Predicting species cover of marine macrophyte and invertebrate species combining hyperspectral remote sensing, machine learning and regression techniques

PLoS One. 2013 Jun 3;8(6):e63946. doi: 10.1371/journal.pone.0063946. Print 2014.

Abstract

In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Aquatic Organisms / physiology*
  • Artificial Intelligence*
  • Ecosystem
  • Estonia
  • Geography
  • Invertebrates / physiology*
  • Oceans and Seas
  • Principal Component Analysis
  • Regression Analysis
  • Remote Sensing Technology*
  • Species Specificity

Grants and funding

This work was funded by the Central Baltic Interreg IVa Programme HISPARES. Additional funding for this research was provided by Institutional research funding IUT02-20 of the Estonian Research Council and by the Estonian Science Foundation grants 7813 and 8254. The study has been partly supported by the projects “EstKliima” No 3.2.0802.11-0043 and “The status of marine biodiversity and its potential futures in the Estonian coastal sea” No. 3.2.0802.11-0029 of Environmental protection and –technology programme of European Regional Fund. The authors acknowledge Dr. Kristjan Herkül for providing technical support at various stages of this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.