Plant species richness prediction from DESIS hyperspectral data: A
comparison study on feature extraction procedures and regression models
- URL: http://arxiv.org/abs/2301.01918v1
- Date: Thu, 5 Jan 2023 05:33:56 GMT
- Title: Plant species richness prediction from DESIS hyperspectral data: A
comparison study on feature extraction procedures and regression models
- Authors: Yiqing Guo, Karel Mokany, Cindy Ong, Peyman Moghadam, Simon Ferrier,
Shaun R. Levick
- Abstract summary: 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.
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.
- Score: 1.8757823231879849
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The diversity of terrestrial vascular plants plays a key role in maintaining
the stability and productivity of ecosystems. Monitoring species compositional
diversity across large spatial scales is challenging and time consuming. 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), 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. Our results provide a quantitative reference for future
studies exploring the potential of spaceborne hyperspectral data for plant
biodiversity mapping.
Related papers
- Soil Fertility Prediction Using Combined USB-microscope Based Soil Image, Auxiliary Variables, and Portable X-Ray Fluorescence Spectrometry [3.431158134976364]
The research combined color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model.
Results indicated that integrating image features (IFs) with auxiliary variables (AVs) significantly enhanced prediction accuracy for available B.
A data fusion approach, incorporating IFs, AVs, and PXRF data, further improved predictions for available Mn and SAI with R2 values of 0.72 and 0.70, respectively.
arXiv Detail & Related papers (2024-04-17T17:57:20Z) - A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa [3.6826233660285395]
locust swarms present a major threat to agriculture and food security.
Our study develops an operationally-ready model for predicting locust breeding grounds.
arXiv Detail & Related papers (2024-03-11T16:13:58Z) - SpectralGPT: Spectral Remote Sensing Foundation Model [60.023956954916414]
A universal RS foundation model, named SpectralGPT, is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT)
Compared to existing foundation models, SpectralGPT accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data.
Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience.
arXiv Detail & Related papers (2023-11-13T07:09:30Z) - SaaFormer: Spectral-spatial Axial Aggregation Transformer for
Hyperspectral Image Classification [2.4723464787484812]
Hyperspectral images (HSI) captured from earth observing satellites and aircraft is becoming increasingly important for applications in agriculture, environmental monitoring, mining, etc.
Due to the limited available hyperspectral datasets, the pixel-wise random sampling is the most commonly used training-test dataset partition approach.
We propose a block-wise sampling method to minimize the potential for data leakage.
arXiv Detail & Related papers (2023-06-29T07:55:43Z) - Evaluation of the potential of Near Infrared Hyperspectral Imaging for
monitoring the invasive brown marmorated stink bug [53.682955739083056]
The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops.
The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
arXiv Detail & Related papers (2023-01-19T11:37:20Z) - Quantitative Assessment of DESIS Hyperspectral Data for Plant
Biodiversity Estimation in Australia [1.8757823231879849]
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.
arXiv Detail & Related papers (2022-07-06T07:14:55Z) - Deep Learning Models of the Discrete Component of the Galactic
Interstellar Gamma-Ray Emission [61.26321023273399]
A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data.
We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions.
arXiv Detail & Related papers (2022-06-06T18:00:07Z) - Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet
Transmission Spectra [68.8204255655161]
We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets.
We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations.
We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
arXiv Detail & Related papers (2022-01-07T22:26:33Z) - Spatial machine-learning model diagnostics: a model-agnostic
distance-based approach [91.62936410696409]
This contribution proposes spatial prediction error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as novel model-agnostic assessment and interpretation tools.
The SPEPs and SVIPs of geostatistical methods, linear models, random forest, and hybrid algorithms show striking differences and also relevant similarities.
The novel diagnostic tools enrich the toolkit of spatial data science, and may improve ML model interpretation, selection, and design.
arXiv Detail & Related papers (2021-11-13T01:50:36Z) - Deep Autoregressive Models with Spectral Attention [74.08846528440024]
We propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module.
By characterizing in the spectral domain the embedding of the time series as occurrences of a random process, our method can identify global trends and seasonality patterns.
Two spectral attention models, global and local to the time series, integrate this information within the forecast and perform spectral filtering to remove time series's noise.
arXiv Detail & Related papers (2021-07-13T11:08:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.