NDVI Indicator Based Land Use/Land Cover Change Analysis Using Machine Learning and Geospatial Techn
The normalized difference vegetation index (NDVI) is an essential classification method for identifying the changes in dynamics of land use/land cover (LULC) area and planning for sustainable services. Machine learning and geospatial techniques are the most effective significant tools for change detection of LULC. The study was executed to assess dynamic changes of LULC with the help of NDVI classification using Machine learning and geospatial techniques. Landsat 5 and 8 images are applied in 2000 and 2020 (20 years) to extract NDVI values. The NDVI values are classified into five categories, and two NDVI maps, 2000 and 2020, are generated. Five LULC classes are identified: Water (Deep and Shallow), Built-up/River Sand, Fallow/Wasteland, Agricultural Land/Crop Land, and Dense vegetation. The present study shows that the area’s water areas decreased from 4.1% in 2000 to 1.9% in 2020, and Built-up/River sand also decreased from 8.1% to 1.7% from 2000 to 2020, respectively. The dense vegetation area was also found at 11.8% in 2020, and Agroforestry/Sparse Vegetation areas increased from 2.1 to 34.7% in the last 20 years.
Multi-spectral images and sensor
AI University , Montana
AI For Research Student Community