Genetically modified Cotton species detection by LISS-III satellite data

It is possible to infer the genetically modified species by using remotely sensed data. Using ERDAS software the algorithm of BT ( Bacillus thuringiensis) Cotton in Punjab, India was developed successfully. GPS enabled space technology has the potential to identify the exact location of Bt Cotton by generating Normalized Difference Vegetation Index (NDVI) for the calculation of total area covered by this species. It was possible to develop a correlation in between genetically modified Cotton crop and NDVI value. In parts of Bhatinda district of Punjab the yield of Bt Cotton and NDVI showing R 2 value of more than 4.5 in regression analysis. A correlation matrix was also generated which shows that NDVI values of BT cotton has reasonably acceptable correlation with Total Dissolved Solids (TDS) of soil and water also

It is possible to infer the genetically modified species by using remotely sensed data.Using ERDAS software the algorithm of BT (Bacillus thuringiensis) Cotton in Punjab, India was developed successfully.GPS enabled space technology has the potential to identify the exact location of Bt Cotton by generating Normalized Difference Vegetation Index (NDVI) for the calculation of total area covered by this species.It was possible to develop a correlation in between genetically modified Cotton crop and NDVI value.In parts of Bhatinda district of Punjab the yield of Bt Cotton and NDVI showing R 2 value of more than 4.5 in regression analysis.A correlation matrix was also generated which shows that NDVI values of BT cotton has reasonably acceptable correlation with Total Dissolved Solids (TDS) of soil and water also.Through remote sensing it is possible to quantify on a global scale the total acreage dedicated to these and other crops at any time.Of greater importance is accurately (best case 90%) estimating the expected yields of each crop locally, regionally or globally.It can be done by first computing the areas dedicated to each crop and then incorporating reliable yield assessment per unit area, which can be measured at representative ground truth sites.Usually, the yield estimates obtained from satellite data are more comprehensive and earlier (often by weeks) than determined conventionally as harvesting approaches 1 .Use of Satellite data for genetically modified crop needs to generate location specific spectral anomalies 2,3 .Use of multispectral satellite data helps to generate NDVI (Normalized Difference Vegetation Index), which can be correlated with the landuse, landcover, soil moisture, soil quality and groundwater quality to estimate the deterministic yield of BT cotton crops 4,5,6 .

CORE
Metadata, citation and similar papers at core.ac.uk Provided by Nature Precedings The study area is Bathinda District (Figure 1  Since this is the time of cotton harvesting so in most of the area the crops were harvested. The areas which had sown the crop a little late were only seen with cotton crops.Some of the spectral signatures were mixed up with wheat or potato crops which were at seedling stage at the time of sampling.In September 2007 total cotton area calculated was 13704.74ha comprising 44.82% of the study area (Figure .3).  predict the yield and it was found that a good correlation (0.697) existed between the NDVI yield and the R 2 value was also found to be very significant (0.4587).
Forecasting of anything refers to foresee the future scenario on the basis of present or past situations.Yield estimation of BT cotton deals with perception of the future activity of biotic agents which adversely affect crop production.The physical and genetic condition has tremendous influence on the yield and survival of BT cotton in Bhatinda, Punjab.Estimation of cotton has been done so far with weather and pest parameters.In Punjab the yield of cotton database shows that there were two peaks of month every year.The main peak was during March-April in which mix varieties of crop were there and the second peak was during October in which cotton was only the main crop.
Multispectral and multi-date satellite data were interpreted to develop a component for identification of location specific yield of BT cotton.
Normalized difference vegetation index (NDVI) for BT cotton was generated by using LISS-III sensor with spatial resolution of 23.5 meters.Ground-truthing was done on the specific anomalous location of NDVI data to correlate with soil texture, soil moisture and other pedological data including pH, TDS, EC, % TOC, Phosphorus and groundwater quality data.
NDVI values September, 2007 were attempted to correlate with other collateral data in GIS environment.A correlation matrix was also generated which shows that NDVI values of Bt cotton has reasonably acceptable correlation with TDS of soil and water.In geo-specific location of study area it was found out that if NDVI values of Bt cotton increases the TDS of soil as well as of water also increases.
) situated in the Southern part of Punjab State in the heart of Malwa region, India.It forms part of newly created division Faridkot Revenue Commissioners Division and is situated between 29°33 & 30°36 North latitude and 74°38 and 75°46 East longitudes.The district is surrounded with Sirsa and Fatehabad of Haryana State in the south, Sangrur and Mansa district in the East, Moga in the Northeast and Faridkot & Muktsar in the Northwest.

Figure. 1 .
Figure.1.Study area showing genetically modified BT-Cotton area in satellite image as red patches.Remote sensing has proven to be a powerful tool for assessing the identity, characteristics, and growth potential of most kinds of vegetative matter at several levels (from biomes to individual plants).Vegetation behavior depends on the nature of the vegetation itself, its interaction with solar radiation and other climate factors, and the availability of chemical nutrients and water between the host medium (usually soil or water in the marine environments).Because many remote sensing devices operate in the green, red and near infrared regions of the electromagnetic spectrum, they can discriminate radiation absorption and reflectance of vegetation.

Figure. 2 .
Figure.2.IRS-1D, LISS III satellite Image showing sampling points done in November 2006 (harvesting time of cotton) of Bhatinda, Punjab with GPS points.