DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE

Anusha NALLAPAREDDY

doi:10.35784/acs-2022-4

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Published: Mar 30, 2022

DOI: https://doi.org/10.35784/acs-2022-4

Issue Vol. 18 No. 1 (2022)

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DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE
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DOI

https://doi.org/10.35784/acs-2022-4

Authors

Anusha NALLAPAREDDY

anusha.nallapareddy@gmail.com

Vidya Jyothi Institute of Technology, Department of Computer Science and Engineering, India

Abstract

Detection and classification of vegetation is a crucial technical task in the management of natural resources since vegetation serves as a foundation for all living things and has a significant impact on climate change such as impacting terrestrial carbon dioxide (CO2). Traditional approaches for acquiring vegetation covers such as field surveys, map interpretation, collateral and data analysis are ineffective as they are time consuming and expensive. In this paper vegetation regions are automatically detected by applying simple but effective vegetation indices Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) on red(R) and near infrared (NIR) bands of Landsat-8 satellite image. Remote sensing technology makes it possible to analyze vegetation cover across wide areas in a cost-effective manner. Using remotely sensed images, the mapping of vegetation requires a number of factors, techniques, and methodologies. The rapid improvement of remote sensing technologies broadens possibilities for image sources making remotely sensed images more accessible. The dataset used in this paper is the R and NIR bands of Level-1 Tier 1 Landsat-8 optical remote sensing image acquired on 6th September 2013, is processed and made available to users on 2nd May 2017. The pre-processing involving sub-setting operation is performed using the ERDAS Imagine tool on R and NIR bands of Landsat-8 image. The NDVI and SAVI are utilized to extract vegetation features automatically by using python language. Finally by establishing a threshold, vegetation cover of the research area is detected and then classified.

Keywords:

classification, remote sensing, soil adjusted vegetation index, normalized difference vegetation index, vegetation

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NALLAPAREDDY, A. (2022). DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE. Applied Computer Science, 18(1), 45–55. https://doi.org/10.35784/acs-2022-4

DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE

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