Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana
LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km.
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dig-cirad-fr-5801762024-01-28T23:25:32Z http://agritrop.cirad.fr/580176/ http://agritrop.cirad.fr/580176/ Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana. Fayad Ibrahim, Baghdadi Nicolas, Bailly Jean Stéphane, Barbier Nicolas, Gond Valéry, Hérault Bruno, El Hajj Mahmoud, Fabre Frédéric, Perrin Jose. 2016. Remote Sensing, 8 (3):e240, 18 p.https://doi.org/10.3390/rs8030240 <https://doi.org/10.3390/rs8030240> Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana Fayad, Ibrahim Baghdadi, Nicolas Bailly, Jean Stéphane Barbier, Nicolas Gond, Valéry Hérault, Bruno El Hajj, Mahmoud Fabre, Frédéric Perrin, Jose eng 2016 Remote Sensing K01 - Foresterie - Considérations générales U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques forêt tropicale inventaire forestier biomasse biomasse aérienne des arbres télédétection satellite radar imagerie par satellite cartographie analyse de données méthode statistique http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_9000132 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_7377 Guyane française France http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/580176/1/Fayad%20et%20al.%20-%202016%20-%20Regional%20Scale%20Rain-Forest%20Height%20Mapping%20Using%20Regression-Kriging%20of%20Spaceborne%20and%20Airborne%20LiDAR%20Data%20Applicati.pdf text Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.3390/rs8030240 10.3390/rs8030240 info:eu-repo/semantics/altIdentifier/doi/10.3390/rs8030240 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.3390/rs8030240 |
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K01 - Foresterie - Considérations générales U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques forêt tropicale inventaire forestier biomasse biomasse aérienne des arbres télédétection satellite radar imagerie par satellite cartographie analyse de données méthode statistique http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_9000132 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 K01 - Foresterie - Considérations générales U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques forêt tropicale inventaire forestier biomasse biomasse aérienne des arbres télédétection satellite radar imagerie par satellite cartographie analyse de données méthode statistique http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_9000132 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
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K01 - Foresterie - Considérations générales U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques forêt tropicale inventaire forestier biomasse biomasse aérienne des arbres télédétection satellite radar imagerie par satellite cartographie analyse de données méthode statistique http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_9000132 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 K01 - Foresterie - Considérations générales U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques forêt tropicale inventaire forestier biomasse biomasse aérienne des arbres télédétection satellite radar imagerie par satellite cartographie analyse de données méthode statistique http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_9000132 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Fayad, Ibrahim Baghdadi, Nicolas Bailly, Jean Stéphane Barbier, Nicolas Gond, Valéry Hérault, Bruno El Hajj, Mahmoud Fabre, Frédéric Perrin, Jose Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana |
description |
LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km. |
format |
article |
topic_facet |
K01 - Foresterie - Considérations générales U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques forêt tropicale inventaire forestier biomasse biomasse aérienne des arbres télédétection satellite radar imagerie par satellite cartographie analyse de données méthode statistique http://aims.fao.org/aos/agrovoc/c_24904 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_926 http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_9000132 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_15962 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Fayad, Ibrahim Baghdadi, Nicolas Bailly, Jean Stéphane Barbier, Nicolas Gond, Valéry Hérault, Bruno El Hajj, Mahmoud Fabre, Frédéric Perrin, Jose |
author_facet |
Fayad, Ibrahim Baghdadi, Nicolas Bailly, Jean Stéphane Barbier, Nicolas Gond, Valéry Hérault, Bruno El Hajj, Mahmoud Fabre, Frédéric Perrin, Jose |
author_sort |
Fayad, Ibrahim |
title |
Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana |
title_short |
Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana |
title_full |
Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana |
title_fullStr |
Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana |
title_full_unstemmed |
Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana |
title_sort |
regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne lidar data: application on french guiana |
url |
http://agritrop.cirad.fr/580176/ http://agritrop.cirad.fr/580176/1/Fayad%20et%20al.%20-%202016%20-%20Regional%20Scale%20Rain-Forest%20Height%20Mapping%20Using%20Regression-Kriging%20of%20Spaceborne%20and%20Airborne%20LiDAR%20Data%20Applicati.pdf |
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