Correlation of leaf and community traits and various spectra in meadows along an elevation gradient

Abstract

The capabilities of remote sensing technology are advancing rapidly, and with it, the applications of those technologies are also advancing. Indices such as Normalized Difference Vegetation Index (NDVI) have long been used to determine vegetative health, but no direct measurements have been made to quantify communities until recently. Studies into the spectral properties of plants and ways in which to expand the quantifiable measurements done with hyperspectral imaging and increasing with the sophistication of imaging technology and modeling techniques. Partial Least Squares Regression (PLSR) is a technique that can pair spectral wavelengths with measured traits. With these relationships, we can measure traits directly from the spectral signature without the need of pervasive and destructive sampling techniques. The PLSR developed with this methodology is built using data from montane and subalpine meadows in the area of Gothic, Colorado, but can be tested on other vegetative communities to assess the model’s resilience in more varied conditions. We used hyperspectral imaging with wavelengths from 400-1000 nanometers and collected leaf and community traits to construct the model. describe your results and conclusions.

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