Grass species differentiation through canopy hyperspectral reflectance

Abstract

This study attempts (1) to evaluate the capability of hyperspectral reflectance to differentiate C 3 and C 4 grass species, both in isolation and in mixed canopies; (2) to identify the critical spectral ranges that differentiate the two groups and individual species within them; and (3) to determine if there is temporal variation in these capabilities. During one year, hyperspectral reflectance of C 3 and C 4 grass species was measured both in single-species and in mixed canopies. Spectral bands with higher differentiating potential were identified and species classified. For singlespecies canopies, hyperspectral reflectance differentiated the two functional groups and most species in all seasons. In mixed canopies, it underestimated the fractional cover of the C 4 component. The green, red, and near infrared above 820 nm spectral ranges were critical both for species and functional group differentiation. In conclusion, hyperspectral information was useful to differentiate pure canopies, but the differentiation algorithms were season-specific. Additionally, we need to improve our understanding of interactive effects of species in order to accurately estimate the composition of assemblages.

Estimation of Canopy Nitrogen Concentration Across C3 and C4 Grasslands Using WorldView-2 Multispectral Data

“…Application of the RF variable selection technique for both classification [33] and regression [46] analyses of remote sensing data has been demonstrated by several studies. Adjorlolo et al [47] showed that the variables that RF identified as most important for classifying selected C3 and C4 species coincided with expectations based on the literature [48].…”

Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests

“…The other studies on arid and similar mountain ecosystems do not map single species, but species composition or vegetation classes [4,5,7,8], plant richness [47]; however, using similar indices. Studies on species separation mainly use hyperspectral data often under experimental conditions [9]. As to expected, elevation was an important predictor apart from spectral information gained by remote sensing in our study region and in all studies in mountain areas [4,5,8].…”

“…However, even most of these studies do not work on single species, but mainly on plant communities. If single species are derived from remote sensing data, often hyperspectral data are used [9].…”

Remote sensing of forage nutrients: Combining ecological and spectral absorption feature data

“…Both LAI and biomass are environmental variables considered important in the description of vegetation condition and structure. Although no grass species map currently exists for this area, studies have shown that imaging spectroscopy potentially provides a means to create such maps (Irisarri et al, 2009;Schmidt and Skidmore, 2003). Species data collected in the field was therefore included in the analysis, based on the premise that given the appropriate collection of data and analysis such a map could be generated from spectroscopic data.…”