Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period

Smart Helmet for Drunk & Drive Detection and Alert System

The rise in road accidents in our country is primarily due to the negligence of not wearing helmets, reckless driving, and drunk driving, which can result in serious head injuries or even death if prompt medical attention is not given. To ensure the safety of bikers, it is crucial to have a system that mandates helmet use. This project presents the development of a smart helmet module with sensors that detect alcohol consumption and helmet use. The module also includes a GSM module that sends out an accident alert along with the GPS module for location tracking and Blink sensor is used to check the sobriety of the driver

Smart Helmet for Drunk & Drive Detection and Alert System

The rise in road accidents in our country is primarily due to the negligence of not wearing helmets, reckless driving, and drunk driving, which can result in serious head injuries or even death if prompt medical attention is not given. To ensure the safety of bikers, it is crucial to have a system that mandates helmet use. This project presents the development of a smart helmet module with sensors that detect alcohol consumption and helmet use. The module also includes a GSM module that sends out an accident alert along with the GPS module for location tracking and Blink sensor is used to check the sobriety of the driver

Generating Soil Parent Material Environmental Covariates Using Sentinel – 2A Images for Delineating Soil Attributes

Soil mapping procedures typically involve the combination of possible soil-forming SCORPAN factors. Among the factors, parent materials/ mineralogy has been considered important for the soil classification besides the Organisms (O) and Relief (R). Inclusion of the parent material covariate for the Digital soil mapping involves implication through geological maps, spectral derivatives and predictive modelling. In this study, the most prominent parent materials identified were derived using the spectral indices formulated based on the Sentinel – 2A multispectral information. While considering the coarse spatial resolution constraints of the existing Landsat -8 bands that may limit certain applications, Sentinel-2 images were used for the indices derivation. The generated mineral maps can support the digital soil mapping of the soil attributes at different spatial scales