Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

Abstract

Numerous spectral indices have been developed to assess plant diversity. However, since they are developed in different areas and vegetation type, it is difficult to make a comprehensive comparison among these indices. The primary objective of this study was to explore the optimum spectral indices that can predict plant species richness across different communities in sandy grassland. We use 7339 spectral indices (7217 we developed and 122 that were extracted from literature) to predict plant richness using a two-year dataset of plant species and spectra information at 270 plots. For this analysis, we employed cluster analysis, correlation analysis, and stepwise linear regression. The spectral variability within the 420–480 nm and 760–900 nm ranges, the first derivative value at the sensitive bands, and the normalized difference at narrow spectral ranges correlated well with plant species richness. Within the 7339 indices that were investigated, the first-order derivative values at 606 and 583 nm, the reflectance combinations on red bands: (R802 − R465)/(R802 + R681) and (R750 − R550)/(R750 + R550) showed a stable performance in both the independent calibration and validation datasets (R2 > 0.27, p < 0.001, RMSE < 1.7). They can be regarded as the best spectral indices to estimate plant species richness in sandy grasslands. In addition to these spectral variation indices, the first derivative values or the normalized difference of the sensitive bands also reflect plant diversity. These results can help to improve the estimation of plant diversity using satellite-based airborne and hand-held hyperspectral sensors.

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

“…Different plant species respond in their own way to incoming solar radiation according to their pigment, water, and biochemical content, as well as leaf and canopy structure. Thus, the variability in the remotely sensed spectra might enable detection of plant species diversity [13][14][15][16][17]. This concept represents the basis of the spectral variability hypothesis (SVH): as the number of plant species increases for a given area, the spectral diversity observed from that area should also increase [18,19].…”

“…A review of the results achieved in previous studies with respect to herbaceous canopies and grassland types is summarized in Table 1. These studies reported a positive correlation (up to R 2 = 0.58) between spectral diversity metrics and α-diversity in grassland ecosystems [13,14,24]. Aneece et al [24] related spectral diversity (expressed as SD) with species diversity (Shannon-Weiner index) and evaluated correlations (R 2 = 0.43) across different spectral regions from visible (VIS) and near-infrared (NIR).…”

“…In another study, Wang et al [13] used the average CV of spectral reflectance calculated over the 430-925 nm wavelength as an indicator of optical diversity and then compared the CV values obtained at different spatial scales ranging from 1 mm to 1 m with α-diversity metrics. Peng et al [14] investigated a natural temperate grassland (with a maximum species richness of 12 in a 0.8 m diameter plot), and reported that the maximum R 2 value of the correlation between optical diversity and α-diversity was 0.40.…”

“…Additionally, phenology status shifts among different species may lead to major structural differences in terms of different rates and different spatial distribution of non-photosynthetic elements (e.g., flowers, dead material [32,33]). This heterogeneity may create a further shift between optical diversity metrics and the measured biodiversity metrics; as a result, the accuracy of the estimation of biodiversity metrics can be reduced [14].…”

Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing

“…In recent years, the use of hyperspectral remote sensing (HRS) data has become common for identifying the composition and physiognomy of forests across large areas [4][5][6][7]. The high spectral resolution of HRS (50-100 nm band width) allows identification of each classified land-cover cluster according to its known spectral signature, thereby enabling detailed analyses of land covers such as mineral composition [8], soil type [9], and vegetation structure and composition [8][9][10][11][12][13][14][15][16][17][18][19].…”