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

• LSTM networks provide efficient cyanobacterial blooms forecasting even with incomplete spatio-temporal data [2.5537500385691594]
  Early warning systems (EWS) for cyanobacterial blooms allow timely implementation of management measures. In this paper, we propose an effective EWS for cyanobacterial bloom forecasting, which uses 6 years incomplete high-frequency-temporal data. Results were analyzed for seven forecasting time horizons ranging from 4 to 28 days evaluated with a hybrid system.
  arXiv  Detail & Related papers  (2024-10-09T15:13:24Z)
• A Novel Fusion of Optical and Radar Satellite Data for Crop Phenology Estimation using Machine Learning and Cloud Computing [0.0]
  In the era of big Earth observation data ubiquity, attempts have been made to accurately predict crop phenology based on Remote Sensing data. Here, we estimate phenological developments for eight major crops and 13 phenological stages across Germany at 30m scale using a novel framework.
  arXiv  Detail & Related papers  (2024-08-16T13:44:35Z)
• Prediction of soil fertility parameters using USB-microscope imagery and portable X-ray fluorescence spectrometry [3.431158134976364]
  This study investigated the use of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis for rapid soil fertility assessment. A total of 1,133 soil samples from diverse agro-climatic zones in Eastern India were analyzed.
  arXiv  Detail & Related papers  (2024-04-17T17:57:20Z)
• 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)
• 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.