Projects
Remote Sensing and GIS Based Projects
Crop Identification and Area Estimation of Major Crops using Multi Temporal SAR data and Optical Imagery.
Used Time series Sentinel-1A SAR (VV, VH Polarization) data and Sentinel 2 data .
Study area was Veppanthattai taluk, Perambalur District, Tamil Nadu State, India.
Preprocessing of Images done using SNAP software.
ML Algorithms used for classification and compared their results – SVM, Neural Net, Max likelihood and Min Distance.
Identified the major crops and estimated their area.
Derived the temporal backscattering of all crops for VV, VH polarization.
Published Articles : Research Paper 1 , Research Paper 2
Google Earth Engine
Land Use/ Land Cover Classification using machine learning algorithms ( such as Random Forest, SVM)
Extraction of Time Series values of NDVI for single point and multi point locations using sentinel 2 and MODIS imagery.
Extraction of Time series values of VV, VH and RVI for single point and multi point locations using sentinel 1A/1B SAR imagery.
Water area detection using sentinel 1A imagery.
Flood Mapping using Sentinel 1A imagery.
Mapping of Mangrove Forest.
DEM image downloading and creationg slope and aspect.
Google Earth Engine App Creation.
Environmental Mapping of Kotturpuram, Chennai, Tamil Nadu, India.
The study area was kotturpuram road and CLRI. We have taken 15 point samples from this surrounding location.
Data Collected for five days from 26th September to 30th september using mobile phone applications.
Data collected such as noise, air temperature, surface temperature, relative humidity using survey technique.
The Interpolation is done for each data in the period of four days.
The output is mapped and it shows the variation of temperature, noise and humidity for each location.
Each Results are compared with other parameters .
Digital Soil Mapping of Rasipuram Block, Tamil Nadu, India.
Soil Profiles have been taken from legacy data.
Study area was salem and namakal block of Tamil nadu state, India.
SCORPAN model was used for prediction.
Predicted the soil properties such as sand, clay, silt and Organic Carbon using Quantile Regression Forest (Machine Learning ) algorithm in R Studio.
Prediction of performance evaluated based on the R2, RMSE, ME and PICP.
Results has average accuracy. In order to improve results has to add more number of training points and high resolution of DEM imagery.
Published Article: Article 1
Other Projects :
Thematic map created using population data such as choropleth map, Dot Map, Located Pie chart and Located Bar chart.
Site Suitability analysis using model builder.
Hydrological modeling using HECRAS.
Least Cost Modeling using ArcGIS .
Calculated the LST ( Land Surface Temperature ) Using ArcGIS and QGIS.
Flood Inundation Mapping.
Morphometric Analysis.
Time Series earthquake analysis in Qgis.
Spatio Temporal variation of association between Sea surface Temperature and Chlorophyl in Indian Ocean.
Mangrove Forest Mapping and time series analysis of ndvi using GEE.
WebGIS app development for dashboard.
Change Detection and Prediction Of LULC using land change modeler ( Idrisi Taiga) .
Sowing Date identification for sugarcane farms using time series trends of vegetation indices.
Water, Built-up and Vegetation area extraction using indices in Google Earth Engine.