Using a multistructural object-based LiDAR approach to estimate vascular plant richness in mediterranean forests with complex structure

Javier Lopatin, Mauricio Galleguillos, Fabian E. Fassnacht, Andrés Ceballos, Jaime Hernández

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

A multistructural object-based LiDAR approach to predict plant richness in complex structure forests is presented. A normalized LiDAR point cloud was split into four height ranges: 1) high canopies (points above 16 m); 2) middle-high canopies (8-16 m); 3) middle-low canopies (2-8 m); and 4) low canopies (0-2 m). A digital canopy model (DCM) was obtained from the full normalized LiDAR point cloud, and four pseudo-DCMs (pDCMs) were obtained from the split point clouds. We applied a multiresolution segmentation algorithm to the DCM and the four pDCMs to obtain crown objects. A partial least squares path model (PLS-PM) algorithm was applied to predict total vascular plant richness using object-based image analysis (OBIA) variables, derived from the delineated crown objects, and topographic variables, derived from a digital terrain model. Results showed that the object-based model was able to predict the total richness with an r2 of 0.64 and a root-mean-square error of four species. Topographic variables showed to be more important than the OBIA variables to predict richness. Furthermore, high-medium canopies (8-16 m) showed the biggest correlation with the total plant richness within the structural segments of the forest.

Original languageEnglish
Article number6990570
Pages (from-to)1008-1012
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume12
Issue number5
DOIs
StatePublished - May 2015

Keywords

  • Bootstrapping
  • LiDAR
  • object-based analysis
  • partial least squares path model (PLS-PM)
  • vascular plant richness

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