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6- monthly NDVI average for Australia, 1 Dec 2012 to 31 May 2013 [1]. A vegetation index (VI) is a spectral imaging transformation of two or more image bands designed to enhance the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations.
The normalized difference vegetation index (NDVI) is a widely-used metric for quantifying the health and density of vegetation using sensor data. It is calculated from spectrometric data at two specific bands: red and near-infrared.
The index is given as: = (+) (+ +) where L is a canopy background adjustment factor. An L value of 0.5 in reflectance space was found to minimize soil brightness variations and eliminate the need for additional calibration for different soils. The transformation was found to nearly eliminate soil-induced variations in vegetation indices. [1]
2011 Enhanced vegetation index based on MODIS Terra data. The enhanced vegetation index (EVI) is an 'optimized' vegetation index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences.
(global vegetation phenology) MxD13Q1 MxD13A1 MxD13A2 MxD13C1 — 16-day Terra, Aqua Vegetation indices — — MxD13A3 MxD13C2 — Monthly Terra, Aqua Vegetation indices — MCD43A1 MCD43B1 MCD43C1 — 16-day Terra+Aqua BRDF/albedo model parameters — MCD43A3 MCD43B3 MCD43C3 — 16-day Terra+Aqua Albedo — MCD43A4 MCD43B4 MCD43C4 — 16-day ...
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.
Tasseled Cap Band 2 (greenness, a measured value for the vegetation) Tasseled Cap Band 3 (wetness, a measured value for interactions of soil and canopy moisture) The algorithm for these three levels of information is a weighted sum of the Landsat bands (without the thermal channel 6), where each band is multiplied by the specific coefficients.
Landsat imagery, and satellite imagery in general, have contributed to understanding fire science; fire danger, wildfire behavior and the effects of wildfire on certain areas. It has helped understanding of how different features and vegetation fuel fires, change temperature, and affect the spreading speed. [30] [31]