Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data

Matthew S. Colgan, Claire A. Baldeck, Jean baptiste Féret, Gregory P. Asner

Research output: Contribution to journalArticle

129 Scopus citations

Abstract

Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR) and hyperspectral imagery. Here we build upon previous work on airborne species detection by using a two-stage support vector machine (SVM) classifier to first predict species from hyperspectral data at the pixel scale. Tree crowns are segmented from the lidar imagery such that crown-level information, such as maximum tree height, can then be combined with the pixel-level species probabilities to predict the species of each tree. An overall prediction accuracy of 76% was achieved for 15 species. We also show that bidirectional reflectance distribution (BRDF) effects caused by anisotropic scattering properties of savanna vegetation can result in flight line artifacts evident in species probability maps, yet these can be largely mitigated by applying a semi-empirical BRDF model to the hyperspectral data. We find that confronting these three challenges-reflectance anisotropy, integration of pixel- and crown-level data, and crown delineation over large areas-enables species mapping at ecosystem scales for monitoring biodiversity and ecosystem function.

Original languageEnglish (US)
Pages (from-to)3462-3480
Number of pages19
JournalRemote Sensing
Volume4
Issue number11
DOIs
StatePublished - Nov 1 2012
Externally publishedYes

Keywords

  • CAO
  • Carnegie airborne observatory
  • Crown segmentation
  • Kruger national park
  • SVM
  • South Africa
  • Species mapping

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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