SENSORIAMENTO REMOTO E ÍNDICES DE VEGETAÇÃO: CONCEITOS E APLICAÇÕES - REVISÃO DE LITERATURA

• DOI: https://doi.org/10.22533/at.ed.0692412063

• Palavras-chave: Processamento de imagens, sensores, ondas eletromagnéticas, NDVI.

• Keywords: Image processing, sensors, electromagnetic waves, NDVI.

• Abstract: The definition of remote sensing is clearly specified by stating that capturing images of an object is performed by the sensor through measurements of electromagnetic radiation such as sunlight reflected from the surface of any object. Only sensors that obtain images through direct detection of electromagnetic radiation should be categorized as remote sensing. Over the last four decades, planet Earth has been the object of observation and analysis using image sensors, which record a wide range of phenomena present on its surface, including any changes that may occur. This approach mainly aims to acquire periodic images to monitor the environment on a global scale, with the aim of providing more accurate information about natural resources, whether renewable or not. Based on the above, the objective of this literature review is to elucidate the historical aspects of remote sensing, highlighting its principles, concepts and applications in various scientific and related areas. Furthermore, the characterization of the sensors used in the image capture process will be addressed. Through this literature review, it was found that remote sensing is fundamental for monitoring and studying various aspects of the Earth, including vegetation, land cover, land use and environmental changes over time. Its history reveals constant evolution in terms of technology and applications. Therefore, understanding the principles and concepts of remote sensing is the basis for exploring data and transforming it into useful and influential information for decision-making. In this context, vegetation indices are essential tools for extracting data from vegetated areas, allowing to evaluate the health, density and distribution of vegetation in different environments, making it possible to acquire and analyze data that would not be possible without the use of vegetation indices.