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<title lang=en>Regional scale rain-forest height mapping using regression-kriging of spaceborneand airborne lidar data: application on French Guiana</title>
<creator>Fayad, I.</creator>
<creator>Baghdadi, N.</creator>
<creator>Bailly, Jean-Stéphane</creator>
<creator>Barbier, N.</creator>
<creator>Gond, V.</creator>
<creator>Hérault, B.</creator>
<creator>El Hajj, M.</creator>
<creator>Lochard, J.</creator>
<creator>Perrin, José</creator>
<contributor>Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - AgroParisTech - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)</contributor>
<contributor>AgroParisTech</contributor>
<contributor>Laboratoire d'étude des interactions entre sols, agrosystèmes et hydrosystèmes (LISAH) ; Institut National de la Recherche Agronomique (INRA)</contributor>
<contributor>Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut national de la recherche agronomique [Montpellier] (INRA Montpellier) - Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS) - Institut de Recherche pour le Développement (IRD [France-Sud])</contributor>
<contributor>Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD)</contributor>
<contributor>Ecologie des forêts de Guyane (ECOFOG) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut National de la Recherche Agronomique (INRA) - Université des Antilles et de la Guyane (UAG) - AgroParisTech - Université de Guyane (UG) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>NOVELTIS [Sté]</contributor>
<contributor>Airbus Defence and Space [Toulouse]</contributor>
<contributor>Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)</contributor>
<description>IGARSS 2015, Milan, ITA, 26-/07/2015 - 31/07/2015</description>
<description>International audience</description>
<source>Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International</source>
<source>IGARSS 2015</source>
<coverage>Milan, Italy</coverage>
<identifier>hal-01244677</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01244677</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01244677/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01244677/file/mt2015-pub00044297.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01244677</source>
<source>IGARSS 2015, Jul 2015, Milan, Italy. Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, pp.4109-4112, 2015, 〈10.1109/IGARSS.2015.7326729 〉</source>
<identifier>DOI : 10.1109/IGARSS.2015.7326729 </identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1109/IGARSS.2015.7326729 </relation>
<identifier>IRSTEA : PUB00044297</identifier>
<language>en</language>
<subject lang=en>SIGNAL PROCESSING</subject>
<subject lang=en>CANOPY</subject>
<subject lang=fr>TELEDETECTION</subject>
<subject lang=fr>LIDAR</subject>
<subject lang=fr>TRAITEMENT DU SIGNAL</subject>
<subject lang=fr>ARBRE</subject>
<subject lang=fr>CANOPEE</subject>
<subject>[SDE] Environmental Sciences</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution.In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset.</description>
<date>2015-07-26</date>
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