Paper Info
Title
Using a Multistructural Object-Based LiDAR Approach to Estimate Vascular Plant Richness in Mediterranean Forests With Complex Structure
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.
Year
DOI
Venue
2015
10.1109/LGRS.2014.2372875
IEEE Geosci. Remote Sensing Lett.
Keywords
DocType
Volume
laser radar,image segmentation,predictive models,digital terrain model,image resolution,lidar,remote sensing,bootstrapping,parameter estimation,biodiversity,root mean square error
Journal
12
Issue
ISSN
Citations 
5
1545-598X
5
PageRank 
References 
Authors
0.55
4
5
Name
Order
Citations
PageRank
Javier Lopatin161.26
Mauricio Galleguillos2183.64
Fabian Ewald Fassnacht3949.85
Andres Ceballos4101.09
Jaime Hernández5162.36