Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure
We predict stand basal area (BA) from small footprint LiDAR data in 129 one-ha tropical forest plots across four sites in French Guiana and encompassing a great diversity of forest structures resulting from natural (soil and geological substrate) and anthropogenic effects (unlogged and logged forests). We use predictors extracted from the Canopy Height Model to compare models of varying complexity: single or multiple regressions and nested models that predict BA by independent estimates of stem density and quadratic mean diameter. Direct multiple regression was the most accurate, giving a 9.6% Root Mean Squared Error of Prediction (RMSEP). The magnitude of the various errors introduced during the data collection stage is evaluated and their contribution to MSEP is analyzed. It was found that these errors accounted for less than 10% of model MSEP, suggesting that there is considerable scope for model improvement. Although site-specific models showed lower MSEP than global models, stratification by site may not be the optimal solution. The key to future improvement would appear to lie in a stratification that captures variations in relations between LiDAR and forest structure.
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dig-cirad-fr-5649682024-01-28T20:37:35Z http://agritrop.cirad.fr/564968/ http://agritrop.cirad.fr/564968/ Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure. Vincent Grégoire, Sabatier Daniel, Blanc Lilian, Chave Jérôme, Weissenbacher Emilien, Pélissier Raphaël, Fonty E., Molino Jean-François, Couteron Pierre. 2012. Remote Sensing of Environment, 125 : 23-33.https://doi.org/10.1016/j.rse.2012.06.019 <https://doi.org/10.1016/j.rse.2012.06.019> Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure Vincent, Grégoire Sabatier, Daniel Blanc, Lilian Chave, Jérôme Weissenbacher, Emilien Pélissier, Raphaël Fonty, E. Molino, Jean-François Couteron, Pierre eng 2012 Remote Sensing of Environment U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales forêt tropicale humide peuplement forestier structure du peuplement télédétection laser dynamique des populations croissance modèle de croissance forestière abattage d'arbres facteur édaphique forêt http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_34911 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_26026 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3394 http://aims.fao.org/aos/agrovoc/c_1374844914634 http://aims.fao.org/aos/agrovoc/c_2847 http://aims.fao.org/aos/agrovoc/c_15617 http://aims.fao.org/aos/agrovoc/c_3062 Guyane française France http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 We predict stand basal area (BA) from small footprint LiDAR data in 129 one-ha tropical forest plots across four sites in French Guiana and encompassing a great diversity of forest structures resulting from natural (soil and geological substrate) and anthropogenic effects (unlogged and logged forests). We use predictors extracted from the Canopy Height Model to compare models of varying complexity: single or multiple regressions and nested models that predict BA by independent estimates of stem density and quadratic mean diameter. Direct multiple regression was the most accurate, giving a 9.6% Root Mean Squared Error of Prediction (RMSEP). The magnitude of the various errors introduced during the data collection stage is evaluated and their contribution to MSEP is analyzed. It was found that these errors accounted for less than 10% of model MSEP, suggesting that there is considerable scope for model improvement. Although site-specific models showed lower MSEP than global models, stratification by site may not be the optimal solution. The key to future improvement would appear to lie in a stratification that captures variations in relations between LiDAR and forest structure. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/564968/1/document_564968.pdf application/pdf Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.rse.2012.06.019 10.1016/j.rse.2012.06.019 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2012.06.019 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.rse.2012.06.019 |
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U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales forêt tropicale humide peuplement forestier structure du peuplement télédétection laser dynamique des populations croissance modèle de croissance forestière abattage d'arbres facteur édaphique forêt http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_34911 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_26026 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3394 http://aims.fao.org/aos/agrovoc/c_1374844914634 http://aims.fao.org/aos/agrovoc/c_2847 http://aims.fao.org/aos/agrovoc/c_15617 http://aims.fao.org/aos/agrovoc/c_3062 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales forêt tropicale humide peuplement forestier structure du peuplement télédétection laser dynamique des populations croissance modèle de croissance forestière abattage d'arbres facteur édaphique forêt http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_34911 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_26026 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3394 http://aims.fao.org/aos/agrovoc/c_1374844914634 http://aims.fao.org/aos/agrovoc/c_2847 http://aims.fao.org/aos/agrovoc/c_15617 http://aims.fao.org/aos/agrovoc/c_3062 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
spellingShingle |
U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales forêt tropicale humide peuplement forestier structure du peuplement télédétection laser dynamique des populations croissance modèle de croissance forestière abattage d'arbres facteur édaphique forêt http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_34911 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_26026 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3394 http://aims.fao.org/aos/agrovoc/c_1374844914634 http://aims.fao.org/aos/agrovoc/c_2847 http://aims.fao.org/aos/agrovoc/c_15617 http://aims.fao.org/aos/agrovoc/c_3062 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales forêt tropicale humide peuplement forestier structure du peuplement télédétection laser dynamique des populations croissance modèle de croissance forestière abattage d'arbres facteur édaphique forêt http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_34911 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_26026 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3394 http://aims.fao.org/aos/agrovoc/c_1374844914634 http://aims.fao.org/aos/agrovoc/c_2847 http://aims.fao.org/aos/agrovoc/c_15617 http://aims.fao.org/aos/agrovoc/c_3062 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Vincent, Grégoire Sabatier, Daniel Blanc, Lilian Chave, Jérôme Weissenbacher, Emilien Pélissier, Raphaël Fonty, E. Molino, Jean-François Couteron, Pierre Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure |
description |
We predict stand basal area (BA) from small footprint LiDAR data in 129 one-ha tropical forest plots across four sites in French Guiana and encompassing a great diversity of forest structures resulting from natural (soil and geological substrate) and anthropogenic effects (unlogged and logged forests). We use predictors extracted from the Canopy Height Model to compare models of varying complexity: single or multiple regressions and nested models that predict BA by independent estimates of stem density and quadratic mean diameter. Direct multiple regression was the most accurate, giving a 9.6% Root Mean Squared Error of Prediction (RMSEP). The magnitude of the various errors introduced during the data collection stage is evaluated and their contribution to MSEP is analyzed. It was found that these errors accounted for less than 10% of model MSEP, suggesting that there is considerable scope for model improvement. Although site-specific models showed lower MSEP than global models, stratification by site may not be the optimal solution. The key to future improvement would appear to lie in a stratification that captures variations in relations between LiDAR and forest structure. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques K01 - Foresterie - Considérations générales forêt tropicale humide peuplement forestier structure du peuplement télédétection laser dynamique des populations croissance modèle de croissance forestière abattage d'arbres facteur édaphique forêt http://aims.fao.org/aos/agrovoc/c_7976 http://aims.fao.org/aos/agrovoc/c_28080 http://aims.fao.org/aos/agrovoc/c_34911 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_26026 http://aims.fao.org/aos/agrovoc/c_6111 http://aims.fao.org/aos/agrovoc/c_3394 http://aims.fao.org/aos/agrovoc/c_1374844914634 http://aims.fao.org/aos/agrovoc/c_2847 http://aims.fao.org/aos/agrovoc/c_15617 http://aims.fao.org/aos/agrovoc/c_3062 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Vincent, Grégoire Sabatier, Daniel Blanc, Lilian Chave, Jérôme Weissenbacher, Emilien Pélissier, Raphaël Fonty, E. Molino, Jean-François Couteron, Pierre |
author_facet |
Vincent, Grégoire Sabatier, Daniel Blanc, Lilian Chave, Jérôme Weissenbacher, Emilien Pélissier, Raphaël Fonty, E. Molino, Jean-François Couteron, Pierre |
author_sort |
Vincent, Grégoire |
title |
Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure |
title_short |
Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure |
title_full |
Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure |
title_fullStr |
Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure |
title_full_unstemmed |
Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure |
title_sort |
accuracy of small footprint airborne lidar in its predictions of tropical moist forest stand structure |
url |
http://agritrop.cirad.fr/564968/ http://agritrop.cirad.fr/564968/1/document_564968.pdf |
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