Abstract
Fast and nondestructive approaches of measuring plant species diversity have been a subject of excessive scientific curiosity and disquiet to environmentalists and field ecologists worldwide. In this study, we measured the hyperspectral reflectances and plant species diversity indices at a fine scale (0.8 meter) in central Hunshandak Sandland of Inner Mongolia, China. The first-order derivative value (FD) at each waveband and 37 hyperspectral indices were used to assess plant species diversity. Results demonstrated that the stepwise linear regression of FD can accurately estimate the Simpson (R2 = 0.83), Pielou (R2 = 0.87) and Shannon-Wiener index (R2 = 0.88). Stepwise linear regression of FD (R2 = 0.81, R2 = 0.82) and spectral vegetation indices (R2 = 0.51, R2 = 0.58) significantly predicted the Margalef and Gleason index. It was proposed that the Simpson, Pielou and Shannon-Wiener indices, which are widely used as plant species diversity indicators, can be precisely estimated through hyperspectral indices at a fine scale. This research promotes the development of methods for assessment of plant diversity using hyperspectral data.
Highlights
The fast and nondestructive estimation of plant species diversity has received increasingly more attention from ecologists in recent decades[1,2]
Linear regression[11], hierarchical agglomerative cluster[16], standard deviations[17] as well as the first[17,18] and second[18] order derivatives of reflectance values were all used for diversity material extraction and validated a good fit between the outcomes and plant diversity indices
Even though previous researchers have discovered the near connection between plant diversity and spectral indices, the coarse spectral and spatial resolutions have limited the estimation accuracy[19,20]
Summary
The fast and nondestructive estimation of plant species diversity has received increasingly more attention from ecologists in recent decades[1,2]. Remote sensing facts offer composite data which can sense the features of an item and mirror its real standing, agreeing on a considerable decrease in field survey costs and labor Such methods display great potential for estimating plant diversity[3]. Near infrared[4], middle infrared[5] and thermal infrared bands[6,7] have been strongly suggested for species diversity discrimination Their combinations were verified robust indicators of plant diversity. Average for several aspects—such as healthy and diseased leaves, stems, and even the shadows and orientation of the woody plant shoot rather than canopy alone These data are largely affected by the illumination conditions of species measuring and canopy reflectance, the biodiversity estimation[24]. The high cost, atmospheric noise, and coarse resolution have considerably limited the wide direct application of hyperspectral data from airborne imagery
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