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

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.

Estimation of Woody and Herbaceous Leaf Area Index in Sub‐Saharan Africa Using MODIS Data

“…During recent decades, the remote sensing community and Earth system modelers have made substantial progress in developing products to characterize global vegetation dynamics (Houborg, Fisher, & Skidmore, 2015;Thenkabail & Lyon, 2016;Xie, Sha, & Yu, 2008;Xue & Su, 2017). However, savanna ecosystems remain a challenge due to the presence of mixed woody and herbaceous components at scales much finer than most medium-and coarse-resolution remote sensing data.…”

Analysis of drought and vulnerability in the North Darfur region of Sudan

“…Instead, satellite remote sensing provides spatially distributed information on vegetation status at regular time intervals. Many studies have proven that vegetation indices derived from multiband satellite imagery can be used to observe drought over a large area (Keshavarz, Vazifedoust, & Alizadeh, ; Xue & Su, ). The NDVI index is one of the most well‐known vegetation indices and can be converted to the Vegetation Condition Index (VCI) to quantify the vegetation status (Tucker, ): where NDVI is the NDVI time series on a given time scale, and NDVI min and NDVI max are the multiyear minimum and maximum values of the NDVI series, respectively.…”

Automated Recognition of Vegetation and Water Bodies on the Territory of Megacities in Satellite Images of Visible and Ir Bands

“…As a source of multispectral data, a SuperView-1A satellite was chosen that performs the survey in the visible and infrared ranges, and also has a sufficiently high radiometric and spatial resolution. The works carried out within the framework of the studies included the following stages of image processing and analysis: -preliminary processing of satellite images (Figure 3), including orthorectification and augmentation of spatial resolution (Gnatushenko, 2013, Hnatushenko, 2015; -thematic processing of satellite images (Figure 4), including calculation of spectral indices, binarization, morphological filtration and vectorization of recognized vegetation and water bodies (Hnatushenko, 2015, Jinru, 2017. The volume of data files received from satellites of sub-meter spatial resolution is quite large -a scene taken in the visible and infrared range can occupy several gigabytes.…”