Detection and Classification of Vegetation Areas From Red and Near Infrared Bands of Landsat-8 Optical Satellite Image

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.

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

“…By 2023, the importance of Band 4 in chlorophyll absorption remained critical for vegetation health identification; Band 2 was pivotal in providing textural details due to its moisture and shadow sensitivity, and Band 1 was essential for atmospheric corrections to improve forest health assessments. This expanding understanding is vital for establishing adaptive forest management techniques that respond to changing climatic circumstances and enable sustainable water resource management in geothermal fields, thus improving conservation efforts and maintaining ecological balance [101,102].…”