A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests

Mélaine Aubry-Kientz,Antonio Ferraz,Sassan Saatchi,David Coomes,Jonathan Williams,Raphaël Dutrieux,Hamid Hamraz,Alexandre Piboule,Grégoire Vincent

doi:10.3390/rs11091086

Abstract

Tropical forest canopies are comprised of tree crowns of multiple species varying in shape and height, and ground inventories do not usually reliably describe their structure. Airborne laser scanning data can be used to characterize these individual crowns, but analytical tools developed for boreal or temperate forests may require to be adjusted before they can be applied to tropical environments. Therefore, we compared results from six different segmentation methods applied to six plots (39 ha) from a study site in French Guiana. We measured the overlap of automatically segmented crowns projection with selected crowns manually delineated on high-resolution photography. We also evaluated the goodness of fit following automatic matching with field inventory data using a model linking tree diameter to tree crown width. The different methods tested in this benchmark segmented highly different numbers of crowns having different characteristics. Segmentation methods based on the point cloud (AMS3D and Graph-Cut) globally outperformed methods based on the Canopy Height Models, especially for small crowns; the AMS3D method outperformed the other methods tested for the overlap analysis, and AMS3D and Graph-Cut performed the best for the automatic matching validation. Nevertheless, other methods based on the Canopy Height Model performed better for very large emergent crowns. The dense foliage of tropical moist forests prevents sufficient point densities in the understory to segment subcanopy trees accurately, regardless of the segmentation method.

Highlights

Airborne and space-borne remote sensing technologies are developing fast and hold great promise to improve our understanding of tropical forest ecosystem structure and function
Remote sensing brings consistent measurements recorded over large scales, complementing field inventories, which provide more detailed information but sample only limited areas
Individual Tree Crown (ITC) delineation from Airborne laser scanning (ALS) can be merged with hyperspectral data to identify tree species from the spectral information at the crown level, which gives better results than at the pixel level [15,16,17,18,19]

Summary

Introduction

Airborne and space-borne remote sensing technologies are developing fast and hold great promise to improve our understanding of tropical forest ecosystem structure and function. Detection and segmentation of crowns have long been addressed in boreal and temperate forests (e.g., [6,7,8,9,10,11,12]), and perform better in coniferous forests than in mixed forest [9,13,14] These algorithms may not transfer readily to tropical dense forests characterized by closed, evergreen, structurally complex canopies. ITC delineation from ALS can be merged with hyperspectral data to identify tree species from the spectral information at the crown level, which gives better results than at the pixel level [15,16,17,18,19]. This implies a good ITC delineation, especially of the upper canopy crowns for which the hyperspectral information is available

Methods

Results

Discussion

Conclusion