EXPLORING CAPABILITIES OF SENTINEL-2 FOR VEGETATION MAPPING USING RANDOM FOREST

EXPLORING CAPABILITIES OF SENTINEL-2 FOR VEGETATION MAPPING USING RANDOM FOREST

Blog Article

Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice.This study aims to explore the capabilities of Sentinel-2 data over Landsat-8 Operational Land Imager (OLI) data for vegetation mapping.Two combination of Sentinel-2 dataset have been considered, first combination is 4-band dataset at 10m resolution which consists of NIR, R, G and B bands, while second combination is generated by stacking 4 bands having 10 m resolution along with other six sharpened bands using 2 Piece Outdoor Sofa Sectional with Chair Gram-Schmidt algorithm.

For Landsat-8 OLI dataset, six multispectral bands have been pan-sharpened to have a spatial resolution of 15 m using Gram-Schmidt algorithm.Random Forest (RF) and Maximum Likelihood classifier (MLC) have been selected for classification of images.It is found that, overall accuracy achieved by RF for 4-band, 10-band dataset of Sentinel-2 and Landsat-8 OLI are 88.

38 %, 90.05 % and 86.68 % respectively.

While, MLC give an overall accuracy of 85.12 %, 87.14 % and 83.

56 % for 4-band, 10-band Sentinel and Landsat-8 OLI respectively.Results shown that 10-band Sentinel-2 dataset gives highest accuracy and shows a rise Hyaluronic Acid of 3.37 % for RF and 3.

58 % for MLC compared to Landsat-8 OLI.However, all the classes show significant improvement in accuracy but a major rise in accuracy is observed for Sugarcane, Wheat and Fodder for Sentinel 10-band imagery.This study substantiates the fact that Sentinel-2 data can be utilized for mapping of vegetation with a good degree of accuracy when compared to Landsat-8 OLI specifically when objective is to map a sub class of vegetation.