A Method of Using Influencing Parameters by GIS based Intersections for Obtaining a Cost Effective Biomass Assessment using RSD P. Rajeshwari CGPL Indian Institute of Science Bangalore rajeshwari@cgpl.iisc.ernet.in  B. K. Ashwini Kumar CGPL Indian Institute of Science Bangalore ashwin@cgpl.iisc.ernet.in  G. S. Sheshagiri CGPL Indian Institute of Science Bangalore sheshagiri@cgpl.iisc.ernet.in  N. K. S. Rajan CGPL Indian Institute of Science Bangalore nksr@cgpl.iisc.ernet.in Abstract The Biomass Residues from agricultural crops and other sources are currently considered to be a potential alternative resource for renewable energy and is currently emerging significant in the background of the prevailing energy crisis and fossil fuel depletions and costs. Estimation of the available biomass residues with periodic updates is a major step in promoting activities of this renewable energy sector. The present work is focused to contribute in this aspect. The Remote Sensing Data (RSD) used for the current analysis has been obtained with the main criteria of analysis for the derived data as the Agricultural land usage as one of the classifications and NDVI (Normalized Differential Vegetation Index) as the other used along with additional parameters such as Isohyets and other local spatial information on the GIS layers. The analysis considers land-use NDVI and Isohyets as the main influencing parameters for the identification of crops in spatially resolved polygons. The GIS layers for NDVI, Agricultural land use classification and the Isohyets contours are intersected with demographic boundaries to generate spatial polygons overlaid with the intersecting layers, thereby providing a dataset of the influencing parameters in the region. The data used is obtained from a lower resolution RSD and with the method of analysis adopted using multiple data sets, as described above, resulting in identification of the crops that would otherwise have demanded for an expensive solution with much higher resolution of the satellite data. The effectiveness of the approach is considered important in this perspective that enables coverage of data of the entire country managed with resource made out of today’s PC family, a cost effective solution for the problem considered. Introduction: Biomass is a renewable energy resource derived from different sources of vegetation including the by-products of the agricultural crops, waste materials from the forest and others of this kind. With the continuously increasing cost of petroleum based fuels, biomass based energy is gaining importance in rural sector of developing countries. With the recent technological developments leading to obtaining of clean gaseous fuel and generation of power with high efficiency from biomass, has paved way in its recognition as an important alternative source for energy and has been established to be economically viable. Polygonal intersection Technique is usually applied to remote sensing data to embed the information from multiple layers into a single Layer. Intersection technique uses agriculture vector Polygons [embedded into Geographical information system] (GeoConcept software) derived out of the RSD [Remote Sense Data] Image to be integrated into a single layer. This data is available with a ground resolution of 188m per pixel. The resulting layer containing all crop influencing parameters such as NDVI and Rainfall will be available for the crop classification enabling the spatial biomass assessment for a particular region. In order to make Map data with multi-various classifications more 'intelligent' for the assessment of biomass, a GIS tool compatible to the programming environment was found to be "Geo-Concept". GeoConcept is a Geographic Information System designed to manipulate geographic objects represented on multiple layers supported by its API. The objects can be either vector or raster image. GeoConcept interpret and integrate files of data expressed in all standard graphics formats and acquired from other GIS applications. ArcInfo’s E00, Shp (ArcView’s ShapeFile), and Mif/Mid (MapInfo) are some of the formats recognized by GeoConcept. The data in these formats are imported into GeoConcept objects and object attributes and similarly the data can be exported back to the original file format from GeoConcept. The necessary software adopting the methodology of intersections is developed using Visual basic. Methodology The Polygonal intersection method makes use of spatial polygons which are extracted from the map in different layers for different spatial crop influencing parameters such as NDVI and Rainfall in this case. It is necessary that these crop attributes are available during classification of vectors for spatially distributed crops. To enable such a requirement, it is necessary to embed the parameter value into one Agricultural land use layer. This is possible by pipelining the selection of Agricultural land use polygons and the NDVI polygons for intersection. Similarly the rainfall is selected and further intersected to embed the information into a single layer.  NDVI = (nir - red) / (nir + red) Using this index, the difference between vegetation and non vegetation are emphasized. Reflectance value for vegetation is maximum in the near infrared and minimum in the red spectral domain. High values of NDVI (Nearing 1) indicate lush vegetation, values around zero are non vegetated land areas and Negative values are generally associated with water bodies. If there is very little difference in the intensity of near-infrared and red band, then the vegetation is probably thin and may consist of grassland or desert.  The illustrations shown below explain the intersection of various layers using GIS API [Application Program Interface]. Fig 2 shows the superimposition of NDVI [Normalized differential vegetation Index] Layer over Agricultural land use polygons distributed randomly within the taluk boundaries. The Agricultural land use polygons having Agricultural pattern and NDVI polygons having Vegetation index are imported on to the same map as 2 different layers along with the demographic layers. Using functionalities of Geo Concept software interfaced with Visual Basic, a rectangle of slightly bigger area is considered around an Agricultural land use polygon for reducing the processing time. The NDVI polygons selected with in the rectangle is intersected with the Agricultural land use polygon. This polygon set inherits both the Agricultural and Vegetation information.  During the formation of Agricultural Polygons it is found that the geographical areas are formed with different types of ‘islands’. For e.g. an Agricultural area may contain a water body. In such cases the polygon will be complex having a ‘hole’ representing the water body. The complex polygons are also intersected wherever necessary. Results and Discussions Table 1 shows an excerpt of the results of a graphical query made on the geographical region- Taluk – Badlapur, District – Jaunpur, State - Uttar Pradesh, Country – India before and after intersection of all the distinct layers to form a single agricultural layer. Table 1 will have no representative values for the agricultural usage layer before intersection. After the intersection, the parameter belonging to each of the layers of NDVI and Rainfall get embedded into the same Table.1.   The work carried out has used data obtained from a lower resolution RSD and the method of analysis has used multiple data sets together resulting in proper identification of the crops that has found a verification and compliance to the ground-truth obtained independently. Such a result would otherwise have needed much higher resolution of the satellite data, an expensive solution to work with. The Biomass Residues from agricultural crops and other sources estimated in this approach known to be a potential alternative fuel and renewable energy source is considered significant and the work is taken up as a part one of the sponsored research projects of MNES, GOI. The method has evolved a stable approach for estimation of the available biomass residues with periodic updates as a major step in promoting activities of this renewable energy sector. The effectiveness of the approach is considered significant in view of the fact that it enables analyzing and handling of the data for the entire country over currently available PC configurations, providing a cost effective solution in this area of work. References:
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