Title: Green vegetation detection in satellite imagery using the normalised difference vegetation index method

Authors: Goutam Sahu; Mamata Garanayak; Bijay Kumar Paikaray

Addresses: Department of Computer Science and Engineering, Centurion University of Technology and Management, Odisha, India ' Computer Science Department, Kalinga Institute of Social Sciences (Deemed to be University), Bhubaneswar, Odisha, India ' Faculty of Emerging Technologies, Sri Sri University, Cuttack, India

Abstract: With unmatched growth of the human population, it has become difficult for the government to keep track of farmland's correct map, which is critical for estimating the country's agricultural state, resources based on nature, utilisation of land along with mapping, observing and planning of infrastructure. It has also become mandatory for agricultural services to operate with maximum efficiency of our needs. This research uses EarthPy in order to work with geospatial data and focuses on detection of green vegetation from satellite images based on one of the methodologies of remote sensing, i.e., normalised difference vegetation index (NDVI). This utilises the sensing of multi-spectral remote information methods for estimating the density of green on an area of land and to classify whether the area comprises high vegetation, low vegetation, or no vegetation directly from satellite image with the help of very less band merging of the remotely sensed information.

Keywords: normalised difference vegetation index; NDVI; EarthPy; satellite imaging; multi-spectral remote sensing; green vegetation.

DOI: 10.1504/IJBRA.2023.135367

International Journal of Bioinformatics Research and Applications, 2023 Vol.19 No.4, pp.327 - 342

Received: 01 Apr 2023
Accepted: 28 Jun 2023
Published online: 06 Dec 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article