Biodiversity mapping in a tropical West African forest with airborne hyperspectral data

PLoS One. 2014 Jun 17;9(6):e97910. doi: 10.1371/journal.pone.0097910. eCollection 2014.


Tropical forests are major repositories of biodiversity, but are fast disappearing as land is converted to agriculture. Decision-makers need to know which of the remaining forests to prioritize for conservation, but the only spatial information on forest biodiversity has, until recently, come from a sparse network of ground-based plots. Here we explore whether airborne hyperspectral imagery can be used to predict the alpha diversity of upper canopy trees in a West African forest. The abundance of tree species were collected from 64 plots (each 1250 m(2) in size) within a Sierra Leonean national park, and Shannon-Wiener biodiversity indices were calculated. An airborne spectrometer measured reflectances of 186 bands in the visible and near-infrared spectral range at 1 m(2) resolution. The standard deviations of these reflectance values and their first-order derivatives were calculated for each plot from the c. 1250 pixels of hyperspectral information within them. Shannon-Wiener indices were then predicted from these plot-based reflectance statistics using a machine-learning algorithm (Random Forest). The regression model fitted the data well (pseudo-R(2) = 84.9%), and we show that standard deviations of green-band reflectances and infra-red region derivatives had the strongest explanatory powers. Our work shows that airborne hyperspectral sensing can be very effective at mapping canopy tree diversity, because its high spatial resolution allows within-plot heterogeneity in reflectance to be characterized, making it an effective tool for monitoring forest biodiversity over large geographic scales.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence
  • Biodiversity
  • Computer Simulation
  • Conservation of Natural Resources
  • Data Collection / methods
  • Rainforest*
  • Regression Analysis
  • Sierra Leone
  • Signal Processing, Computer-Assisted
  • Trees*
  • Tropical Climate

Grant support

The authors acknowledge the ERC grant Africa GHG #247349 and the Cambridge Conservation Initiative for providing support to the investigation. This work is also supported by the Industry-Academia Partnerships and Pathways programme of the European Commission, Project AIRFORS, contract no: 286079. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.