untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T18:22:01Z</responseDate>
<request identifier=oai:HAL:hal-01361343v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:hal-01361343v1</identifier>
<datestamp>2018-01-11</datestamp>
<setSpec>type:ART</setSpec>
<setSpec>subject:sdv</setSpec>
<setSpec>subject:sde</setSpec>
<setSpec>collection:CNRS</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:IRSTEA</setSpec>
<setSpec>collection:INRA</setSpec>
<setSpec>collection:SDE</setSpec>
<setSpec>collection:AMAP</setSpec>
<setSpec>collection:ECOFOG</setSpec>
<setSpec>collection:BRGM</setSpec>
<setSpec>collection:AGROPOLIS</setSpec>
<setSpec>collection:APT-TELEDETECTION</setSpec>
<setSpec>collection:TETIS</setSpec>
<setSpec>collection:GIP-BE</setSpec>
<setSpec>collection:CIRAD</setSpec>
<setSpec>collection:GUYANE</setSpec>
<setSpec>collection:IRD</setSpec>
<setSpec>collection:AGROPARISTECH</setSpec>
<setSpec>collection:AGREENIUM</setSpec>
<setSpec>collection:AGROPARISTECH-SIAFEE</setSpec>
<setSpec>collection:AGROPARISTECH-ORG</setSpec>
<setSpec>collection:B3ESTE</setSpec>
<setSpec>collection:UNIV-MONTPELLIER</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Regional scale rain-forest height mapping using regression-kriging of spaceborne and 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>Fabre, F.</creator>
<creator>Perrin, José</creator>
<contributor>Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) - AgroParisTech - Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD)</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>Biens et services des écosystèmes forestiers tropicaux (UPR BSEF) ; 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 Operation S.A.S. ; AIRBUS</contributor>
<contributor>Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)</contributor>
<description>International audience</description>
<source>ISSN: 2072-4292</source>
<source>Remote Sensing</source>
<publisher>MDPI</publisher>
<identifier>hal-01361343</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01361343</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01361343/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01361343/file/mt2016-pub00048036.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01361343</source>
<source>Remote Sensing, MDPI, 2016, 8 (240), pp.1-18. 〈10.3390/rs8030240〉</source>
<identifier>DOI : 10.3390/rs8030240</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.3390/rs8030240</relation>
<language>en</language>
<subject lang=en>remote sensing</subject>
<subject lang=en>cartography</subject>
<subject lang=en>lidar</subject>
<subject lang=en>canopy</subject>
<subject lang=en>sampling</subject>
<subject lang=en>environmental data</subject>
<subject lang=fr>CANOPEE</subject>
<subject lang=fr>CARTOGRAPHIE</subject>
<subject lang=fr>TELEDETECTION</subject>
<subject lang=fr>ECHANTILLONNAGE</subject>
<subject lang=fr>DONNEE ENVIRONNEMENTALE</subject>
<subject>[SDV.EE.ECO] Life Sciences [q-bio]/Ecology, environment/Ecosystems</subject>
<subject>[SDE.IE] Environmental Sciences/Environmental Engineering</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>LiDAR (Light Detection And Ranging) remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and above ground 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 (e.g. geology, slope, vegetation indices, etc.). 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 random forest 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. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on the precision of canopy height estimates, six subsets were derived from the initial airborne LiDAR dataset with flight line spacing of 5, 10, 20, 30, 40 and 50 km (corresponding to 0.29, 0.11, 0.08, 0.05, 0.04, and 0.03 points/km² respectively). Results indicated that using the regression-kriging approach achieved a precision of 1.8 m on the canopy height map with flight line spacing of 5 km and achieved an average RMSE of 4.8m for the configuration for the 50 km flight line spacing.</description>
<date>2016</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
