Quantitative Assessment of DESIS Hyperspectral Data for Plant
Biodiversity Estimation in Australia
- URL: http://arxiv.org/abs/2207.02482v1
- Date: Wed, 6 Jul 2022 07:14:55 GMT
- Title: Quantitative Assessment of DESIS Hyperspectral Data for Plant
Biodiversity Estimation in Australia
- Authors: Yiqing Guo, Karel Mokany, Cindy Ong, Peyman Moghadam, Simon Ferrier,
Shaun R. Levick
- Abstract summary: This study assessed the ability of hyperspectral data captured by the DLR Earth Sensing Imaging Spectrometer (DESIS) for estimating plant species richness.
With the best performing model, $r$ is 0.71 and RMSE is 5.99 for the Southern Tablelands region, while $r$ is 0.62 and RMSE is 6.20 for the Snowy Mountains region.
- Score: 1.8757823231879849
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Diversity of terrestrial plants plays a key role in maintaining a stable,
healthy, and productive ecosystem. Though remote sensing has been seen as a
promising and cost-effective proxy for estimating plant diversity, there is a
lack of quantitative studies on how confidently plant diversity can be inferred
from spaceborne hyperspectral data. In this study, we assessed the ability of
hyperspectral data captured by the DLR Earth Sensing Imaging Spectrometer
(DESIS) for estimating plant species richness in the Southern Tablelands and
Snowy Mountains regions in southeast Australia. Spectral features were firstly
extracted from DESIS spectra with principal component analysis, canonical
correlation analysis, and partial least squares analysis. Then regression was
conducted between the extracted features and plant species richness with
ordinary least squares regression, kernel ridge regression, and Gaussian
process regression. Results were assessed with the coefficient of correlation
($r$) and Root-Mean-Square Error (RMSE), based on a two-fold cross validation
scheme. With the best performing model, $r$ is 0.71 and RMSE is 5.99 for the
Southern Tablelands region, while $r$ is 0.62 and RMSE is 6.20 for the Snowy
Mountains region. The assessment results reported in this study provide
supports for future studies on understanding the relationship between
spaceborne hyperspectral measurements and terrestrial plant biodiversity.
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