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Affiner la recherche Interroger des sources externesAboveground-biomass estimation of a complex tropical forest in India using Lidar / Cédric Vega in Remote sensing, vol 7 n° 8 (August 2015)
Titre : Aboveground-biomass estimation of a complex tropical forest in India using Lidar Type de document : Article/Communication Auteurs : Cédric Vega Année de publication : 2015 Projets : 3-projet - voir note / Article en page(s) : pp 10607 - 10625 Note générale : bibliographie
The research has been supported by IFPCAR (Indo-French Promotion Center for Advanced Research) through the joint project number 4509-1 “Controlling for Uncertainty in Assessment of Forest Aboveground Biomass in the Western Ghats of India”between UMR AMAP, Montpellier and the National Remote Sensing Centre, Hyderabad. The authors also greatly acknowledge the French Institute of Pondicherry (IFP) for its financial support to Udayalakshmi Vepakomma for visiting IFPand for providing field control data from its long term monitoring plot in Uppangala.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] biomasse aérienne
[Termes IGN] canopée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] Ghats occidentaux
[Termes IGN] Inde
[Termes IGN] pente
[Termes IGN] profil en travers
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] volume en boisRésumé : (auteur) Light Detection and Ranging (Lidar) is a state of the art technology to assess forest aboveground biomass (AGB). To date, methods developed to relate Lidar metrics with forest parameters were built upon the vertical component of the data. In multi-layered tropical forests, signal penetration might be restricted, limiting the efficiency of these methods. A potential way for improving AGB models in such forests would be to combine traditional approaches by descriptors of the horizontal canopy structure. We assessed the capability and complementarity of three recently proposed methods for assessing AGB at the plot level using point distributional approach (DM), canopy volume profile approach (CVP), 2D canopy grain approach (FOTO), and further evaluated the potential of a topographical complexity index (TCI) to explain part of the variability of AGB with slope. This research has been conducted in a mountainous wet evergreen tropical forest of Western Ghats in India. AGB biomass models were developed using a best subset regression approach, and model performance was assessed through cross-validation. Results demonstrated that the variability in AGB could be efficiently captured when variables describing both the vertical (DM or CVP) and horizontal (FOTO) structure were combined. Integrating FOTO metrics with those of either DM or CVP decreased the root mean squared error of the models by 4.42% and 6.01%, respectively. These results are of high interest for AGB mapping in the tropics and could significantly contribute to the REDD+ program. Model quality could be further enhanced by improving the robustness of field-based biomass models and influence of topography on area-based Lidar descriptors of the forest structure. Numéro de notice : A2015--081 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs70810607 Date de publication en ligne : 18/08/2015 En ligne : https://doi.org/10.3390/rs70810607 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84559
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