# Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models

arXiv: https://arxiv.org/abs/2301.01918 · HTML: https://mlsanity.com/paper/2301.01918/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 67 references that are in the map.
- First posted on arXiv 5 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification](https://mlsanity.com/paper/1706.04719/) (2017)
- [Quantitative Assessment of DESIS Hyperspectral Data for Plant Biodiversity Estimation in Australia](https://mlsanity.com/paper/2207.02482/) (2022)

## Built on by (most cited first, 5 of 5)

- [FactoFormer: Factorized Hyperspectral Transformers with Self-Supervised Pretraining](https://mlsanity.com/paper/2309.09431/) (2023)
- [SpecDETR: A transformer-based hyperspectral point object detection network](https://mlsanity.com/paper/2405.10148/) (2024)
- [Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Prediction](https://mlsanity.com/paper/2410.19256/) (2024)
- [Decadal analysis of sea surface temperature patterns, climatology, and anomalies in temperate coastal waters with Landsat-8 TIRS observations](https://mlsanity.com/paper/2503.05843/) (2025)
- [Hyperspectral in situ remote sensing of water surface nitrate in the Fitzroy River estuary, Queensland, Australia, using deep learning](https://mlsanity.com/paper/2505.17483/) (2025)

## Abstract (arXiv)

The diversity of terrestrial vascular plants plays a key role in maintaining the stability and productivity of ecosystems. Airborne hyperspectral imaging has shown promise for measuring plant diversity remotely, but to operationalise these efforts over large regions we need to advance satellite-based alternatives. The advanced spectral and spatial specification of the recently launched DESIS (the DLR Earth Sensing Imaging Spectrometer) instrument provides a unique opportunity to test the potential for monitoring plant species diversity with spaceborne hyperspectral data. This study provides a quantitative assessment on the ability of DESIS hyperspectral data for predicting plant species richness in two different habitat types in southeast Australia. Spectral features were first extracted from the DESIS spectra, then regressed against on-ground estimates of plant species richness, with a two-fold cross validation scheme to assess the predictive performance. We tested and compared the effectiveness of Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and Partial Least Squares analysis (PLS) for feature extraction, and Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), and Random Forest Regression (RFR) for species richness prediction. The best prediction results were r=0.76 and RMSE=5.89 for the Southern Tablelands region, and r=0.68 and RMSE=5.95 for the Snowy Mountains region. Relative importance analysis for the DESIS spectral bands showed that the red-edge, red, and blue spectral regions were more important for predicting plant species richness than the green bands and the near-infrared bands beyond red-edge. We also found that the DESIS hyperspectral data performed better than Sentinel-2 multispectral data in the prediction of plant species richness.