Tree crown detection in high resolution optical and LiDAR images of tropical forest

Jia Zhou 1 Christophe Proisy 1 Xavier Descombes 2 Ihssen Hedhli 2 Nicolas Barbier 1 Josiane Zerubia 2 Jean-Philippe Gastellu-Etchegorry 3 Pierre Couteron 1
1 UMR AMAP - Botanique et Bioinformatique de l'Architecture des Plantes
2 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , SIS - Signal, Images et Systèmes
3 CESBIO - Centre d'études spatiales de la biosphère
Abstract : Tropical forests are complex ecosystems where the potential of remote sensing has not yet been fully realized. The increasing availability of satellite metric imagery along with canopy altimetry from airborne LiDAR open new prospects to detect individual trees. For this objective, we optimized, calibrated and applied a model based on marked point processes to detect trees in high biomass mangroves of French Guiana by considering a set of 1m pixel images including 1) panchromatic images from the IKONOS sensor 2) LiDAR-derived canopy 2D altimetry and 3) reflectance panchromatic images simulated by the DART-model. The relevance of detection is then discussed considering: (i) the agreement in space of detected crown centers locations with known true locations for the DART images and also the detection agreement for each pair of IKONOS and LiDAR images, and (ii) the comparison between the frequency distributions of the diameters of the detected crowns and of the tree trunks measured in the field. Both distributions are expected to be related due to the allometry relationships between trunk and crown.
Keywords : Tropical forest tree detection mangrove marked point process IKONOS LiDAR DART
Type de document :
Communication dans un congrès
Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, Sep 2010, Toulouse, France. SPIE, 7824, 2010, <10.1117/12.865068>
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Jia Zhou, Christophe Proisy, Xavier Descombes, Ihssen Hedhli, Nicolas Barbier, et al.. Tree crown detection in high resolution optical and LiDAR images of tropical forest. Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, Sep 2010, Toulouse, France. SPIE, 7824, 2010, <10.1117/12.865068>. <inria-00534791>

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