generalised rainfall data was therefore considered as insufficient. 7.3 Methods Adopted During this research a range of GIS and Remote techniques, outlined below, were attempted to help evaluate the study areas, some of these proved useful. Mostly issues arose due to the limited area of the study sites. The following section outlines the analyses that were conducted but that were ultimately excluded in the final assessment. 7.3.1 Solar Irradiance Map Solar irradiance was initially intended to form part of the analyses for determining suitable lands. However, after the results were derived it was realised that the sun hour duration per day was broadly similar across the study area (with just few minutes between the highest and lowest areas) as shown in (Figure 13.). This was deemed insufficient for discriminating between suitable lands. It is however a requirement for sugar cane and this method can very well be adopted for larger geographical areas which will have variations is the daily amount of sun hours and so further analyses can be made.Solar irradiance was created from the digital elevation model using the area solar radiation tool in ArcGIS. This was calibrated for the local sun angle over one year to produce an accurate figure for solar radiation which was given in wh/m2.In order to convert this to represent duration of sun hours per day, conversions were made to the derived solar radiation. The standard unit conversion adopted was 1kwh/m2 being equal to 1 peak hour of sun wh/m2, it was divided by 1,000 to get kwh/m2 and then divided by 365 days which then gives a daily sun hours received by the whole study area per square meter. 7.3.2 NDVI VS. EVI Some vegetation indices were derived from Landsat data which helped in differentiating between the greenness of vegetation and un-vegetated areas (bare soil or built up areas), both indices were calculated and had a minimal difference in the index values (Figure 14.), during this




research it was noted that NDVI can easily become saturated in its reflectance and therefore cannot easily distinguish patches of bare soils between vegetation. 8.CONCLUSION Based on the proposed maps on methodology generated and concluded our project has sought to analyse one of the approaches in the literature within the variety of land evaluation techniques, with the numerous methods applicable to land suitability analyses it was however possible to imitate a feasible method within the given time of this research. Data assembling was possible using GIS to build a spatial database holding several datasets including soil, contemporary and historic data with attribute tables in order to identify potential sugar cane sites for sustainable production. A GIS based traditional boolean and weighted overlay method was applied to the produced thematic layers which helped in the process of segmenting the land based on suitability classes for sugar cane. In this research, a GIS weighted overlay method proved more advantageous over the traditional boolean method in combining several data to help in a multi-criteria decision analyses with potential of it being extended to other areas. This project therefore hopes to serve as an initial approach to land suitability analyses and guide towards field survey activities in order to effectively make decisions and how further land management can be made. Through our results drainage map, geology map, soil order, slope map, geomorphology map will be explored. For sugaercane site suitability will be find out based on above maps. Also the results will catagorize exact suitable map, not suitable, highly suitable areas. References [1] T.Subramani, and R. Elangovan, “Planning Of A Ring

Road Formation For Salem Corporation Using GIS”, International Journal of Engineering Research And Industrial Applications, Vol.5, No.II, pp 109-120, 2012

[2] T.Subramani,, S.Krishnan. and P.K.Kumaresan.., “Study of Ground Water Quality with GIS Application for Coonur Taluk In Nilgiri District.”, International Journal of Modern Engineering Research,Vol.2, No.3, pp 586-592, 2012.

[3] T.Subramani, and S.Nandakumar,, “National Highway Alignment Using Gis” International Journal of Engineering Research and Applications, Vol.2, Issue.4, pp 427-436, 2012.

[4] T.Subramani, and P.Malaisamy,“Design of Ring Road For Erode District Using GIS”, International Journal of Modern Engineering Research,Vol.2, No.4, pp 1914 - 1919,2012.

[5] T.Subramani., P.Krishnamurthi., “Geostatical Modelling For Ground Water Pollution in Salem by Using GIS”, International Journal of Engineering Research and Applications ,Vol. 4, Issue 6( Version 2), pp.165-172, 2014.

[6] T.Subramani., T.Manikandan., “Analysis Of Urban Growth And Its Impact On Groundwater Tanneries By Using Gis”, International Journal of Engineering

Research and Applications, Vol. 4, Issue 6( Version 2), pp.274-282, 2014.

[7] T.Subramani., P.Someswari, “Identification And Analysis Of Pollution In Thirumani Muthar River Using Remote Sensing”, International Journal of Engineering Research and Applications, Vol. 4, Issue 6( Version 2), pp.198-207, 2014.

[8] T.Subramani., S.Krishnan., C.Kathirvel. S.K.Bharathi Devi., “National Highway Alignment from Namakkal to Erode Using GIS” , International Journal of Engineering Research and Applications ,Vol. 4, Issue 8( Version 6), pp.79-89, 2014.

[9] T.Subramani., A.Subramanian.,C.Kathirvel.,S.K. Bharathi Devi., “ Analysis and Site Suitability Evaluation for Textile Sewage Water Treatment Plant in Salem Corporation, Tamilnadu Using Remote Sensing Techniques” , International Journal of Engineering Research and Applications , Vol. 4, Issue 8( Version 6), pp.90-102, 2014.

[10] T.Subramani. C.T.Sivakumar., C.Kathirvel., S.Sekar.,”

Identification Of Ground Water Potential Zones In Tamil Nadu By Remote Sensing And GIS Technique” International Journal of Engineering Research and Applications , Vol. 4 , Issue 12(Version 3), pp.127-138, 2014.

[11] T.Subramani., S.Sekar., C.Kathirvel. C.T. Sivakumar, “Geomatics Based Landslide Vulnerability Zonation Mapping - Parts Of Nilgiri District, Tamil Nadu, India”, International Journal of Engineering Research and Applications, Vol. 4, Issue 12(Version 3), pp.139-149, 2014.

[12] T.Subramani., S.Sekar., C.Kathirvel. C.T. Sivakumar, ”Identification Of Soil Erosion Prone Zones Using Geomatics Technology In Parts Of North Arcot And Dharmapuri District”, International Journal of Engineering Research and Applications, Vol. 4, Issue 12(Version 3), pp.150-159, 2014

[13] T.Subramani, R.Vasantha Kumar, C.Krishnan “Air Quality Monitoring In Palladam Taluk Using Geo Spatial Data”, International Journal of Applied Engineering Research (IJAER),Volume 10, Number 32, Special Issues pp.24026-24031,2015

[14] T.Subramani,”Identification Of Ground Water Potential Zone By Using GIS”, International Journal of Applied Engineering Research (IJAER), Volume 10, Number 38, Special Issues, pp.28134-28138, 2015

[15] T.Subramani, M.Sivagnanam , " Suburban Changes In Salem By Using Remote Sensing Data" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 4, Issue 5, May 2015 , pp. 178-187 , ISSN 2319 - 4847. 2015

[16] T.Subramani, P.Malathi , " Drainage And Irrigation Management System For Salem Dist Tamilnadu Using GIS" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 4, Issue 5, pp. 199-210 , 2015

[17] T.Subramani, P.Malathi , " Land Slides Hazardous Zones By Using Remote Sensing And GIS" , International Journal of Application or Innovation in




Engineering & Management (IJAIEM) , Volume 4, Issue 5, pp. 211-222 , 2015

[18] T.Subramani, D.Pari, “Highway Alignment Using Geographical Information System” , IOSR Journal of Engineering, Volume 5 ~ Issue 5 ,Version 3, pp 32-42, 2015

[19] T.Subramani, G.Raghu Prakash , " Rice Based Irrigated Agriculture Using GIS" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 5, Issue 3, pp. 114-124 , 2016.

[20] T.Subramani, E.S.M.Tamil Bharath , " Remote Sensing Based Irrigation And Drainage Management System For Namakkal District" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 5, Issue 3, pp. 071-080 , 2016.

[21] T.Subramani, A.Janaki , " Identification Of Aquifer And Its Management Of Ground Water Resource Using GIS In Karur" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 5, Issue 3, pp. 081-092 , 2016.

[22] T.Subramani, C.Kathirvel , " Water Shed Management For Erode District Using Gis " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 5, Issue 3, pp. 093-103 , 2016.

[23] T.Subramani, A.Kumaravel , " Analysis Of Polymer Fibre Reinforced Concrete Pavements By Using ANSYS" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 5, Issue 5, pp. 132-139 , 2016 .

[24] T.Subramani, S.Sounder , " A Case Study And Analysis Of Noise Pollution For Chennai Using GIS" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 5, Issue 3, pp. 125-134 , 2016.

[25] T.Subramani, K.M.Vijaya , " Planning And Design Of Irrigation System For A Farm In Tanjavur By Using Remote Sensing" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 5, Issue 3, pp. 135-146, 2016.

[26] T.Subramani, G.Kaliappan , " Water Table Contour For Salem District Tamilnadu using GIS" , International Journal of Emerging Trends & 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.

AUTHOR

Prof. Dr.T.Subramani Working as a Professor and Dean of Civil Engineering in VMKV Engineering College, Vinayaka Missions University, Salem, TamilNadu, India. Having more than 27 years of Teaching experience in Various Engineering Colleges. He is a Chartered Civil Engineer and

Approved Valuer for many banks. Chairman and Member in Board of Studies of Civil Engineering branch. Question paper setter and Valuer for UG and PG Courses of Civil Engineering in number of Universities. Life Fellow in Institution of Engineers (India) and Institution of Valuers. Life member in number of Technical Societies and Educational bodies. Guided more than 400 students in UG projects and 300 students in PG projects. He is a reviewer for number of International Journals and published 174 International Journal Publications and presented more than 25 papers in International Conferences.

K.Sukumar received his B.Tech. Degree in the branch of Chemical Engineering in Ahhiyaman Engineering College, Hosur. VMKV.Engineering College, Vinayaka Missions University, Salem, TamilNadu, India.. Now, he is working as an Assistant Professor in

Sri Nandhanam College of Engineering and Technology in Trupatur. Currently he is doing his ME Degree in the branch of Environmental Engineering in the division of Civil Engineering in VMKV Engineering College, Salem.

S.Priyanka is persuing B.E. Degree in the branch of Civil Engineering in V.M.K.V. Engineering College , Vinayaka Missions University, Salem. She has illustrious career in her intermediate and matriculation exams, her hobby is cooking and surfing internet.

sugar cane modelling using gis and remote sensing for ... · sugar cane modelling using gis and...

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