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      Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

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      Journal of Sensors
      Hindawi Limited

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          Abstract

          Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.

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          Most cited references87

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                Author and article information

                Journal
                Journal of Sensors
                Journal of Sensors
                Hindawi Limited
                1687-725X
                1687-7268
                2017
                2017
                : 2017
                :
                : 1-17
                Article
                10.1155/2017/1353691
                322e2de7-f0a7-4bb0-a5da-620b19242b60
                © 2017

                http://creativecommons.org/licenses/by/4.0/

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