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Vegetation Index: Ideas, Methods, Influences, and Trends

Vegetation Index: Ideas, Methods, Influences, and Trends

Suresh Kumar Nagarajan, Arun Kumar Sangaiah
Copyright: © 2018 |Pages: 40
ISBN13: 9781522552048|ISBN10: 1522552049|EISBN13: 9781522552055
DOI: 10.4018/978-1-5225-5204-8.ch065
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MLA

Nagarajan, Suresh Kumar, and Arun Kumar Sangaiah. "Vegetation Index: Ideas, Methods, Influences, and Trends." Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 1560-1599. https://doi.org/10.4018/978-1-5225-5204-8.ch065

APA

Nagarajan, S. K. & Sangaiah, A. K. (2018). Vegetation Index: Ideas, Methods, Influences, and Trends. In I. Management Association (Ed.), Computer Vision: Concepts, Methodologies, Tools, and Applications (pp. 1560-1599). IGI Global. https://doi.org/10.4018/978-1-5225-5204-8.ch065

Chicago

Nagarajan, Suresh Kumar, and Arun Kumar Sangaiah. "Vegetation Index: Ideas, Methods, Influences, and Trends." In Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1560-1599. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5204-8.ch065

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Abstract

This is the survey for finding vegetation, deforestation of earth images from various related papers from different authors. This survey deals with remote sensing and normalized difference vegetation index with various techniques. We survey almost 100 theoretical and empirical contributions in the current decade related to image processing, NDVI generation by using various new techniques. We also discuss significant challenges involved in the adaptation of existing image processing techniques to generation NDVI systems that can be useful in the real world. The resolution of remote sensing images increases every day, raising the level of detail and the heterogeneity of the scenes. Most of the existing geographic information systems classification tools have used the same methods for years. With these new high resolution images, basic classification methods do not provide satisfactory results.

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