Influence of tree species complexity on discrimination performance of vegetation Indices

Abstract

Performance of different vegetation indices (VIs) in combination with single-and multipleendmember (SEM and MEM) for discriminating Corsican and Scots pines with different ages and Broadleaves tree species is demonstrated by using an airborne hyperspectral data. The analysis is performed in three different complexity levels. The results show by increasing tree species complexity, overall accuracy significantly reduced. An overall accuracy up to 90% is obtained from the first category with the least complexity; however, it is reduced to 55% in the third category with the highest complexity. By employing MEM, performance of normalized difference vegetation index (NDVI) is increased by 10%.

Above-ground biomass estimation forQuercus rotundifoliausing vegetation indices derived from high spatial resolution satellite images

“…Most of these studies use vegetation indices that are mathematical functions that combine two or more spectral bands, condensing data in a quantitative numeric manner (Rizzi, 2004). This spectral information contributes to the differentiation and identification of tree species, and are related to some important biophysical properties, such as leaf area index, biomass and vegetation cover (Ahmed, Tian, Zhang, & Ting, 2011;Ghiyamat, Shafri, & Shariff, 2016) and have been used to develop aboveground biomass functions (Chen, Weber, & Gokhale, 2011;Hall, Skakun, Arsenault, & Case, 2006;Ji et al, 2012;Lu, 2006;Muukkonen & Heiskanen, 2007;Tomppo, Nilsson, Rosengren, Aalto, & Kennedy, 2002).…”

Identifying the Spectral Signatures of Invasive and Native Plant Species in Two Protected Areas of Pakistan through Field Spectroscopy

“…Different narrow-banded hyperspectral vegetation indices were derived from spectral measurements to allow species-level identification of plants [45,54]. These indices were based on the variations in chlorophyll absorption, greenness, water absorption, and other pigments at different wavelengths in the electromagnetic spectrum.…”

“…These studies are based on the variations between spectral signatures of different plant species. However, the potential of hyperspectral indices, as well as red-edge parameters (REPs), has also previously been studied for discriminating between the spectral diversity of plant species [44][45][46] Cho et al [47] evaluated the potential of spectral indices by using leaf and canopy spectra of six different species and found REP, NDVI, and PRI as good indices for the spectral identification of plant species. Similarly, invasive Acacia longifolia (Andrews) Willd.…”

Mapping Succession in Non-Forest Habitats by Means of Remote Sensing: Is the Data Acquisition Time Critical for Species Discrimination?

“…Specific spectral bands were also used to compute a set of 65 vegetation indices, including the following vegetation categories: Broadband greenness, narrowband greenness, canopy water content, canopy nitrogen, dry or senescent carbon, leaf pigments and light use efficiency. Both feature extraction methods have been shown to be highly effective in research studies of species differentiation [55,[74][75][76][77].…”