Biodiversity Mapping in a Tropical West African Forest with Airborne Hyperspectral Data
Open Access
- 17 June 2014
- journal article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 9 (6), e97910
- https://doi.org/10.1371/journal.pone.0097910
Abstract
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 m2 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 m2 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-R2 = 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.Keywords
This publication has 68 references indexed in Scilit:
- Spatial and temporal variation of carbon stocks in a lowland tropical forest in West AfricaForest Ecology and Management, 2013
- Modelling Forest α-Diversity and Floristic Composition — On the Added Value of LiDAR plus Hyperspectral Remote SensingRemote Sensing, 2012
- Species-Level Differences in Hyperspectral Metrics among Tropical Rainforest Trees as Determined by a Tree-Based ClassifierRemote Sensing, 2012
- Biodiversity Conservation in the REDDCarbon Balance and Management, 2010
- Variable selection using random forestsPattern Recognition Letters, 2010
- Mapping the species richness and composition of tropical forests from remotely sensed data with neural networksEcological Modelling, 2006
- Extinction risk from climate changeNature, 2004
- Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stagesRemote Sensing of Environment, 2002
- Consequences of changing biodiversityNature, 2000
- Benefits of plant diversity to ecosystems: immediate, filter and founder effectsJournal of Ecology, 1998